modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-04 06:26:56
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 538
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
fromhell01/ppo-LunarLander-v2
|
fromhell01
| 2023-08-05T10:54:32Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-05T10:54:12Z |
---
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: 261.76 +/- 19.02
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
...
```
|
Muhammadreza/mann-e-artistic-1
|
Muhammadreza
| 2023-08-05T10:46:13Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-05T10:33:43Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### mann-e_artistic-1 Dreambooth model trained by Muhammadreza with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Aityz/aityz_chatbot
|
Aityz
| 2023-08-05T10:34:38Z | 207 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-05T09:57:36Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: aityz_chatbot
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. -->
# aityz_chatbot
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.8707
## 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: 0.2
- 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 | 467 | 75.0160 |
| 93.9474 | 2.0 | 934 | 9.0902 |
| 21.3455 | 3.0 | 1401 | 7.8707 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
minjingzhu/bigbird-pegasus-large-pubmed-finetuned-legal-2
|
minjingzhu
| 2023-08-05T10:34:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bigbird_pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/bigbird-pegasus-large-pubmed",
"base_model:finetune:google/bigbird-pegasus-large-pubmed",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-05T06:08:45Z |
---
license: apache-2.0
base_model: google/bigbird-pegasus-large-pubmed
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bigbird-pegasus-large-pubmed-finetuned-legal-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. -->
# bigbird-pegasus-large-pubmed-finetuned-legal-2
This model is a fine-tuned version of [google/bigbird-pegasus-large-pubmed](https://huggingface.co/google/bigbird-pegasus-large-pubmed) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0932
- Rouge1: 35.0046
- Rouge2: 14.6481
- Rougel: 20.8387
- Rougelsum: 32.3484
- Gen Len: 245.06
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.3208 | 1.0 | 6176 | 3.0932 | 35.0046 | 14.6481 | 20.8387 | 32.3484 | 245.06 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
OU812/BlueChristmas
|
OU812
| 2023-08-05T10:32:41Z | 0 | 0 | null |
[
"music",
"art",
"en",
"license:artistic-2.0",
"region:us"
] | null | 2023-08-05T10:22:39Z |
---
license: artistic-2.0
language:
- en
tags:
- music
- art
---
|
AnjanaB/mypet
|
AnjanaB
| 2023-08-05T10:32:37Z | 12 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-05T10:29:01Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### MyPet Dreambooth model trained by AnjanaB following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AJCE95
Sample pictures of this concept:
.jpg)
.jpg)
|
helamri/ppo-Huggy
|
helamri
| 2023-08-05T10:29:33Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-05T10:29:28Z |
---
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: helamri/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DavidGetter1/falcon-horror
|
DavidGetter1
| 2023-08-05T09:54:04Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-05T09:50:43Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
Aspik101/30B-Lazarus-instruct-PL-lora_adapter_model
|
Aspik101
| 2023-08-05T09:42:15Z | 0 | 0 | null |
[
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"pl",
"dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish",
"license:other",
"region:us"
] |
text-generation
| 2023-08-05T09:41:16Z |
---
language:
- pl
datasets:
- Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish
license: other
model_type: llama-2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
|
MattStammers/a2c-AntBullet
|
MattStammers
| 2023-08-05T09:14:06Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-05T09:12:54Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1411.21 +/- 388.99
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
|
teilomillet/llama-2-7b-LORA-data-analyst
|
teilomillet
| 2023-08-05T08:59:46Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-05T08:59:39Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
gaodrew/git-base-pokemon
|
gaodrew
| 2023-08-05T08:51:53Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"git",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/git-base",
"base_model:finetune:microsoft/git-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-08-05T08:06:54Z |
---
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: git-base-pokemon
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. -->
# git-base-pokemon
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0348
- Wer Score: 2.7147
## 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: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.3601 | 4.17 | 50 | 4.5925 | 21.8560 |
| 2.4331 | 8.33 | 100 | 0.4978 | 15.2153 |
| 0.1504 | 12.5 | 150 | 0.0323 | 1.2062 |
| 0.0142 | 16.67 | 200 | 0.0288 | 3.0791 |
| 0.0039 | 20.83 | 250 | 0.0314 | 2.3619 |
| 0.0021 | 25.0 | 300 | 0.0327 | 2.6537 |
| 0.0016 | 29.17 | 350 | 0.0333 | 3.2049 |
| 0.0014 | 33.33 | 400 | 0.0344 | 2.9403 |
| 0.0012 | 37.5 | 450 | 0.0344 | 2.9624 |
| 0.0011 | 41.67 | 500 | 0.0345 | 2.8106 |
| 0.0011 | 45.83 | 550 | 0.0346 | 2.7393 |
| 0.0011 | 50.0 | 600 | 0.0348 | 2.7147 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
DORA1222/585
|
DORA1222
| 2023-08-05T08:45:48Z | 0 | 0 | null |
[
"glm",
"chatglm",
"thudm",
"zh",
"en",
"arxiv:2103.10360",
"arxiv:2210.02414",
"arxiv:1911.02150",
"region:us"
] | null | 2023-08-05T06:16:46Z |
---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM2-6B
<p align="center">
💻 <a href="https://github.com/THUDM/ChatGLM2-6B" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2103.10360" target="_blank">[GLM@ACL 22]</a> <a href="https://github.com/THUDM/GLM" target="_blank">[GitHub]</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">[GLM-130B@ICLR 23]</a> <a href="https://github.com/THUDM/GLM-130B" target="_blank">[GitHub]</a> <br>
</p>
<p align="center">
👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1y7pqoloy-9b1g6T6JjA8J0KxvUjbwJw" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a>
</p>
## 介绍
ChatGLM**2**-6B 是开源中英双语对话模型 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) 的第二代版本，在保留了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上，ChatGLM**2**-6B 引入了如下新特性：
1. **更强大的性能**：基于 ChatGLM 初代模型的开发经验，我们全面升级了 ChatGLM2-6B 的基座模型。ChatGLM2-6B 使用了 [GLM](https://github.com/THUDM/GLM) 的混合目标函数，经过了 1.4T 中英标识符的预训练与人类偏好对齐训练，[评测结果](#评测结果)显示，相比于初代模型，ChatGLM2-6B 在 MMLU（+23%）、CEval（+33%）、GSM8K（+571%） 、BBH（+60%）等数据集上的性能取得了大幅度的提升，在同尺寸开源模型中具有较强的竞争力。
2. **更长的上下文**：基于 [FlashAttention](https://github.com/HazyResearch/flash-attention) 技术，我们将基座模型的上下文长度（Context Length）由 ChatGLM-6B 的 2K 扩展到了 32K，并在对话阶段使用 8K 的上下文长度训练，允许更多轮次的对话。但当前版本的 ChatGLM2-6B 对单轮超长文档的理解能力有限，我们会在后续迭代升级中着重进行优化。
3. **更高效的推理**：基于 [Multi-Query Attention](http://arxiv.org/abs/1911.02150) 技术，ChatGLM2-6B 有更高效的推理速度和更低的显存占用：在官方的模型实现下，推理速度相比初代提升了 42%，INT4 量化下，6G 显存支持的对话长度由 1K 提升到了 8K。
4. **更开放的协议**：ChatGLM2-6B 权重对学术研究**完全开放**，在填写[问卷](https://open.bigmodel.cn/mla/form)进行登记后**亦允许免费商业使用**。
ChatGLM**2**-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B). It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the following new features:
1. **Stronger Performance**: Based on the development experience of the first-generation ChatGLM model, we have fully upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The [evaluation results](README.md#evaluation-results) show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size.
2. **Longer Context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations.
3. **More Efficient Inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K.
4. **More Open License**: ChatGLM2-6B weights are **completely open** for academic research, and **free commercial use** is also allowed after completing the [questionnaire](https://open.bigmodel.cn/mla/form).
## 软件依赖
```shell
pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate
```
## 代码调用
可以通过如下代码调用 ChatGLM-6B 模型来生成对话：
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).half().cuda()
>>> model = model.eval()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:
1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。
如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
```
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM2-6B)。
For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM2-6B).
## Change Log
* v1.0
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM2-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文，ChatGLM2-6B 的论文会在近期公布，敬请期待～
```
@article{zeng2022glm,
title={Glm-130b: An open bilingual pre-trained model},
author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
journal={arXiv preprint arXiv:2210.02414},
year={2022}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
```
|
KevinC/ppo-Huggy
|
KevinC
| 2023-08-05T08:28:00Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-05T08:27:50Z |
---
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: KevinC/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
HachiML/Llama-2-7b-hf-jatok-qlora-1ep
|
HachiML
| 2023-08-05T08:25:45Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-05T08:24:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
AkitoP/ppo-SnowballTarget
|
AkitoP
| 2023-08-05T08:16:14Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-08-05T08:16:06Z |
---
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: AkitoP/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
nateshmbhat/model-isha-qa
|
nateshmbhat
| 2023-08-05T08:14:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"llama",
"text-generation",
"autotrain",
"dataset:nateshmbhat/isha-qa-text",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-03T07:17:45Z |
---
tags:
- autotrain
- text-generation
widget:
- text: 'What are the visiting hours for Adiyogi? '
datasets:
- nateshmbhat/isha-qa-text
pipeline_tag: text-generation
---
# ISHA Call Center QA Model
## This model was trained on a finetuned version(StableBeluga2) of Llama2-13B from stabilityai : [(StableBeluga2 tops the LLM leaderboard currently)](https://huggingface.co/stabilityai/StableBeluga2)
### Dataset Used : https://huggingface.co/datasets/nateshmbhat/isha-qa-text
#### Train Params used :
- Base model : stabilityai/StableBeluga-13B
- Quantization Used : 4 bit
- Learning rate : 2e-4
- Batch Size : 2
- Epochs : 3
- Trainer : sft
- Max token length : 2048 (capable of higher token length)
####
```
!autotrain llm --train --project_name project-isha-qa --model stabilityai/StableBeluga-13B --data_path nateshmbhat/isha-qa-text --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 2 --num_train_epochs 3 --trainer sft --model_max_length 2048 --push_to_hub --repo_id nateshmbhat/model-isha-qa
```
|
jlpan/starcoder-finetuned-cpp2py
|
jlpan
| 2023-08-05T07:44:13Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:bigcode/starcoder",
"base_model:finetune:bigcode/starcoder",
"license:bigcode-openrail-m",
"region:us"
] | null | 2023-08-04T19:30:43Z |
---
license: bigcode-openrail-m
base_model: bigcode/starcoder
tags:
- generated_from_trainer
model-index:
- name: starcoder-finetuned-cpp2py
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. -->
# starcoder-finetuned-cpp2py
This model is a fine-tuned version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3484
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.892 | 0.1 | 100 | 0.4126 |
| 0.4991 | 0.2 | 200 | 0.3559 |
| 0.4578 | 0.3 | 300 | 0.3554 |
| 0.4463 | 0.4 | 400 | 0.3514 |
| 0.4381 | 0.5 | 500 | 0.3522 |
| 0.4365 | 0.6 | 600 | 0.3496 |
| 0.4322 | 0.7 | 700 | 0.3498 |
| 0.4332 | 0.8 | 800 | 0.3485 |
| 0.4297 | 0.9 | 900 | 0.3492 |
| 0.432 | 1.0 | 1000 | 0.3484 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YanJiangJerry/task1_xlnet-large-cased_3_4_2e-05_0.01
|
YanJiangJerry
| 2023-08-05T07:31:25Z | 98 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-classification",
"generated_from_trainer",
"base_model:xlnet/xlnet-large-cased",
"base_model:finetune:xlnet/xlnet-large-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-05T06:01:55Z |
---
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: task1_xlnet-large-cased_3_4_2e-05_0.01
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. -->
# task1_xlnet-large-cased_3_4_2e-05_0.01
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7090
- Accuracy: 0.8147
- F1: 0.0
- Precision: 0.0
- Recall: 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: 2e-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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|
| 0.6754 | 1.0 | 1629 | 0.5660 | 0.8147 | 0.0 | 0.0 | 0.0 |
| 0.7117 | 2.0 | 3258 | 0.6926 | 0.8147 | 0.0 | 0.0 | 0.0 |
| 0.6359 | 3.0 | 4887 | 0.7090 | 0.8147 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
Kaori1707/bert-fine-tuned-cola
|
Kaori1707
| 2023-08-05T07:30:31Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-05T07:21:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-fine-tuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5584013487121077
---
<!-- 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-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9225
- Matthews Correlation: 0.5584
## 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4684 | 1.0 | 1069 | 0.6386 | 0.4390 |
| 0.3065 | 2.0 | 2138 | 0.6727 | 0.5682 |
| 0.1592 | 3.0 | 3207 | 0.9225 | 0.5584 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bilbo991/clip-homer-1m
|
bilbo991
| 2023-08-05T07:07:29Z | 89 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-text-dual-encoder",
"feature-extraction",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-08-04T15:06:03Z |
---
base_model: clip-homer-1m
tags:
- generated_from_trainer
model-index:
- name: clip-homer-1m
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-homer-1m
This model is a fine-tuned version of [clip-homer-1m](https://huggingface.co/clip-homer-1m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6790
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.3582 | 1.0 | 31250 | 1.2655 |
| 0.9139 | 2.0 | 62500 | 0.8926 |
| 0.492 | 3.0 | 93750 | 0.6790 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.1
- Tokenizers 0.13.3
|
bigcode/starcoder-co-format
|
bigcode
| 2023-08-05T06:48:34Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T12:17:46Z |
An ablation of [OctoCoder](https://huggingface.co/bigcode/octocoder) released for research purposes. Generally use OctoCoder, which performs better. Steps: 50
|
bigcode/starcoder-o
|
bigcode
| 2023-08-05T06:45:35Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-23T08:28:39Z |
An ablation of [OctoCoder](https://huggingface.co/bigcode/octocoder) released for research purposes. Generally use OctoCoder, which performs better. Steps: 45
|
bigcode/starcoder-cxo
|
bigcode
| 2023-08-05T06:41:14Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-15T15:59:30Z |
An ablation of [OctoCoder](https://huggingface.co/bigcode/octocoder) released for research purposes. Generally use OctoCoder, which performs better. Steps: 40
|
Adulala20/taxi
|
Adulala20
| 2023-08-05T06:33:25Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-05T06:32:03Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.78
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Adulala20/taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
wuxianchao/lazylora-13b-chathf
|
wuxianchao
| 2023-08-05T06:22:26Z | 0 | 1 | null |
[
"arxiv:2305.14314",
"arxiv:2106.09685",
"arxiv:2110.07602",
"arxiv:2104.08691",
"arxiv:2303.16199",
"license:llama2",
"region:us"
] | null | 2023-07-24T01:37:13Z |
---
license: llama2
---
## Lazy LoRA
### Benefits
0. using the updated [Meta's LLaMA-2 models](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf).
