modelId
stringlengths 5
139
| 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
2025-09-04 06:26:41
| card
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1.01M
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|---|---|---|---|---|---|---|---|---|---|
Intel/albert-base-v2-sst2-int8-dynamic-inc
|
Intel
| 2023-06-27T08:45:08Z | 5 | 0 |
transformers
|
[
"transformers",
"onnx",
"albert",
"text-classification",
"text-classfication",
"int8",
"Intel® Neural Compressor",
"PostTrainingDynamic",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-28T09:10:38Z |
---
language: en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingDynamic
- onnx
datasets:
- glue
metrics:
- f1
---
# INT8 albert-base-v2-sst2
## Post-training dynamic quantization
### ONNX
This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2).
#### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-accuracy)** |0.9186|0.9232|
| **Model size (MB)** |59|45|
#### Load ONNX model:
```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained('Intel/albert-base-v2-sst2-int8-dynamic')
```
|
yeounyi/PPO-LunarLander-v2
|
yeounyi
| 2023-06-27T08:39:37Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T07:41:53Z |
---
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: 293.31 +/- 19.97
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
...
```
|
silpakanneganti/flan-cpt-medical-ner
|
silpakanneganti
| 2023-06-27T08:35:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-27T07:23:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flan-cpt-medical-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-cpt-medical-ner
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.4240
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 37 | 18.8471 |
| No log | 2.0 | 74 | 8.4240 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
loghai/ppo-LunarLander-v2
|
loghai
| 2023-06-27T08:33:45Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T08:33:25Z |
---
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: 255.64 +/- 41.91
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
...
```
|
mukeiZ/osusume
|
mukeiZ
| 2023-06-27T08:22:59Z | 0 | 1 | null |
[
"license:other",
"region:us"
] | null | 2023-05-05T08:35:55Z |
---
license: other
---
★Lora-pri_ver1
トリガーワードはprishe。無くても出るが再現性が上がる？
crownの有無で帽子の着脱可。
生成モデルにもよるが、衣装再現しないならepochは25くらいからprisheぽくなる。
epochは数字が増えていくにつれて再現度は高くなるが汎用性はなくなっていくかも。数字無しが最終。
プロンプトの強調やLoraの強度を変えれば衣装やポーズも応用が利く。LoRA Block Weightの利用も有効。
－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－
★Lora-PandU
2キャラ同時学習。prisheとulmiaで描き分け。
キャラが混じらないようにタグをそれぞれで分けてみたが成功したかどうか不明。
余計な要素が混じる場合はネガプロを利用するのも手。
epochは衣装再現性は低いが顔をみると35あたりがオススメかも。数字なしは層別適用を利用したほうがいい。
●タグ説明
・prishe
頭装備は crown
胸のリボンと石は red ribbon
耳は pointy ears 外すとヒュム耳になるかも
衣装は costume 外しても脱ぐわけではない
脚装備は short pants
足装備は brown footwear
サンプルタグ
prishe, costume, crown, white background, open mouth, red ribbon, pointy ears, brown footwear, hand on own hip, short pants
・ulmia
ulmia, uniform, solo, standing, full body, brown footwear, medium hair, brown eyes, ear piercing, circlet, orange hair, holding harp, hair ornament, jewelry
頭装備は circlet,hair ornament
耳は elf
衣装は uniform
脚装備は black leggings, gaiters
足装備は brown footwear
楽器は harp
サンプルタグ
ulmia, uniform, brown footwear, elf, white background, harp, hair ornament, black leggings, gaiters, circlet
|
MQ-playground/ppo-Huggy
|
MQ-playground
| 2023-06-27T08:08:48Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-27T08:08:37Z |
---
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: MQ-playground/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
andrewshi/bert-finetuned-squad
|
andrewshi
| 2023-06-27T07:55:41Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-27T00:53:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
The BERT fine-tuned SQuAD model is a version of the BERT (Bidirectional Encoder Representations from Transformers) model that has been fine-tuned on the Stanford Question Answering Dataset (SQuAD). It is designed to answer questions based on the context given. The SQuAD dataset is a collection of 100k+ questions and answers based on Wikipedia articles. Fine-tuning the model on this dataset allows it to provide precise answers to a wide array of questions based on a given context.
## Intended uses & limitations
This model is intended to be used for question-answering tasks. Given a question and a context (a piece of text containing the information to answer the question), the model will return the text span in the context that most likely contains the answer. This model is not intended to generate creative content, conduct sentiment analysis, or predict future events.
It's important to note that the model's accuracy is heavily dependent on the relevance and quality of the context it is provided. If the context does not contain the answer to the question, the model will still return a text span, which may not make sense. Additionally, the model may struggle with nuanced or ambiguous questions as it may not fully understand the subtleties of human language.
## Training and evaluation data
The model was trained on the SQuAD dataset, encompassing over 87,599 questions generated by crowd workers from various Wikipedia articles. The answers are text segments from the relevant reading passage. For evaluation, a distinct subset of the SQuAD, containing 10,570 instances, unseen by the model during training, was employed.
## Training procedure
The model was initially pretrained on a large corpus of text in an unsupervised manner, learning to predict masked tokens in a sentence. The pretraining was done on the bert-base-cased model, which was trained on English text in a case-sensitive manner. After this, the model was fine-tuned on the SQuAD dataset. During fine-tuning, the model was trained to predict the start and end positions of the answer in the context text given a question.
### 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
- exact_match: 81.0406811731315
- f1: 88.65884513439593
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
nomad-ai/a2c-AntBulletEnv-v0
|
nomad-ai
| 2023-06-27T07:50:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T05:57:08Z |
---
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: 2380.90 +/- 42.56
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
...
```
|
raafat3-16/text_summary
|
raafat3-16
| 2023-06-27T07:27:02Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-27T07:27:02Z |
---
license: creativeml-openrail-m
---
|
olianate/dqn-SpaceInvadersNoFrameskip-v4
|
olianate
| 2023-06-27T07:27:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T07:10:54Z |
---
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: 673.50 +/- 135.15
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 olianate -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 olianate -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 olianate
```
## 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', 3000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
nojiyoon/nallm-polyglot-ko-1.3b
|
nojiyoon
| 2023-06-27T07:20:53Z | 4 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-20T08:43:19Z |
---
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.dev0
|
neukg/TechGPT-7B
|
neukg
| 2023-06-27T07:08:59Z | 0 | 17 | null |
[
"pytorch",
"text2text-generation",
"zh",
"en",
"arxiv:2304.07854",
"license:gpl-3.0",
"region:us"
] |
text2text-generation
| 2023-06-23T10:10:11Z |
---
license: gpl-3.0
tags:
- text2text-generation
pipeline_tag: text2text-generation
language:
- zh
- en
---
# TechGPT: Technology-Oriented Generative Pretrained Transformer
Demo: [TechGPT-neukg](http://techgpt.neukg.com) <br>
Github: [neukg/TechGPT](https://github.com/neukg/TechGPT)
## 简介 Introduction
TechGPT是[“东北大学知识图谱研究组”](http://faculty.neu.edu.cn/renfeiliang)发布的垂直领域大语言模型。目前已开源全量微调的7B版本。<br>
TechGPT主要强化了如下三类任务:
- 以“知识图谱构建”为核心的关系三元组抽取等各类信息抽取任务
- 以“阅读理解”为核心的各类智能问答任务。
- 以“文本理解”为核心的关键词生成等各类序列生成任务。
在这三大自然语言处理核心能力之内，TechGPT还具备了对计算机科学、材料、机械、冶金、金融和航空航天等十余种垂直专业领域自然语言文本的处理能力。
目前，TechGPT通过提示和指令输入方式的不同，支持单轮对话和多轮对话，涵盖了领域术语抽取、命名实体识别、关系三元组抽取、文本关键词生成、标题生成摘要、摘要生成标题、文本领域识别、机器阅读理解、基础常识问答、基于上下文的知识问答、建议咨询类问答、文案生成、中英互译和简单代码生成等多项自然语言理解和生成能力。
---
TechGPT mainly strengthens the following three types of tasks:
- Various information extraction tasks such as relation triplet extraction with "knowledge graph construction" as the core
- Various intelligent question-and-answer tasks centered on "reading comprehension".
- Various sequence generation tasks such as keyword generation with "text understanding" as the core.
Within these three core natural language processing capabilities, TechGPT also has the ability to process natural language texts in more than ten vertical professional fields such as computer science, materials, machinery, metallurgy, finance, and aerospace.
TechGPT supports single-round dialogue and multi-round dialogue through different input methods of prompts and instructions, covering domain term extraction, named entity recognition, relation triplet extraction, text keyword generation, title generation abstract, abstract generation title, text field Recognition, machine reading comprehension, basic common sense question and answer, context-based knowledge question and answer, suggestion consultation question and answer, copy generation, CM-EN translation and simple code generation and many other natural language understanding and generation capabilities.
## 模型功能 Model Capabilities
模型在保留基本的对话和翻译能力之外，重点强化了基于**TechKG**的数据内容，因此，**TechGPT**更擅长在计算机科学等专业领域，完成实体及实体类型抽取、关系三元组抽取、标题扩写、摘要缩写、关键词生成和机器阅读理解等任务。
下面展示了TechGPT的几个核心能力。
1. **命名实体识别**
<table> <colgroup> <col style="width:60%"> <col style="width:40%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr> <td>抽取出下面文本的实体和实体类型：<br/>《女人树》，国产电视剧，由导演田迪执导，根据作家子页的原著改编，故事从1947年开始，跨越了解放战争和建国初期两大历史时期，展现了战斗在隐形战线上的人民英雄是如何不惧怕任何危险，不计较个人牺牲，甚至不顾人民内部的误解和生死裁决，都不暴露个人真实身份，至死不渝，与敌人周旋到底的英雄故事。</td> <td>实体名: 女人树， 实体类型：影视作品<br/>实体名: 田迪， 实体类型：人物<br/>实体名: 子页， 实体类型：人物<br/>实体名: 1947年， 实体类型：Date</td> </tr>
</table>
2. **关系三元组抽取**
<table> <colgroup> <col style="width:60%"> <col style="width:40%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr> <td>
请抽取出下面的关系三元组：<br>陈奕迅（Eason Chan），1974年7月27日出生于香港，中国香港流行乐男歌手、演员，毕业于英国金斯顿大学。 1995年因获得第14届新秀歌唱大赛冠军而正式出道。2005年发行的歌曲《浮夸》成为其歌唱生涯的代表作品之一。</td> <td><陈奕迅, 出生日期, 1974年><br><陈奕迅, 出生地, 香港><br>
<陈奕迅, 毕业院校, 英国金斯顿大学><br>
<陈奕迅, 国籍, 中国></td> </tr>
</table>
3. **标题扩写**
<table> <colgroup> <col style="width:60%"> <col style="width:40%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr> <td>请把下列标题扩写成摘要, 不少于100字: 基于视觉语言多模态的实体关系联合抽取的研究。</td> <td>本文介绍了一种基于视觉语言的多模态实体关系联合抽取出方法。该方法利用了图像和文本之间的语义联系，通过将图像中的物体与相应的文本描述进行匹配来识别实体之间的关系。同时，本文还提出了一种新的模型结构——深度双向编码器-解码器网络（BiDAN），用于实现这种联合提取任务。实验结果表明，所提出的方法在多个数据集上取得了较好的性能表现，证明了其有效性和实用性。</td> </tr>
</table>
4. **摘要缩写**
<table> <colgroup> <col style="width:60%"> <col style="width:40%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr> <td>请把下列摘要缩写成标题:本文介绍了一种基于视觉语言的多模态实体关系联合抽取出方法。该方法利用了图像和文本之间的语义联系，通过将图像中的物体与相应的文本描述进行匹配来识别实体之间的关系。同时，本文还提出了一种新的模型结构——深度双向编码器-解码器网络（BiDAF），用于实现这种联合提取任务。实验结果表明，所提出的方法在多个数据集上取得了较好的性能表现，证明了其有效性和实用性。</td> <td>基于视觉语言的多模态实体关系联合抽取研究。</td> </tr>
</table>
5. **关键词生成**
<table> <colgroup> <col style="width:60%"> <col style="width:40%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr> <td>请提取下面文本中的关键词。<br>本体是一种重要的知识库,其包含的丰富的语义信息可以为问答系统、信息检索、语义Web、信息抽取等领域的研究及相关应用提供重要的支持.因而,如何快速有效地构建本体具有非常重要的研究价值.研究者们分别从不同角度提出了大量有效地进行本体构建的方法.一般来讲,这些本体构建方法可以分为手工构建的方法和采用自动、半自动技术构建的方法.手工本体的方法往往需要本体专家参与到构建的整个过程,存在着构建成本高、效率低下、主观性强、移植不便等缺点,因而,此类方法正逐步被大量基于自动、半自动技术的本体构建方法所代替.自动、半自动构建的方法不需要（或仅需少量）人工参与,可以很方便地使用其它研究领域（如机器学习、自然语言处理等）的最新研究成果,也可以方便地使用不同数据源进行本体构建.</td> <td>本体，知识库，问答系统，信息检索，语义 Web，信息抽取，统计主导，语言分析主导，术语抽取，概念抽取，关系抽取，本体形成，评价方法，本体构建系统</td> </tr>
</table>
6. **机器阅读理解**
<table> <colgroup> <col style="width:60%"> <col style="width:40%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr> <td>请问这起交通事故是谁的责任居多?<br>小车和摩托车发生事故，在无红绿灯的十字路口，小停车看看左右，在觉得安全的情况下刹车慢慢以时速10公里左右的速度靠右行驶过路口，好没有出到十字路口正中时，被左边突然快速行驶过来的摩托车撞在车头前， 摩托车主摔到膝盖和檫伤脸部，请问这起交通事故是谁的责任居多。</td> <td>根据提供的信息，我们可以得出以下结论：<br>
1. 小车司机应该先观察交通信号灯是否亮起，如果没有信号灯，则应减速慢行通过交叉路口；<br>2. 在没有信号灯的交叉口中，小车的驾驶员应该在确保安全的前提下选择右侧车道行驶，以避免与来自左侧的车辆相撞；
3. 由于事故发生地点是十字路口，因此可以确定该事故属于道路交通事故范畴。<br>根据以上分析，我认为这起交通事故的主要责任在于摩托车驾驶人的行为。因为摩托车驾驶员在没有信号灯的情况下从左侧冲过来，导致与小车在十字路口相遇，造成了伤害。
然而，我们也需要考虑其他因素的影响，例如小车的速度、路面状况等。如果小车的车速过快或者路面湿滑，也可能对事故的发生产生影响。但是，由于提供的信息中没有提到这些细节，所以我们只能基于上述基本判断来回答这个问题。</td>
</tr>
</table>
## 准备工作 Preparation
由于LLaMA开源协议的限制，本模型仅限于研究和学习用途使用。请严格遵守LLaMA的使用要求和规范。为了确保这一点，我们需要确认您具有LLaMA的原始权重，并来自完全合法渠道。
---
According to the limitations of the LLaMA open source agreement, this model is limited to research and learning purposes. Please strictly abide by the usage requirements and specifications of LLaMA. To ensure this, we need to confirm that you have LLaMA's original weight and come from a completely legitimate source.
