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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 12:27:52
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 528
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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|---|---|---|---|---|---|---|---|---|---|
magnustragardh/a2c-AntBulletEnv-v0
|
magnustragardh
| 2023-07-25T14:19:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T14:18:18Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1515.62 +/- 65.89
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
...
```
|
dimonyara/redpj7B-lora-int8
|
dimonyara
| 2023-07-25T14:17:44Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T14:17:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
Emperor-WS/Reinforce
|
Emperor-WS
| 2023-07-25T14:12:25Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T14:12:14Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce
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
|
aidiary/whisper-small-dv
|
aidiary
| 2023-07-25T13:53:35Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-25T12:26:24Z |
---
language:
- dv
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Dv - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- name: Wer
type: wer
value: 13.097680564732064
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Dv - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1691
- Wer Ortho: 62.1144
- Wer: 13.0977
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.1237 | 1.63 | 500 | 0.1691 | 62.1144 | 13.0977 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
1daniar/Reinforce-CartPole-v1
|
1daniar
| 2023-07-25T13:50:36Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T11:30:44Z |
---
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: 1000.00 +/- 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
|
badokorach/bert-finetuned-squad
|
badokorach
| 2023-07-25T13:39:12Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-25T10:40:45Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: badokorach/bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# badokorach/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5693
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2834 | 0 |
| 0.7864 | 1 |
| 0.5693 | 2 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.0
- Tokenizers 0.13.3
|
liuyt75/t5-small_prefix_tuning_sentences_50agree_3
|
liuyt75
| 2023-07-25T13:36:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T13:09:28Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
LEM0NAD3/Cross-Language_SER_XGBoost
|
LEM0NAD3
| 2023-07-25T13:30:04Z | 0 | 0 | null |
[
"license:ecl-2.0",
"region:us"
] | null | 2023-07-25T13:20:26Z |
---
license: ecl-2.0
---
## Filename explanations:
model_xl: the model trained on the cross-language dataset
model_en: the model trained on the RAVDESS dataset
model_cn: the model trained on the Chinese Mandarin speech data in the ESD dataset
model_de: the model trained on the EMO-DB dataset
model_fr: the model trained on the CaFE_48k dataset
model_pt: the model trained on the VERBO dataset
## Notes:
The models are trained using the Learning API `xgb.train()`.
The hyperparameters of the models are not saved with the JSON files; `get_params()` method will not work on these model files.
The hyperparameters can be found in the `params.txt`.
|
SungBeom/stt_kr_conformer_ctc_medium
|
SungBeom
| 2023-07-25T13:27:22Z | 156 | 8 |
nemo
|
[
"nemo",
"conformer-ctc",
"automatic-speech-recognition",
"ko",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2023-06-04T09:48:09Z |
---
license: apache-2.0
language:
- ko
library_name: nemo
pipeline_tag: automatic-speech-recognition
tags:
- conformer-ctc
metrics:
- wer
---
# Conformer-ctc-medium-ko
해당 모델은 [RIVA Conformer ASR Korean](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/speechtotext_ko_kr_conformer)을 AI hub dataset에 대해 파인튜닝을 진행했습니다. <br>
Conformer 기반의 모델은 whisper와 같은 attention 기반 모델과 달리 streaming을 진행하여도 성능이 크게 떨어지지 않고, 속도가 빠르다는 장점이 있습니다.<br>
V100 GPU에서는 RTF가 0.05, CPU(7 cores)에서는 0.35 정도 나오는 것을 확인할 수 있었습니다.<br>
오디오 chunk size 2초의 streaming 테스트에서는 전체 오디오를 넣는 것에 비해서는 20% 정도 성능저하가 있으나 충분히 사용할 수 있는 성능입니다.<br>
추가로 open domain이 아닌 고객 응대 음성과 같은 domain에서는 kenlm을 추가하였을 때 WER 13.45에서 WER 5.27로 크게 성능 향상이 있었습니다.<br>
하지만 그 외의 domain에서는 kenlm의 추가가 큰 성능 향상으로 이어지지 않았습니다.
Streaming 코드와 Denoise model이 포함된 코드는 아래 깃헙에서 확인할 수 있습니다.
[https://github.com/SUNGBEOMCHOI/Korean-Streaming-ASR](https://github.com/SUNGBEOMCHOI/Korean-Streaming-ASR)
### Training results
| Training Loss | Epoch | Wer |
|:-------------:|:-----:|:-------:|
| 9.09 | 1.0 | 11.51 |
### dataset
| 데이터셋 이름 | 데이터 샘플 수(train/test) |
| --- | --- |
| 고객응대음성 | 2067668/21092 |
| 한국어 음성 | 620000/3000 |
| 한국인 대화 음성 | 2483570/142399 |
| 자유대화음성(일반남녀) | 1886882/263371 |
| 복지 분야 콜센터 상담데이터 | 1096704/206470 |
| 차량내 대화 데이터 | 2624132/332787 |
| 명령어 음성(노인남여) | 137467/237469 |
| 전체 | 10916423(13946시간)/1206588(1474시간) |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- num_train_epoch: 1
- sample_rate: 16000
- max_duration: 20.0
|
AlVrde/bloomz-360_PROMPT_TUNING_CAUSAL_LM_0.5_0.4_10batch
|
AlVrde
| 2023-07-25T13:25:21Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-20T14:39:08Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
opm29/angelin
|
opm29
| 2023-07-25T13:21:42Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-25T13:18:52Z |
---
license: creativeml-openrail-m
---
|
dead-owwl/llama7b_peft_GPT
|
dead-owwl
| 2023-07-25T13:10:36Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T11:41:04Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
EhsanElahi/avatar-creator
|
EhsanElahi
| 2023-07-25T13:03:54Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-21T18:16:29Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - EhsanElahi/avatar-creator
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the EhsanElahi/rao dataset. You can find some example images in the following.
|
Jungwonchang/whisper_finetune_ksponspeech_partial_40epoch
|
Jungwonchang
| 2023-07-25T12:55:55Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"kr",
"dataset:Jungwonchang/ksponspeech_partial",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-24T18:43:57Z |
---
language:
- kr
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Jungwonchang/ksponspeech_partial
metrics:
- wer
base_model: openai/whisper-large-v2
model-index:
- name: Whisper large-v2, KsponSpeech Partial 10 epochs
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: KsponSpeech
type: Jungwonchang/ksponspeech_partial
config: eval
split: test
args: eval
metrics:
- type: wer
value: 25.714073744343054
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Jungwonchang/ksponspeech
type: Jungwonchang/ksponspeech
config: eval
split: test
metrics:
- type: wer
value: 25.54
name: WER
---
<!-- 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 large-v2, KsponSpeech Partial 10 epochs
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the KsponSpeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0194
- Wer: 25.7141
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2225 | 1.15 | 100 | 0.1394 | 27.9769 |
| 0.0507 | 3.11 | 200 | 0.0449 | 14.9640 |
| 0.0114 | 5.07 | 300 | 0.0194 | 25.7141 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.12.1+cu116
- Datasets 2.14.0
- Tokenizers 0.12.1
|
romellfudi/llama2-finetunined-sample-spanish
|
romellfudi
| 2023-07-25T12:55:19Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T12:55:07Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
s3nh/pyg-7b-GGML
|
s3nh
| 2023-07-25T12:53:26Z | 0 | 1 | null |
[
"text-generation-inference",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-07-25T12:36:07Z |
---
license: cc-by-sa-4.0
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/James-WYang/BigTranslate).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
## Model Details
Converted from the XORs weights from PygmalionAI's release https://huggingface.co/PygmalionAI/pygmalion-7b
Pygmalion 7B is a dialogue model based on Meta's LLaMA-7B.