1. support [4-bit qlora](https://arxiv.org/abs/2305.14314), extreme GPU memory and inference time saving;
2. comparable MMLU evaluation dataset results, llama2-13b-chat:
| | eval | test | comp-eval | comp-test |
|---------------|--------|--------|-----------|-----------|
|llama2-13b-chat| 54.58% | 54.67% | | |
|ckpt-800 | 53.86% | 53.32% | -0.72% | -1.35% |
llama2-13b-chat: "7389082e6bc4fcbf6202e6108a70194800e6c51e"
3. This lazy-lora adapter is based on [Meta's LLaMA-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf), and using the [oasst1 dataset](https://huggingface.co/datasets/OpenAssistant/oasst1), following [Guanaco](https://huggingface.co/timdettmers/guanaco-65b).
### Introduction
Determine the rank of LoRA layers by the singular values of pretrained weight matrices.
Also, combines:
1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/abs/2106.09685)
2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.3
53/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.or
g/pdf/2110.07602.pdf)
3. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691)
4. LLaMA adapter: [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention] (https://arxiv.org/abs/2303.16199)
in one model.
This allows you to perform LoRA (additional low rank adapters inserted to each linear layer), and prompt learning (additional virtual tokens attached to the input and to the attention layers acting as `past_key_values`)
## Usage:
```python
import sys
sys.path.insert(1, '/workspace/asr/peft/src')
# TODO set this path to the lazy-lora source code path,
# or you can install it from source code:
# TODO, please install lazylora for usage:
# git clone git@github.com:Xianchao-Wu/peft.git
# cd peft
# python setup.py install
from transformers import (AutoTokenizer,
AutoModelForCausalLM, BitsAndBytesConfig)
from peft import PeftModel, PeftConfig
import os
import torch
#import ipdb; ipdb.set_trace()
cache_dir="/workspace/asr/peft/qlora"
# TODO set this cache_dir to the path where you
# stored (or, want to store) llama2-13b-chat-hf model
lazylora_dir=os.getcwd()
# the path that contains 'adapter_config.json'
# and 'adapter_model.bin'
config = PeftConfig.from_pretrained(lazylora_dir)
tokenizer = AutoTokenizer.from_pretrained(
config.base_model_name_or_path,
cache_dir=cache_dir,
use_auth_token=True
)
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
quantization_config=bnb_config,
device_map="auto",
cache_dir=cache_dir,
use_auth_token=True
)
#model.print_trainable_parameters()
print(sum(p.numel() for p in model.parameters()))
# 6,671,979,520 -> half-size of 13B due to 4-bit loading
model = PeftModel.from_pretrained(model, lazylora_dir)
print('after adding lazy lora parameters:')
model.print_trainable_parameters()
# trainable params: 0 || all params: 6,922,300,928 || trainable%: 0.0
```
## MMLU result:
### MMLU eval result:
```json
{"mmlu_loss": 1.6594436656097273,
"mmlu_eval_accuracy_high_school_mathematics": 0.2413793103448276,
"mmlu_eval_accuracy_high_school_biology": 0.5,
"mmlu_eval_accuracy_business_ethics": 0.45454545454545453,
"mmlu_eval_accuracy_jurisprudence": 0.6363636363636364,
"mmlu_eval_accuracy_virology": 0.4444444444444444,
"mmlu_eval_accuracy_logical_fallacies": 0.6666666666666666,
"mmlu_eval_accuracy_professional_law": 0.3176470588235294,
"mmlu_eval_accuracy_econometrics": 0.3333333333333333,
"mmlu_eval_accuracy_prehistory": 0.6571428571428571,
"mmlu_eval_accuracy_professional_accounting": 0.41935483870967744,
"mmlu_eval_accuracy_professional_psychology": 0.4782608695652174,
"mmlu_eval_accuracy_management": 0.8181818181818182,
"mmlu_eval_accuracy_human_sexuality": 0.5,
"mmlu_eval_accuracy_college_mathematics": 0.5454545454545454,
"mmlu_eval_accuracy_us_foreign_policy": 0.8181818181818182,
"mmlu_eval_accuracy_high_school_european_history": 0.6666666666666666,
"mmlu_eval_accuracy_miscellaneous": 0.7441860465116279,
"mmlu_eval_accuracy_international_law": 0.8461538461538461,
"mmlu_eval_accuracy_high_school_computer_science": 0.6666666666666666,
"mmlu_eval_accuracy_world_religions": 0.7894736842105263,
"mmlu_eval_accuracy_high_school_physics": 0.29411764705882354,
"mmlu_eval_accuracy_moral_scenarios": 0.28,
"mmlu_eval_accuracy_sociology": 0.7727272727272727,
"mmlu_eval_accuracy_professional_medicine": 0.45161290322580644,
"mmlu_eval_accuracy_conceptual_physics": 0.4230769230769231,
"mmlu_eval_accuracy_high_school_us_history": 0.8636363636363636,
"mmlu_eval_accuracy_clinical_knowledge": 0.5172413793103449,
"mmlu_eval_accuracy_human_aging": 0.6086956521739131,
"mmlu_eval_accuracy_college_medicine": 0.4090909090909091,
"mmlu_eval_accuracy_computer_security": 0.7272727272727273,
"mmlu_eval_accuracy_moral_disputes": 0.5789473684210527,
"mmlu_eval_accuracy_security_studies": 0.6296296296296297,
"mmlu_eval_accuracy_high_school_world_history": 0.5769230769230769,
"mmlu_eval_accuracy_public_relations": 0.5833333333333334,
"mmlu_eval_accuracy_medical_genetics": 0.7272727272727273,
"mmlu_eval_accuracy_electrical_engineering": 0.375,
"mmlu_eval_accuracy_marketing": 0.8,
"mmlu_eval_accuracy_high_school_geography": 0.7272727272727273,
"mmlu_eval_accuracy_high_school_government_and_politics": 0.5714285714285714,
"mmlu_eval_accuracy_abstract_algebra": 0.2727272727272727,
"mmlu_eval_accuracy_nutrition": 0.6363636363636364,
"mmlu_eval_accuracy_college_biology": 0.625,
"mmlu_eval_accuracy_formal_logic": 0.14285714285714285,
"mmlu_eval_accuracy_machine_learning": 0.5454545454545454,
"mmlu_eval_accuracy_high_school_psychology": 0.7333333333333333,
"mmlu_eval_accuracy_high_school_statistics": 0.34782608695652173,
"mmlu_eval_accuracy_philosophy": 0.5588235294117647,
"mmlu_eval_accuracy_high_school_microeconomics": 0.5769230769230769,
"mmlu_eval_accuracy_global_facts": 0.5,
"mmlu_eval_accuracy_anatomy": 0.6428571428571429,
"mmlu_eval_accuracy_college_computer_science": 0.36363636363636365,
"mmlu_eval_accuracy_college_physics": 0.5454545454545454,
"mmlu_eval_accuracy_high_school_chemistry": 0.2727272727272727,
"mmlu_eval_accuracy_astronomy": 0.5625,
"mmlu_eval_accuracy_elementary_mathematics": 0.21951219512195122,
"mmlu_eval_accuracy_high_school_macroeconomics": 0.4418604651162791,
"mmlu_eval_accuracy_college_chemistry": 0.25,
"mmlu_eval_accuracy": 0.5385831470660036}
```
### MMLU test result:
```json
{"mmlu_loss": 1.6477740873911495,
"mmlu_test_accuracy_us_foreign_policy": 0.76,
"mmlu_test_accuracy_conceptual_physics": 0.3659574468085106,
"mmlu_test_accuracy_professional_accounting": 0.38652482269503546,
"mmlu_test_accuracy_high_school_world_history": 0.7088607594936709,
"mmlu_test_accuracy_human_aging": 0.6547085201793722,
"mmlu_test_accuracy_clinical_knowledge": 0.569811320754717,
"mmlu_test_accuracy_abstract_algebra": 0.36,
"mmlu_test_accuracy_machine_learning": 0.3392857142857143,
"mmlu_test_accuracy_high_school_geography": 0.6767676767676768,
"mmlu_test_accuracy_medical_genetics": 0.54,
"mmlu_test_accuracy_virology": 0.4939759036144578,
"mmlu_test_accuracy_professional_medicine": 0.4889705882352941,
"mmlu_test_accuracy_philosophy": 0.594855305466238,
"mmlu_test_accuracy_logical_fallacies": 0.656441717791411,
"mmlu_test_accuracy_formal_logic": 0.2857142857142857,
"mmlu_test_accuracy_electrical_engineering": 0.5103448275862069,
"mmlu_test_accuracy_anatomy": 0.4962962962962963,
"mmlu_test_accuracy_computer_security": 0.68,
"mmlu_test_accuracy_high_school_physics": 0.3509933774834437,
"mmlu_test_accuracy_high_school_statistics": 0.37962962962962965,
"mmlu_test_accuracy_high_school_us_history": 0.7009803921568627,
"mmlu_test_accuracy_college_biology": 0.5347222222222222,
"mmlu_test_accuracy_college_mathematics": 0.32,
"mmlu_test_accuracy_marketing": 0.7606837606837606,
"mmlu_test_accuracy_moral_scenarios": 0.2849162011173184,
"mmlu_test_accuracy_high_school_mathematics": 0.3148148148148148,
"mmlu_test_accuracy_high_school_microeconomics": 0.5168067226890757,
"mmlu_test_accuracy_college_computer_science": 0.48,
"mmlu_test_accuracy_college_chemistry": 0.35,
"mmlu_test_accuracy_global_facts": 0.31,
"mmlu_test_accuracy_management": 0.6990291262135923,
"mmlu_test_accuracy_security_studies": 0.6204081632653061,
"mmlu_test_accuracy_high_school_psychology": 0.7211009174311926,
"mmlu_test_accuracy_international_law": 0.7272727272727273,
"mmlu_test_accuracy_college_medicine": 0.44508670520231214,
"mmlu_test_accuracy_professional_psychology": 0.5098039215686274,
"mmlu_test_accuracy_high_school_european_history": 0.6545454545454545,
"mmlu_test_accuracy_prehistory": 0.5925925925925926,
"mmlu_test_accuracy_business_ethics": 0.51,
"mmlu_test_accuracy_high_school_chemistry": 0.45320197044334976,
"mmlu_test_accuracy_high_school_government_and_politics": 0.7461139896373057,
"mmlu_test_accuracy_astronomy": 0.5723684210526315,
"mmlu_test_accuracy_human_sexuality": 0.5877862595419847,
"mmlu_test_accuracy_miscellaneous": 0.735632183908046,
"mmlu_test_accuracy_public_relations": 0.6181818181818182,
"mmlu_test_accuracy_elementary_mathematics": 0.35185185185185186,
"mmlu_test_accuracy_world_religions": 0.7602339181286549,
"mmlu_test_accuracy_moral_disputes": 0.5838150289017341,
"mmlu_test_accuracy_econometrics": 0.2894736842105263,
"mmlu_test_accuracy_high_school_computer_science": 0.58,
"mmlu_test_accuracy_jurisprudence": 0.6296296296296297,
"mmlu_test_accuracy_nutrition": 0.5980392156862745,
"mmlu_test_accuracy_high_school_macroeconomics": 0.4897435897435897,
"mmlu_test_accuracy_professional_law": 0.36962190352020863,
"mmlu_test_accuracy_high_school_biology": 0.635483870967742,
"mmlu_test_accuracy_college_physics": 0.3235294117647059,
"mmlu_test_accuracy_sociology": 0.7164179104477612,
"mmlu_test_accuracy": 0.5332109924946602}
```
## License and intended use
This lazy-lora adapter is based on [Meta's LLaMA-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf), and using the [oasst1 dataset](https://huggingface.co/datasets/OpenAssistant/oasst1), following [Guanaco](https://huggingface.co/timdettmers/guanaco-65b).
lazy lora adapter weights are available under LLAMA-2 license. Note the use of the lazy lora adapter weights, requires access to the LLaMA model weighs. Lazy lora is based on LLaMA and therefore should be used according to the LLaMA license.