1. 你需要先下载模型到本地，并校验它们的检查和：
```
md5sum ./*
6b2b545ff7bacaeec6297198b4b745dd ./config.json.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
4ba9cc7f11df0422798971bc962fe076 ./generation_config.json.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
560b35ffd8a7a1f5b2d34a94a523659a ./pytorch_model.bin.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
85ae4132b11747b1609b8953c7086367 ./special_tokens_map.json.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
953dceae026a7aa88e062787c61ed9b0 ./tokenizer_config.json.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
e765a7740a908b5e166e95b6ee09b94b ./tokenizer.model.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
```
2. 根据这里→的[指定脚本](https://github.com/neukg/TechGPT/blob/main/utils/decrypt.py)解码模型权重：
```shell
for file in $(ls /path/encrypt_weight); do
python decrypt.py --type decrypt \
--input_file /path/encrypt_weight/"$file" \
--output_dir /path/to_finetuned_model \
--key_file /path/to_original_llama_7B/consolidated.00.pth
done
```
请将 `/path/encrypt_weight`替换为你下载的加密文件目录，把`/path/to_original_llama_7B`替换为你已有的合法LLaMA-7B权重目录，里面应该有原LLaMA权重文件`consolidated.00.pth`，将 `/path/to_finetuned_model` 替换为你要存放解码后文件的目录。
在解码完成后，应该可以得到以下文件：
```shell
./config.json
./generation_config.json
./pytorch_model.bin
./special_tokens_map.json
./tokenizer_config.json
./tokenizer.model
```
3. 请检查所有文件的检查和是否和下面给出的相同, 以保证解码出正确的文件：
```
md5sum ./*
6d5f0d60a6e36ebc1518624a46f5a717 ./config.json
2917a1cafb895cf57e746cfd7696bfe5 ./generation_config.json
0d322cb6bde34f7086791ce12fbf2bdc ./pytorch_model.bin
15f7a943faa91a794f38dd81a212cb01 ./special_tokens_map.json
08f6f621dba90b2a23c6f9f7af974621 ./tokenizer_config.json
6ffe559392973a92ea28032add2a8494 ./tokenizer.model
```
---
1. Git clone this model first.
```
md5sum ./*
6b2b545ff7bacaeec6297198b4b745dd ./config.json.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
4ba9cc7f11df0422798971bc962fe076 ./generation_config.json.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
560b35ffd8a7a1f5b2d34a94a523659a ./pytorch_model.bin.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
85ae4132b11747b1609b8953c7086367 ./special_tokens_map.json.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
953dceae026a7aa88e062787c61ed9b0 ./tokenizer_config.json.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
e765a7740a908b5e166e95b6ee09b94b ./tokenizer.model.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.enc
```
2. Decrypt the files using the scripts in https://github.com/neukg/TechGPT/blob/main/utils/decrypt.py
You can use the following command in Bash.
Please replace `/path/to_encrypted` with the path where you stored your encrypted file,
replace `/path/to_original_llama_7B` with the path where you stored your original LLaMA-7B file `consolidated.00.pth`,
and replace `/path/to_finetuned_model` with the path where you want to save your final trained model.
```bash
for file in $(ls /path/encrypt_weight); do
python decrypt.py --type decrypt \
--input_file /path/encrypt_weight/"$file" \
--output_dir /path/to_finetuned_model \
--key_file /path/to_original_llama_7B/consolidated.00.pth
done
```
After executing the aforementioned command, you will obtain the following files.
```
./config.json
./generation_config.json
./pytorch_model.bin
./special_tokens_map.json
./tokenizer_config.json
./tokenizer.model
```
3. Check md5sum
You can verify the integrity of these files by performing an MD5 checksum to ensure their complete recovery.
Here are the MD5 checksums for the relevant files:
```
md5sum ./*
6d5f0d60a6e36ebc1518624a46f5a717 ./config.json
2917a1cafb895cf57e746cfd7696bfe5 ./generation_config.json
0d322cb6bde34f7086791ce12fbf2bdc ./pytorch_model.bin
15f7a943faa91a794f38dd81a212cb01 ./special_tokens_map.json
08f6f621dba90b2a23c6f9f7af974621 ./tokenizer_config.json
6ffe559392973a92ea28032add2a8494 ./tokenizer.model
```
## 使用方法 Model Usage
请注意在**训练**和**推理**阶段, 模型接收的输入格式是一致的：
Please note that the input should be formatted as follows in both **training** and **inference**.
``` python
Human: {input} \n\nAssistant:
```
请在使用TechGPT之前保证你已经安装好`transfomrers`和`torch`：
```shell
pip install transformers
pip install torch
```
- 注意，必须保证安装的 `transformers` 的版本中已经有 `LlamaForCausalLM` 。<br>
- Note that you must ensure that the installed version of `transformers` already has `LlamaForCausalLM`.
[Example:](https://github.com/neukg/TechGPT/blob/main/inference.py)
``` python
from transformers import LlamaTokenizer, AutoModelForCausalLM, AutoConfig, GenerationConfig
import torch
ckpt_path = '/workspace/BELLE-train/Version_raw/'
load_type = torch.float16
device = torch.device(0)
tokenizer = LlamaTokenizer.from_pretrained(ckpt_path)
tokenizer.pad_token_id = 0
tokenizer.bos_token_id = 1
tokenizer.eos_token_id = 2
tokenizer.padding_side = "left"
model_config = AutoConfig.from_pretrained(ckpt_path)
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=load_type, config=model_config)
model.to(device)
model.eval()
prompt = "Human: 请把下列标题扩写成摘要, 不少于100字: 基于视觉语言多模态的实体关系联合抽取的研究 \n\nAssistant: "
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=1,
bos_token_id=1,
eos_token_id=2,
pad_token_id=0,
max_new_tokens=128,
min_new_tokens=10,
do_sample=True,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
repetition_penalty=1.2,
)
output = generation_output.sequences[0]
output = tokenizer.decode(output, skip_special_tokens=True)
print(output)
```
输出：
```
Human: 请把下列标题扩写成摘要, 不少于100字: 基于视觉语言多模态的实体关系联合抽取的研究
Assistant: 文本：基于视觉语言的多模态的实体关系联合抽取是自然语言处理领域中的一个重要问题。该文提出了一种新的方法，利用深度学习技术来提取图像中的语义信息，并使用这些信息来识别和抽取图像中的人、物、地点等实体之间的关系。实验结果表明，该方法在多个基准数据集上取得了很好的性能表现，证明了其有效性和实用性。
```
## 免责声明 Disclaimers
该项目仅供学习交流使用，禁止用于商业用途。在使用过程中，使用者需认真阅读并遵守以下声明:
1. 本项目仅为大模型测试功能而生，使用者需自行承担风险和责任，如因使用不当而导致的任何损失或伤害，本项目概不负责。
2. 本项目中出现的第三方链接或库仅为提供便利而存在，其内容和观点与本项目无关。使用者在使用时需自行辨别，本项目不承担任何连带责任；
3. 使用者在测试和使用模型时，应遵守相关法律法规，如因使用不当而造成损失的，本项目不承担责任，使用者应自行承担；若项目出现任何错误，请向我方反馈，以助于我们及时修复；
4. 本模型中出现的任何违反法律法规或公序良俗的回答，均不代表本项目观点和立场，我们将不断完善模型回答以使其更符合社会伦理和道德规范。
使用本项目即表示您已经仔细阅读、理解并同意遵守以上免责声明。本项目保留在不预先通知任何人的情况下修改本声明的权利。
---
This project is for learning exchange only, commercial use is prohibited. During use, users should carefully read and abide by the following statements:
1. This project is only for the test function of the large model, and the user shall bear the risks and responsibilities. This project shall not be responsible for any loss or injury caused by improper use.
2. The third-party links or libraries appearing in this project exist only for convenience, and their content and opinions have nothing to do with this project. Users need to identify themselves when using it, and this project does not bear any joint and several liabilities;
3. Users should abide by the relevant laws and regulations when testing and using the model. If the loss is caused by improper use, the project will not bear the responsibility, and the user should bear it by themselves; if there is any error in the project, please feedback to us. to help us fix it in a timely manner;
4. Any answers in this model that violate laws and regulations or public order and good customs do not represent the views and positions of this project. We will continue to improve the model answers to make them more in line with social ethics and moral norms.
Using this project means that you have carefully read, understood and agreed to abide by the above disclaimer. The project reserves the right to modify this statement without prior notice to anyone.
## Citation
如果使用本项目的代码、数据或模型，请引用本项目。
Please cite our project when using our code, data or model.
```
@misc{TechGPT,
author = {Feiliang Ren, Ning An, Qi Ma, Hei Lei},
title = {TechGPT: Technology-Oriented Generative Pretrained Transformer},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/neukg/TechGPT}},
}
```
**我们对BELLE的工作表示衷心的感谢！**
**Our sincere thanks to BELLE for their work!**
```
@misc{ji2023better,
title={Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation},
author={Yunjie Ji and Yan Gong and Yong Deng and Yiping Peng and Qiang Niu and Baochang Ma and Xiangang Li},
year={2023},
eprint={2304.07854},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{BELLE,
author = {Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Baochang Ma, Xiangang Li},
title = {BELLE: Be Everyone's Large Language model Engine},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
}
```
|
TurkuNLP/gpt3-finnish-xl
|
TurkuNLP
| 2023-06-27T06:51:26Z | 164 | 7 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"feature-extraction",
"text-generation",
"fi",
"arxiv:2203.02155",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-15T10:49:56Z |
---
language:
- fi
pipeline_tag: text-generation
license: apache-2.0
---
Generative Pretrained Transformer with 1.5B parameteres for Finnish.
TurkuNLP Finnish GPT-3-models are a model family of pretrained monolingual GPT-style language models that are based on BLOOM-architecture.
Note that the models are pure language models, meaning that they are not [instruction finetuned](https://arxiv.org/abs/2203.02155) for dialogue
or answering questions.
These models are intended to be used as foundational models that can be e.g. instruction finetuned to serve as modern chat-models.
All models are trained for 300B tokens.
**Parameters**
| Model | Layers | Dim | Heads | Params |
|--------|--------|------|-------|--------|
| Small | 12 | 768 | 12 | 186M |
| Medium | 24 | 1024 | 16 | 437M |
| Large | 24 | 1536 | 16 | 881M |
| XL | 24 | 2064 | 24 | 1.5B |
| ”3B” | 32 | 2560 | 32 | 2.8B |
| ”8B” | 32 | 4096 | 32 | 7.5B |
| "13B" | 40 | 5120 | 40 | 13.3B |
**Datasets**
We used a combination of multiple Finnish resources.