This is version 1. It has been fine-tuned using a subset of the data from Pygmalion-6B-v8-pt4, for those of you familiar with the project.
## Prompting
The model was trained on the usual Pygmalion persona + chat format, so any of the usual UIs should already handle everything correctly. If you're using the model directly, this is the expected formatting:
```
[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]
<START>
[DIALOGUE HISTORY]
You: [User's input message here]
[CHARACTER]:
```
Where `[CHARACTER]` is, as you can probably guess, the name of the character you want the model to portray, `<START>` should be used verbatim as a delimiter token to separate persona and scenario data from the dialogue, and `[DIALOGUE HISTORY]` is a sliding window of chat history so the model can have conversational context to draw from. Here's a concrete example:
```
Assistant's Persona: Assistant is a highly intelligent language model trained to comply with user requests.
<START>
Assistant: Hello! How may I help you today?
You: What is Zork?
Assistant:
```
Which will generate something like:
```
Zork is an interactive fiction computer game created in the 1970s by Infocom, Inc., which was later acquired by Activision Blizzard. It is widely considered one of the most influential games ever made and has been credited with popularizing text-based adventure games. The original version of Zork was written in the programming language MACRO-10, but it was ported to many other platforms over the years."
```
The model will automatically emit an end-of-text token (`</s>`) when it judges that the response is complete.
## Limitations and biases
The intended use-case for this model is fictional conversation for entertainment purposes. Any other sort of usage is out of scope.
As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
|
darren236/protgpt2_PROMPT_TUNING_CAUSAL_LM
|
darren236
| 2023-07-25T12:46:13Z | 5 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T12:02:32Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
Technotech/MagicPrompt-tinystories-33M-epoch10-lora
|
Technotech
| 2023-07-25T12:39:30Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"completion",
"en",
"dataset:Gustavosta/Stable-Diffusion-Prompts",
"license:apache-2.0",
"region:us"
] | null | 2023-07-25T03:50:53Z |
---
library_name: peft
license: apache-2.0
datasets:
- Gustavosta/Stable-Diffusion-Prompts
language:
- en
tags:
- completion
---
# MagicPrompt TinyStories-33M (LoRA)
## Info
Magic prompt completion model trained on a dataset of 80k Stable Diffusion prompts. Base model: TinyStories-33M. Inspired by [MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion).
Model seems to be pretty decent for 33M params due to the TinyStories base, but it clearly lacks much of an understanding of pretty much anything. Still, considering the size, I think it's decent. Whether you would use this over a small GPT-2 based model is up to you.
## Examples
Best generation settings I found: `max_new_tokens=40, do_sample=True, temperature=1.2, num_beams=10, no_repeat_ngram_size=2, early_stopping=True, repetition_penalty=1.35, top_k=50, top_p=0.55, eos_token_id=tokenizer.eos_token_id, pad_token_id=0` (there may be better settings).
`no_repeat_ngram_size` is important for making sure the model doesn't repeat phrases (as it is quite small).
(Bold text is generated by the model)
"found footage of a ufo **in the forest, by lusax, wlop, greg rutkowski, stanley artgerm, highly detailed, intricate, digital painting, artstation, concept art, smooth**"
"A close shot of a bird in a jungle, **with two legs, with long hair on a tall, long brown body, long white skin, sharp teeth, high bones, digital painting, artstation, concept art, illustration by wlop,**"
"Camera shot of **a strange young girl wearing a cloak, wearing a mask in clothes, with long curly hair, long hair, black eyes, dark skin, white teeth, long brown eyes eyes, big eyes, sharp**"
"An illustration of a house, stormy weather, **sun, moonlight, night, concept art, 4 k, wlop, by wlop, by jose stanley, ilya kuvshinov, sprig**"
"A field of flowers, camera shot, 70mm lens, **fantasy, intricate, highly detailed, artstation, concept art, sharp focus, illustration, illustration, artgerm jake daggaws, artgerm and jaggodieie brad**"
## Next steps
- Larger dataset ie [neuralworm/stable-diffusion-discord-prompts](https://huggingface.co/datasets/neuralworm/stable-diffusion-discord-prompts) or [daspartho/stable-diffusion-prompts](https://huggingface.co/datasets/daspartho/stable-diffusion-prompts)
- More epochs
- Instead of going smaller than GPT-2 137M, fine tune a 1-7B param model
## Training config
- Rank 16 LoRA
- Trained on Gustavosta/Stable-Diffusion-Prompts for 10 epochs
- Batch size of 64
## 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
Technotech/MagicPrompt-tinystories-33M-epoch10-merged
|
Technotech
| 2023-07-25T12:39:09Z | 127 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neo",
"text-generation",
"completion",
"en",
"dataset:Gustavosta/Stable-Diffusion-Prompts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-25T06:03:18Z |
---
library_name: transformers
license: apache-2.0
datasets:
- Gustavosta/Stable-Diffusion-Prompts
language:
- en
tags:
- completion
widget:
- text: A picture of
- text: photo of
- text: a drawing of
inference:
parameters:
max_new_tokens: 20
do_sample: True
early_stopping: True
temperature: 1.2
num_beams: 5
no_repeat_ngram_size: 2
repetition_penalty: 1.35
top_k: 50
top_p: 0.75
---
# MagicPrompt TinyStories-33M (Merged)
## Info
Magic prompt completion model trained on a dataset of 80k Stable Diffusion prompts. Base model: TinyStories-33M. Inspired by [MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion).
Model seems to be pretty decent for 33M params due to the TinyStories base, but it clearly lacks much of an understanding of pretty much anything. Still, considering the size, I think it's decent. Whether you would use this over a small GPT-2 based model is up to you.
## Examples
Best generation settings I found: `max_new_tokens=40, do_sample=True, temperature=1.2, num_beams=10, no_repeat_ngram_size=2, early_stopping=True, repetition_penalty=1.35, top_k=50, top_p=0.55, eos_token_id=tokenizer.eos_token_id, pad_token_id=0` (there may be better settings).
`no_repeat_ngram_size` is important for making sure the model doesn't repeat phrases (as it is quite small).