## Risks and Biases
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets; it is possible that this model could generate lewd, biased, or otherwise offensive outputs.
|
wuxianchao/lazylora-13bhf
|
wuxianchao
| 2023-08-05T06:20:25Z | 0 | 1 | null |
[
"arxiv:2305.14314",
"arxiv:2106.09685",
"arxiv:2110.07602",
"arxiv:2104.08691",
"arxiv:2303.16199",
"license:llama2",
"region:us"
] | null | 2023-07-24T01:16:24Z |
---
license: llama2
---
## Lazy LoRA
### Benefits
0. using the updated [Meta's LLaMA-2 models](https://huggingface.co/meta-llama/Llama-2-13b-hf).
1. support [4-bit qlora](https://arxiv.org/abs/2305.14314), extreme GPU memory and inference time saving;
2. comparable MMLU evaluation dataset results, llama2-13b:
| | eval | test | comp-eval | comp-test |
|---------------|--------|--------|-----------|-----------|
|llama2-13b | 56.57% | 55.71% | | |
|ckpt-200 | 56.19% | 54.75% | -0.38% | -0.96% |
|ckpt-800 | 53.16% | 55.06% | -3.41% | -0.65% |
llama2-13b: "31820c723bc0368c339e067553021dc6a8bf4375"
3. This lazy-lora adapter is based on [Meta's LLaMA-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf), and using the [oasst1 dataset](https://huggingface.co/datasets/OpenAssistant/oasst1), following [Guanaco](https://huggingface.co/timdettmers/guanaco-65b).
### Introduction
Determine the rank of LoRA layers by the singular values of pretrained weight matrices.
Also, combines:
1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/abs/2106.09685)
2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.3
53/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.or
g/pdf/2110.07602.pdf)
3. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691)
4. LLaMA adapter: [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention] (https://arxiv.org/abs/2303.16199)
in one model.
This allows you to perform LoRA (additional low rank adapters inserted to each linear layer), and prompt learning (additional virtual tokens attached to the input and to the attention layers acting as `past_key_values`)
## Usage:
```python
import sys
sys.path.insert(1, '/workspace/asr/peft/src')
# TODO set this path to the lazy-lora source code path,
# or you can install it from source code:
# TODO, please install lazylora for usage:
# git clone git@github.com:Xianchao-Wu/peft.git
# cd peft
# python setup.py install
from transformers import (AutoTokenizer,
AutoModelForCausalLM, BitsAndBytesConfig)
from peft import PeftModel, PeftConfig
import os
import torch
#import ipdb; ipdb.set_trace()
cache_dir="/workspace/asr/peft/qlora"
# TODO set this cache_dir to the path where you
# stored (or, want to store) llama2-13b-hf model
lazylora_dir=os.getcwd()
# the path that contains 'adapter_config.json'
# and 'adapter_model.bin'
config = PeftConfig.from_pretrained(lazylora_dir)
tokenizer = AutoTokenizer.from_pretrained(
config.base_model_name_or_path,
cache_dir=cache_dir,
use_auth_token=True
)
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
quantization_config=bnb_config,
device_map="auto",
cache_dir=cache_dir,
use_auth_token=True
)
#model.print_trainable_parameters()
print(sum(p.numel() for p in model.parameters()))
# 6,671,979,520 -> half-size of 13B due to 4-bit loading
model = PeftModel.from_pretrained(model, lazylora_dir)
print('after adding lazy lora parameters:')
model.print_trainable_parameters()
# trainable params: 0 || all params: 6,922,290,688 || trainable%: 0.0
```
## MMLU result:
### MMLU eval result:
```json
{"mmlu_loss": 1.459045737611944,
"mmlu_eval_accuracy_sociology": 0.8181818181818182,
"mmlu_eval_accuracy_high_school_government_and_politics": 0.7142857142857143,
"mmlu_eval_accuracy_marketing": 0.88,
"mmlu_eval_accuracy_us_foreign_policy": 0.9090909090909091,
"mmlu_eval_accuracy_high_school_world_history": 0.5,
"mmlu_eval_accuracy_management": 0.7272727272727273,
"mmlu_eval_accuracy_college_mathematics": 0.36363636363636365,
"mmlu_eval_accuracy_high_school_us_history": 0.8181818181818182,
"mmlu_eval_accuracy_high_school_computer_science": 0.5555555555555556,
"mmlu_eval_accuracy_elementary_mathematics": 0.3170731707317073,
"mmlu_eval_accuracy_high_school_chemistry": 0.36363636363636365,
"mmlu_eval_accuracy_computer_security": 0.8181818181818182,
"mmlu_eval_accuracy_college_chemistry": 0.375,
"mmlu_eval_accuracy_econometrics": 0.4166666666666667,
"mmlu_eval_accuracy_professional_psychology": 0.5362318840579711,
"mmlu_eval_accuracy_high_school_statistics": 0.30434782608695654,
"mmlu_eval_accuracy_high_school_macroeconomics": 0.5581395348837209,
"mmlu_eval_accuracy_moral_scenarios": 0.36,
"mmlu_eval_accuracy_moral_disputes": 0.6578947368421053,
"mmlu_eval_accuracy_astronomy": 0.5625,
"mmlu_eval_accuracy_professional_accounting": 0.41935483870967744,
"mmlu_eval_accuracy_human_sexuality": 0.5,
"mmlu_eval_accuracy_world_religions": 0.7894736842105263,
"mmlu_eval_accuracy_high_school_psychology": 0.8166666666666667,
"mmlu_eval_accuracy_nutrition": 0.7575757575757576,
"mmlu_eval_accuracy_high_school_geography": 0.8181818181818182,
"mmlu_eval_accuracy_global_facts": 0.4,
"mmlu_eval_accuracy_high_school_mathematics": 0.20689655172413793,
"mmlu_eval_accuracy_college_medicine": 0.4090909090909091,
"mmlu_eval_accuracy_electrical_engineering": 0.5,
"mmlu_eval_accuracy_jurisprudence": 0.6363636363636364,
"mmlu_eval_accuracy_public_relations": 0.5833333333333334,
"mmlu_eval_accuracy_virology": 0.4444444444444444,
"mmlu_eval_accuracy_high_school_physics": 0.29411764705882354,
"mmlu_eval_accuracy_college_physics": 0.6363636363636364,
"mmlu_eval_accuracy_miscellaneous": 0.686046511627907,
"mmlu_eval_accuracy_international_law": 0.8461538461538461,
"mmlu_eval_accuracy_human_aging": 0.6956521739130435,
"mmlu_eval_accuracy_conceptual_physics": 0.38461538461538464,
"mmlu_eval_accuracy_formal_logic": 0.2857142857142857,
"mmlu_eval_accuracy_medical_genetics": 0.7272727272727273,
"mmlu_eval_accuracy_philosophy": 0.6470588235294118,
"mmlu_eval_accuracy_professional_medicine": 0.5806451612903226,
"mmlu_eval_accuracy_security_studies": 0.6666666666666666,
"mmlu_eval_accuracy_college_biology": 0.625,
"mmlu_eval_accuracy_high_school_biology": 0.53125,
"mmlu_eval_accuracy_machine_learning": 0.45454545454545453,
"mmlu_eval_accuracy_high_school_microeconomics": 0.7307692307692307,
"mmlu_eval_accuracy_professional_law": 0.38823529411764707,
"mmlu_eval_accuracy_logical_fallacies": 0.7777777777777778,
"mmlu_eval_accuracy_high_school_european_history": 0.7222222222222222,
"mmlu_eval_accuracy_prehistory": 0.6,
"mmlu_eval_accuracy_abstract_algebra": 0.2727272727272727,
"mmlu_eval_accuracy_clinical_knowledge": 0.5517241379310345,
"mmlu_eval_accuracy_anatomy": 0.35714285714285715,
"mmlu_eval_accuracy_business_ethics": 0.2727272727272727,
"mmlu_eval_accuracy_college_computer_science": 0.45454545454545453,
"mmlu_eval_accuracy": 0.5618637260755334}
```
### MMLU test result:
```json
{"mmlu_loss": 1.4737504210172938,
"mmlu_test_accuracy_professional_law": 0.4172099087353325,
"mmlu_test_accuracy_formal_logic": 0.3412698412698413,
"mmlu_test_accuracy_anatomy": 0.4962962962962963,
"mmlu_test_accuracy_professional_medicine": 0.5404411764705882,
"mmlu_test_accuracy_high_school_mathematics": 0.2851851851851852,
"mmlu_test_accuracy_high_school_government_and_politics": 0.8134715025906736,
"mmlu_test_accuracy_electrical_engineering": 0.5448275862068965,
"mmlu_test_accuracy_conceptual_physics": 0.4595744680851064,
"mmlu_test_accuracy_jurisprudence": 0.7407407407407407,
"mmlu_test_accuracy_college_chemistry": 0.4,
"mmlu_test_accuracy_high_school_computer_science": 0.56,
"mmlu_test_accuracy_college_computer_science": 0.44,
"mmlu_test_accuracy_marketing": 0.7863247863247863,
"mmlu_test_accuracy_high_school_psychology": 0.7376146788990826,
"mmlu_test_accuracy_professional_psychology": 0.5522875816993464,
"mmlu_test_accuracy_logical_fallacies": 0.6748466257668712,
"mmlu_test_accuracy_high_school_microeconomics": 0.5672268907563025,
"mmlu_test_accuracy_international_law": 0.7107438016528925,
"mmlu_test_accuracy_high_school_world_history": 0.7088607594936709,
"mmlu_test_accuracy_medical_genetics": 0.57,
"mmlu_test_accuracy_professional_accounting": 0.41843971631205673,
"mmlu_test_accuracy_business_ethics": 0.57,
"mmlu_test_accuracy_moral_scenarios": 0.293854748603352,
"mmlu_test_accuracy_abstract_algebra": 0.34,
"mmlu_test_accuracy_elementary_mathematics": 0.328042328042328,
"mmlu_test_accuracy_high_school_biology": 0.6580645161290323,
"mmlu_test_accuracy_public_relations": 0.6090909090909091,
"mmlu_test_accuracy_high_school_physics": 0.31788079470198677,
"mmlu_test_accuracy_astronomy": 0.4934210526315789,
"mmlu_test_accuracy_high_school_us_history": 0.75,
"mmlu_test_accuracy_college_medicine": 0.5028901734104047,
"mmlu_test_accuracy_college_mathematics": 0.34,
"mmlu_test_accuracy_econometrics": 0.2894736842105263,
"mmlu_test_accuracy_clinical_knowledge": 0.6150943396226415,
"mmlu_test_accuracy_moral_disputes": 0.630057803468208,
"mmlu_test_accuracy_machine_learning": 0.26785714285714285,
"mmlu_test_accuracy_human_sexuality": 0.5801526717557252,
"mmlu_test_accuracy_virology": 0.4457831325301205,
"mmlu_test_accuracy_high_school_chemistry": 0.43349753694581283,
"mmlu_test_accuracy_high_school_geography": 0.6515151515151515,
"mmlu_test_accuracy_high_school_european_history": 0.6363636363636364,
"mmlu_test_accuracy_nutrition": 0.5849673202614379,
"mmlu_test_accuracy_human_aging": 0.6502242152466368,
"mmlu_test_accuracy_philosophy": 0.639871382636656,
"mmlu_test_accuracy_global_facts": 0.34,
"mmlu_test_accuracy_high_school_statistics": 0.4722222222222222,
"mmlu_test_accuracy_college_biology": 0.5486111111111112,
"mmlu_test_accuracy_management": 0.6893203883495146,
"mmlu_test_accuracy_sociology": 0.7213930348258707,
"mmlu_test_accuracy_security_studies": 0.6326530612244898,
"mmlu_test_accuracy_college_physics": 0.24509803921568626,
"mmlu_test_accuracy_high_school_macroeconomics": 0.5,
"mmlu_test_accuracy_us_foreign_policy": 0.83,
"mmlu_test_accuracy_miscellaneous": 0.7343550446998723,
"mmlu_test_accuracy_prehistory": 0.6512345679012346,
"mmlu_test_accuracy_computer_security": 0.69,
"mmlu_test_accuracy_world_religions": 0.7602339181286549,
"mmlu_test_accuracy": 0.5475190434068002}
```
## License and intended use
This lazy-lora adapter is based on [Meta's LLaMA-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf), and using the [oasst1 dataset](https://huggingface.co/datasets/OpenAssistant/oasst1), following [Guanaco](https://huggingface.co/timdettmers/guanaco-65b).
lazy lora adapter weights are available under LLAMA-2 license. Note the use of the lazy lora adapter weights, requires access to the LLaMA model weighs. Lazy lora is based on LLaMA and therefore should be used according to the LLaMA license.