* Finnish Internet Parsebank https://turkunlp.org/finnish_nlp.html
mC4 multilingual colossal, cleaned Common Crawl https://huggingface.co/datasets/mc4
* Common Crawl Finnish https://TODO
* Finnish Wikipedia https://fi.wikipedia.org/wiki
* Lönnrot Projekti Lönnrot http://www.lonnrot.net/
* ePub National library ”epub” collection
* National library ”lehdet” collection
* Suomi24 The Suomi 24 Corpus 2001-2020 http://urn.fi/urn:nbn:fi:lb-2021101527
* Reddit r/Suomi submissions and comments https://www.reddit.com/r/Suomi
* STT Finnish News Agency Archive 1992-2018 http://urn.fi/urn:nbn:fi:lb-2019041501
* Yle Finnish News Archive 2011-2018 http://urn.fi/urn:nbn:fi:lb-2017070501
* Yle Finnish News Archive 2019-2020 http://urn.fi/urn:nbn:fi:lb-2021050401
* Yle News Archive Easy-to-read Finnish 2011-2018 http://urn.fi/urn:nbn:fi:lb-2019050901
* Yle News Archive Easy-to-read Finnish 2019-2020 http://urn.fi/urn:nbn:fi:lb-2021050701
* ROOTS TODO
**Sampling ratios**
|Dataset | Chars | Ratio | Weight | W.Ratio |
|----------|--------|---------|--------|---------|
|Parsebank | 35.0B | 16.9\% | 1.5 | 22.7\%|
|mC4-Fi | 46.3B | 22.4\% | 1.0 | 20.0\%|
|CC-Fi | 79.6B | 38.5\% | 1.0 | 34.4\%|
|Fiwiki | 0.8B | 0.4\% | 3.0 | 1.0\%|
|Lönnrot | 0.8B | 0.4\% | 3.0 | 1.0\%|
|Yle | 1.6B | 0.8\% | 2.0 | 1.4\%|
|STT | 2.2B | 1.1\% | 2.0 | 1.9\%|
|ePub | 13.5B | 6.5\% | 1.0 | 5.8\%|
|Lehdet | 5.8B | 2.8\% | 1.0 | 2.5\%|
|Suomi24 | 20.6B | 9.9\% | 1.0 | 8.9\%|
|Reddit-Fi | 0.7B | 0.4\% | 1.0 | 0.3\%|
|**TOTAL** | **207.0B** | **100.0\%** | **N/A** | **100.0\%** |
More documentation and a paper coming soon.
|
TurkuNLP/gpt3-finnish-3B
|
TurkuNLP
| 2023-06-27T06:48:57Z | 78 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"feature-extraction",
"text-generation",
"fi",
"arxiv:2203.02155",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-15T11:40:09Z |
---
language:
- fi
pipeline_tag: text-generation
license: apache-2.0
---
Generative Pretrained Transformer with 3B parameteres for Finnish.
TurkuNLP Finnish GPT-3-models are a model family of pretrained monolingual GPT-style language models that are based on BLOOM-architecture.
Note that the models are pure language models, meaning that they are not [instruction finetuned](https://arxiv.org/abs/2203.02155) for dialogue
or answering questions.
These models are intended to be used as foundational models that can be e.g. instruction finetuned to serve as modern chat-models.
All models are trained for 300B tokens.
**Parameters**
| Model | Layers | Dim | Heads | Params |
|--------|--------|------|-------|--------|
| Small | 12 | 768 | 12 | 186M |
| Medium | 24 | 1024 | 16 | 437M |
| Large | 24 | 1536 | 16 | 881M |
| XL | 24 | 2064 | 24 | 1.5B |
| ”3B” | 32 | 2560 | 32 | 2.8B |
| ”8B” | 32 | 4096 | 32 | 7.5B |
| "13B" | 40 | 5120 | 40 | 13.3B |
**Datasets**
We used a combination of multiple Finnish resources.
* Finnish Internet Parsebank https://turkunlp.org/finnish_nlp.html
mC4 multilingual colossal, cleaned Common Crawl https://huggingface.co/datasets/mc4
* Common Crawl Finnish https://TODO
* Finnish Wikipedia https://fi.wikipedia.org/wiki
* Lönnrot Projekti Lönnrot http://www.lonnrot.net/
* ePub National library ”epub” collection
* National library ”lehdet” collection
* Suomi24 The Suomi 24 Corpus 2001-2020 http://urn.fi/urn:nbn:fi:lb-2021101527
* Reddit r/Suomi submissions and comments https://www.reddit.com/r/Suomi
* STT Finnish News Agency Archive 1992-2018 http://urn.fi/urn:nbn:fi:lb-2019041501
* Yle Finnish News Archive 2011-2018 http://urn.fi/urn:nbn:fi:lb-2017070501
* Yle Finnish News Archive 2019-2020 http://urn.fi/urn:nbn:fi:lb-2021050401
* Yle News Archive Easy-to-read Finnish 2011-2018 http://urn.fi/urn:nbn:fi:lb-2019050901
* Yle News Archive Easy-to-read Finnish 2019-2020 http://urn.fi/urn:nbn:fi:lb-2021050701
* ROOTS TODO
**Sampling ratios**
|Dataset | Chars | Ratio | Weight | W.Ratio |
|----------|--------|---------|--------|---------|
|Parsebank | 35.0B | 16.9\% | 1.5 | 22.7\%|
|mC4-Fi | 46.3B | 22.4\% | 1.0 | 20.0\%|
|CC-Fi | 79.6B | 38.5\% | 1.0 | 34.4\%|
|Fiwiki | 0.8B | 0.4\% | 3.0 | 1.0\%|
|Lönnrot | 0.8B | 0.4\% | 3.0 | 1.0\%|
|Yle | 1.6B | 0.8\% | 2.0 | 1.4\%|
|STT | 2.2B | 1.1\% | 2.0 | 1.9\%|
|ePub | 13.5B | 6.5\% | 1.0 | 5.8\%|
|Lehdet | 5.8B | 2.8\% | 1.0 | 2.5\%|
|Suomi24 | 20.6B | 9.9\% | 1.0 | 8.9\%|
|Reddit-Fi | 0.7B | 0.4\% | 1.0 | 0.3\%|
|**TOTAL** | **207.0B** | **100.0\%** | **N/A** | **100.0\%** |
More documentation and a paper coming soon.
|
TurkuNLP/gpt3-finnish-small
|
TurkuNLP
| 2023-06-27T06:48:35Z | 3,087 | 12 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"feature-extraction",
"text-generation",
"fi",
"arxiv:2203.02155",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-15T10:08:16Z |
---
language:
- fi
pipeline_tag: text-generation
license: apache-2.0
---
Generative Pretrained Transformer with 186M parameteres for Finnish.
TurkuNLP Finnish GPT-3-models are a model family of pretrained monolingual GPT-style language models that are based on BLOOM-architecture.
Note that the models are pure language models, meaning that they are not [instruction finetuned](https://arxiv.org/abs/2203.02155) for dialogue
or answering questions.
These models are intended to be used as foundational models that can be e.g. instruction finetuned to serve as modern chat-models.
All models are trained for 300B tokens.
**Parameters**
| Model | Layers | Dim | Heads | Params |
|--------|--------|------|-------|--------|
| Small | 12 | 768 | 12 | 186M |
| Medium | 24 | 1024 | 16 | 437M |
| Large | 24 | 1536 | 16 | 881M |
| XL | 24 | 2064 | 24 | 1.5B |
| ”3B” | 32 | 2560 | 32 | 2.8B |
| ”8B” | 32 | 4096 | 32 | 7.5B |
| "13B" | 40 | 5120 | 40 | 13.3B |
**Datasets**
We used a combination of multiple Finnish resources.
* Finnish Internet Parsebank https://turkunlp.org/finnish_nlp.html
mC4 multilingual colossal, cleaned Common Crawl https://huggingface.co/datasets/mc4
* Common Crawl Finnish https://TODO
* Finnish Wikipedia https://fi.wikipedia.org/wiki
* Lönnrot Projekti Lönnrot http://www.lonnrot.net/
* ePub National library ”epub” collection
* National library ”lehdet” collection
* Suomi24 The Suomi 24 Corpus 2001-2020 http://urn.fi/urn:nbn:fi:lb-2021101527
* Reddit r/Suomi submissions and comments https://www.reddit.com/r/Suomi
* STT Finnish News Agency Archive 1992-2018 http://urn.fi/urn:nbn:fi:lb-2019041501
* Yle Finnish News Archive 2011-2018 http://urn.fi/urn:nbn:fi:lb-2017070501
* Yle Finnish News Archive 2019-2020 http://urn.fi/urn:nbn:fi:lb-2021050401
* Yle News Archive Easy-to-read Finnish 2011-2018 http://urn.fi/urn:nbn:fi:lb-2019050901
* Yle News Archive Easy-to-read Finnish 2019-2020 http://urn.fi/urn:nbn:fi:lb-2021050701
* ROOTS TODO
**Sampling ratios**
|Dataset | Chars | Ratio | Weight | W.Ratio |
|----------|--------|---------|--------|---------|
|Parsebank | 35.0B | 16.9\% | 1.5 | 22.7\%|
|mC4-Fi | 46.3B | 22.4\% | 1.0 | 20.0\%|
|CC-Fi | 79.6B | 38.5\% | 1.0 | 34.4\%|
|Fiwiki | 0.8B | 0.4\% | 3.0 | 1.0\%|
|Lönnrot | 0.8B | 0.4\% | 3.0 | 1.0\%|
|Yle | 1.6B | 0.8\% | 2.0 | 1.4\%|
|STT | 2.2B | 1.1\% | 2.0 | 1.9\%|
|ePub | 13.5B | 6.5\% | 1.0 | 5.8\%|
|Lehdet | 5.8B | 2.8\% | 1.0 | 2.5\%|
|Suomi24 | 20.6B | 9.9\% | 1.0 | 8.9\%|
|Reddit-Fi | 0.7B | 0.4\% | 1.0 | 0.3\%|
|**TOTAL** | **207.0B** | **100.0\%** | **N/A** | **100.0\%** |
More documentation and a paper coming soon.
|
TurkuNLP/gpt3-finnish-large
|
TurkuNLP
| 2023-06-27T06:48:11Z | 10,788 | 7 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"feature-extraction",
"text-generation",
"fi",
"arxiv:2203.02155",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-15T10:26:29Z |
---
language:
- fi
pipeline_tag: text-generation
license: apache-2.0
---
Generative Pretrained Transformer with 881M parameteres for Finnish.
TurkuNLP Finnish GPT-3-models are a model family of pretrained monolingual GPT-style language models that are based on BLOOM-architecture.
Note that the models are pure language models, meaning that they are not [instruction finetuned](https://arxiv.org/abs/2203.02155) for dialogue
or answering questions.
These models are intended to be used as foundational models that can be e.g. instruction finetuned to serve as modern chat-models.
All models are trained for 300B tokens.
**Parameters**
| Model | Layers | Dim | Heads | Params |
|--------|--------|------|-------|--------|
| Small | 12 | 768 | 12 | 186M |
| Medium | 24 | 1024 | 16 | 437M |
| Large | 24 | 1536 | 16 | 881M |
| XL | 24 | 2064 | 24 | 1.5B |
| ”3B” | 32 | 2560 | 32 | 2.8B |
| ”8B” | 32 | 4096 | 32 | 7.5B |
| "13B" | 40 | 5120 | 40 | 13.3B |
**Datasets**
We used a combination of multiple Finnish resources.
* Finnish Internet Parsebank https://turkunlp.org/finnish_nlp.html
mC4 multilingual colossal, cleaned Common Crawl https://huggingface.co/datasets/mc4
* Common Crawl Finnish https://TODO
* Finnish Wikipedia https://fi.wikipedia.org/wiki
* Lönnrot Projekti Lönnrot http://www.lonnrot.net/
* ePub National library ”epub” collection
* National library ”lehdet” collection
* Suomi24 The Suomi 24 Corpus 2001-2020 http://urn.fi/urn:nbn:fi:lb-2021101527
* Reddit r/Suomi submissions and comments https://www.reddit.com/r/Suomi
* STT Finnish News Agency Archive 1992-2018 http://urn.fi/urn:nbn:fi:lb-2019041501
* Yle Finnish News Archive 2011-2018 http://urn.fi/urn:nbn:fi:lb-2017070501
* Yle Finnish News Archive 2019-2020 http://urn.fi/urn:nbn:fi:lb-2021050401
* Yle News Archive Easy-to-read Finnish 2011-2018 http://urn.fi/urn:nbn:fi:lb-2019050901
* Yle News Archive Easy-to-read Finnish 2019-2020 http://urn.fi/urn:nbn:fi:lb-2021050701
* ROOTS TODO
**Sampling ratios**
|Dataset | Chars | Ratio | Weight | W.Ratio |
|----------|--------|---------|--------|---------|
|Parsebank | 35.0B | 16.9\% | 1.5 | 22.7\%|
|mC4-Fi | 46.3B | 22.4\% | 1.0 | 20.0\%|
|CC-Fi | 79.6B | 38.5\% | 1.0 | 34.4\%|
|Fiwiki | 0.8B | 0.4\% | 3.0 | 1.0\%|
|Lönnrot | 0.8B | 0.4\% | 3.0 | 1.0\%|
|Yle | 1.6B | 0.8\% | 2.0 | 1.4\%|
|STT | 2.2B | 1.1\% | 2.0 | 1.9\%|
|ePub | 13.5B | 6.5\% | 1.0 | 5.8\%|
|Lehdet | 5.8B | 2.8\% | 1.0 | 2.5\%|
|Suomi24 | 20.6B | 9.9\% | 1.0 | 8.9\%|
|Reddit-Fi | 0.7B | 0.4\% | 1.0 | 0.3\%|
|**TOTAL** | **207.0B** | **100.0\%** | **N/A** | **100.0\%** |
More documentation and a paper coming soon.
|
Paulsunny/whisper-small-hi
|
Paulsunny
| 2023-06-27T06:47:09Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-27T06:40:16Z |
---
language:
- hi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
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 Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
spitfire4794/Kandinsky_2.1
|
spitfire4794
| 2023-06-27T06:45:56Z | 0 | 0 |
pytorch
|
[
"pytorch",
"Kandinsky",
"text-image",
"text2image",
"diffusion",
"latent diffusion",
"mCLIP-XLMR",
"mT5",
"text-to-image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2023-06-27T05:54:36Z |
---
license: apache-2.0
tags:
- Kandinsky
- text-image
- text2image
- diffusion
- latent diffusion
- mCLIP-XLMR
- mT5
pipeline_tag: text-to-image
library_name: pytorch
---
# Kandinsky 2.1
[Open In Colab](https://colab.research.google.com/drive/1xSbu-b-EwYd6GdaFPRVgvXBX_mciZ41e?usp=sharing)
[GitHub repository](https://github.com/ai-forever/Kandinsky-2)
[Habr post](https://habr.com/ru/company/sberbank/blog/725282/)
[Demo](https://rudalle.ru/)
## Architecture
Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas.