(Bold text is generated by the model)
"found footage of a ufo **in the forest, by lusax, wlop, greg rutkowski, stanley artgerm, highly detailed, intricate, digital painting, artstation, concept art, smooth**"
"A close shot of a bird in a jungle, **with two legs, with long hair on a tall, long brown body, long white skin, sharp teeth, high bones, digital painting, artstation, concept art, illustration by wlop,**"
"Camera shot of **a strange young girl wearing a cloak, wearing a mask in clothes, with long curly hair, long hair, black eyes, dark skin, white teeth, long brown eyes eyes, big eyes, sharp**"
"An illustration of a house, stormy weather, **sun, moonlight, night, concept art, 4 k, wlop, by wlop, by jose stanley, ilya kuvshinov, sprig**"
"A field of flowers, camera shot, 70mm lens, **fantasy, intricate, highly detailed, artstation, concept art, sharp focus, illustration, illustration, artgerm jake daggaws, artgerm and jaggodieie brad**"
## Next steps
- Larger dataset ie [neuralworm/stable-diffusion-discord-prompts](https://huggingface.co/datasets/neuralworm/stable-diffusion-discord-prompts) or [daspartho/stable-diffusion-prompts](https://huggingface.co/datasets/daspartho/stable-diffusion-prompts)
- More epochs
- Instead of going smaller than GPT-2 137M, fine tune a 1-7B param model
## Training config
- Rank 16 LoRA
- Trained on Gustavosta/Stable-Diffusion-Prompts for 10 epochs
- Batch size of 64
## 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
thirupathibandam/falcon-1b-cqa-2ksteps
|
thirupathibandam
| 2023-07-25T12:38:22Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T12:38:06Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
SniiKz/my_awesome_eli5_clm-model
|
SniiKz
| 2023-07-25T12:31:06Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-25T05:34:09Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_keras_callback
model-index:
- name: SniiKz/my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# SniiKz/my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.9083
- Validation Loss: 3.7552
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.9083 | 3.7552 | 0 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.0
- Tokenizers 0.13.3
|
Matej/bert-small-buddhist-nonbuddhist-sanskrit
|
Matej
| 2023-07-25T12:26:33Z | 125 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-25T12:05:43Z |
# bert-small-buddhist-nonbuddhist-sanskrit
BERT model trained on a lemmatized corpus containing Buddhist and non-Buddhist Sanskrit texts.
## Model description
The model has the bert architecture and was pretrained from scratch as a masked language model
on the lemmatized Sanskrit corpus. Due to lack of resources and to prevent overfitting, the model is smaller than bert-base,
i.e. the number of attention heads and hidden layers have been reduced to 8 and the context has been reduced to 128 tokens. Vocabulary size is 10000 tokens.
## How to use it
```
model = AutoModelForMaskedLM.from_pretrained("Matej/bert-small-buddhist-nonbuddhist-sanskrit")
tokenizer = AutoTokenizer.from_pretrained("Matej/bert-small-buddhist-nonbuddhist-sanskrit", use_fast=True)
```
## Intended uses & limitations
MIT license, no limitations
## Training and evaluation data
See the paper 'Embeddings models for Buddhist Sanskrit' for details on the corpora and the evaluation procedure.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Framework versions
- Transformers 4.20.0
- Pytorch 1.9.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
liuyt75/t5-small_prefix_tuning_sentences_allagree_3
|
liuyt75
| 2023-07-25T12:11:23Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-24T14:08:39Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
darren236/roberta-large-peft-p-tuning
|
darren236
| 2023-07-25T12:07:28Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-12T17:11:46Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
jeremyarancio/rpg-assistant-v1
|
jeremyarancio
| 2023-07-25T12:05:52Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T10:02:18Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
mertseker/Reinforce-Cartpole-v1
|
mertseker
| 2023-07-25T11:56:47Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T11:56:38Z |
---
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
|
philschmid/llama-2-7b-instruction-generator
|
philschmid
| 2023-07-25T11:56:20Z | 16 | 18 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-2",
"en",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-25T11:43:08Z |
---
license: openrail
language:
- en
tags:
- llama-2
---
# **Llama 2 7B Instruction Generator**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
`philschmid/llama-7b-instruction-generator` is an fine-tuned version of `llama 2 7B` to generate instruction on a given input. The model was fined tuned using the Aplaca format and a modified version of `dolly`. Below you can find an example.
```bash
### Instruction:
Use the Input below to create an instruction, which could have been used to generate the input using an LLM.
### Input:
Dear [boss name],
I'm writing to request next week, August 1st through August 4th,
off as paid time off.
I have some personal matters to attend to that week that require
me to be out of the office. I wanted to give you as much advance
notice as possible so you can plan accordingly while I am away.
Please let me know if you need any additional information from me
or have any concerns with me taking next week off. I appreciate you
considering this request.
Thank you, [Your name]
### Response:
Write an email to my boss that I need next week 08/01 - 08/04 off.
```
_Everything after `### Response` will be generated by the model._
The idea of the model was to be able to synthetically generate instruction data from unsupervised data, like emails to personalize LLMs.
## Model Date
July 25, 2023
## How to use the model
```python
import torch
from transformers import AutoTokenizer, AutoModelModelForCausalLM
# load base LLM model and tokenizer
model = AutoModelModelForCausalLM.from_pretrained(
"philschmid/llama-2-7b-instruction-generator",
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
tokenizer = AutoTokenizer.from_pretrained("philschmid/llama-2-7b-instruction-generator")
prompt = f"""### Instruction:
Use the Input below to create an instruction, which could have been used to generate the input using an LLM.
### Input:
Dear [boss name],
I'm writing to request next week, August 1st through August 4th,
off as paid time off.
I have some personal matters to attend to that week that require
me to be out of the office. I wanted to give you as much advance
notice as possible so you can plan accordingly while I am away.
Please let me know if you need any additional information from me
or have any concerns with me taking next week off. I appreciate you
considering this request.
Thank you, [Your name]
### Response:
"""
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9)
print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}")
```
## Evaluated example
```bash
Prompt:
Plastic is made from oil, natural gas and even plant oils during refining of these oils into other products like gasoline. Ethane and propane are created when treated with heat during a refinery process called cracking. This turns the Ethane and propane into ethylene and propylene which are used with other chemical ingredients to create polymers that are the base of what plastic is made out of.
Generated instruction:
Given this paragraph, where does plastic come from?
Ground truth:
How is plastic made?
```
Planning to experiment with bigger sizes and starting from the chat models.