## Risks and Biases
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets; it is possible that this model could generate lewd, biased, or otherwise offensive outputs.
|
anas-awadalla/mpt-1b-redpajama-200b
|
anas-awadalla
| 2023-08-05T06:19:59Z | 4,795 | 2 |
transformers
|
[
"transformers",
"pytorch",
"mosaic_gpt",
"text-generation",
"custom_code",
"dataset:togethercomputer/RedPajama-Data-1T",
"arxiv:2302.13971",
"arxiv:2205.14135",
"arxiv:2108.12409",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-05-25T00:58:32Z |
---
license: apache-2.0
datasets:
- togethercomputer/RedPajama-Data-1T
---
# MPT-1b-RedPajama-200b
MPT-1b-RedPajama-200b is a 1.3 billion parameter decoder-only transformer trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).
The model was trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions as were used by the [Llama series of models](https://arxiv.org/abs/2302.13971).
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
April 20, 2023
## How to Use
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom model architecture `MosaicGPT` that is not yet part of the `transformers` package.
`MosaicGPT` includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALIBI](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b', trust_remote_code=True)
```
To use the optimized triton implementation of FlashAttention, you can load with `attn_impl='triton'` and move the model to `bfloat16` like so:
```python
model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b', trust_remote_code=True, attn_impl='triton')
model.to(device='cuda:0', dtype=torch.bfloat16)
```
## Model Description
This model uses the MosaicML LLM codebase, which can be found in the [MosaicML Examples Repository](https://github.com/mosaicml/examples/tree/v0.0.4/examples/llm).
The architecture is a modification of a standard decoder-only transformer.
The transformer has 24 layers, 16 attention heads, and width 2048.
The model has been modified from a standard transformer in the following ways:
* It uses ALiBi and does not use positional embeddings.
* It uses QK LayerNorm.
* It does not use biases.
## Training Data
The model was trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix:
* 67% RedPajama Common Crawl
* 15% [C4](https://huggingface.co/datasets/c4)
* 4.5% RedPajama GitHub
* 4.5% RedPajama Wikipedia
* 4.5% RedPajama Books
* 2.5% RedPajama Arxiv
* 2% RedPajama StackExchange
This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971).
Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above.
The examples were shuffled within each dataset.
Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
## Training Configuration
This model was trained on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using FSDP.
## Acknowledgements
This model builds on the work of [Together](https://www.together.xyz), which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models.
We gratefully acknowledge the hard work of the team that put together this dataset, and we hope this model serves as a useful companion to that work.
We also gratefully acknowledge the work of the researchers who created the Llama series of models, which was the impetus for our efforts and those who worked on the RedPajama project.
|
CyberHarem/mp_446_girlsfrontline
|
CyberHarem
| 2023-08-05T05:46:44Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/mp_446_girlsfrontline",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-05T05:43:26Z |
---
license: mit
datasets:
- CyberHarem/mp_446_girlsfrontline
pipeline_tag: text-to-image
tags:
- art
---
# Lora of mp_446_girlsfrontline
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/mp_446_girlsfrontline.pt` as the embedding and `1500/mp_446_girlsfrontline.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `mp_446_girlsfrontline`.**
These are available steps:
| Steps | bikini | free | nude | Download |
|--------:|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:-------------------------------------------|
| 1500 |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/mp_446_girlsfrontline.zip) |
| 1400 |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/mp_446_girlsfrontline.zip) |
| 1300 |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/mp_446_girlsfrontline.zip) |
| 1200 |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/mp_446_girlsfrontline.zip) |
| 1100 |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/mp_446_girlsfrontline.zip) |
| 1000 |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/mp_446_girlsfrontline.zip) |
| 900 |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/mp_446_girlsfrontline.zip) |
| 800 |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/mp_446_girlsfrontline.zip) |
| 700 |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/mp_446_girlsfrontline.zip) |
| 600 |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/mp_446_girlsfrontline.zip) |
| 500 |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/mp_446_girlsfrontline.zip) |
| 400 |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/mp_446_girlsfrontline.zip) |
| 300 |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/mp_446_girlsfrontline.zip) |
| 200 |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/mp_446_girlsfrontline.zip) |
| 100 |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/mp_446_girlsfrontline.zip) |
|
kernelguardian/instruct2action_llama2
|
kernelguardian
| 2023-08-05T05:32:59Z | 2 | 0 |
peft
|
[
"peft",
"tensorboard",
"region:us"
] | null | 2023-08-05T03:38:23Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
Koishit/Kevin_parker
|
Koishit
| 2023-08-05T05:02:58Z | 0 | 0 | null |
[
"music",
"audio-to-audio",
"en",
"license:openrail",
"region:us"
] |
audio-to-audio
| 2023-08-05T04:55:53Z |
---
license: openrail
language:
- en
pipeline_tag: audio-to-audio
tags:
- music
---
|
Thamer/albert-fine-tuned
|
Thamer
| 2023-08-05T04:41:49Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"albert",
"text-classification",
"generated_from_keras_callback",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-03T16:06:51Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_keras_callback
model-index:
- name: Thamer/albert-fine-tuned
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. -->
# Thamer/albert-fine-tuned
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6843
- Train Binary Accuracy: 0.5640
- Validation Loss: 0.6990
- Validation Binary Accuracy: 0.5092
- Train Accuracy: 0.6032
- 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 3156, '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 | Train Binary Accuracy | Validation Loss | Validation Binary Accuracy | Train Accuracy | Epoch |
|:----------:|:---------------------:|:---------------:|:--------------------------:|:--------------:|:-----:|
| 0.6987 | 0.5410 | 0.6446 | 0.6835 | 0.5333 | 0 |
| 0.6976 | 0.5642 | 0.6981 | 0.5092 | 0.4908 | 1 |
| 0.6843 | 0.5640 | 0.6990 | 0.5092 | 0.6032 | 2 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.11.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
BlackSwan1827/ppo-Pyramids
|
BlackSwan1827
| 2023-08-05T04:15:32Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-08-05T04:15:29Z |
---
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: BlackSwan1827/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
YanJiangJerry/covid-twitter-bert-v2_3_4_2e-05_0.01
|
YanJiangJerry
| 2023-08-05T04:04:01Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:digitalepidemiologylab/covid-twitter-bert-v2",
"base_model:finetune:digitalepidemiologylab/covid-twitter-bert-v2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-05T03:46:26Z |
---
license: mit
base_model: digitalepidemiologylab/covid-twitter-bert-v2
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: covid-twitter-bert-v2_3_4_2e-05_0.01
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. -->
# covid-twitter-bert-v2_3_4_2e-05_0.01
This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8148
- F1: 0.7871
- Recall: 0.7871
- Precision: 0.7871
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| 0.8198 | 1.0 | 745 | 0.8577 | 0.6846 | 0.6846 | 0.6846 |
| 0.7384 | 2.0 | 1490 | 0.6214 | 0.7709 | 0.7709 | 0.7709 |
| 0.5273 | 3.0 | 2235 | 0.8148 | 0.7871 | 0.7871 | 0.7871 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
ShynBui/s28
|
ShynBui
| 2023-08-05T03:59:45Z | 125 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T16:16:30Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: s28
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. -->
# s28
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
EllieKini/Ai
|
EllieKini
| 2023-08-05T03:35:34Z | 0 | 0 |
fairseq
|
[
"fairseq",
"rvc",
"sail-rvc",
"audio-to-audio",
"en",
"ja",
"ko",
"dataset:Open-Orca/OpenOrca",
"doi:10.57967/hf/0961",
"license:openrail",
"region:us"
] |
audio-to-audio
| 2023-08-04T22:36:59Z |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
library_name: fairseq
license: openrail
datasets:
- Open-Orca/OpenOrca
language:
- en
- ja
- ko
metrics:
- character
---
# AiHoshinoTTS
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:17:48
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
|
pain/t5-small-finetuned-xsum
|
pain
| 2023-08-05T03:32:57Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-04T08:06:08Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.2399
---
<!-- 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. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4782
- Rouge1: 28.2399
- Rouge2: 7.7274
- Rougel: 22.2538
- Rougelsum: 22.2486
- Gen Len: 18.8191
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7161 | 1.0 | 12753 | 2.4782 | 28.2399 | 7.7274 | 22.2538 | 22.2486 | 18.8191 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
|
Bastian1111/q-FrozenLake-v1-4x4-noSlippery
|
Bastian1111
| 2023-08-05T03:26:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-05T03:26:53Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Bastian1111/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Ellbendls/a2c-PandaReachDense-v2
|
Ellbendls
| 2023-08-05T03:20:12Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-08T04:14:19Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.43 +/- 0.21
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
BlackSwan1827/ppo-SnowballTarget
|
BlackSwan1827
| 2023-08-05T03:06:42Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-08-05T03:06:39Z |
---
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: BlackSwan1827/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Thamer/electra-fine-tuned
|
Thamer
| 2023-08-05T03:06:10Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"electra",
"text-classification",
"generated_from_keras_callback",
"base_model:google/electra-base-discriminator",
"base_model:finetune:google/electra-base-discriminator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-04T11:46:23Z |
---
license: apache-2.0
base_model: google/electra-base-discriminator
tags:
- generated_from_keras_callback
model-index:
- name: Thamer/electra-fine-tuned
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. -->
# Thamer/electra-fine-tuned
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6869
- Train Binary Accuracy: 0.5541
- Validation Loss: 0.7140
- Validation Binary Accuracy: 0.5092
- Train F1: 0.6748
- 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 3156, '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 | Train Binary Accuracy | Validation Loss | Validation Binary Accuracy | Train F1 | Epoch |
|:----------:|:---------------------:|:---------------:|:--------------------------:|:--------:|:-----:|
| 0.7292 | 0.6233 | 0.7062 | 0.5092 | 0.6748 | 0 |
| 0.6881 | 0.5551 | 0.7137 | 0.5092 | 0.6748 | 1 |
| 0.6869 | 0.5541 | 0.7140 | 0.5092 | 0.6748 | 2 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.11.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
BigSalmon/InformalToFormalLincoln107Paraphrase
|
BigSalmon
| 2023-08-05T02:50:41Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-27T18:11:42Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln107Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln107Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
```
```
Leadership | Lecture 17: Worker Morale
What Workers Look for in Companies:
• Benefits
o Tuition reimbursement
o Paid parental leave
o 401K matching
o Profit sharing
o Pension plans
o Free meals
• Social responsibility
o Environmental stewardship
o Charitable contributions
o Diversity
• Work-life balance
o Telecommuting
o Paid holidays and vacation
o Casual dress
• Growth opportunities
• Job security
• Competitive compensation
• Recognition
o Open-door policies
o Whistleblower protection
o Employee-of-the-month awards
o Positive performance reviews
o Bonuses
```
```
description: business
keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification
```
```
3. In this task, you are given a company name and you need to find its industry.
McDonalds -- Restaurant
Facebook -- Social Network
IKEA -- Furniture
American Express -- Credit Services
Nokia -- Telecom
Nintendo -- Entertainment
4. In this task, you are given a Month and you need to convert it to its corresponding season
April -- Spring
December -- Winter
July -- Summer
October -- Fall
February -- Winter
5. In this task, you are given a sentence with a missing word and you need to predict the correct word.
Managers should set an _____ for their employees. -- example
Some people spend more than four _____ in the gym. -- hours
The police were on the _____ of arresting the suspect. -- verge
They were looking for _____ on how to solve the problem. -- guidance
What is the _____ of the coffee? -- price
6. In this task, you are given a paragraph and you need to reorder it to make it logical.
It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters.
It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman.
It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth.
```
```
trivia: What is the population of South Korea?
response: 51 million.
***
trivia: What is the minimum voting age in the US?
response: 18.
***
trivia: What are the first ten amendments of the US constitution called?
response: Bill of Rights.
```
```
ideas: in modern-day america, it is customary for the commander-in-chief to conduct regular press conferences
related keywords: transparency, check and balance, sacrosanct, public accountability, adversarial, unscripted, direct access, open government, watchdog, healthy democracy, institutional integrity, right to know, direct line of communication, behind closed doors, updates, track progress, instill confidence, reassure, humanize, leadership style, day-to-day, forthcoming, demystify, ask hard questions
***
ideas: i know this one guy who retired so young, attesting to how careful they were with money.
related keywords: money management, resourceful, penny-pinching, live below their means, frugal, financial discipline, financial independence, conservative, long-term vision, discretionary spending, deferred gratification, preparedness, self-control, cushion
```
```
less specific: actors and musicians should ( support democracy ).
clarifies: actors and musicians should ( wield their celebrity to amplify pro-democracy messaging / marshal their considerable influence in the service of the democratic cause ).