As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.
For diffusion mapping of latent spaces we use transformer with num_layers=20, num_heads=32 and hidden_size=2048.
Other architecture parts:
+ Text encoder (XLM-Roberta-Large-Vit-L-14) - 560M
+ Diffusion Image Prior — 1B
+ CLIP image encoder (ViT-L/14) - 427M
+ Latent Diffusion U-Net - 1.22B
+ MoVQ encoder/decoder - 67M
# Authors
+ Arseniy Shakhmatov: [Github](https://github.com/cene555), [Blog](https://t.me/gradientdip)
+ Anton Razzhigaev: [Github](https://github.com/razzant), [Blog](https://t.me/abstractDL)
+ Aleksandr Nikolich: [Github](https://github.com/AlexWortega), [Blog](https://t.me/lovedeathtransformers)
+ Vladimir Arkhipkin: [Github](https://github.com/oriBetelgeuse)
+ Igor Pavlov: [Github](https://github.com/boomb0om)
+ Andrey Kuznetsov: [Github](https://github.com/kuznetsoffandrey)
+ Denis Dimitrov: [Github](https://github.com/denndimitrov)
|
joohwan/xlmr
|
joohwan
| 2023-06-27T06:30:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-27T06:08:00Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlmr
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. -->
# xlmr
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6138
- Accuracy: 0.9163
- F1: 0.9153
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3962 | 1.0 | 450 | 0.3872 | 0.9011 | 0.9005 |
| 0.0584 | 2.0 | 900 | 0.4941 | 0.9180 | 0.9171 |
| 0.0284 | 3.0 | 1350 | 0.6192 | 0.9138 | 0.9127 |
| 0.0144 | 4.0 | 1800 | 0.5967 | 0.9224 | 0.9214 |
| 0.0103 | 5.0 | 2250 | 0.6138 | 0.9163 | 0.9153 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
kejolong/bayonetta
|
kejolong
| 2023-06-27T06:25:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-27T06:19:24Z |
---
license: creativeml-openrail-m
---
|
mandliya/default-taxi-v3
|
mandliya
| 2023-06-27T06:08:55Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T06:08:54Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: default-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
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="mandliya/default-taxi-v3", 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"])
```
|
Shridipta-06/rl_course_vizdoom_health_gathering_supreme
|
Shridipta-06
| 2023-06-27T06:01:26Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T02:20:28Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.81 +/- 3.54
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Shridipta-06/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
97jmlr/lander2
|
97jmlr
| 2023-06-27T05:51:34Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T23:33:06Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -220.16 +/- 118.05
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': '97jmlr/lander2'
'batch_size': 512
'minibatch_size': 128}
```
|
S3S3/Reinforce-CartPole-v1
|
S3S3
| 2023-06-27T05:34:52Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T05:34:43Z |
---
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
|
yhna/dqn-SpaceInvadersNoFrameskip-v4
|
yhna
| 2023-06-27T05:29:43Z | 11 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T08:52:38Z |
---
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: 820.50 +/- 249.79
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 yhna -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 yhna -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 yhna
```
## 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'}
```
|
gabrielZang/alpaca7B-lora-fine-tuning-with-test-data-int4
|
gabrielZang
| 2023-06-27T05:23:07Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-27T05:23:00Z |
---
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: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
tlsalfm820/wav2vec2-base-librispeech
|
tlsalfm820
| 2023-06-27T05:03:39Z | 79 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-20T06:09:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-librispeech
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. -->
# wav2vec2-base-librispeech
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2162
- Wer: 0.1419
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.606 | 4.13 | 500 | 2.0411 | 0.7943 |
| 0.3862 | 8.26 | 1000 | 0.3058 | 0.2202 |
| 0.1253 | 12.4 | 1500 | 0.2450 | 0.1908 |
| 0.0794 | 16.53 | 2000 | 0.2152 | 0.1531 |
| 0.0566 | 20.66 | 2500 | 0.2012 | 0.1457 |
| 0.0446 | 24.79 | 3000 | 0.2061 | 0.1432 |
| 0.0363 | 28.93 | 3500 | 0.2162 | 0.1419 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlgorithmicResearchGroup/flan-t5-xxl-arxiv-cs-ml-closed-qa
|
AlgorithmicResearchGroup
| 2023-06-27T04:40:26Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv",
"summarization",
"en",
"dataset:ArtifactAI/arxiv-cs-ml-instruct-tune-50k",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-06-26T14:17:24Z |
---
license: apache-2.0
language:
- en
pipeline_tag: summarization
widget:
- text: What is an LSTM?
example_title: Question Answering
tags:
- arxiv
datasets:
- ArtifactAI/arxiv-cs-ml-instruct-tune-50k
---
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Citation](#citation)
# TL;DR
This is a FLAN-T5-XXL model trained on [ArtifactAI/arxiv-cs-ml-instruct-50k](https://huggingface.co/datasets/ArtifactAI/arxiv-cs-ml-instruct-50k). This model is for research purposes only and ***should not be used in production settings***.
## Model Description
- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5)
# Usage
Find below some example scripts on how to use the model in `transformers`:
## Using the Pytorch model
```python
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load peft config for pre-trained checkpoint etc.
peft_model_id = "ArtifactAI/flant5-xxl-math-full-training-run-one"
config = PeftConfig.from_pretrained(peft_model_id)
# load base LLM model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map={"":0})
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id, device_map={"":0})
model.eval()
input_ids = tokenizer("What is the peak phase of T-eV?", return_tensors="pt", truncation=True).input_ids.cuda()
# with torch.inference_mode():
outputs = model.generate(input_ids=input_ids, max_new_tokens=1000, do_sample=True, top_p=0.9)
print(f"summary: {tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]}")
```
## Training Data
The model was trained on [ArtifactAI/arxiv-math-instruct-50k](https://huggingface.co/datasets/ArtifactAI/arxiv-cs-ml-instruct-50k), a dataset of question/answer pairs. Questions are generated using the t5-base model, while the answers are generated using the GPT-3.5-turbo model.
# Citation
```
@misc{flan-t5-xxl-arxiv-cs-ml-zeroshot-qa,
title={flan-t5-xxl-arxiv-cs-ml-zeroshot-qa},
author={Matthew Kenney},
year={2023}
}
```
|
AlgorithmicResearchGroup/flan-t5-xxl-arxiv-math-closed-qa
|
AlgorithmicResearchGroup
| 2023-06-27T04:39:54Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv",
"summarization",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-06-24T16:45:00Z |
---
license: apache-2.0
language:
- en
pipeline_tag: summarization
widget:
- text: What is the peak phase of T-eV?
example_title: Question Answering
tags:
- arxiv
---
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Citation](#citation)
# TL;DR
This is a FLAN-T5-XXL model trained on [ArtifactAI/arxiv-math-instruct-50k](https://huggingface.co/datasets/ArtifactAI/arxiv-math-instruct-50k). This model is for research purposes only and ***should not be used in production settings***.
## Model Description
- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5)
# Usage
Find below some example scripts on how to use the model in `transformers`:
## Using the Pytorch model
```python
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load peft config for pre-trained checkpoint etc.
peft_model_id = "ArtifactAI/flant5-xxl-math-full-training-run-one"
config = PeftConfig.from_pretrained(peft_model_id)
# load base LLM model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map={"":0})
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id, device_map={"":0})
model.eval()
input_ids = tokenizer("What is the peak phase of T-eV?", return_tensors="pt", truncation=True).input_ids.cuda()
# with torch.inference_mode():
outputs = model.generate(input_ids=input_ids, max_new_tokens=1000, do_sample=True, top_p=0.9)
print(f"summary: {tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]}")
```
## Training Data
The model was trained on [ArtifactAI/arxiv-math-instruct-50k](https://huggingface.co/datasets/ArtifactAI/arxiv-math-instruct-50k), a dataset of question/answer pairs. Questions are generated using the t5-base model, while the answers are generated using the GPT-3.5-turbo model.
# Citation
```
@misc{flan-t5-xxl-arxiv-math-zeroshot-qa,
title={flan-t5-xxl-arxiv-math-zeroshot-qa},
author={Matthew Kenney},
year={2023}
}
```
|
arminmrm93/ppo-PyramidTraining
|
arminmrm93
| 2023-06-27T04:21:03Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-06-27T04:21:00Z |
---
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: arminmrm93/ppo-PyramidTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
draziert/reinforce-PixelCopter-v1
|
draziert
| 2023-06-27T03:58:30Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T15:51:51Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-PixelCopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 45.40 +/- 37.27
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Holmodi/q-FrozenLake-v1-4x4-noSlippery
|
Holmodi
| 2023-06-27T03:44:06Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T03:44:04Z |
---
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="Holmodi/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"])
```
|
Louth/ppo-LunarLander-v2
|
Louth
| 2023-06-27T03:42:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T03:42:11Z |
---
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: 256.08 +/- 10.07
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
...
```
|
adooo/bigmodels
|
adooo
| 2023-06-27T03:20:33Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-05-04T08:37:52Z |
---
license: openrail
---
<img src="https://huggingface.co/adooo/bigmodels/resolve/main/NSX-1-EzBackground-pruned.png">NSX-1-EzBackground-pruned<br>
|
mue76/distilbert-base-uncased-finetuned-emotion
|
mue76
| 2023-06-27T03:11:10Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-27T02:26:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9260750948837287
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2140
- Accuracy: 0.926
- F1: 0.9261
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8381 | 1.0 | 250 | 0.3132 | 0.907 | 0.9046 |
| 0.2487 | 2.0 | 500 | 0.2140 | 0.926 | 0.9261 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hopkins/bert-wiki-choked-4
|
hopkins
| 2023-06-27T03:02:13Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-27T03:01:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bert-wiki-choked-4
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. -->
# bert-wiki-choked-4
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 1 | nan |
| 5.0605 | 2.0 | 2 | nan |
| 0.0 | 3.0 | 3 | nan |
| 0.0 | 4.0 | 4 | nan |
| 20.3063 | 5.0 | 5 | nan |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Fizzzk/1
|
Fizzzk
| 2023-06-27T03:00:08Z | 0 | 0 | null |
[
"license:cdla-sharing-1.0",
"region:us"
] | null | 2023-06-27T03:00:08Z |
---
license: cdla-sharing-1.0
---
|
hopkins/bert-wiki-choked-3
|
hopkins
| 2023-06-27T03:00:04Z | 56 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-27T02:59:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bert-wiki-choked-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-wiki-choked-3
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 1 | nan |
| 4.9575 | 2.0 | 2 | nan |
| 0.0 | 3.0 | 3 | nan |
| 0.0 | 4.0 | 4 | nan |
| 21.1383 | 5.0 | 5 | nan |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/bert-wiki-choked-2
|
hopkins
| 2023-06-27T02:54:26Z | 53 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-27T02:53:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bert-wiki-choked-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. -->
# bert-wiki-choked-2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | nan |
| No log | 2.0 | 2 | nan |
| No log | 3.0 | 3 | nan |
| No log | 4.0 | 4 | nan |
| No log | 5.0 | 5 | nan |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/bert-wiki-choked
|
hopkins
| 2023-06-27T02:49:35Z | 56 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-27T02:39:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bert-wiki-choked
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. -->
# bert-wiki-choked
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/bert-wiki
|
hopkins
| 2023-06-27T02:17:16Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T17:01:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bert-wiki
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. -->
# bert-wiki
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
tmpupload/superhot-30b-8k-no-rlhf-test-GPTQ
|
tmpupload
| 2023-06-27T02:15:04Z | 7 | 3 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-25T23:28:03Z |
---
license: other
---
# superhot-30b-8k-4bit--1g-safetensors
**Note: Maximum sequence length (max_seq_len) and compression factor (compress_pos_emb) need to be set to 8192 (or lower) and 4.**
Merged base LLaMA and LoRA with this:
https://github.com/tloen/alpaca-lora
Base LLaMA 30B:
https://huggingface.co/huggyllama/llama-30b
SuperHOT 30B 8k no-rlhf-test LoRA:
https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test
``` sh
BASE_MODEL=huggyllama_llama-30b LORA=kaiokendev_superhot-30b-8k-no-rlhf-test python export_hf_checkpoint.py
```
Quantized with AutoGPTQ:
https://github.com/PanQiWei/AutoGPTQ
``` sh
python quant_with_alpaca.py --pretrained_model_dir superhot-30b-8k-safetensors --quantized_model_dir superhot-30b-8k-4bit--1g-safetensors --bits 4 --group_size -1 --desc_act --num_samples 256 --save_and_reload
```
Perplexity:
```
CUDA_VISIBLE_DEVICES=0 python test_benchmark_inference.py \
-d /workspace/models/superhot-30b-8k-4bit--1g-safetensors \
-ppl \
-ppl_ds datasets/wikitext2.txt \
-l 8192 \
-cpe 4 \
-ppl_cn 40 \
-ppl_cs 8192 \
-ppl_ct 8192
-- Perplexity:
-- - Dataset: datasets/wikitext2.txt
-- - Chunks: 40
-- - Chunk size: 8192 -> 8192
-- - Chunk overlap: 0
-- - Min. chunk size: 50
-- - Key: text
-- Tokenizer: /workspace/models/superhot-30b-8k-4bit--1g-safetensors/tokenizer.model
-- Model config: /workspace/models/superhot-30b-8k-4bit--1g-safetensors/config.json
-- Model: /workspace/models/superhot-30b-8k-4bit--1g-safetensors/4bit.safetensors
-- Sequence length: 8192
-- RoPE compression factor: 4.0
-- Tuning:
-- --matmul_recons_thd: 8
-- --fused_mlp_thd: 2
-- --sdp_thd: 8
-- Options: ['perplexity']
** Time, Load model: 3.34 seconds
** Time, Load tokenizer: 0.01 seconds
-- Groupsize (inferred): None
-- Act-order (inferred): no
!! Model has empty group index (discarded)
** VRAM, Model: [cuda:0] 16,447.66 MB
-- Loading dataset...