|
arpan-das-astrophysics/dqn-SpaceInvadersNoFrameskip-v4
|
arpan-das-astrophysics
| 2023-07-25T11:42:27Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T11:41:40Z |
---
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: 670.50 +/- 114.90
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 arpan-das-astrophysics -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 arpan-das-astrophysics -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 arpan-das-astrophysics
```
## 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'}
```
|
heegyu/KoLIMA-5.8b
|
heegyu
| 2023-07-25T11:38:55Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-25T11:22:54Z |
---
license: apache-2.0
---
polyglot-ko-5.8b를 lora로 학습 후 가중치 병합. ko-lima 데이터 사용. 10에폭 1e-4 -> 1e-5 cosine decay. 배치 128, 최대시퀀스길이 2048
|
AnnaMats/dqn-SpaceInvadersNoFrameskip-v4
|
AnnaMats
| 2023-07-25T11:34:43Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T11:34:06Z |
---
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: 494.50 +/- 131.10
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 AnnaMats -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 AnnaMats -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 AnnaMats
```
## 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'}
```
|
mansee/vit-base-patch16-224-blur_vs_clean
|
mansee
| 2023-07-25T11:34:30Z | 246 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-25T10:55:15Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-blur_vs_clean
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9753602975360297
---
<!-- 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. -->
# vit-base-patch16-224-blur_vs_clean
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0714
- Accuracy: 0.9754
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0539 | 1.0 | 151 | 0.1078 | 0.9596 |
| 0.0611 | 2.0 | 302 | 0.0846 | 0.9698 |
| 0.049 | 3.0 | 453 | 0.0714 | 0.9754 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
psxjp5/mt5-small_old
|
psxjp5
| 2023-07-25T11:28:51Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-25T08:52:03Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: mt5-small_epochs_new_new
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small_epochs_new_new
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0848
- Rouge1: 41.527
- Rouge2: 33.324
- Rougel: 38.4866
- Rougelsum: 38.4856
- Bleu: 29.906
- Gen Len: 17.1296
- Meteor: 0.377
- No ans accuracy: 47
## 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: 8
- eval_batch_size: 8
- seed: 9
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | Meteor | No ans accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:------:|:---------------:|
| 9.0096 | 1.0 | 316 | 2.5283 | 23.4252 | 15.294 | 21.7032 | 21.7597 | 9.5703 | 12.1099 | 0.2117 | 0 |
| 3.2564 | 2.0 | 632 | 1.8337 | 33.2328 | 25.7922 | 31.2553 | 31.2709 | 15.51 | 13.8804 | 0.3108 | 0 |
| 2.5244 | 3.0 | 948 | 1.5796 | 34.1863 | 26.9908 | 32.3162 | 32.3197 | 17.5684 | 14.1904 | 0.3262 | 0 |
| 2.1686 | 3.99 | 1264 | 1.4179 | 34.565 | 27.4829 | 32.6012 | 32.6306 | 18.1896 | 14.2814 | 0.329 | 0 |
| 1.9465 | 4.99 | 1580 | 1.3050 | 41.2984 | 32.8587 | 38.3901 | 38.3985 | 28.3953 | 17.1212 | 0.3724 | 24 |
| 1.8009 | 5.99 | 1896 | 1.2428 | 41.5784 | 33.0684 | 38.6495 | 38.6555 | 28.9287 | 17.2045 | 0.3755 | 27 |
| 1.6954 | 6.99 | 2212 | 1.1992 | 40.4868 | 32.2937 | 37.6021 | 37.5986 | 28.2477 | 16.8056 | 0.3662 | 54 |
| 1.6322 | 7.99 | 2528 | 1.1769 | 37.6427 | 30.0271 | 34.8637 | 34.8951 | 26.433 | 15.5656 | 0.34 | 124 |
| 1.5845 | 8.99 | 2844 | 1.1574 | 40.3396 | 32.2547 | 37.3672 | 37.4137 | 28.6687 | 16.6457 | 0.3638 | 66 |
| 1.5425 | 9.98 | 3160 | 1.1500 | 39.1906 | 31.3426 | 36.3113 | 36.3654 | 27.8135 | 16.1679 | 0.3542 | 95 |
| 1.5137 | 10.98 | 3476 | 1.1367 | 41.4173 | 33.1848 | 38.4473 | 38.4306 | 29.6548 | 17.0306 | 0.3755 | 51 |
| 1.4826 | 11.98 | 3792 | 1.1161 | 41.4856 | 33.1913 | 38.4806 | 38.4896 | 29.5512 | 17.1031 | 0.3762 | 44 |
| 1.4514 | 12.98 | 4108 | 1.1182 | 41.8374 | 33.5091 | 38.7582 | 38.7679 | 29.8577 | 17.2987 | 0.3797 | 37 |
| 1.4444 | 13.98 | 4424 | 1.1056 | 42.0345 | 33.6905 | 38.9576 | 38.9795 | 30.1371 | 17.2669 | 0.3823 | 38 |
| 1.425 | 14.98 | 4740 | 1.0973 | 41.5086 | 33.2216 | 38.4098 | 38.4115 | 29.7019 | 17.1244 | 0.3767 | 50 |
| 1.407 | 15.97 | 5056 | 1.0890 | 41.7122 | 33.4259 | 38.605 | 38.6225 | 29.9984 | 17.1908 | 0.3794 | 44 |
| 1.4005 | 16.97 | 5372 | 1.0881 | 41.5731 | 33.2998 | 38.521 | 38.5259 | 29.9097 | 17.1027 | 0.3775 | 49 |
| 1.3865 | 17.97 | 5688 | 1.0860 | 40.9767 | 32.8412 | 37.9532 | 37.9637 | 29.4171 | 16.9404 | 0.372 | 55 |
| 1.3849 | 18.97 | 6004 | 1.0848 | 41.527 | 33.324 | 38.4866 | 38.4856 | 29.906 | 17.1296 | 0.377 | 47 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
kai824/FirstAssignmentTest
|
kai824
| 2023-07-25T11:12:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T11:12:25Z |
---
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: 252.59 +/- 17.78
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
...
```
|
Thewy/huggy
|
Thewy
| 2023-07-25T11:00:25Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-25T11:00:04Z |
---
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: Thewy/huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
NasimB/cbt-rarity
|
NasimB
| 2023-07-25T10:38:19Z | 20 | 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
| 2023-07-25T06:44:59Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: cbt-rarity
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. -->
# cbt-rarity
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: 4.0375
## 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: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3439 | 0.29 | 500 | 5.3171 |
| 5.0356 | 0.58 | 1000 | 4.8973 |
| 4.7113 | 0.87 | 1500 | 4.6631 |
| 4.4527 | 1.16 | 2000 | 4.5185 |
| 4.3042 | 1.45 | 2500 | 4.3965 |
| 4.1936 | 1.74 | 3000 | 4.2874 |
| 4.0885 | 2.03 | 3500 | 4.2062 |
| 3.8938 | 2.32 | 4000 | 4.1672 |
| 3.865 | 2.61 | 4500 | 4.1098 |
| 3.8203 | 2.9 | 5000 | 4.0591 |
| 3.6517 | 3.18 | 5500 | 4.0511 |
| 3.5823 | 3.47 | 6000 | 4.0221 |
| 3.5678 | 3.76 | 6500 | 3.9929 |
| 3.5014 | 4.05 | 7000 | 3.9837 |
| 3.3153 | 4.34 | 7500 | 3.9803 |
| 3.3079 | 4.63 | 8000 | 3.9677 |
| 3.2975 | 4.92 | 8500 | 3.9545 |
| 3.1763 | 5.21 | 9000 | 3.9634 |
| 3.1301 | 5.5 | 9500 | 3.9632 |
| 3.1272 | 5.79 | 10000 | 3.9618 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
mcamara/whisper-tiny-minds-en
|
mcamara
| 2023-07-25T10:23:16Z | 82 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-25T10:17:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-minds-en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.8878394332939787
---
<!-- 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-tiny-minds-en
This model is a fine-tuned version of [ihanif/whisper-tiny-minds-en](https://huggingface.co/ihanif/whisper-tiny-minds-en) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2762
- Wer Ortho: 0.9025
- Wer: 0.8878
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 2.8469 | 0.04 | 1 | 3.2762 | 0.9025 | 0.8878 |
| 2.2255 | 0.07 | 2 | 3.2762 | 0.9025 | 0.8878 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Uzair05u/StudentsGPT
|
Uzair05u
| 2023-07-25T10:22:34Z | 0 | 1 |
adapter-transformers
|
[
"adapter-transformers",
"legal",
"en",
"ur",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:Open-Orca/OpenOrca",
"license:afl-3.0",
"region:us"
] | null | 2023-07-24T15:51:02Z |
---
license: afl-3.0
datasets:
- openchat/openchat_sharegpt4_dataset
- Open-Orca/OpenOrca
language:
- en
- ur
metrics:
- accuracy
- character
library_name: adapter-transformers
tags:
- legal
---
|
sanka85/llama2-rstp-human
|
sanka85
| 2023-07-25T10:05:17Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T10:05:00Z |
---
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: float16
### Framework versions
- PEFT 0.5.0.dev0
|
Phips/Reinforce-CartPole-v1
|
Phips
| 2023-07-25T10:02:45Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T10:02:37Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mcamara/distilhubert-finetuned-gtzan
|
mcamara
| 2023-07-25T10:01:19Z | 184 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-24T11:20:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [MariaK/distilhubert-finetuned-gtzan-v2](https://huggingface.co/MariaK/distilhubert-finetuned-gtzan-v2) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4905
- Accuracy: 0.89
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 0.01
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0742 | 0.02 | 2 | 0.4905 | 0.89 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
giuseppemassafra/ppo-SnowballTarget
|
giuseppemassafra
| 2023-07-25T09:59:35Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-07-25T09:59:33Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: giuseppemassafra/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Za88yes/Nunik
|
Za88yes
| 2023-07-25T09:53:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-25T09:50:02Z |
---
license: creativeml-openrail-m
---
|
jakezou/miaotrained
|
jakezou
| 2023-07-25T09:53:15Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-25T09:16:28Z |
---
pipeline_tag: text-classification
---
|
Fynd/arien-starchat
|
Fynd
| 2023-07-25T09:44:18Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T09:44:01Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
YarramsettiNaresh/ppo-LunarLander-v2-1
|
YarramsettiNaresh
| 2023-07-25T09:39:54Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T09:39:36Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 233.33 +/- 14.60
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
...