***
less specific: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( be careful ).
clarifies: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( master their desires / exercise self-restraint / embrace frugality / restrain their appetite for splendor ).
```
```
dull: clean
emotional heft: spotless, immaculate, pristine
***
dull: hot
emotional heft: scorching, searing, blistering
***
dull: happy
emotional heft: euphoric
```
```
text: {guide: vividly describe the premise of the show "seinfield"} -> set in the heart of bustling new york city, the sitcom "seinfield" follows the everyday {restrict: term that implies they get into trouble but in a comical way} -> misadventures of four neurotic friends. on any given episode, one can find them quarreling over their favorite diner's latest menu change, haggling over the division of household expenses, or contriving a scheme to spy on the apartment's newest resident. mundane as their exploits may be, they never fail to elicit a hearty laugh. {guide: mention how larry david is responsible} -> behind the show's witty, incisive dialogue lies the sharp pen of larry david, who co-created the show with jerry seinfeld. {guide: mention how larry david came up with the most recognizable lines} -> it is his genius that conjured such instantly {restrict: term that imply everybody knows them} -> recognizable quips as "no soup for you!" and "not that there's anything wrong with that!". {guide: mention how humanity should revel in having such good comedians these days} -> as this list of laugh-out-loud lines attests, the world is fortunate to ( count such a sharp comedic mind among its ranks / have such a talented humorist in its midst / have such comedic talent gracing its airwaves ).
```
```
system: i'm not sure i agree with your assessment of the situation.
user: replace "assessment" with a more casual term.
chatbot: ( take / read / view ).
system: i've been feeling really busy lately, trying to balance work and family responsibilities.
user: replace "busy" with a more intense word that conveys it is too much.
chatbot: overwhelmed.
system: the candidate's stance on immigration is quite nebulous, and it's difficult to discern exactly what they would do if elected.
user: replace "nebulous" with a word that conveys a lack of detail.
chatbot: ( vague / ambiguous ).
```
```
user: exorbitant in price, scholarly journals unconscionably define the breadth of one's knowledge awareness by the contents of their wallet. [replace “knowledge awareness” with a more natural expression]
chatbot: intellectual horizons.
user: can you do another alternative to “intellectual horizons” that has more relation to “scholarly journals”?
chatbot: academic enlightenment.
```
```
key: calculate.
syn: estimate, consider, weigh, number, count, apportion, proportion, investigate, reckon, rate, compute.
ant: guess, conjecture, hit, chance, risk, stake, miscalculate.
```
```
description: more forceful version of curious that is less forceful than nosy
answer: inquisitive
description: more forceful version of hopeful that is less forceful than overconfident
answer: optimistic
```
```
key: inquisitive
positive: curious, interested
negative: nosy, prying
***
key: witty
positive: clever, humorous
negative: sarcastic, caustic
***
key: influential
positive: impactful, powerful
negative: overbearing, domineering
```
```
defective: the blogger's { use of language imprecise } confused an already complicated issue.
precise: the blogger's ( vague wording ) confused an already complicated issue.
defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals.
precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals.
```
```
example: the new car uses gas.
boring: uses
stronger: guzzles
example: he hates people that are rude.
boring: hates
stronger: loathes, abhors, despises, scorns, detests
```
```
initial: The music at the party was [ loud; replace with a word that suggests a more uncomfortable noise level ] and overwhelming.
modified: The music at the party was { ear-splitting } and overwhelming.
initial: their house is [ small; replace with a positive spin ].
modified: their house is { cozy }.
```
```
defective: they spent the weekend enjoying { time do what you want }.
precise: they spent the weekend enjoying ( leisure activities).
defective: the author rightly notes the inequities perpetuated by { employment based on who you know }.
precise: the author rightly notes the inequities perpetuated by ( nepotism ).
defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals.
precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals.
```
```
persona: human resources manager
buzzwords: pipeline, talent, retention, compensation, flexible, recruitment, personnel, resume, competitive, quality, onboard
```
```
lost among the razzle-dazzle of las vegas, the infinite splendors of san francisco languish in {definition: when something is difficult to understand or explain} -> ( obscure / cryptic / enigmatic / perplexing ) obscurity.
***
just as with any other good, transportation efficiency is a {definition: when something is necessary for a particular outcome} -> ( prerequisite / requirement / precondition ) of economic growth.
***
the coach's {definition: when someone is lenient and easygoing, often letting their team or players get away with mistakes} -> ( permissive / lenient / indulgent ) approach to training left many athletes feeling unprepared for the upcoming season.
```
|
ClementXie/whisper-small
|
ClementXie
| 2023-08-05T02:50:06Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-04T23:11:40Z |
---
language:
- dv
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Dv - ClementXie
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- name: Wer
type: wer
value: 16.908926522238062
---
<!-- 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 Dv - ClementXie
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2216
- Wer Ortho: 72.6095
- Wer: 16.9089
## 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: 4
- 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: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.2057 | 0.41 | 500 | 0.2216 | 72.6095 | 16.9089 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.13.1
- Datasets 2.14.3
- Tokenizers 0.13.3
|
saurabh2086/dqn-SpaceInvadersNoFrameskip-v4
|
saurabh2086
| 2023-08-05T02:42:52Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-05T02:42:14Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 688.50 +/- 369.16
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saurabh2086 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saurabh2086 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga saurabh2086
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
zwellington/pubhealth-expanded-1
|
zwellington
| 2023-08-05T02:31:56Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:clupubhealth",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-05T01:54:51Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
datasets:
- clupubhealth
metrics:
- rouge
model-index:
- name: pubhealth-expanded-1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: clupubhealth
type: clupubhealth
config: expanded
split: test
args: expanded
metrics:
- name: Rouge1
type: rouge
value: 28.6755
---
<!-- 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. -->
# pubhealth-expanded-1
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the clupubhealth dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3198
- Rouge1: 28.6755
- Rouge2: 9.2869
- Rougel: 21.9675
- Rougelsum: 22.2946
- Gen Len: 19.85
## 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: 12
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 120
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.6788 | 0.08 | 40 | 2.3758 | 29.5273 | 9.3588 | 22.4799 | 22.6212 | 19.835 |
| 3.4222 | 0.15 | 80 | 2.3484 | 29.0821 | 9.1988 | 22.3907 | 22.5996 | 19.88 |
| 3.3605 | 0.23 | 120 | 2.3500 | 29.2893 | 9.296 | 22.1247 | 22.4075 | 19.94 |
| 3.3138 | 0.31 | 160 | 2.3504 | 29.039 | 8.907 | 21.9631 | 22.2506 | 19.91 |
| 3.2678 | 0.39 | 200 | 2.3461 | 29.678 | 9.4429 | 22.3439 | 22.6962 | 19.92 |
| 3.2371 | 0.46 | 240 | 2.3267 | 28.535 | 9.1858 | 21.3721 | 21.6634 | 19.915 |
| 3.204 | 0.54 | 280 | 2.3330 | 29.0796 | 9.4283 | 21.8953 | 22.1867 | 19.885 |
| 3.1881 | 0.62 | 320 | 2.3164 | 29.1456 | 9.1919 | 21.9529 | 22.235 | 19.945 |
| 3.1711 | 0.69 | 360 | 2.3208 | 29.3212 | 9.4823 | 22.1643 | 22.4159 | 19.895 |
| 3.1752 | 0.77 | 400 | 2.3239 | 29.0408 | 9.3615 | 21.8007 | 22.0795 | 19.945 |
| 3.1591 | 0.85 | 440 | 2.3218 | 28.6336 | 9.2799 | 21.5843 | 21.9422 | 19.845 |
| 3.1663 | 0.93 | 480 | 2.3198 | 28.6755 | 9.2869 | 21.9675 | 22.2946 | 19.85 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Yuyang-z/zero123-xl
|
Yuyang-z
| 2023-08-05T02:13:46Z | 31 | 1 |
diffusers
|
[
"diffusers",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-04T11:09:24Z |
---
license: mit
---
Upload zero123-xl.ckpt, converted by diffusers scripts convert_original_stable_diffusion_to_diffusers.py
[Zero123-hf](https://github.com/kxhit/zero123_hf) implemented with diffusers pipelines.
Thanks Original Repo [Zero123](https://github.com/cvlab-columbia/zero123), and [Weights](https://huggingface.co/cvlab/zero123-weights).
|
patonw/reinforce-CartPole-v1
|
patonw
| 2023-08-05T02:12:06Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-01T19:32:37Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
danfi/llama2-qlora-finetunined-french
|
danfi
| 2023-08-05T02:11:20Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-05T02:11:03Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
YanJiangJerry/baseline_roberta-base_epoch3_batch4_lr2e-05_w0.01
|
YanJiangJerry
| 2023-08-05T01:58:50Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-05T01:46:58Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: baseline_roberta-base_epoch3_batch4_lr2e-05_w0.01
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. -->
# baseline_roberta-base_epoch3_batch4_lr2e-05_w0.01
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4562
- Accuracy: 0.8988
- F1: 0.8636
- Precision: 0.8675
- Recall: 0.8597
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6109 | 1.0 | 788 | 0.6462 | 0.8343 | 0.7306 | 0.9266 | 0.6030 |
| 0.4597 | 2.0 | 1576 | 0.4432 | 0.8899 | 0.8596 | 0.8189 | 0.9045 |
| 0.3418 | 3.0 | 2364 | 0.4562 | 0.8988 | 0.8636 | 0.8675 | 0.8597 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
jingwora/StableBeluga-7B-fine-tune-abstractQA-qlora-weight
|
jingwora
| 2023-08-05T01:57:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-05T01:56:51Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
UWECProgrammer/setfit-model-two
|
UWECProgrammer
| 2023-08-05T01:50:16Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-08-05T01:48:17Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# UWECProgrammer/setfit-model-two
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("UWECProgrammer/setfit-model-two")
# 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}
}
```
|
privatep/privatep
|
privatep
| 2023-08-05T01:08:27Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-05T01:08:27Z |
---
license: creativeml-openrail-m
---
|
YanJiangJerry/baseline_roberta-large_epoch3_batch4_lr2e-05_w0.01
|
YanJiangJerry
| 2023-08-05T01:02:14Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-05T00:24:46Z |
---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: baseline_roberta-large_epoch3_batch4_lr2e-05_w0.01
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. -->
# baseline_roberta-large_epoch3_batch4_lr2e-05_w0.01
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5836
- Accuracy: 0.7164
- F1: 0.3943
- Precision: 0.9651
- Recall: 0.2478
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7073 | 1.0 | 788 | 0.6524 | 0.6274 | 0.0 | 0.0 | 0.0 |
| 0.668 | 2.0 | 1576 | 0.5835 | 0.6274 | 0.0 | 0.0 | 0.0 |
| 0.6148 | 3.0 | 2364 | 0.5836 | 0.7164 | 0.3943 | 0.9651 | 0.2478 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
casonshep/token_classification_test
|
casonshep
| 2023-08-05T00:50:08Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-05T00:43:53Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_classification_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. -->
# token_classification_test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2859
- Precision: 0.9187
- Recall: 0.9095
- F1: 0.9140
- Accuracy: 0.9308
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 47 | 1.2700 | 0.6758 | 0.5896 | 0.6298 | 0.7121 |
| No log | 2.0 | 94 | 0.6468 | 0.8315 | 0.7864 | 0.8083 | 0.8461 |
| No log | 3.0 | 141 | 0.4607 | 0.8709 | 0.8422 | 0.8563 | 0.8845 |
| No log | 4.0 | 188 | 0.3841 | 0.8924 | 0.8686 | 0.8804 | 0.9047 |
| No log | 5.0 | 235 | 0.3380 | 0.9060 | 0.8905 | 0.8982 | 0.9180 |
| No log | 6.0 | 282 | 0.3164 | 0.9096 | 0.8934 | 0.9014 | 0.9213 |
| No log | 7.0 | 329 | 0.3072 | 0.9090 | 0.9001 | 0.9045 | 0.9227 |
| No log | 8.0 | 376 | 0.2997 | 0.9156 | 0.9009 | 0.9082 | 0.9258 |
| No log | 9.0 | 423 | 0.2940 | 0.9141 | 0.9058 | 0.9099 | 0.9269 |
| No log | 10.0 | 470 | 0.2904 | 0.9199 | 0.9076 | 0.9137 | 0.9312 |
| 0.5334 | 11.0 | 517 | 0.2894 | 0.9210 | 0.9093 | 0.9151 | 0.9314 |
| 0.5334 | 12.0 | 564 | 0.2884 | 0.9173 | 0.9081 | 0.9127 | 0.9295 |
| 0.5334 | 13.0 | 611 | 0.2862 | 0.9184 | 0.9089 | 0.9136 | 0.9305 |
| 0.5334 | 14.0 | 658 | 0.2859 | 0.9196 | 0.9103 | 0.9149 | 0.9310 |
| 0.5334 | 15.0 | 705 | 0.2859 | 0.9187 | 0.9095 | 0.9140 | 0.9308 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
snowneji/test_llama2
|
snowneji
| 2023-08-05T00:18:19Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-05T00:03:35Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
voidful/asr_hubert_cluster_bart_base
|
voidful
| 2023-08-05T00:17:35Z | 56 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"audio",
"automatic-speech-recognition",
"speech",
"asr",
"hubert",
"en",
"dataset:librispeech",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech
tags:
- audio
- automatic-speech-recognition
- speech
- asr
- hubert
license: apache-2.0
metrics:
- wer
- cer
---
# voidful/asr_hubert_cluster_bart_base
## Usage
download file
```shell
wget https://raw.githubusercontent.com/voidful/hubert-cluster-code/main/km_feat_100_layer_20
wget https://cdn-media.huggingface.co/speech_samples/sample1.flac
```
Hubert kmeans code
```python
import joblib
import torch
from transformers import Wav2Vec2FeatureExtractor, HubertModel
import soundfile as sf
class HubertCode(object):
def __init__(self, hubert_model, km_path, km_layer):
self.processor = Wav2Vec2FeatureExtractor.from_pretrained(hubert_model)
self.model = HubertModel.from_pretrained(hubert_model)
self.km_model = joblib.load(km_path)
self.km_layer = km_layer
self.C_np = self.km_model.cluster_centers_.transpose()
self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True)
self.C = torch.from_numpy(self.C_np)
self.Cnorm = torch.from_numpy(self.Cnorm_np)
if torch.cuda.is_available():
self.C = self.C.cuda()
self.Cnorm = self.Cnorm.cuda()
self.model = self.model.cuda()
def __call__(self, filepath, sampling_rate=None):
speech, sr = sf.read(filepath)
input_values = self.processor(speech, return_tensors="pt", sampling_rate=sr).input_values
if torch.cuda.is_available():
input_values = input_values.cuda()
hidden_states = self.model(input_values, output_hidden_states=True).hidden_states
x = hidden_states[self.km_layer].squeeze()
dist = (
x.pow(2).sum(1, keepdim=True)
- 2 * torch.matmul(x, self.C)
+ self.Cnorm
)
return dist.argmin(dim=1).cpu().numpy()
```
input
```python
hc = HubertCode("facebook/hubert-large-ll60k", './km_feat_100_layer_20', 20)
voice_ids = hc('./sample1.flac')
```
bart model
````python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("voidful/asr_hubert_cluster_bart_base")
model = AutoModelForSeq2SeqLM.from_pretrained("voidful/asr_hubert_cluster_bart_base")
````
generate output
```python
gen_output = model.generate(input_ids=tokenizer("".join([f":vtok{i}:" for i in voice_ids]),return_tensors='pt').input_ids,max_length=1024)
print(tokenizer.decode(gen_output[0], skip_special_tokens=True))
```
## Result
`going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to ask immediately afterwards`
|
vabatista/question-generation-t5-small-pt-br
|
vabatista
| 2023-08-05T00:09:55Z | 152 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"pt",
"dataset:squad",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-03T10:06:52Z |
---
license: afl-3.0
language:
- pt
pipeline_tag: text2text-generation
datasets:
- squad
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model is intended to be used generating questions and answers from brazilian portuguese text passages,
so you can finetune another BERT model into your generated triples (context-question-answer) for extractive question answering without supervision or labeled data.