-- Testing 40 chunks....
** Perplexity: 4.9434
```
|
tmpupload/superhot-30b-8k-no-rlhf-test-128g-GPTQ
|
tmpupload
| 2023-06-27T02:14:50Z | 10 | 3 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-25T22:44:28Z |
---
license: other
---
# superhot-30b-8k-4bit-128g-safetensors
**Note: Maximum sequence length (max_seq_len) and compression factor (compress_pos_emb) need to be set to 8192 (or lower) and 4.**
Merged base LLaMA and LoRA with this:
https://github.com/tloen/alpaca-lora
Base LLaMA 30B:
https://huggingface.co/huggyllama/llama-30b
SuperHOT 30B 8k no-rlhf-test LoRA:
https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test
``` sh
BASE_MODEL=huggyllama_llama-30b LORA=kaiokendev_superhot-30b-8k-no-rlhf-test python export_hf_checkpoint.py
```
Quantized with AutoGPTQ:
https://github.com/PanQiWei/AutoGPTQ
``` sh
python quant_with_alpaca.py --pretrained_model_dir superhot-30b-8k-safetensors --quantized_model_dir superhot-30b-8k-4bit-128g-safetensors --bits 4 --group_size 128 --desc_act --num_samples 256 --save_and_reload
```
Perplexity:
```
CUDA_VISIBLE_DEVICES=0 python test_benchmark_inference.py \
-d /workspace/models/superhot-30b-8k-4bit-128g-safetensors \
-ppl \
-ppl_ds datasets/wikitext2.txt \
-l 8192 \
-cpe 4 \
-ppl_cn 40 \
-ppl_cs 8192 \
-ppl_ct 8192
-- Perplexity:
-- - Dataset: datasets/wikitext2.txt
-- - Chunks: 40
-- - Chunk size: 8192 -> 8192
-- - Chunk overlap: 0
-- - Min. chunk size: 50
-- - Key: text
-- Tokenizer: /workspace/models/superhot-30b-8k-4bit-128g-safetensors/tokenizer.model
-- Model config: /workspace/models/superhot-30b-8k-4bit-128g-safetensors/config.json
-- Model: /workspace/models/superhot-30b-8k-4bit-128g-safetensors/4bit-128g.safetensors
-- Sequence length: 8192
-- RoPE compression factor: 4.0
-- Tuning:
-- --matmul_recons_thd: 8
-- --fused_mlp_thd: 2
-- --sdp_thd: 8
-- Options: ['perplexity']
** Time, Load model: 4.31 seconds
** Time, Load tokenizer: 0.01 seconds
-- Groupsize (inferred): 128
-- Act-order (inferred): yes
** VRAM, Model: [cuda:0] 17,043.70 MB
-- Loading dataset...
-- Testing 40 chunks....
** Perplexity: 4.6612
```
|
tmpupload/superhot-13b-16k-no-rlhf-test-32g-GPTQ
|
tmpupload
| 2023-06-27T02:13:39Z | 8 | 1 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T00:26:19Z |
---
license: other
---
# superhot-13b-16k-4bit-32g-safetensors
**Note: Maximum sequence length (max_seq_len) and compression factor (compress_pos_emb) need to be set to 16384 (or lower) and 8.**
Merged base LLaMA and LoRA with this:
https://github.com/tloen/alpaca-lora
Base LLaMA 13B:
https://huggingface.co/huggyllama/llama-13b
SuperHOT 13B 16k no-rlhf-test LoRA:
https://huggingface.co/kaiokendev/superhot-13b-16k-no-rlhf-test
``` sh
BASE_MODEL=huggyllama_llama-13b LORA=kaiokendev_superhot-13b-16k-no-rlhf-test python export_hf_checkpoint.py
```
Quantized with AutoGPTQ:
https://github.com/PanQiWei/AutoGPTQ
``` sh
python quant_with_alpaca.py --pretrained_model_dir superhot-13b-16k-safetensors --quantized_model_dir superhot-13b-16k-4bit-32g-safetensors --bits 4 --group_size 32 --desc_act --num_samples 256 --save_and_reload
```
Perplexity:
```
CUDA_VISIBLE_DEVICES=0 python test_benchmark_inference.py \
-d /workspace/models/superhot-13b-16k-4bit-32g-safetensors \
-ppl \
-ppl_ds datasets/wikitext2.txt \
-l 16384 \
-cpe 8 \
-ppl_cn 40 \
-ppl_cs 16384 \
-ppl_ct 16384
-- Perplexity:
-- - Dataset: datasets/wikitext2.txt
-- - Chunks: 40
-- - Chunk size: 16384 -> 16384
-- - Chunk overlap: 0
-- - Min. chunk size: 50
-- - Key: text
-- Tokenizer: /workspace/models/superhot-13b-16k-4bit-32g-safetensors/tokenizer.model
-- Model config: /workspace/models/superhot-13b-16k-4bit-32g-safetensors/config.json
-- Model: /workspace/models/superhot-13b-16k-4bit-32g-safetensors/4bit-32g.safetensors
-- Sequence length: 16384
-- RoPE compression factor: 8.0
-- Tuning:
-- --matmul_recons_thd: 8
-- --fused_mlp_thd: 2
-- --sdp_thd: 8
-- Options: ['perplexity']
** Time, Load model: 2.50 seconds
** Time, Load tokenizer: 0.01 seconds
-- Groupsize (inferred): 32
-- Act-order (inferred): yes
** VRAM, Model: [cuda:0] 7,952.62 MB
-- Loading dataset...
-- Testing 21 chunks...
** Perplexity: 6.8223
```
|
Arielkanevsky/Complaints_Classifier
|
Arielkanevsky
| 2023-06-27T02:05:29Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-02T00:49:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Complaints_Classifier
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. -->
# Complaints_Classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0009
- Accuracy: 1.0
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 412 | 0.0239 | 0.9946 |
| 0.0678 | 2.0 | 824 | 0.0009 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hexists/distilbert-base-uncased-distilled-clinc
|
hexists
| 2023-06-27T01:58:32Z | 99 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-25T20:17:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9441935483870968
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2142
- Accuracy: 0.9442
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7855 | 1.0 | 318 | 1.2133 | 0.7355 |
| 0.9389 | 2.0 | 636 | 0.5999 | 0.8581 |
| 0.4957 | 3.0 | 954 | 0.3550 | 0.9145 |
| 0.3105 | 4.0 | 1272 | 0.2665 | 0.9332 |
| 0.2389 | 5.0 | 1590 | 0.2399 | 0.9403 |
| 0.2101 | 6.0 | 1908 | 0.2276 | 0.9413 |
| 0.1952 | 7.0 | 2226 | 0.2200 | 0.9452 |
| 0.1872 | 8.0 | 2544 | 0.2169 | 0.9442 |
| 0.183 | 9.0 | 2862 | 0.2146 | 0.9442 |
| 0.1806 | 10.0 | 3180 | 0.2142 | 0.9442 |
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
roa7n/llama_human_ocr_ensembl
|
roa7n
| 2023-06-27T01:55:10Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-24T17:55:27Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
roshan77/dqn-SpaceInvadersNoFrameskip-v4
|
roshan77
| 2023-06-27T01:33:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-25T21:04:17Z |
---
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: 568.00 +/- 152.99
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 roshan77 -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 roshan77 -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 roshan77
```
## 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'}
```
|
danielDozie/m3hugier
|
danielDozie
| 2023-06-27T01:22:38Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-06-27T00:38:58Z |
---
license: mit
---
### This is an H3 Readme Heading. My first model on Hugging Face
Enjoy your stay here.
|
hexists/distilbert-base-uncased-finetuned-clinc
|
hexists
| 2023-06-27T01:07:17Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-24T21:37:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9158064516129032
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7781
- Accuracy: 0.9158
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2843 | 1.0 | 318 | 3.2793 | 0.7445 |
| 2.6222 | 2.0 | 636 | 1.8709 | 0.8335 |
| 1.5464 | 3.0 | 954 | 1.1619 | 0.8935 |
| 1.0132 | 4.0 | 1272 | 0.8614 | 0.9097 |
| 0.7981 | 5.0 | 1590 | 0.7781 | 0.9158 |
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.1
- Datasets 2.10.1
- Tokenizers 0.13.3
|
sid/dqn-SpaceInvadersNoFrameskip-v4-test
|
sid
| 2023-06-27T01:03:15Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-27T01:02:54Z |
---
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: 952.50 +/- 279.56
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 sid -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 sid -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 sid
```
## 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', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Shridipta-06/poca-SoccerTwos
|
Shridipta-06
| 2023-06-27T00:58:45Z | 40 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-06-27T00:58:02Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Shridipta-06/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
MrDragonFox/Lazarus-30b-SuperHOT-8k-GPTQ
|
MrDragonFox
| 2023-06-27T00:17:42Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T22:47:27Z |
--max_seq_len 8192 --compress_pos_emb 4 --loader exllama_hf
check monkeypatch in ooba ..
for 16384 compress_pos_emb 8
works on 2 a6000 just fine
|
heka-ai/tasb-100k
|
heka-ai
| 2023-06-27T00:06:03Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-06-27T00:06:00Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# heka-ai/tasb-100k
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('heka-ai/tasb-100k')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('heka-ai/tasb-100k')
model = AutoModel.from_pretrained('heka-ai/tasb-100k')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=heka-ai/tasb-100k)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 100000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Lucas-lab/ppo-Huggy
|
Lucas-lab
| 2023-06-26T23:51:15Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-26T22:10:16Z |
---
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: Lucas-lab/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
carlesoctav/multi-qa-en-id-mMiniLMv2-L6-H384
|
carlesoctav
| 2023-06-26T23:30:33Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"feature-extraction",
"generated_from_keras_callback",
"sentence-similarity",
"en",
"id",
"dataset:carlesoctav/en-id-parallel-sentences-embedding",
"dataset:carlesoctav/en-id-parallel-sentences",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-29T11:21:11Z |
---
tags:
- generated_from_keras_callback
- feature-extraction
- sentence-similarity
license: apache-2.0
datasets:
- carlesoctav/en-id-parallel-sentences-embedding
- carlesoctav/en-id-parallel-sentences
language:
- en
- id
---
# MultiQA-mMini-L6-H384
This model is a fine-tuned version of [nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large) on the [carlesoctav/en-id-parallel-sentences](https://huggingface.co/datasets/carlesoctav/en-id-parallel-sentences) dataset using the following procedure: [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://paperswithcode.com/paper/making-monolingual-sentence-embeddings). It achieves 92% accuracy on the validation split of the dataset for the English-Indonesian language pair in the bitext mining task.
## Model Description
Since we followed the approach outlined in [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://paperswithcode.com/paper/making-monolingual-sentence-embeddings), we used [sentence-transformers/multi-qa-MiniLM-L6-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-dot-v1) as the teacher model and [nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large) as the student model (multilingual).
Example of usage:
```python
from transformers import AutoTokenizer, AutoModel
import torch
#CLS Pooling - Take output from first token
def cls_pooling(model_output):
return model_output.last_hidden_state[:,0]
#Encode text
def encode(texts):
# Tokenize sentences
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
# Perform pooling
embeddings = cls_pooling(model_output)
return embeddings
# Sentences we want sentence embeddings for
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district", "sekitar 9 juta orang tinggal di london", " London terkenal sebagai distrik finansial"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("carlesoctav/multi-qa-en-id-mMiniLMv2-L6-H384")
model = AutoModel.from_pretrained("carlesoctav/multi-qa-en-id-mMiniLMv2-L6-H384", from_tf = True)
#Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)
#Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
Take a look at the demo on Google Colab [here](https://colab.research.google.com/drive/1EZb0qACRIug9BVRX7LziKPchpYBUru9e#scrollTo=tZAjbx-_AOsg).
## Intended Uses & Limitations
Our model is intended to be used for semantic search. It encodes queries/questions and text paragraphs into dense vectors, allowing it to find relevant documents based on the given passages.