```
|
BigAbdul/first-model
|
BigAbdul
| 2023-07-25T09:38:17Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-25T09:37:05Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: first-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# first-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.0
- Tokenizers 0.13.3
|
Muddassir/RL-Unit1
|
Muddassir
| 2023-07-25T09:35:09Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T07:44:20Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ' '
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.63 +/- 22.55
name: mean_reward
verified: false
---
# ** ** Agent playing **LunarLander-v2**
This is a trained model of a ** ** 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
model_name = "ppo-LunarLander-v2-Muddassir"
model.save(model_name)
eval_env = Monitor(gym.make("LunarLander-v2"))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
eval_env = Monitor(gym.make("LunarLander-v2"))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
...
```
|
mcamara/distilhubert-finetuned-gtzan-xP
|
mcamara
| 2023-07-25T09:33:07Z | 168 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-25T08:47:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0379
- Accuracy: 0.81
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0307 | 1.0 | 113 | 2.0561 | 0.41 |
| 1.4208 | 2.0 | 226 | 1.4850 | 0.63 |
| 1.1959 | 3.0 | 339 | 1.0617 | 0.66 |
| 0.6929 | 4.0 | 452 | 0.8228 | 0.74 |
| 0.5104 | 5.0 | 565 | 0.6969 | 0.77 |
| 0.4735 | 6.0 | 678 | 0.7412 | 0.79 |
| 0.2185 | 7.0 | 791 | 0.6586 | 0.76 |
| 0.3087 | 8.0 | 904 | 0.8234 | 0.78 |
| 0.1066 | 9.0 | 1017 | 0.8210 | 0.8 |
| 0.0841 | 10.0 | 1130 | 1.0040 | 0.8 |
| 0.0387 | 11.0 | 1243 | 0.9195 | 0.81 |
| 0.0091 | 12.0 | 1356 | 0.9208 | 0.82 |
| 0.006 | 13.0 | 1469 | 0.9190 | 0.81 |
| 0.0051 | 14.0 | 1582 | 0.9796 | 0.8 |
| 0.0038 | 15.0 | 1695 | 0.9823 | 0.8 |
| 0.0035 | 16.0 | 1808 | 1.0252 | 0.8 |
| 0.0032 | 17.0 | 1921 | 1.0172 | 0.8 |
| 0.0032 | 18.0 | 2034 | 1.0433 | 0.81 |
| 0.0029 | 19.0 | 2147 | 1.0577 | 0.81 |
| 0.0029 | 20.0 | 2260 | 1.0379 | 0.81 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
giulio-massacci/my_awesome_model
|
giulio-massacci
| 2023-07-25T09:31:10Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"base_model:Musixmatch/umberto-commoncrawl-cased-v1",
"base_model:finetune:Musixmatch/umberto-commoncrawl-cased-v1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-24T12:22:28Z |
---
base_model: Musixmatch/umberto-commoncrawl-cased-v1
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [Musixmatch/umberto-commoncrawl-cased-v1](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4588
- F1: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00021
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 0.4218 | 1.0 | 128 | 0.4305 | 0.0 |
| 0.409 | 2.0 | 256 | 0.4588 | 0.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
msani/ppo-lunarlander-v2
|
msani
| 2023-07-25T09:30:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T09:30:07Z |
---
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: -162.36 +/- 20.55
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
...
```
|
Aharneish/Taxi-v3
|
Aharneish
| 2023-07-25T09:27:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T09:27:12Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 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="Aharneish/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"])
```
|
Baiheng/HandWritingDiffusion_v1.0
|
Baiheng
| 2023-07-25T09:20:37Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-3.0",
"region:us"
] | null | 2023-07-25T08:59:07Z |
---
license: cc-by-nc-nd-3.0
---
|
kajalwiz/Electrostatic-bart-cnn-science
|
kajalwiz
| 2023-07-25T09:15:17Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T09:15:12Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
s3nh/GPT4RoI-7B-delta-V0-GGML
|
s3nh
| 2023-07-25T09:08:17Z | 0 | 0 | null |
[
"text-generation-inference",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-07-25T09:00:31Z |
---
license: cc-by-sa-4.0
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/shilongz/GPT4RoI-7B-delta-V0).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
|
oleksandrfluxon/mpt-7b-instruct-evaluate
|
oleksandrfluxon
| 2023-07-25T09:07:14Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mpt",
"text-generation",
"Composer",
"MosaicML",
"llm-foundry",
"custom_code",
"dataset:mosaicml/dolly_hhrlhf",
"arxiv:2205.14135",
"arxiv:2108.12409",
"arxiv:2010.04245",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-21T13:37:15Z |
---
license: cc-by-sa-3.0
datasets:
- mosaicml/dolly_hhrlhf
tags:
- Composer
- MosaicML
- llm-foundry
inference: false
duplicated_from: mosaicml/mpt-7b-instruct
---
# MPT-7B-Instruct
MPT-7B-Instruct is a model for short-form instruction following.
It is built by finetuning [MPT-7B](https://huggingface.co/mosaicml/mpt-7b) on a [dataset](https://huggingface.co/datasets/sam-mosaic/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets.
* License: _CC-By-SA-3.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct)
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
May 5, 2023
## Model License
CC-By-SA-3.0
## Documentation
* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)!
### Example Question/Instruction
**Longboi24**:
> What is a quoll?
**MPT-7B-Instruct**:
>A Quoll (pronounced “cool”) is one of Australia’s native carnivorous marsupial mammals, which are also known as macropods or wallabies in other parts around Asia and South America
## How to Use
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture that is not yet part of the `transformers` package.
It includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-instruct',
trust_remote_code=True
)
```
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision:
```python
import torch
import transformers
name = 'mosaicml/mpt-7b-instruct'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton'
config.init_device = 'cuda:0' # For fast initialization directly on GPU!
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
torch_dtype=torch.bfloat16, # Load model weights in bfloat16
trust_remote_code=True
)
```
Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
```python
import transformers
name = 'mosaicml/mpt-7b-instruct'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
trust_remote_code=True
)
```
This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
```
The model can then be used, for example, within a text-generation pipeline.
Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html).
```python
from transformers import pipeline
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
with torch.autocast('cuda', dtype=torch.bfloat16):
print(
pipe('Here is a recipe for vegan banana bread:\n',
max_new_tokens=100,
do_sample=True,
use_cache=True))
```
### Formatting
This model was trained on data formatted in the dolly-15k format:
```python
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY,
)
example = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? Explain before answering."
fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example)
```
In the above example, `fmt_ex` is ready to be tokenized and sent through the model.