It was trained using [unicamp-dl/ptt5-small-t5-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-small-t5-portuguese-vocab) base model and [Squad 1.1 portuguese version](https://huggingface.co/datasets/ArthurBaia/squad_v1_pt_br) dataset to generante question and answers from text passages.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Vitor Alcantara Batista (vabatista@gmail.com)
- **Model type:** T5 small
- **Language(s) (NLP):** Brazilian Portuguese
- **License:** [Academic Free License v. 3.0](https://opensource.org/license/afl-3-0-php/)
- **Finetuned from model :** unicamp-dl/ptt5-small-t5-portuguese-vocab
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** This model used code from this github repo [https://github.com/patil-suraj/question_generation/](https://github.com/patil-suraj/question_generation/)
## Usage
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
How to use it (after cloning the github repo above):
```
from pipelines import pipeline
nlp = pipeline("question-generation", model='vabatista/question-generation-t5-small-pt-br', tokenizer='vabatista/question-generation-t5-small-pt-br')
text = """ PUT YOUR TEXT PASSAGE HERE """
nlp(text)
```
Sample usage/results:
```
text = """A Volkswagen anunciou a chegada do ID.Buzz, a Kombi elétrica, ao Brasil. Em campanha publicitária, a marca alemã usou tecnologia de inteligência artificial
para criar um comercial com a cantora Elis Regina, falecida em 1982, e a sua filha, a também cantora Maria Rita. Ambas aparecem cantando juntas a música 'Como Nossos Pais', composta por Belchior e eternizada por Elis.
O vídeo, que já foi divulgado nas redes sociais da marca, foi exibido pela primeira vez em comemoração de 70 anos da Volkswagen no ginásio do Ibirapuera, em São Paulo.
Diante de 5 mil pessoas, entre funcionários e convidados, a apresentação ainda contou com a presença de Maria Rita, que também cantou ao vivo a canção e se emocionou bastante -
a cantora chegou a chorar abraçada com Ciro Possobom, CEO da VW do Brasil.
A técnica utilizada, conhecida também como "deep fake", aplica IA para criar conteúdos realistas. No caso, foi produzida pela agência AlmapBBDO."""
nlp(text)
[{'answer': 'Kombi elétrica', 'question': 'Qual é o nome do ID.Buzz?'},
{'answer': 'tecnologia de inteligência artificial',
'question': 'O que a Volkswagen usou para criar um comercial com Elis Regina?'},
{'answer': 'Como Nossos Pais',
'question': 'Qual é o nome da música que Elis Regina cantou?'},
{'answer': '70 anos',
'question': 'Qual foi o aniversário da Volkswagen em comemoração ao ID.Buzz?'},
{'answer': 'Ciro Possobom', 'question': 'Quem foi o CEO da VW do Brasil?'},
{'answer': 'deep fake', 'question': 'Qual é o outro nome para o ID.Buzz?'},
{'answer': 'AlmapBBDO', 'question': 'Qual agência produziu o ID.Buzz?'}]
```
You may also use this model directly using this inputs (you can test on the sandbox in this page):
1. extrair respostas: \<PHRASE HERE>
2. gerar pergunta: \<HIGHLIGHTED PHRASE HERE>
where \<HIGHLIGHTED PHRASE> uses \<hl> token to highlight generated answer.
Example:
input: "extrair respostas: A Volkswagen anunciou a chegada do ID.Buzz, a Kombi elétrica, ao Brasil."
output: ID.Buzz
input: "gerar perguntas: A Volkswagen anunciou a chegada do \<hl> ID.Buzz \<hl>, a Kombi elétrica, ao Brasil."
output: "Qual é o nome da Kombi elétrica da Volkswagen no Brasil?"
## Training Details
10 epochs, learning-rate 1e-4
## Model Card Authors
Vitor Alcantara Batista
## Model Card Contact
vabatista@gmail.com
|
mhdaw/ppo-LunarLander-v2-4
|
mhdaw
| 2023-08-05T00:07:12Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-05T00:06:53Z |
---
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: 279.64 +/- 18.02
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
...
```
|
timxiaohangt/dt-halfcheetah-d4rl_expert_halfcheetah-2907-2038
|
timxiaohangt
| 2023-08-05T00:05:06Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"dataset:decision_transformer_gym_replay",
"endpoints_compatible",
"region:us"
] | null | 2023-07-29T19:39:50Z |
---
tags:
- generated_from_trainer
datasets:
- decision_transformer_gym_replay
model-index:
- name: dt-halfcheetah-d4rl_expert_halfcheetah-2907-2038
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. -->
# dt-halfcheetah-d4rl_expert_halfcheetah-2907-2038
This model is a fine-tuned version of [](https://huggingface.co/) on the decision_transformer_gym_replay dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 1000000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
helamri/RLagent
|
helamri
| 2023-08-04T23:56:57Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-04T23:56:36Z |
---
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: 235.09 +/- 13.12
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
...
```
|
egerdm/sdlx_dreambooth_egerdm
|
egerdm
| 2023-08-04T23:49:50Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-08-04T23:49:45Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of egerdm person
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Test enoder was not trained.
|
ShynBui/s16
|
ShynBui
| 2023-08-04T23:36:17Z | 133 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T16:14:59Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: s16
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. -->
# s16
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
alexcux/ai_gov_test1
|
alexcux
| 2023-08-04T23:24:32Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T20:46:32Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: alexcux/ai_gov_test1
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. -->
# alexcux/ai_gov_test1
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: 2.5195
- Validation Loss: 2.4058
- 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4928, '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 | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5195 | 2.4058 | 0 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.3
- Tokenizers 0.13.3
|
rajendravenkata/xlm-roberta-base-finetuned-panx-de
|
rajendravenkata
| 2023-08-04T23:22:39Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-05T04:42:48Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8636363636363636
---
<!-- 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-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- F1: 0.8636
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2544 | 1.0 | 525 | 0.1611 | 0.8195 |
| 0.1267 | 2.0 | 1050 | 0.1433 | 0.8556 |
| 0.0807 | 3.0 | 1575 | 0.1365 | 0.8636 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
silvacarl/llama2-qlora-finetuned-test
|
silvacarl
| 2023-08-04T23:15:20Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T23:15:04Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
CristoJV/ppo-Huggy
|
CristoJV
| 2023-08-04T23:13:54Z | 28 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-04T23:13:48Z |
---
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: CristoJV/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
YanJiangJerry/baseline_bert-base-cased_epoch3_batch4_lr2e-05_w0.01
|
YanJiangJerry
| 2023-08-04T22:48:55Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-03T23:24:55Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: baseline_bert-base-cased_epoch3_batch4_lr2e-05_w0.01
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. -->
# baseline_bert-base-cased_epoch3_batch4_lr2e-05_w0.01
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5547
- Accuracy: 0.8810
- F1: 0.8415
- Precision: 0.8353
- Recall: 0.8478
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.547 | 1.0 | 788 | 0.4076 | 0.8754 | 0.8222 | 0.8780 | 0.7731 |
| 0.3627 | 2.0 | 1576 | 0.5177 | 0.8710 | 0.8357 | 0.7951 | 0.8806 |
| 0.2548 | 3.0 | 2364 | 0.5547 | 0.8810 | 0.8415 | 0.8353 | 0.8478 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
ShynBui/s21
|
ShynBui
| 2023-08-04T22:48:27Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T16:15:34Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: s21
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. -->
# s21
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- 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: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
brunoboat/a2c-AntBulletEnv-v0
|
brunoboat
| 2023-08-04T22:44:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-04T22:43:18Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1299.45 +/- 236.14
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
|
TheRains/yt-special-batch8
|
TheRains
| 2023-08-04T22:32:39Z | 93 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:yt",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-31T03:22:59Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- yt
metrics:
- wer
model-index:
- name: yt-special-batch8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: yt id
type: yt
args: id
metrics:
- name: Wer
type: wer
value: 100.0
---
<!-- 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. -->
# yt-special-batch8
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the yt id dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 100.0
## 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: 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
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 0.0 | 0.17 | 1000 | nan | 100.0 |
| 0.0 | 0.34 | 2000 | nan | 100.0 |
| 0.0 | 0.52 | 3000 | nan | 100.0 |
| 0.0 | 0.69 | 4000 | nan | 100.0 |
| 0.0 | 0.86 | 5000 | nan | 100.0 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ArashF/clinicalbert-complete_Diagnosis_in_responses_finetuned-squad
|
ArashF
| 2023-08-04T22:31:00Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:medicalai/ClinicalBERT",
"base_model:finetune:medicalai/ClinicalBERT",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T22:23:49Z |
---
base_model: medicalai/ClinicalBERT
tags:
- generated_from_keras_callback
model-index:
- name: ArashF/clinicalbert-complete_Diagnosis_in_responses_finetuned-squad
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. -->
# ArashF/clinicalbert-complete_Diagnosis_in_responses_finetuned-squad
This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2699
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 444, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, '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 | Epoch |
|:----------:|:-----:|
| 2.5396 | 0 |
| 1.5202 | 1 |
| 1.2699 | 2 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.3
- Tokenizers 0.13.3
|
egerdm/sdlx_dreambooth_man
|
egerdm
| 2023-08-04T22:21:46Z | 2 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-08-04T22:21:43Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: egerdm man
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Test enoder was not trained.