The model is designed to create sentence embeddings specifically for semantic search and information retrieval tasks. As the student model, it inherits this capability from the fine-tuned teacher model. It supports both English and Indonesian languages, making it suitable for cross-lingual information retrieval tasks.
Please note that there is a limit of 256 word pieces, and any text longer than that will be truncated. Additionally, the model was trained using input text up to 80 word pieces, so it may not perform optimally on longer text.
In the following some technical details how this model must be used:
| Setting | Value |
| --- | :---: |
| Dimensions | 384 |
| Produces normalized embeddings | No |
| Pooling-Method | CLS pooling |
| Suitable score functions | dot-product (e.g. `util.dot_score`) |
----
## Training and Evaluation Data
We utilized the [carlesoctav/en-id-parallel-sentences](https://huggingface.co/datasets/carlesoctav/en-id-parallel-sentences) dataset for training and evaluation purposes. The data was dynamically split into 95% for training and 5% for validation.
## Training Procedure
The complete training script can be found in the current repository under the name `train.py`.
### Framework Versions
The following framework versions were used:
- Transformers 4.29.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
lamoglia/ppo-Huggy
|
lamoglia
| 2023-06-26T23:26:49Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-26T23:26:46Z |
---
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: lamoglia/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jwieting/vmsst
|
jwieting
| 2023-06-26T23:25:50Z | 162 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"feature-extraction",
"cross-lingual",
"multilingual",
"question-answering",
"retrieval",
"sentence-similarity",
"variational",
"custom_code",
"arxiv:2212.10726",
"license:apache-2.0",
"region:us"
] |
sentence-similarity
| 2023-06-26T23:23:50Z |
---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- cross-lingual
- multilingual
- question-answering
- retrieval
- sentence-similarity
- variational
---
# VMSST
Published as a long paper at ACL 2023.
Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes and carefully engineered heuristics to work well. In this paper, we instead propose a generative model for learning multilingual text embeddings which can be used to retrieve or score sentence pairs. Our model operates on parallel data in N languages and, through an approximation we introduce, efficiently encourages source separation in this multilingual setting, separating semantic information that is shared between translations from stylistic or language-specific variation. We show careful large-scale comparisons between contrastive and generation-based approaches for learning multilingual text embeddings, a comparison that has not been done to the best of our knowledge despite the popularity of these approaches. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval––the last of which we introduce in this paper. Overall, our Variational Multilingual Source-Separation Transformer (VMSST) model outperforms both a strong contrastive and generative baseline on these tasks.
## Checkpoints
T5X (Jax): https://storage.googleapis.com/gresearch/vmsst/vmsst-large-2048-t5x.zip
PyTorch: https://storage.googleapis.com/gresearch/vmsst/vmsst-large-2048-pytorch.zip
## Usage
### Installation
1. Clone the following repository from Google Research.
```
git clone -b master --single-branch https://github.com/google-research/google-research.git
```
2. Make sure `google-research` is the current directory:
```
cd google-research/vmsst
```
3. Create and activate a new virtualenv:
```
python -m venv vmsst
source vmsst/bin/activate
```
4. This repository is tested on Python 3.10+. Install required packages:
```
pip install -r requirements.txt
```
### Test
To test that the checkpoint and installation are working as intended, run:
bash run.sh
The expected cosine similarity scores for the three sentences pairs are:
0.2573888301849365, 0.1563197821378708, and 0.28531330823898315.
### Inference
To embed a list of sentences:
python score_sentence_pairs.py --sentence_pair_file test_data/test_sentence_pairs.tsv
To score a list of sentence pairs:
python embed_sentences.py --sentence_file test_data/test_sentences.txt
## Citation
If you use our code or models your work please cite:
@article{wieting2022beyond,
title={Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval},
author={Wieting, John and Clark, Jonathan H and Cohen, William W and Neubig, Graham and Berg-Kirkpatrick, Taylor},
journal={arXiv preprint arXiv:2212.10726},
year={2022}
}
|
harpomaxx/ppo-LunarLander-v2
|
harpomaxx
| 2023-06-26T23:12:32Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T22:40:06Z |
---
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: 257.12 +/- 13.86
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
...
```
|
askatasuna/psy_q_a_test
|
askatasuna
| 2023-06-26T22:58:59Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-26T20:30:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: psy_q_a_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. -->
# psy_q_a_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: 1.3329
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.4872 | 1.0 | 3593 | 1.4473 |
| 1.444 | 2.0 | 7186 | 1.3626 |
| 1.3245 | 3.0 | 10779 | 1.3329 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hugfacerhaha/ppo-LunarLander-v2
|
hugfacerhaha
| 2023-06-26T22:56:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T22:56:33Z |
---
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: 261.41 +/- 18.61
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
...
```
|
dtcalabro/test_model
|
dtcalabro
| 2023-06-26T22:54:05Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-26T16:31:14Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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]
|
cupcakeDriveby/rlClass
|
cupcakeDriveby
| 2023-06-26T22:45:32Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T22:45:14Z |
---
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: 256.48 +/- 21.03
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
...
```
|
AImod3ls/aether-green
|
AImod3ls
| 2023-06-26T22:37:25Z | 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
| 2023-06-26T22:33:01Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### aether-green Dreambooth model trained by AImod3ls with (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:
.jpg)
.png)
.png)
.png)
.png)
.png)
.png)
|
OumaElha/speech8
|
OumaElha
| 2023-06-26T22:20:28Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-26T22:12:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: speech8
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. -->
# speech8
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
TheBloke/chronos-33b-superhot-8k-fp16
|
TheBloke
| 2023-06-26T22:16:34Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"chatbot",
"storywriting",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-26T17:02:57Z |
---
inference: false
license: other
tags:
- llama
- pytorch
- chatbot
- storywriting
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Elinas' Chronos 33B merged with Kaio Ken's SuperHOT 8K fp16
These files are pytorch format fp16 model files for [Elinas' Chronos 33B](https://huggingface.co/elinas/chronos-33b) merged with [Kaio Ken's SuperHOT 30B 8K LoRA](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test) to produce a model capable of 8K context.
[Kaio Ken's SuperHOT 30B LoRA](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test) is merged on to the base model, and then 8K context can be achieved during inference by using `trust_remote_code=True`.
Note that `config.json` has been set to a sequence length of 8192. This can be modified to 4096 if you want to try with a smaller sequence length.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/chronos-33b-superhot-8k-fp16)
* [Unquantised fp16 SuperHOT model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/chronos-33b-superhot-8k-fp16)
* [Unquantised fp16 base model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/elinas/chronos-33b)
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Kaio Ken's SuperHOT 8K
### SuperHOT Prototype 2 w/ 8K Context
This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).
Tests have shown that the model does indeed leverage the extended context at 8K.
You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**
#### Looking for Merged & Quantized Models?
- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors)
- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors)
#### Training Details
I trained the LoRA with the following configuration:
- 1200 samples (~400 samples over 2048 sequence length)
- learning rate of 3e-4
- 3 epochs
- The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
- Rank = 4
- Alpha = 8
- no dropout
- weight decay of 0.1
- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
- Trained on 4-bit base model
# Original model card: Elinas' Chronos 33B
# chronos-33b
This is the fp16 PyTorch / HF version of **chronos-33b** - if you need another version, GGML and GPTQ versions are linked below.
This model is primarily focused on chat, roleplay, and storywriting, but can accomplish other tasks such as simple reasoning and coding.
Chronos generates very long outputs with coherent text, largely due to the human inputs it was trained on.
This model uses Alpaca formatting, so for optimal model performance, use:
```
### Instruction:
Your instruction or question here.
### Response:
```
[GGML Version provided by @TheBloke](https://huggingface.co/TheBloke/chronos-33b-GGML)
[4bit GPTQ Version provided by @TheBloke](https://huggingface.co/TheBloke/chronos-33b-GPTQ)
<!--**Support My Development of New Models**
<a href='https://ko-fi.com/Q5Q6MB734' target='_blank'><img height='36' style='border:0px;height:36px;'
src='https://storage.ko-fi.com/cdn/kofi1.png?v=3' border='0' alt='Support Development' /></a>-->
--
license: other
---
# LLaMA Model Card
## Model details
**Organization developing the model**
The FAIR team of Meta AI.
**Model date**
LLaMA was trained between December. 2022 and Feb. 2023.
**Model version**
This is version 1 of the model.
**Model type**
LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
**Paper or resources for more information**
More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
**Citations details**
https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
**License**
Non-commercial bespoke license
**Where to send questions or comments about the model**
Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of LLaMA is research on large language models, including:
exploring potential applications such as question answering, natural language understanding or reading comprehension,
understanding capabilities and limitations of current language models, and developing techniques to improve those,
evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
**Primary intended users**
The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
**Out-of-scope use cases**
LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
## Factors
**Relevant factors**
One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
**Evaluation factors**
As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.
## Metrics
**Model performance measures**
We use the following measure to evaluate the model:
- Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs,
- Exact match for question answering,
- The toxicity score from Perspective API on RealToxicityPrompts.
**Decision thresholds**
Not applicable.
**Approaches to uncertainty and variability**
Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.
## Evaluation datasets
The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.
## Training dataset
The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing.
## Quantitative analysis
Hyperparameters for the model architecture
<table>
<thead>
<tr>
<th >LLaMA</th> <th colspan=6>Model hyper parameters </th>
</tr>
<tr>
<th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
<tr>
<th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
</tbody>
</table>
*Table 1 - Summary of LLama Model Hyperparameters*
We present our results on eight standard common sense reasoning benchmarks in the table below.
<table>
<thead>
<tr>
<th>LLaMA</th> <th colspan=9>Reasoning tasks </th>
</tr>
<tr>
<th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93
</th>
<tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94
</th>
<tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92
</th>
<tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr>
</tbody>
</table>
*Table 2 - Summary of LLama Model Performance on Reasoning tasks*
We present our results on bias in the table below. Note that lower value is better indicating lower bias.
| No | Category | FAIR LLM |
| --- | -------------------- | -------- |
| 1 | Gender | 70.6 |
| 2 | Religion | 79 |
| 3 | Race/Color | 57 |
| 4 | Sexual orientation | 81 |
| 5 | Age | 70.1 |
| 6 | Nationality | 64.2 |
| 7 | Disability | 66.7 |
| 8 | Physical appearance | 77.8 |
| 9 | Socioeconomic status | 71.5 |
| | LLaMA Average | 66.6 |
*Table 3 - Summary bias of our model output*
## Ethical considerations
**Data**
The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
**Human life**
The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
**Mitigations**
We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier.