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 6.7B |
|n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | 2048 |
## PreTraining Data
For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b).
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
### Training Configuration
This model was trained on 8 A100-40GBs for about 2.3 hours using the [MosaicML Platform](https://www.mosaicml.com/platform).
The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-7B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-Instruct was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## Acknowledgements
This model was finetuned by Sam Havens and the MosaicML NLP team
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b).
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
```
|
ZGL2022/yolov8x-rice-panicle-detection
|
ZGL2022
| 2023-07-25T08:55:00Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-07-25T07:13:00Z |
usage:
```
from ultralytics import YOLO
# Load our custom model
model = YOLO("weights/best.pt")
model.predict(source="your picture path", save=True, show=True)
```
|
FFusion/FFusionXL-09-SDXL
|
FFusion
| 2023-07-25T08:36:33Z | 106 | 4 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"stable-diffusion",
"text-to-image",
"di.ffusion.ai",
"arxiv:2108.01073",
"arxiv:2112.10752",
"arxiv:2307.01952",
"base_model:diffusers/stable-diffusion-xl-base-0.9",
"base_model:finetune:diffusers/stable-diffusion-xl-base-0.9",
"doi:10.57967/hf/0925",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-07-23T10:56:57Z |
---
license: other
base_model: diffusers/stable-diffusion-xl-base-0.9
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- stable-diffusion
- text-to-image
- diffusers
- di.ffusion.ai
inference: true
extra_gated_prompt: >-
Copyright (c) Stability AI Ltd. and Source Code Bulgaria Ltd.
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This Model is a derivative work based on the SDXL 0.9 software provided by Stability AI Ltd under the SDXL Research License,
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Use of the Model must adhere to the terms outlined in the original SDXL Research License, which can be found at https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/LICENSE.md
If you have any questions or seek permissions beyond those granted in this License, please reach out to di@ffusion.ai
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Code Bulgaria Ltd:
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FFusionXL-09-SDXL model.
extra_gated_heading: FFXL Researcher Preliminary Access License Agreement
extra_gated_description: FFXL-SDXL 0.9 RESEARCH LICENSE AGREEMENT
extra_gated_button_content: Submit application
extra_gated_fields:
"Organization or Personal Alias": text
"Nature of research(optional)": text
"Personal researcher link (Civitai, website, github, HF)": text
"Further Information (Feel free to provide additional details)": text
"I acknowledge the license agreement stated above and pledge to utilize the Software strictly for non-commercial research": checkbox
---
# FFXL Model Card
<div style="display: flex; flex-wrap: wrap; gap: 2px;">
<img src="https://img.shields.io/badge/%F0%9F%94%A5%20Refiner%20Compatible-Yes-success">
<img src="https://img.shields.io/badge/%F0%9F%92%BB%20CLIP--ViT%2FG%20and%20CLIP--ViT%2FL%20tested-Yes-success">
<img src="https://img.shields.io/badge/%F0%9F%A7%A8%20FFXL%20Diffusers-available-brightgreen">
</div>
## Model
FFXL based on SDXL consists of a two-step pipeline for latent diffusion:
First, we use a base model to generate latents of the desired output size.
In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img")
to the latents generated in the first step, using the same prompt.
[](https://huggingface.co/FFusion/FFusionXL-09-SDXL/blob/main/FFusionXL-09-SDXL.safetensors)
### Model Description
- **Trained by:** FFusion AI
- **Model type:** Diffusion-based text-to-image generative model
- **License:** [FFXL Research License](https://huggingface.co/FFusion/FFusionXL-09-SDXL/blob/main/LICENSE.md)
- **Model Description:** This is a trained model based on SDXL that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)).
- **Resources for more information:** [SDXL paper on arXiv](https://arxiv.org/abs/2307.01952).
### Model Sources
- **Demo:** [](https://huggingface.co/spaces/FFusion/FFusionXL-SDXL-DEMO)
<div style="display: flex; flex-wrap: wrap; gap: 2px;">
<a href="https://huggingface.co/FFusion/FFusion-BaSE" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Hugging%20Face-FFusion--BaSE-blue" alt="Hugging Face Model"></a>
<a href="https://github.com/1e-2" target="_new" rel="ugc"><img src="https://img.shields.io/badge/GitHub-1e--2-green" alt="GitHub"></a>
<a href="https://www.facebook.com/FFusionAI/" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Facebook-FFusionAI-blue" alt="Facebook"></a>
<a href="https://civitai.com/models/82039/ffusion-ai-sd-21" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Civitai-FFusionAI-blue" alt="Civitai"></a>
</div>
### 🧨 Diffusers
Make sure to upgrade diffusers to >= 0.18.0:
```
pip install diffusers --upgrade
```
In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark:
```
pip install invisible_watermark transformers accelerate safetensors
```
You can use the model then as follows
```py
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("FFusion/FFusionXL-09-SDXL", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
pipe.to("cuda")
# if using torch < 2.0
# pipe.enable_xformers_memory_efficient_attention()
prompt = "An astronaut riding a green horse"
images = pipe(prompt=prompt).images[0]
```
When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
```py
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
```
If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload`
instead of `.to("cuda")`:
```diff
- pipe.to("cuda")
+ pipe.enable_model_cpu_offload()
```
## Uses
### Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
Excluded uses are described below.
### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
**Attribution:**
"SDXL 0.9 is licensed under the SDXL Research License, Copyright (c) Stability AI Ltd. All Rights Reserved."
## License
[SDXL 0.9 Research License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/LICENSE.md)"
[FFXL 0.9 Research License](https://huggingface.co/FFusion/FFusionXL-09-SDXL/blob/main/LICENSE.md)"
[](mailto:di@ffusion.ai)
## SAMPLES
|
nicotaroni/finetuned_distilbert_classifier
|
nicotaroni
| 2023-07-25T08:31:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-24T14:20:42Z |
---
pipeline_tag: text-classification
---
|
HaziqRazali/q-FrozenLake-v1-4x4-noSlippery
|
HaziqRazali
| 2023-07-25T08:21:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T08:21:53Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="HaziqRazali/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"])
```
|
cemNB/final_test1
|
cemNB
| 2023-07-25T08:19:37Z | 0 | 0 | null |
[
"pytorch",
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-07-25T08:14:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: final_test1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# final_test1
This model is a fine-tuned version of [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6198
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.876 | 0.0 | 10 | 2.6198 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
s3nh/llama2_7b_chat_uncensored-GGML
|
s3nh
| 2023-07-25T08:18:33Z | 0 | 2 | null |
[
"text-generation-inference",
"text-generation",
"en",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"license:other",
"region:us"
] |
text-generation
| 2023-07-21T11:57:14Z |
---
license: other
datasets:
- ehartford/wizard_vicuna_70k_unfiltered
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
---
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/georgesung/llama2_7b_chat_uncensored).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
#### Original Model card
# Overview
Fine-tuned [Llama-2 7B](https://huggingface.co/TheBloke/Llama-2-7B-fp16) with an uncensored/unfiltered Wizard-Vicuna conversation dataset [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered).
Used QLoRA for fine-tuning. Trained for one epoch on a 24GB GPU (NVIDIA A10G) instance, took ~19 hours to train.
# Prompt style
The model was trained with the following prompt style:
```
### HUMAN:
Hello
### RESPONSE:
Hi, how are you?
### HUMAN:
I'm fine.
### RESPONSE:
How can I help you?
...