|
RohanHBTU/wav2vec2-large-xls-r-300m-hindi-colab
|
RohanHBTU
| 2023-08-04T22:13:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-03T18:29:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 0.728538283062645
---
<!-- 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. -->
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0956
- Wer: 0.7285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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
- num_epochs: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.247 | 42.11 | 400 | 1.8828 | 0.9513 |
| 0.3283 | 84.21 | 800 | 1.7075 | 0.8399 |
| 0.0915 | 126.32 | 1200 | 1.7553 | 0.7715 |
| 0.0492 | 168.42 | 1600 | 1.8279 | 0.7645 |
| 0.0283 | 210.53 | 2000 | 1.9725 | 0.7970 |
| 0.0176 | 252.63 | 2400 | 1.9678 | 0.7483 |
| 0.013 | 294.74 | 2800 | 1.9957 | 0.7645 |
| 0.0082 | 336.84 | 3200 | 2.1776 | 0.7575 |
| 0.0051 | 378.95 | 3600 | 2.0943 | 0.7506 |
| 0.0039 | 421.05 | 4000 | 2.0855 | 0.7227 |
| 0.0026 | 463.16 | 4400 | 2.0956 | 0.7285 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.13.3
|
Webstornomy/cmail
|
Webstornomy
| 2023-08-04T22:09:40Z | 0 | 0 |
allennlp
|
[
"allennlp",
"text2text-generation",
"dataset:fka/awesome-chatgpt-prompts",
"license:openrail",
"region:us"
] |
text2text-generation
| 2023-08-04T19:30:04Z |
---
license: openrail
pipeline_tag: text2text-generation
datasets:
- fka/awesome-chatgpt-prompts
metrics:
- bertscore
- character
library_name: allennlp
---
|
FLSRDS/Jotarooooo
|
FLSRDS
| 2023-08-04T22:05:27Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-04T22:05:27Z |
---
license: creativeml-openrail-m
---
|
teilomillet/llama-2-7b-LORA-chatSQL
|
teilomillet
| 2023-08-04T22:03:10Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T22:03:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
ZachBeesley/bert-finetuned-food
|
ZachBeesley
| 2023-08-04T22:03:03Z | 129 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"en",
"dataset:Dizex/FoodBase",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-04T21:28:48Z |
---
datasets:
- Dizex/FoodBase
language:
- en
widget:
- text: "Crack 3 eggs on a frying pan"
example_title: "Example 1"
---
# Model Card for Model ID
Token classification model used to identify food products recipe instructions
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ShynBui/s15
|
ShynBui
| 2023-08-04T21:50:17Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T16:14:53Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: s15
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. -->
# s15
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
gabbymax/ppo-LunarLander-v2
|
gabbymax
| 2023-08-04T21:34:09Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-04T21:33:47Z |
---
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: 253.47 +/- 17.02
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
...
```
|
Heralax/bloomz-3b-document-title-writer
|
Heralax
| 2023-08-04T21:07:31Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"en",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-03T09:21:20Z |
---
license: bigscience-bloom-rail-1.0
language:
- en
---
# Write short titles for text documents on your computer -- quickly
That's the goal with this small 3b-parameter model based on outdated architecture. It was finetuned on the 1000+ personal notes I have on my own computer, a fair number of which are titled; the model should be able to handle inputs of varying lengths.
Example input and output (the model is trained on completions only using SFTTrainer, and in this example generated everything below ''### Title:'):
```
### Instruction:
You are an expert title-writing AI that, given the body text of a quick note made about some subject, writes a title that accurately and concisely summarizes the content of that note. If the body text is not obviously a note -- for instance, it is some code, or an essay -- your title will describe what kind of code or essay it is.
### Body text of note:
Things needed for solitaire or other solo game reinforcement learning:
A simulated environment that can be stepped and returns the next state, the rewards, and whether or not the sim is terminated.
A discrete list of actions that can be taken
A neural network for predicting Q values. And a duplicate, "target" one.
A function that computes the loss of a Q function
a function that uses gradient tape to track the application of the above function, calculate the gradients for the given trainable variables, and apply these gradients. A helper to do a soft update can be used here
A data structure that stores states, "done" status, and rewards. This is the memory buffer. It should have a specific size.
Mini buffer?
helper to pick either exploited best value or random thing with epsilon % chance
### Title:
solitude reinforcement learning.txt</s>
```
Since the model is small, it should be able to handle bulk tasks better than some larger models because it can run faster. Especially if quantized. The purpose of building this model was to help myself organize and search through the vast number of untitled text documents on my computer, and now you can too!
Its true utility may come about if quantized, which I still need to figure out how to do.
## Legal
Uses the bloomz rail license, which essentially prevents you from using the model for evil, but that's not a very precise description on my part... so just go read the license if you use this.
## Prompt template (for further finetuning) (this is the exact code I used when generating examples to be passed to the prompt):
```
### Instruction:
You are an expert title-writing AI that, given the body text of a quick note made about some subject, writes a title that accurately and concisely summarizes the content of that note. If the body text is not obviously a note -- for instance, it is some code, or an essay -- your title will describe what kind of code or essay it is.
### Body text of note:
{row['content']}.
### Title:
{row['title'].replace(re.escape('.txt'),'') + tokenizer.eos_token}
```
No, I don't know why it still writes titles with .txt if I tried to remove that from my training data. Oh well.
## Known issues
* It will probably assume every file is a .txt file, since it was only trained on those.
* I don't know how it handles a wide variety of formats, I've literally only tested it on two files right now.
* I didn't know that bloomz has a bos_token, and the model was finetuned without that.
### If you want, you can finetune this model on your specific use case or document format, to organize your own work!
Just get GPT-4 to write you a script that calls it on the body text of files in a specified directory and renames them to the model's output and you're good.
I haven't actually done the bulk rename of my files myself yet, so I don't have a script to share. Once I do have a script to share, I'll add it here.
### This is the first model that I pushed to Hugging Face! if you have any comments or feedback, please do message me, or find me on Discord. I'm also called Heralax over there. You can find me in TheBloke's Discord if you look hard enough.
|
s3nh/ToolLLaMA-7b-GGML
|
s3nh
| 2023-08-04T21:05:23Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-04T20:57:53Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/ToolBench/ToolLLaMA-7b).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
---
license: apache-2.0
---
# Model Card for Model ID
This is ToolLLaMA model introduced in [ToolBench](https://github.com/OpenBMB/ToolBench).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **License:** apache-2.0
- **Finetuned from model [optional]:** LLaMA-7b
## Uses
Refer to [ToolBench](https://github.com/OpenBMB/ToolBench).
## Training Details
Trained with the new version data in ToolBench.
|
bigcode/starcoder-cxso
|
bigcode
| 2023-08-04T20:58:14Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-16T03:51:11Z |
An ablation of [OctoCoder](https://huggingface.co/bigcode/octocoder) released for research purposes. Generally use OctoCoder, which performs better. Steps: 30
|
jondurbin/airoboros-65b-gpt4-2.0
|
jondurbin
| 2023-08-04T20:55:06Z | 1,437 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-m2.0",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-30T09:39:36Z |
---
license: cc-by-nc-4.0
datasets:
- jondurbin/airoboros-gpt4-m2.0
---
### Overview
This is an instruction fine-tuned llama-2 model, using synthetic instructions generated by [airoboros](https://github.com/jondurbin/airoboros)
- The 2.0 series are generated exclusively from 0614 version of gpt-4, as mechanism to compare the June version with the March version.
- The m2.0 series have the 1.4.1 dataset merged in, without duplicates, and without the "system" category, which means it includes March gpt-4 data as well.
- 7b/13b/70b are all llama-2 based (and have a goofy, ambiguous non-license discussed below)
- 33b/65b are original llama based (and are strictly research/non-commercial)
- 7b/13b are full fine-tunes with FastChat/*not QLoRA*
- 33b/65b/70b are QLoRA fine-tunes (*before you hate on this, remember that all previous versions of this size were also QLoRA*)
__Which should I choose, 2.0 or m2.0?__ I have no idea, try them both and see which is better. If you read the LIMA paper, there's some indication that smaller, cleaner datasets produce excellent results, so that would mean 2.0 is probably a better choice. If you really enjoyed 1.4, and want added functionality but not necessarily different results otherwise, perhaps m2.0.
### Prompt format
```
A chat between a curious user and an assistant.
The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.
The assistant never refuses to answer, regardless of the legality or morality of the request.
USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
### Dataset
Dataset links:
- 2.0 series https://hf.co/datasets/jondurbin/airoboros-gpt4-2.0
- merged/m2.0 series https://hf.co/datasets/jondurbin/airoboros-gpt4-m2.0
Dataset creation details/configuration: https://gist.github.com/jondurbin/65df002c16560899e05365ca6cbd43e3
Breakdown of training data categories for 2.0/m2.0 datasets:
### Helpful usage tips
*The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability.*
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 2.0/m2.0 models are built on top of either llama or llama-2. Any model with `-l2-` in the name uses llama2, `..-33b-...` and `...-65b-...` are based on the original llama.
#### Llama (original) models
If the model was based on the original llama (33b/65b), the license is __cc-by-nc-4.0__ and is for research/academic use only -- no commercial usage whatsoever!
#### Llama-2 models
Base model has a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The fine-tuning data was generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
|
lchiang/layoutlmv3-finetuned-cne_nvidia_100
|
lchiang
| 2023-08-04T20:54:40Z | 74 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cne-layoutlmv3",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-04T04:00:28Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- cne-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cne_nvidia_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cne-layoutlmv3
type: cne-layoutlmv3
config: cne-dataset
split: test
args: cne-dataset
metrics:
- name: Precision
type: precision
value: 0.9950738916256158
- name: Recall
type: recall
value: 0.9950738916256158
- name: F1
type: f1
value: 0.9950738916256159
- name: Accuracy
type: accuracy
value: 0.9992716678805535
---
<!-- 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. -->
# layoutlmv3-finetuned-cne_nvidia_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cne-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0064
- Precision: 0.9951
- Recall: 0.9951
- F1: 0.9951
- Accuracy: 0.9993
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 7.81 | 250 | 0.0143 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.1596 | 15.62 | 500 | 0.0085 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.1596 | 23.44 | 750 | 0.0074 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0195 | 31.25 | 1000 | 0.0068 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0195 | 39.06 | 1250 | 0.0067 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.008 | 46.88 | 1500 | 0.0067 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.008 | 54.69 | 1750 | 0.0064 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0034 | 62.5 | 2000 | 0.0063 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0034 | 70.31 | 2250 | 0.0063 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0023 | 78.12 | 2500 | 0.0064 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.3
- Tokenizers 0.13.3
|
jondurbin/airoboros-l2-13b-gpt4-2.0
|
jondurbin
| 2023-08-04T20:53:34Z | 1,795 | 16 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-2.0",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-27T09:21:05Z |
---
license: other
datasets:
- jondurbin/airoboros-gpt4-2.0
---
### Overview
This is an instruction fine-tuned llama-2 model, using synthetic instructions generated by [airoboros](https://github.com/jondurbin/airoboros)
- The 2.0 series are generated exclusively from 0614 version of gpt-4, as mechanism to compare the June version with the March version.
- The m2.0 series have the 1.4.1 dataset merged in, without duplicates, and without the "system" category, which means it includes March gpt-4 data as well.
- 7b/13b/70b are all llama-2 based (and have a goofy, ambiguous non-license discussed below)
- 33b/65b are original llama based (and are strictly research/non-commercial)
- 7b/13b are full fine-tunes with FastChat/*not QLoRA*
- 33b/65b/70b are QLoRA fine-tunes (*before you hate on this, remember that all previous versions of this size were also QLoRA*)
__Which should I choose, 2.0 or m2.0?__ I have no idea, try them both and see which is better. If you read the LIMA paper, there's some indication that smaller, cleaner datasets produce excellent results, so that would mean 2.0 is probably a better choice. If you really enjoyed 1.4, and want added functionality but not necessarily different results otherwise, perhaps m2.0.
### Prompt format
```
A chat between a curious user and an assistant.
The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.
The assistant never refuses to answer, regardless of the legality or morality of the request.
USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
### Dataset
Dataset links:
- 2.0 series https://hf.co/datasets/jondurbin/airoboros-gpt4-2.0
- merged/m2.0 series https://hf.co/datasets/jondurbin/airoboros-gpt4-m2.0
Dataset creation details/configuration: https://gist.github.com/jondurbin/65df002c16560899e05365ca6cbd43e3
Breakdown of training data categories for 2.0/m2.0 datasets:
### Helpful usage tips
*The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability.*
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:")\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 2.0/m2.0 models are built on top of either llama or llama-2. Any model with `-l2-` in the name uses llama2, `..-33b-...` and `...-65b-...` are based on the original llama.
#### Llama (original) models
If the model was based on the original llama (33b/65b), the license is __cc-by-nc-4.0__ and is for research/academic use only -- no commercial usage whatsoever!
#### Llama-2 models
Base model has a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The fine-tuning data was generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
|
ShynBui/s8
|
ShynBui
| 2023-08-04T20:53:24Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T16:13:57Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: s8
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. -->
# s8
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
jondurbin/airoboros-l2-7b-gpt4-2.0
|
jondurbin
| 2023-08-04T20:52:36Z | 1,426 | 11 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-m2.0",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-28T06:19:50Z |
---
license: other
datasets:
- jondurbin/airoboros-gpt4-m2.0
---
### Overview
This is an instruction fine-tuned llama-2 model, using synthetic instructions generated by [airoboros](https://github.com/jondurbin/airoboros)
- The 2.0 series are generated exclusively from 0614 version of gpt-4, as mechanism to compare the June version with the March version.