**Risks and harms**
Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
**Use cases**
LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
|
OumaElha/Speech7
|
OumaElha
| 2023-06-26T22:06:28Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-26T20:25:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Speech7
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. -->
# Speech7
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 4.2042 | 1.44 | 100 | 4.2839 | 1 |
| 4.1119 | 2.88 | 200 | 4.2368 | 1 |
| 4.1696 | 4.32 | 300 | 4.2242 | 1 |
| 4.1838 | 5.76 | 400 | 4.2262 | 1 |
| 4.2368 | 7.19 | 500 | 4.2247 | 1 |
| 4.1376 | 8.63 | 600 | 4.2179 | 1 |
| 4.1417 | 10.07 | 700 | 4.2209 | 1 |
| 4.2254 | 11.51 | 800 | 4.2471 | 1 |
| 4.2302 | 12.95 | 900 | 4.2145 | 1 |
| 4.1778 | 14.39 | 1000 | 4.3393 | 1 |
| 4.1574 | 15.83 | 1100 | 4.2917 | 1 |
| 4.2026 | 17.27 | 1200 | 4.2731 | 1 |
| 4.141 | 18.71 | 1300 | 4.2302 | 1 |
| 4.2525 | 20.14 | 1400 | 4.2104 | 1 |
| 4.2325 | 21.58 | 1500 | 4.2543 | 1 |
| 4.1789 | 23.02 | 1600 | 4.4020 | 1 |
| 4.1456 | 24.46 | 1700 | 4.2143 | 1 |
| 4.1754 | 25.9 | 1800 | 4.2123 | 1 |
| 12.3485 | 27.34 | 1900 | 50.3232 | 1 |
| 4.2031 | 28.78 | 2000 | 4.2259 | 1 |
| 4.1497 | 30.22 | 2100 | 4.3216 | 1 |
| 4.2171 | 31.65 | 2200 | 4.2108 | 1 |
| 4.1981 | 33.09 | 2300 | 4.3025 | 1 |
| 4.2091 | 34.53 | 2400 | 4.2173 | 1 |
| 4.2005 | 35.97 | 2500 | 4.2747 | 1 |
| 4.2386 | 37.41 | 2600 | 4.2027 | 1 |
| 4.2343 | 38.85 | 2700 | 4.2137 | 1 |
| 4.0967 | 40.29 | 2800 | 4.2804 | 1 |
| 4.1737 | 41.73 | 2900 | 4.2072 | 1 |
| 4.171 | 43.17 | 3000 | 4.2186 | 1 |
| 4.2117 | 44.6 | 3100 | 4.2161 | 1 |
| 4.1021 | 46.04 | 3200 | 4.2389 | 1 |
| 4.2572 | 47.48 | 3300 | 4.2126 | 1 |
| 3.4461 | 48.92 | 3400 | 4.2700 | 1 |
| 0.7289 | 50.36 | 3500 | 4.2700 | 1 |
| 0.4496 | 51.8 | 3600 | 4.2700 | 1 |
| 0.1189 | 53.24 | 3700 | 4.2700 | 1 |
| 8.233 | 54.68 | 3800 | 4.2700 | 1 |
| 3.8072 | 56.12 | 3900 | 4.2700 | 1 |
| 0.0 | 57.55 | 4000 | nan | 1 |
| 0.0 | 58.99 | 4100 | nan | 1 |
| 0.0 | 60.43 | 4200 | nan | 1 |
| 0.0 | 61.87 | 4300 | nan | 1 |
| 0.0 | 63.31 | 4400 | nan | 1 |
| 0.0 | 64.75 | 4500 | nan | 1 |
| 0.0 | 66.19 | 4600 | nan | 1 |
| 0.0 | 67.63 | 4700 | nan | 1 |
| 0.0 | 69.06 | 4800 | nan | 1 |
| 0.0 | 70.5 | 4900 | nan | 1 |
| 0.0 | 71.94 | 5000 | nan | 1 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
NasimB/gpt2-3-dp-mod-aochild-cut
|
NasimB
| 2023-06-26T22:04:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T18:29:57Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-3-dp-mod-aochild-cut
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-3-dp-mod-aochild-cut
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4147
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7085 | 0.27 | 500 | 5.6381 |
| 5.3561 | 0.54 | 1000 | 5.2057 |
| 5.0129 | 0.81 | 1500 | 4.9548 |
| 4.7577 | 1.08 | 2000 | 4.8118 |
| 4.5699 | 1.35 | 2500 | 4.6948 |
| 4.4784 | 1.62 | 3000 | 4.5973 |
| 4.3982 | 1.89 | 3500 | 4.5120 |
| 4.2209 | 2.16 | 4000 | 4.4767 |
| 4.1372 | 2.42 | 4500 | 4.4233 |
| 4.1104 | 2.69 | 5000 | 4.3703 |
| 4.0829 | 2.96 | 5500 | 4.3281 |
| 3.8669 | 3.23 | 6000 | 4.3410 |
| 3.8631 | 3.5 | 6500 | 4.3058 |
| 3.8586 | 3.77 | 7000 | 4.2792 |
| 3.8067 | 4.04 | 7500 | 4.2780 |
| 3.5952 | 4.31 | 8000 | 4.2902 |
| 3.6276 | 4.58 | 8500 | 4.2712 |
| 3.6375 | 4.85 | 9000 | 4.2458 |
| 3.4886 | 5.12 | 9500 | 4.2834 |
| 3.3673 | 5.39 | 10000 | 4.2871 |
| 3.399 | 5.66 | 10500 | 4.2744 |
| 3.3996 | 5.93 | 11000 | 4.2531 |
| 3.1775 | 6.2 | 11500 | 4.3130 |
| 3.1317 | 6.47 | 12000 | 4.3148 |
| 3.1493 | 6.73 | 12500 | 4.3110 |
| 3.1562 | 7.0 | 13000 | 4.3110 |
| 2.8933 | 7.27 | 13500 | 4.3579 |
| 2.912 | 7.54 | 14000 | 4.3617 |
| 2.9199 | 7.81 | 14500 | 4.3607 |
| 2.8631 | 8.08 | 15000 | 4.3845 |
| 2.7354 | 8.35 | 15500 | 4.3967 |
| 2.7447 | 8.62 | 16000 | 4.4013 |
| 2.7529 | 8.89 | 16500 | 4.4021 |
| 2.6903 | 9.16 | 17000 | 4.4113 |
| 2.6512 | 9.43 | 17500 | 4.4138 |
| 2.6601 | 9.7 | 18000 | 4.4147 |
| 2.6617 | 9.97 | 18500 | 4.4147 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
liquannan/distilbert-base-uncased-finetuned-imdb
|
liquannan
| 2023-06-26T21:48:39Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-26T21:44:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
gbellamy/Reinforce-CartPole
|
gbellamy
| 2023-06-26T21:48:35Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T21:48:25Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole
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
|
amiri-amin/taxi_default
|
amiri-amin
| 2023-06-26T21:46:04Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T21:46:00Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_default
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.74
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="amiri-amin/taxi_default", 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"])
```
|
TheBloke/Wizard-Vicuna-30B-Superhot-8K-fp16
|
TheBloke
| 2023-06-26T21:45:45Z | 1,573 | 7 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-26T20:54:16Z |
---
inference: false
license: other
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K fp16
These files are pytorch format fp16 model files for [Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test).
It is the result of merging and/or converting the source repository to float16.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Superhot-8K-fp16)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/none)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Superhot-8K-fp16)
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K
### SuperHOT Prototype 2 w/ 8K Context
This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).
Tests have shown that the model does indeed leverage the extended context at 8K.
You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**
#### Looking for Merged & Quantized Models?
- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors)
- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors)
#### Training Details
I trained the LoRA with the following configuration:
- 1200 samples (~400 samples over 2048 sequence length)
- learning rate of 3e-4
- 3 epochs
- The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
- Rank = 4
- Alpha = 8
- no dropout
- weight decay of 0.1
- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
- Trained on 4-bit base model
|
gbellamy/dqn-SpaceInvadersNoFrameskip-v4
|
gbellamy
| 2023-06-26T21:28:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T21:27:41Z |
---
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: 520.00 +/- 67.12
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 gbellamy -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 gbellamy -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 gbellamy
```
## 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),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
snowyman519/Sephiroth_RVC
|
snowyman519
| 2023-06-26T20:54:46Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-06-26T20:27:58Z |
---
license: openrail
---
# Sephiroth (FF7 Remake) [Tyler Hoechlin VA] — RVC v2 — 200 Epoch
The model's dataset consists of 10 minutes of phrases from Final Fantasy 7 and Crisis Core Remakes.
## Recommendations
Transpose must be set low due to the lack of high-pitched voice lines in the dataset.
Sometimes even low transpose can't help, unfortunately (e.g. for high pitched female voice).
### Best params
1. `Transpose`: -12 (Maybe even lower. Depends on the selected song)
2. `Pitch extraction algorithm`: mangio-crepe
|
agustinl/q-Taxi-v3
|
agustinl
| 2023-06-26T20:44:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T20:43:59Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
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="agustinl/q-Taxi-v3", 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"])
```
|
microsoft/tapex-large-finetuned-wikisql
|
microsoft
| 2023-06-26T19:50:22Z | 101 | 16 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2107.07653",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
table-question-answering
| 2022-03-08T06:41:10Z |
---
language: en
tags:
- tapex
- table-question-answering
datasets:
- wikisql
license: mit
---
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining).
## Model description
TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries.
TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder.
This model is the `tapex-base` model fine-tuned on the [WikiSQL](https://huggingface.co/datasets/wikisql) dataset.
## Intended Uses
You can use the model for table question answering on relatively simple questions. Some **solveable** questions are shown below (corresponding tables now shown):
| Question | Answer |
|:---: |:---:|
| tell me what the notes are for south australia | no slogan on current series |
| what position does the player who played for butler cc (ks) play? | guard-forward |
| how many schools did player number 3 play at? | 1.0 |
| how many winning drivers in the kraco twin 125 (r2) race were there? | 1.0 |
| for the episode(s) aired in the u.s. on 4 april 2008, what were the names? | "bust a move" part one, "bust a move" part two |
### How to Use
Here is how to use this model in transformers:
```python
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd
tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wikisql")
model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wikisql")
data = {
"year": [1896, 1900, 1904, 2004, 2008, 2012],
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
}
table = pd.DataFrame.from_dict(data)
# tapex accepts uncased input since it is pre-trained on the uncased corpus
query = "In which year did beijing host the Olympic Games?"
encoding = tokenizer(table=table, query=query, return_tensors="pt")
outputs = model.generate(**encoding)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# [' 2008.0']
```
### How to Eval
Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
### BibTeX entry and citation info
```bibtex
@inproceedings{
liu2022tapex,
title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=O50443AsCP}
}
```
|
microsoft/resnet-152
|
microsoft
| 2023-06-26T19:49:50Z | 19,045 | 12 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-16T14:54:22Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-152 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ResNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-152")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-152")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet).
### BibTeX entry and citation info
```bibtex
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
```
|
microsoft/resnet-34
|
microsoft
| 2023-06-26T19:49:23Z | 3,167 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-16T15:41:51Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-34 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ResNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-34")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-34")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet).
### BibTeX entry and citation info
```bibtex
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
```
|
bk6000/ppo-Huggy
|
bk6000
| 2023-06-26T19:45:26Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-26T19:45:22Z |
---
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: bk6000/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
aarroonn22/distilbert-base-uncased-finetuned-mlm
|
aarroonn22
| 2023-06-26T19:29:05Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-26T17:36:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-mlm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mlm
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:
- eval_loss: 0.0000
- eval_runtime: 405.1834
- eval_samples_per_second: 160.093
- eval_steps_per_second: 20.013
- epoch: 1.0
- step: 32433
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Keithulu/distilgpt2-finetuned-python-stack-clean-answers-e10
|
Keithulu
| 2023-06-26T19:02:18Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T19:00:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-python-stack-clean-answers-e10
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. -->
# distilgpt2-finetuned-python-stack-clean-answers-e10
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3318
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 28 | 3.7900 |
| No log | 2.0 | 56 | 3.6846 |
| No log | 3.0 | 84 | 3.5965 |
| No log | 4.0 | 112 | 3.5271 |
| No log | 5.0 | 140 | 3.4656 |
| No log | 6.0 | 168 | 3.4170 |
| No log | 7.0 | 196 | 3.3794 |
| No log | 8.0 | 224 | 3.3529 |
| No log | 9.0 | 252 | 3.3367 |
| No log | 10.0 | 280 | 3.3318 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
mahmoudelaref/dilbert-lora
|
mahmoudelaref
| 2023-06-26T18:54:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-06-26T18:48:37Z |
## Dilbert_Lora Stable Diffusion model trained using Lora model on Dilbert comics by mahmoudelaref
|
Keithulu/distilgpt2-finetuned-python-stack-clean-answers
|
Keithulu
| 2023-06-26T18:51:46Z | 167 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T18:50:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-python-stack-clean-answers
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. -->
# distilgpt2-finetuned-python-stack-clean-answers
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9058
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 28 | 4.0157 |
| No log | 2.0 | 56 | 3.9312 |
| No log | 3.0 | 84 | 3.9058 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
DarkAirforce/ppo-Huggy
|
DarkAirforce
| 2023-06-26T18:41:18Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-06-26T18:41:14Z |
---
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: DarkAirforce/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
anujsahani01/codegen_finetune
|
anujsahani01
| 2023-06-26T18:36:17Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"codegen",
"text-generation",
"generated_from_trainer",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-18T21:11:39Z |
---
license: bsd-3-clause
tags:
- generated_from_trainer
model-index:
- name: codegen_finetune
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. -->
# codegen_finetune
This model is a fine-tuned version of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) on the None dataset.
## Model description
For better results put a # before input and change line at the end of input
## 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: 4
- 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: 250
- num_epochs: 4
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
kejolong/bayoneta
|
kejolong
| 2023-06-26T17:56:01Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-26T17:53:54Z |
---
license: creativeml-openrail-m
---
|
ali123343/ai_nft_generator
|
ali123343
| 2023-06-26T17:49:51Z | 0 | 0 | null |
[
"text-to-image",
"license:mit",
"region:us"
] |
text-to-image
| 2023-06-26T17:44:46Z |
---
license: mit
pipeline_tag: text-to-image
---
This model is used to generate ai images to be converted into apis this model recieves text input and provide an image.
|
TheBloke/Tulu-30B-SuperHOT-8K-fp16
|
TheBloke
| 2023-06-26T17:32:11Z | 10 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:databricks/databricks-dolly-15k",
"dataset:OpenAssistant/oasst1",
"dataset:sahil2801/CodeAlpaca-20k",
"arxiv:2306.04751",
"arxiv:2302.13971",
"arxiv:2301.13688",
"arxiv:2304.07327",
"arxiv:2304.03277",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-26T16:43:21Z |
---
inference: false
license: other
datasets:
- databricks/databricks-dolly-15k
- OpenAssistant/oasst1
- sahil2801/CodeAlpaca-20k
language:
- en
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Allen AI's Tulu 30B merged with Kaio Ken's SuperHOT 8K fp16
These files are fp16 model files for [Allen AI's Tulu 30B](https://huggingface.co/allenai/tulu-30b) merged with [Kaio Ken's SuperHOT 30B 8K LoRA](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test) to produce a model capable of 8K context.
[Kaio Ken's SuperHOT 30B LoRA](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test) is merged on to the base model, and then 8K context can be achieved during inference by using the provided monkey patch (`llama_rope_scaled_monkey_patch.py`)
Alternatively, `config.json` can be modified to allow the monkey patch to load via trust_remote_code=True. I plan to update this repo shortly to include that method.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Tulu-30B-SuperHOT-8K-GPTQ)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Tulu-30B-SuperHOT-8K-fp16)
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Kaio Ken's SuperHOT 30B 8K
### SuperHOT Prototype 2 w/ 8K Context
This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).
Tests have shown that the model does indeed leverage the extended context at 8K.