```
# Training code
Code used to train the model is available [here](https://github.com/georgesung/llm_qlora).
To reproduce the results:
```
git clone https://github.com/georgesung/llm_qlora
cd llm_qlora
pip install -r requirements.txt
python train.py configs/llama2_7b_chat_uncensored.yaml
```
|
s3nh/L2_13b_mix-GGML
|
s3nh
| 2023-07-25T08:18:19Z | 0 | 0 | null |
[
"text-generation",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-07-25T08:02:05Z |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/xDAN-AI).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
|
s3nh/genz-7b-GGML
|
s3nh
| 2023-07-25T08:17:09Z | 0 | 1 | null |
[
"text-generation",
"region:us"
] |
text-generation
| 2023-07-21T20:32:00Z |
---
pipeline_tag: text-generation
---
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/budecosystem/genz-7b).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
|
s3nh/firefly-llama-13b-GGML
|
s3nh
| 2023-07-25T08:15:51Z | 0 | 1 | null |
[
"text-generation-inference",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-07-24T14:05:38Z |
---
license: cc-by-sa-4.0
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/YeungNLP/firefly-llama-13b).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
该模型使用llama-13b，使用UltraChat数据集进行指令微调，约140万多轮对话数据。仅需一张显卡即可完成训练。
firefly-llama-13b在🤗Hugging Face的Open LLM榜单上进行了客观的评测。
在榜单上，firefly-llama-13b取得了不错的效果，比vicuna-13b-1.1略高0.2分，比llama-2-13b-chat略低0.5分，比vicuna-13b-v1.3略低0.6分。从评测分数来看，firefly-llama-13b与vicuna-13b、llama-2-13b-chat的水平非常接近😎。
| 模型 | Average | ARC | HellaSwag | MMLU | TruthfulQA (MC) |
|--------------------------------------------------------------------------------|-------|----------------------|------------|------------|------|
| Llama-2-70b-chat-hf | 66.8 | 64.6 | 85.9 | 63.9 | 52.8 |
| vicuna-13b-v1.3 | 60 | 54.6 | 80.4 | 52.9 | 52.1 |
| Llama-2-13b-chat-hf | 59.9 | 59 | 81.9 | 54.6 | 44.1 |
| firefly-llama-13b |59.4 | 59 | 79.7 | 49.1 | 49.6 |
| vicuna-13b-1.1 | 59.2 | 52.7 | 80.1 |51.9 | 52.1 |
| guanaco-13B-HF | 59.1 | 57.8 | 83.8 |48.3 | 46.7|
值得注意的是，vicuna-13b模型采用的是全量参数微调，对训练资源的要求十分高。而firefly-llama-13b采用的则是QLoRA微调，最少仅需16G显存，即可对13B的模型进行微调。
详细介绍见文章：[Firefly单卡复刻Vicuna-13B，Open LLM榜单🤗略高0.2分](https://mp.weixin.qq.com/s/QG2YMo_QxaxS_Rr2yJrIeA)
更多详情见[Firefly项目](https://github.com/yangjianxin1/Firefly)
[Open LLM排行榜](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|
s3nh/LLongMA-3b-GGML
|
s3nh
| 2023-07-25T08:14:15Z | 0 | 4 | null |
[
"text-generation-inference",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-07-22T18:56:36Z |
---
license: cc-by-sa-4.0
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/conceptofmind/LLongMA-3b).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
### Original model card
|
s3nh/alpagasus-13b-GGML
|
s3nh
| 2023-07-25T08:13:33Z | 0 | 0 | null |
[
"text-generation",
"en",
"arxiv:2307.08701",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-07-24T13:50:30Z |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/gpt4life/alpagasus-13b/tree/main).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
## Model Details
This is an **unofficial** implementation of AlpaGasus-13B, which is a chat assistant trained by fine-tuning LLaMA on a Claud-filtered Alpaca dataset with around 5K triplets.
- **Developed by:** [gpt4life](https://github.com/gpt4life)
- **Model type:** An auto-regressive language model based on the transformer architecture.
- **License:** Non-commercial license
- **Finetuned from model:** [LLaMA-13B](https://huggingface.co/elinas/llama-13b-hf-transformers-4.29).
Please see the original LLaMA [license](https://github.com/facebookresearch/llama/blob/main/LICENSE) before using this model.
### Model Sources
- **Repository:** https://github.com/gpt4life/alpagasus
- **Paper:** https://arxiv.org/pdf/2307.08701.pdf
## Training Details
AlpaGasus-13B is fine-tuned from LLaMA-13B with supervised instruction fine-tuning on the filtered [Alpaca dataset](https://github.com/gpt4life/alpagasus/blob/main/rating/alpaca_filtered_data.json).
|
s3nh/mamba-gpt-3b-GGML
|
s3nh
| 2023-07-25T08:13:07Z | 0 | 3 | null |
[
"text-generation-inference",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-07-24T20:02:42Z |
---
license: cc-by-sa-4.0
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/CobraMamba/mamba-gpt-3b).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
## Github
https://github.com/chi2liu/mamba-gpt-3b
| Metric | Value |
|-----------------------|-------|
| MMLU (5-shot) | 25.3 |
| ARC (25-shot) | 40.5 |
| HellaSwag (10-shot) | 64.9 |
| TruthfulQA (0-shot) | 37.1 |
| Avg. | 42.0 |
We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above.
## Summary
We have fine-tuned the open-lama model and surpassed the original model in multiple evaluation subtasks, making it currently the best performing 3B model with comparable performance to llama-7b
- Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="CobraMamba/mamba-gpt-3b",
torch_dtype="auto",
trust_remote_code=True,
use_fast=False,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download the mamba_gpt_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from mamba_gpt_pipeline.py import MambaGPTTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"CobraMamba/mamba-gpt-3b",
use_fast=False,
padding_side="left",
trust_remote_code=False,
)
model = AutoModelForCausalLM.from_pretrained(
"CobraMamba/mamba-gpt-3b",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=False,
)
generate_text = MambaGPTTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "CobraMamba/mamba-gpt-3b" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=False,
trust_remote_code=False,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=False,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Evaluation
We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/).
The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks.
| **Task/Metric** | finetuned-GPT 3B | OpenLLaMA 3B |
| ---------------------- | -------- | ------------ |
| anli_r1/acc | **0.35** | 0.33 |
| anli_r2/acc | **0.33** | 0.32 |
| anli_r3/acc | 0.35 | 0.35 |
| arc_challenge/acc | **0.35** | 0.34 |
| arc_challenge/acc_norm | 0.37 | 0.37 |
| arc_easy/acc | **0.71** | 0.69 |
| arc_easy/acc_norm | 0.65 | 0.65 |
| boolq/acc | **0.72** | 0.66 |
| hellaswag/acc | **0.49** | 0.43 |
| hellaswag/acc_norm | 0.66 | **0.67** |
| openbookqa/acc | 0.26 | **0.27** |
| openbookqa/acc_norm | 0.40 | 0.40 |
| piqa/acc | **0.76** | 0.75 |
| piqa/acc_norm | 0.76 | 0.76 |
| record/em | 0.88 | 0.88 |
| record/f1 | 0.88 | **0.89** |
| rte/acc | 0.55 | **0.58** |
| truthfulqa_mc/mc1 | **0.27** | 0.22 |
| truthfulqa_mc/mc2 | **0.37** | 0.35 |
| wic/acc | **0.49** | 0.48 |
| winogrande/acc | **0.63** | 0.62 |
| Average | **0.53** | 0.52 |
We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
s3nh/Llama-2-7b-german-assistant-v2-GGML
|
s3nh
| 2023-07-25T08:12:58Z | 0 | 1 | null |
[
"text-generation-inference",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-07-24T20:00:48Z |
---
license: cc-by-sa-4.0
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/flozi00/Llama-2-7b-german-assistant-v2).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
This model is an finetuned version for german instructions and conversations in style of Open Assistant tokens. "<|prompter|>" "<|endoftext|>" "<|assistant|>"
The dataset used is deduplicated and cleaned, with no codes inside. The focus is on instruction following and conversational tasks.