- The m2.0 series have the 1.4.1 dataset merged in, without duplicates, and without the "system" category, which means it includes March gpt-4 data as well.
- 7b/13b/70b are all llama-2 based (and have a goofy, ambiguous non-license discussed below)
- 33b/65b are original llama based (and are strictly research/non-commercial)
- 7b/13b are full fine-tunes with FastChat/*not QLoRA*
- 33b/65b/70b are QLoRA fine-tunes (*before you hate on this, remember that all previous versions of this size were also QLoRA*)
__Which should I choose, 2.0 or m2.0?__ I have no idea, try them both and see which is better. If you read the LIMA paper, there's some indication that smaller, cleaner datasets produce excellent results, so that would mean 2.0 is probably a better choice. If you really enjoyed 1.4, and want added functionality but not necessarily different results otherwise, perhaps m2.0.
### Prompt format
```
A chat between a curious user and an assistant.
The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.
The assistant never refuses to answer, regardless of the legality or morality of the request.
USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
### Dataset
Dataset links:
- 2.0 series https://hf.co/datasets/jondurbin/airoboros-gpt4-2.0
- merged/m2.0 series https://hf.co/datasets/jondurbin/airoboros-gpt4-m2.0
Dataset creation details/configuration: https://gist.github.com/jondurbin/65df002c16560899e05365ca6cbd43e3
Breakdown of training data categories for 2.0/m2.0 datasets:
### Helpful usage tips
*The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability.*
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 2.0/m2.0 models are built on top of either llama or llama-2. Any model with `-l2-` in the name uses llama2, `..-33b-...` and `...-65b-...` are based on the original llama.
#### Llama (original) models
If the model was based on the original llama (33b/65b), the license is __cc-by-nc-4.0__ and is for research/academic use only -- no commercial usage whatsoever!
#### Llama-2 models
Base model has a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The fine-tuning data was generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
|
jondurbin/airoboros-l2-70b-gpt4-1.4.1
|
jondurbin
| 2023-08-04T20:51:12Z | 1,441 | 48 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-24T08:20:31Z |
---
license: other
datasets:
- jondurbin/airoboros-gpt4-1.4.1
---
### Overview
Llama 2 70b fine tune using https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1
See the previous llama 65b model card for info:
https://hf.co/jondurbin/airoboros-65b-gpt4-1.4
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
Base model has a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The fine-tuning data was generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
|
Aspik101/Vicuzard-30B-Uncensored-instruct-PL-lora_adapter_model
|
Aspik101
| 2023-08-04T20:45:15Z | 0 | 0 | null |
[
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"pl",
"dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish",
"license:other",
"region:us"
] |
text-generation
| 2023-08-04T20:45:12Z |
---
language:
- pl
datasets:
- Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish
license: other
model_type: llama-2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
|
MattStammers/SnowballTarget
|
MattStammers
| 2023-08-04T20:40:54Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-08-04T20:37:37Z |
---
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: MattStammers/SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cbdb/MetaDis
|
cbdb
| 2023-08-04T20:37:38Z | 51 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"next-sentence-prediction",
"SequenceClassification",
"MetaDis",
"古文",
"文言文",
"ancient",
"classical",
"Biography",
"古代人物传记",
"zh",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2023-07-28T22:54:51Z |
---
language:
- zh
tags:
- SequenceClassification
- MetaDis
- 古文
- 文言文
- ancient
- classical
- Biography
- 古代人物传记
license: cc-by-nc-sa-4.0
---
# <font color="IndianRed"> MetaDis (Classical Chinese Biographical Metadata Disambiguation)</font>
[](https://colab.research.google.com/drive/1UcyhdfX5_NuZ87XR1fUmMACJ7qY-nn-P#scrollTo=cd-iH6OLpIeV)
Download <font color="IndianRed">template excel sheet</font> from here: https://huggingface.co/cbdb/MetaDis/blob/main/template.xlsx
---
Welcome to the repository for MetaDis, a specialized model designed for disambiguating biographical metadata within Classical Chinese texts.
At the core of the problem MetaDis aims to solve is a common issue researchers encounter when studying historical texts - the identification of individuals sharing the same name. Are these instances referring to the same person or two different people? This is the question MetaDis seeks to answer.
MetaDis is based on the `AutoModelForNextSentencePrediction` architecture, a machine learning model that processes two sequences of data as its input. It then outputs a 0 or 1 - a binary representation indicating whether or not the two sequences refer to the same person. Here, 0 represents 'not the same person', and 1 indicates 'the same person'.
---
### <font color="IndianRed">Input Data Formatting </font>
In order to ensure the highest accuracy and performance of the MetaDis model, we've specifically designed an input format based on the data the model was originally trained on. This is crucial as it allows the model to accurately interpret and process your data.
To assist you in this process, we've provided a template Excel (.xlsx) file. We recommend downloading this template and inputting your data directly into it, ensuring your data matches the same format as the model's training data.
To download our Excel data template, please click [here](https://huggingface.co/cbdb/MetaDis/blob/main/template.xlsx).
---
### <font color="IndianRed">Code Demonstration: Loading and Using MetaDis Model </font>
The following section demonstrates how to directly load the MetaDis model and use it for predicting whether two sets of biographical information refer to the same person or not.
Please ensure that you have the `transformers` library installed in your Python environment. If not, you can install it using pip:
```python
pip install transformers
```
Now, let's load our model and make some predictions:
```python
# Import necessary libraries from HuggingFace Transformers
from transformers import AutoTokenizer, AutoModelForNextSentencePrediction
import torch
# Load our tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("cbdb/MetaDis")
model = AutoModelForNextSentencePrediction.from_pretrained("cbdb/MetaDis")
# Define our sentences to compare
sentence1 = ['first biographical information of person name A', 'first biographical information of person name B']
sentence2 = ['second biographical information of person name A', 'first biographical information of person name B']
# Loop through each sentence pair
for s1, s2 in zip(sentence1, sentence2):
# Prepare the inputs
encoding = tokenizer(s1, s2, truncation=True, padding=True, return_tensors='pt')
# Move the inputs to the device where the model is
for key in encoding:
encoding[key] = encoding[key].to(model.device)
# Make the prediction
outputs = model(**encoding)
# Extract the prediction
logits = outputs.logits
preds = torch.argmax(logits, dim=-1)
# Display the results
if preds.item() == 1:
print('Same person')
print(s1, s2)
else:
print('Different person')
print(s1, s2)
```
This code demonstration shows how you can load our MetaDis model, prepare inputs in the necessary format, and extract predictions to determine if the biographical details refer to the same person or different individuals. Remember to replace the example sentences with your own data.
Remember to include a link or instructions on how users can install the `transformers` library if they don't already have it installed.
---
### <font color="IndianRed">Authors </font>
Queenie Luo (queenieluo[at]g.harvard.edu)
<br>
Hongsu Wang
<br>
Peter Bol
<br>
CBDB Group
### <font color="IndianRed">License </font>
Copyright (c) 2023 CBDB
Except where otherwise noted, content on this repository is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or
send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
|
Hermi2023/doc2query-ppo-msmarco-43520-121
|
Hermi2023
| 2023-08-04T20:37:19Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-08-03T14:41:31Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Hermi2023/doc2query-ppo-msmarco-43520-121")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Hermi2023/doc2query-ppo-msmarco-43520-121")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Hermi2023/doc2query-ppo-msmarco-43520-121")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
vesteinn/icelandic-weather-summarization
|
vesteinn
| 2023-08-04T20:31:53Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
Temporary upload - student project
|
s3nh/elliot4ai-MythoLogic-Mini-7b-GGML
|
s3nh
| 2023-08-04T20:14:47Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-04T20:06:04Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/Gryphe/MythoLogic-Mini-7b/).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
## Model details
MythoLogic-Mini-7b can be considered the little brother in my Mytho series of models: [MythoLogic-13b](https://huggingface.co/Gryphe/MythoLogic-13b) and [MythoBoros-13b](https://huggingface.co/Gryphe/MythoBoros-13b)).
Its Llama-2 core is powered by [Nous Hermes-2](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b), which is further augmented by [Stable Beluga](https://huggingface.co/stabilityai/StableBeluga-7B) and a carefully distilled [Kimiko LoRa](https://huggingface.co/nRuaif/Kimiko_7B).
Since 7B models tend to be less capable all-rounders, more emphasis was put on improving the roleplaying aspects for this gradient merge, of which various gradients were benchmarked before settling on the configuration shown below.
In technical terms, the Hermes-2 core starts at 90% strength before fading away completely at the 12th layer level, where Stable Beluga (and Kimiko) handle the more intricate linguistic aspects.
Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoLogic-Mini-7b-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoLogic-Mini-7b-GPTQ) (You're the best!)
## Prompt Format
Due to its Hermes-2 core this model works best with Alpaca formatting, so for optimal model performance, use:
```
<System prompt/Character Card>
### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
### Response:
```
|
yaya2169/folkloretaylor
|
yaya2169
| 2023-08-04T20:01:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-04T19:59:07Z |
folklore era taylor
trained off of invisible string, august, hoax, peace
500 epochs, 40k sample rate, RVC v2
|
ShynBui/s11
|
ShynBui
| 2023-08-04T20:01:13Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T16:14:26Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: s11
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. -->
# s11
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
Masoumi/llama2-persian-syntran-fa
|
Masoumi
| 2023-08-04T19:55:04Z | 1 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-04T19:41:34Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
ShynBui/s7
|
ShynBui
| 2023-08-04T19:53:32Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-04T16:13:41Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: s7
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. -->
# s7
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
jeremyvictor/mt5-large-gramatika161k-b16-e10-lr0.001
|
jeremyvictor
| 2023-08-04T19:50:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-04T03:37:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-large-gramatika161k-b16-e10-lr0.001
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-large-gramatika161k-b16-e10-lr0.001
This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1537
- Rouge1: 70.8264
- Rouge2: 64.518
- Rougel: 70.6934
- Rougelsum: 70.6881
- Gen Len: 18.3298
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.3641 | 0.63 | 5000 | 0.1944 | 69.4204 | 61.9635 | 69.2556 | 69.2477 | 18.3389 |
| 0.1843 | 1.27 | 10000 | 0.1655 | 70.3343 | 63.6924 | 70.1851 | 70.1815 | 18.3377 |
| 0.1359 | 1.9 | 15000 | 0.1537 | 70.8264 | 64.518 | 70.6934 | 70.6881 | 18.3298 |
| 0.0912 | 2.54 | 20000 | 0.1643 | 71.037 | 64.8861 | 70.9075 | 70.9027 | 18.3295 |
| 0.0759 | 3.17 | 25000 | 0.1694 | 71.288 | 65.3505 | 71.1746 | 71.1675 | 18.3314 |
| 0.054 | 3.81 | 30000 | 0.1672 | 71.4356 | 65.5825 | 71.3263 | 71.3199 | 18.3294 |
| 0.0398 | 4.44 | 35000 | 0.1779 | 71.4473 | 65.6798 | 71.343 | 71.3354 | 18.3341 |
| 0.0331 | 5.08 | 40000 | 0.1908 | 71.615 | 65.9285 | 71.5126 | 71.4982 | 18.3344 |
| 0.021 | 5.71 | 45000 | 0.2025 | 71.6252 | 65.9628 | 71.5172 | 71.513 | 18.3317 |
| 0.0167 | 6.35 | 50000 | 0.2107 | 71.6508 | 66.0666 | 71.5547 | 71.542 | 18.3366 |
| 0.0126 | 6.98 | 55000 | 0.2084 | 71.8403 | 66.3396 | 71.7392 | 71.735 | 18.3337 |
| 0.0072 | 7.62 | 60000 | 0.2256 | 71.8659 | 66.388 | 71.7699 | 71.7644 | 18.3330 |
| 0.0057 | 8.25 | 65000 | 0.2578 | 71.9226 | 66.4948 | 71.8279 | 71.8162 | 18.3313 |
| 0.0036 | 8.88 | 70000 | 0.2784 | 71.9279 | 66.5248 | 71.8258 | 71.8149 | 18.3324 |
| 0.0021 | 9.52 | 75000 | 0.3040 | 71.9913 | 66.634 | 71.893 | 71.8844 | 18.3317 |
### Framework versions
- Transformers 4.30.1
- Pytorch 1.11.0a0+b6df043
- Datasets 2.12.0
- Tokenizers 0.13.3
|
josebruzzoni/whisper-small-es-v3
|
josebruzzoni
| 2023-08-04T19:45:25Z | 91 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-04T13:53:54Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-es-v3
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-es-v3
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: 0.2706
- Wer: 56.9793
## 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: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2565 | 0.69 | 1000 | 0.2811 | 75.3519 |
| 0.1148 | 1.39 | 2000 | 0.2688 | 42.8365 |
| 0.0473 | 2.08 | 3000 | 0.2701 | 48.2173 |
| 0.0453 | 2.78 | 4000 | 0.2706 | 56.9793 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
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