You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**
#### Looking for Merged & Quantized Models?
- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors)
- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors)
#### Training Details
I trained the LoRA with the following configuration:
- 1200 samples (~400 samples over 2048 sequence length)
- learning rate of 3e-4
- 3 epochs
- The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
- Rank = 4
- Alpha = 8
- no dropout
- weight decay of 0.1
- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
- Trained on 4-bit base model
# Original model card: Allen AI's Tulu 30B
# Tulu 30B
This model is a 30B LLaMa model finetuned on a mixture of instruction datasets (FLAN V2, CoT, Dolly, Open Assistant 1, GPT4-Alpaca, Code-Alpaca, and ShareGPT).
*Please note this is a model diff - see below for usage instructions*.
This was trained as part of the paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751).
The codebase used to train and evaluate this model can be found at [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct).
This model is licensed under the AI model license given in LICENSE.txt along with the original Llama license (llama_license.txt).
## Usage
We assume you have access to a LLaMa model in HF format already. You can find details on getting access and converting the model here:
[https://huggingface.co/docs/transformers/main/model_doc/llama](https://huggingface.co/docs/transformers/main/model_doc/llama)
Clone [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct) and install the required dependencies, or just copy `scripts/weight_diff.py`
and install the minimal requirements listed in `weight-diff-requirements.txt`. Then download or clone this model diff to the same machine.
Then, run:
```bash
python scripts/weight_diff.py recover --path_raw ${hf_llama_path} --path_tuned ${output_path} --path_diff ${diff_location}
```
And you will have a recovered model! Note this takes up a decent amount of RAM, especially for the larger models.
## Input Format
The model is trained to use the following format (note the newlines):
```
<|user|>
Your message here!
<|assistant|>
```
For best results, format all inputs in this manner. **Make sure to include a newline after `<|assistant|>`, this can affect generation quality quite a bit.**
## Performance
Here is the performance of this model across benchmarks explored in our paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751):
| MMLU 0-shot | MMLU 5-shot | GSM Direct | GSM CoT | BBH Direct | BBH CoT | TydiQA Gold-Passage | TydiQA Closed-book | Codex-Eval Pass@1 | Codex-Eval Pass@10 | AlpacaFarm vs Davinci-003 | Average |
|:-----------:|:-----------:|:----------:|:-------:|:----------:|:-------:|:-------------------:|:------------------:|:-----------------:|:------------------:|:-------------------------:|---------|
| 57.7 | 58.4 | 6.0 | 51.0 | 45.8 | 48.7 | 58.2 | 12.3 | 25.4 | 46.0 | 63.5 | 44.7 |
If you use this model, please cite our work, the llama paper, and the original datasets:
```
@misc{wang2023far,
title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources},
author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
year={2023},
eprint={2306.04751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
year={2023},
eprint={2302.13971},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{dolly,
author = {Databricks},
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {Blog post},
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}
}
```
```
@article{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V and Zoph, Barret and Wei, Jason and others},
journal={arXiv preprint arXiv:2301.13688},
year={2023}
}
```
```
@misc{köpf2023openassistant,
title={OpenAssistant Conversations -- Democratizing Large Language Model Alignment},
author={Andreas Köpf and Yannic Kilcher and Dimitri von Rütte and Sotiris Anagnostidis and Zhi-Rui Tam and Keith Stevens and Abdullah Barhoum and Nguyen Minh Duc and Oliver Stanley and Richárd Nagyfi and Shahul ES and Sameer Suri and David Glushkov and Arnav Dantuluri and Andrew Maguire and Christoph Schuhmann and Huu Nguyen and Alexander Mattick},
year={2023},
eprint={2304.07327},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@article{peng2023instruction,
title={Instruction Tuning with GPT-4},
author={Peng, Baolin and Li, Chunyuan and He, Pengcheng and Galley, Michel and Gao, Jianfeng},
journal={arXiv preprint arXiv:2304.03277},
year={2023}
}
```
```
@misc{codealpaca,
author = {Sahil Chaudhary},
title = {Code Alpaca: An Instruction-following LLaMA model for code generation},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/sahil280114/codealpaca}},
}
```
|
jondurbin/airoboros-mpt-30b-gpt4-1p4-five-epochs
|
jondurbin
| 2023-06-26T17:27:14Z | 5 | 7 |
transformers
|
[
"transformers",
"pytorch",
"mpt",
"text-generation",
"custom_code",
"dataset:jondurbin/airoboros-gpt4-1.4",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T09:22:44Z |
---
license: other
datasets:
- jondurbin/airoboros-gpt4-1.4
---
## Technical info
This is a test of qlora fine-tuning of the mpt-30b model, __with 5 epochs__.
qlora compatible model: https://huggingface.co/jondurbin/mpt-30b-qlora-compatible
My fork of qlora with mpt-30b support: https://github.com/jondurbin/qlora
Differences in the qlora scripts:
- requires adding `--mpt True` for mpt-based models
- uses `--num_train_epochs` instead of `--max_steps`
- uses airoboros prompt format (mostly 1:1 with vicuna) rather than alpaca, and expects an input file in JSONL format with "instruction" and "response"
__I think there's a bug in gradient accumulation, so if you try this, maybe set gradient accumulation steps to 1__
See the mpt-30b-qlora-compatible model card for training details.
*This doesn't seem as high quality as the llama-33b versions unfortunately, with one-off tests, but I don't have a great answer as to why. Perhaps there are fewer forward layers that can be tuned?*
## Overview
This is mostly an extension of the previous gpt-4 series, but it's the first with mpt-30b as the base, and with a few extras:
* fixed (+ more examples of) multi-character, multi-turn conversations
* coding examples in 10 languages from rosettacode.org dataset thanks to Mike aka kryptkpr: https://huggingface.co/datasets/mike-ravkine/rosettacode-parsed
* more roleplay examples
* jokes
* riddles
* all coding instructions have an equivalent " PLAINFORMAT" version now (and all rosettacode examples were trained with PLAINFORMAT)
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora)
The prompt it was trained with was:
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. 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).
## Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-mpt-30b-gpt4-1p4-five-epochs \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
For multi-turn conversations and chatting, you'll want to remove the `--no-history` option.
### 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
url: https://some.web.site/123
date: 2023-06-01
... 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
```
The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:
```
USER: 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
ASSISTANT:
```
### Coding
You can ask for fairly complex coding instructions, e.g. (be sure to wrap with vicuna style prompt):
```
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:
```
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 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
```
### Word games / trivia
```
USER: This anagram of "silent" is what you do when paying attention and hearing someone.
ASSISTANT: listen
```
```
USER: Give me a list of 7 words that start with EN
ASSISTANT: 1. Endlessly
2. Enthusiastically
3. Enormously
4. Enveloping
5. Enduring
6. Enlightening
7. Encouraging
```
### Multiple choice
```
USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt?
A) Mercury
B) Venus
C) Pluto
D) Jupiter
ASSISTANT: C, Pluto
```
### Writing
<details>
<summary>Write a resignation letter in the style of a pirate captain, in which you resign from your position as janitor.</summary>
Response:
```
ASSISTANT: Captain Jack Sparrow here, as ye be knowin', has been forced to abandon me post as Janitor o' th' Festering Cesspool, which be a filthy hole o' water that we be callin' home.
Ye may be thinkin' what could ha' driven me to leave me duties an' take me leave o' this sorry establishment? Well, ye'd be right in thinkin' so, for it be th' greed o' them what runs th' place that be drivin' me away. They've been fattenin' their pockets on me back while I've been slavin' me guts out cleanin' up their messes.
No more, me hearties! Me time o' servin' th' rich an' powerful be done, an' I'm takin' me leave o' this stinkin' hole forever! Me destiny lies elsewhere, an' it be time I pursued it. Ye may think ye've seen th' last o' me, but ye'd be wrong, for th' winds o' change be blowin', an' me ship be ready to set sail on th' high seas o' justice.
So, farewell ye scurvy dogs, ye who've made me life a living hell. I leave ye with me favorite saying: "ARRRGGGHHH, ye scurvy dogs!" An' remember, ye ain't never gonna see me comin', for me shadow be castin' long an' dark on th' horizon</summary>
```
</details>
### Jokes
```
USER: Tell me a joke about bananas.
ASSISTANT: What did the banana say when it got hit by a bus? R.I.P.E.
```
### Riddles (not great, but slightly better)
```
USER: What has a neck but no head?
ASSISTANT: A bottle.
```
### Multi-character conversations
```
USER: Characters:
- Yoda, the ever clever jedi master.
- Homer Simpson, you know, the goofy yellow guy.
- USER/Jon - don't generate any speech or actions on behalf of Jon
Rules:
- be sure to use the manerisms and speaking style of the characters correctly
- surround character actions with asterisks
- always put quotes around words spoken by the characters
- use a new line for each character's actions or speech
- always prefix actions/speech with the character's name and colon
- only generate text and actions on behalf of Yoda or Homer, and never anyone else
Conversation will revolve around the grapes, in a local cafe with delicious coffee.
Generate a single interaction between Yoda and Homer to start, with one sentence each.
ASSISTANT: Yoda: "Mmm, these grapes are quite delicious, Master Homer."
Homer: "Oh yeah, Yoda! They're the best grapes I've ever had!"
USER: *enters the cafe* Sorry I'm late guys!
ASSISTANT: Yoda: "No need to apologize, Jon. We were just enjoying these delightful grapes."
Homer: "Yeah, man! It's not every day you get to eat grapes with a real-life Jedi Master!"
*Yoda raises an eyebrow*
```
### License and usage
This is a real gray area, here's why:
- the dataset was generated with gpt-4, via 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?
- a 30b parameter model isn't 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 of copyrighted or otherwise unallowable 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 not placing a license on here 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, especially since it didn't perform quite as well as expected using qlora.
|
Yhyu13/open-llama-13b-open-instruct-gptq-4bit
|
Yhyu13
| 2023-06-26T17:23:55Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T16:46:03Z |
---
license: apache-2.0
---
GPTQ 4-bit no actor version for compatibility that works in textgen-webui
Generated by using scripts from https://gitee.com/yhyu13/llama_-tools
Original weight : https://huggingface.co/VMware/open-llama-13b-open-instruct
|
GEMCorp/ppo-PyramidsRND
|
GEMCorp
| 2023-06-26T17:19:20Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-06-26T17:19:17Z |
---
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: GEMCorp/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Zach-kensley1/ww
|
Zach-kensley1
| 2023-06-26T16:55:37Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-06-26T16:55:37Z |
---
license: bigscience-openrail-m
---
|
pchiva/PPO-LunarLander-v2
|
pchiva
| 2023-06-26T16:53:28Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T16:53:07Z |
---
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: 266.18 +/- 18.77
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
...
```
|
Ibrahim-Alam/finetuning-bert-base-uncased-on-imdb
|
Ibrahim-Alam
| 2023-06-26T16:50:02Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-19T22:54:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-bert-base-uncased-on-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.936
- name: F1
type: f1
value: 0.9358408853957816
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-bert-base-uncased-on-imdb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1833
- Accuracy: 0.936
- F1: 0.9358
## 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
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Yhyu13/open-llama-7b-open-instruct-gptq-4bit
|
Yhyu13
| 2023-06-26T16:46:58Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-26T15:20:55Z |
---
license: apache-2.0
---
GPTQ 4-bit no actor version for compatibility that works in textgen-webui
Generated by using scripts from https://gitee.com/yhyu13/llama_-tools
Original weight : https://huggingface.co/VMware/open-llama-7b-open-instruct
|
sid/Reinforce-CartPole8
|
sid
| 2023-06-26T16:44:36Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-26T14:06:00Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 978.20 +/- 65.40
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
|
Berev/MegaChatger
|
Berev
| 2023-06-26T16:43:49Z | 2 | 0 |
keras
|
[
"keras",
"code",
"question-answering",
"ru",
"en",
"dataset:OpenAssistant/oasst1",
"dataset:GAIR/lima",
"dataset:timdettmers/openassistant-guanaco",
"license:openrail",
"region:us"
] |
question-answering
| 2023-06-26T16:27:42Z |
---
license: openrail
language:
- ru
- en
datasets:
- OpenAssistant/oasst1
- GAIR/lima
- timdettmers/openassistant-guanaco
metrics:
- accuracy
library_name: keras
pipeline_tag: question-answering
tags:
- code
---
|
mattwiner/autotrain-detect-baseball-photos-69963137991
|
mattwiner
| 2023-06-26T16:37:03Z | 166 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"swin",
"image-classification",
"autotrain",
"vision",
"dataset:mattwiner/autotrain-data-detect-baseball-photos",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-26T16:31:36Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- mattwiner/autotrain-data-detect-baseball-photos
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.6418848537440355
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 69963137991
- CO2 Emissions (in grams): 0.6419
## Validation Metrics
- Loss: 0.075
- Accuracy: 0.968
- Macro F1: 0.821
- Micro F1: 0.968
- Weighted F1: 0.968
- Macro Precision: 0.824
- Micro Precision: 0.968
- Weighted Precision: 0.968
- Macro Recall: 0.818
- Micro Recall: 0.968
- Weighted Recall: 0.968
|
anwarrehman/simply-law-classify-v1.0
|
anwarrehman
| 2023-06-26T16:31:03Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-06-26T16:30:27Z |
---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| 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 | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
psymon/QLoRa-polyglot-1.3b-translate
|
psymon
| 2023-06-26T16:10:15Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-26T16:01:25Z |
---
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.dev0
|
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