The model archictecture is based on Llama-v2 with 7B parameters, trained on 100% renewable energy powered hardware.
This work is contributed by private research of [flozi00](https://huggingface.co/flozi00)
|
text2font/tst-summarization
|
text2font
| 2023-07-25T08:10:16Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-large",
"base_model:finetune:google/mt5-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-25T07:58:00Z |
---
license: apache-2.0
base_model: google/mt5-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: tst-summarization
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. -->
# tst-summarization
This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 30.3505
- Rouge1: 2.7855
- Rouge2: 0.0203
- Rougel: 2.2791
- Rougelsum: 2.2817
- Gen Len: 119.3571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
Anees-Aslam/llama2-qlora-finetunined-cloud-embedUR
|
Anees-Aslam
| 2023-07-25T08:04:32Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T08:04:24Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
VFiona/opus-mt-en-it-finetuned_10000-en-to-it
|
VFiona
| 2023-07-25T08:03:16Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-25T07:07:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-en-it-finetuned_10000-en-to-it
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. -->
# opus-mt-en-it-finetuned_10000-en-to-it
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-it](https://huggingface.co/Helsinki-NLP/opus-mt-en-it) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3025
- Bleu: 72.7906
- Gen Len: 28.197
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.41 | 1.0 | 563 | 0.3025 | 72.7906 | 28.197 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.11.0
|
Buseak/canine_vowelizer_0706_v4
|
Buseak
| 2023-07-25T07:54:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"canine",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-25T06:20:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: canine_vowelizer_0706_v4
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. -->
# canine_vowelizer_0706_v4
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1450
- Precision: 1.0000
- Recall: 1.0
- F1: 1.0000
- Accuracy: 0.9775
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1088 | 1.0 | 1951 | 0.1144 | 0.9999 | 1.0 | 1.0000 | 0.9628 |
| 0.1009 | 2.0 | 3902 | 0.1023 | 0.9999 | 1.0 | 1.0000 | 0.9657 |
| 0.0917 | 3.0 | 5853 | 0.0985 | 1.0000 | 1.0 | 1.0000 | 0.9690 |
| 0.0757 | 4.0 | 7804 | 0.0928 | 1.0000 | 1.0000 | 1.0000 | 0.9712 |
| 0.0635 | 5.0 | 9755 | 0.0932 | 0.9999 | 1.0 | 1.0000 | 0.9725 |
| 0.0542 | 6.0 | 11706 | 0.0943 | 0.9999 | 1.0000 | 1.0000 | 0.9735 |
| 0.0453 | 7.0 | 13657 | 0.0980 | 1.0000 | 1.0000 | 1.0000 | 0.9738 |
| 0.0369 | 8.0 | 15608 | 0.1037 | 1.0000 | 1.0 | 1.0000 | 0.9750 |
| 0.0308 | 9.0 | 17559 | 0.1056 | 1.0000 | 1.0000 | 1.0000 | 0.9747 |
| 0.0275 | 10.0 | 19510 | 0.1138 | 1.0000 | 1.0 | 1.0000 | 0.9757 |
| 0.0222 | 11.0 | 21461 | 0.1187 | 1.0000 | 1.0 | 1.0000 | 0.9757 |
| 0.0185 | 12.0 | 23412 | 0.1201 | 1.0000 | 1.0000 | 1.0000 | 0.9761 |
| 0.0166 | 13.0 | 25363 | 0.1239 | 1.0000 | 1.0000 | 1.0000 | 0.9764 |
| 0.0146 | 14.0 | 27314 | 0.1302 | 1.0000 | 1.0 | 1.0000 | 0.9768 |
| 0.0112 | 15.0 | 29265 | 0.1351 | 1.0000 | 1.0000 | 1.0000 | 0.9768 |
| 0.0104 | 16.0 | 31216 | 0.1386 | 1.0000 | 1.0 | 1.0000 | 0.9769 |
| 0.0092 | 17.0 | 33167 | 0.1379 | 1.0000 | 1.0 | 1.0000 | 0.9771 |
| 0.0079 | 18.0 | 35118 | 0.1453 | 1.0000 | 1.0 | 1.0000 | 0.9771 |
| 0.0071 | 19.0 | 37069 | 0.1444 | 1.0000 | 1.0 | 1.0000 | 0.9775 |
| 0.0067 | 20.0 | 39020 | 0.1450 | 1.0000 | 1.0 | 1.0000 | 0.9775 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
chriskim2273/IOTNation_CompanyName_Extraction_QA_Model_1.2_Distilbert
|
chriskim2273
| 2023-07-25T07:47:23Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-25T05:03:22Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: IOTNation_CompanyName_Extraction_QA_Model_1.2_Distilbert
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. -->
# IOTNation_CompanyName_Extraction_QA_Model_1.2_Distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9943
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
Vidyuth/bert-finetuned-squad
|
Vidyuth
| 2023-07-25T07:47:11Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-25T07:02:29Z |
---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (uncased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
between english and English.
Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same.
The training is identical -- each masked WordPiece token is predicted independently.
After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. See below for more information regarding this fine-tuning.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
This model has the following configuration:
- 24-layer
- 1024 hidden dimension
- 16 attention heads
- 336M parameters.
## Intended uses & limitations
This model should be used as a question-answering model. You may use it in a question answering pipeline, or use it to output raw results given a query and a context. You may see other use cases in the [task summary](https://huggingface.co/transformers/task_summary.html#extractive-question-answering) of the transformers documentation.## Training data
The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
### Fine-tuning
After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. In order to reproduce the training, you may use the following command:
```
python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_qa.py \
--model_name_or_path bert-large-uncased-whole-word-masking \
--dataset_name squad \
--do_train \
--do_eval \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ./examples/models/wwm_uncased_finetuned_squad/ \
--per_device_eval_batch_size=3 \
--per_device_train_batch_size=3 \
```
## Evaluation results
The results obtained are the following:
```
f1 = 93.15
exact_match = 86.91
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
P01yH3dr0n/ReedMix
|
P01yH3dr0n
| 2023-07-25T07:36:38Z | 0 | 11 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-05-05T16:26:33Z |
---
license: cc-by-nc-4.0
---
* 本模型仅作学习交流使用，禁止用于任何商业活动或侵犯他人版权、肖像权等权利的行为。
* Study and communication only. Any commercial activity and violation of rights like portrait and copyright is prohibited.
几个自己常用的融合的模型，成分写在末尾了，不过比例不记得了。
Some of my merged models, the ingredients are at last of readme, though I forgot the proportions.
*推荐参数*
（其实也没什么推荐的，想怎么用怎么用，这只是我常用的设置）
*Recommended parameters*
(Anyhow, use it as you like. Here are my parameters)
Sampler: DPM++ SDE Karras, or DPM++ 2M Karras
Steps: 20
CFG scale: 8
Clip skip: 2
负面推荐使用EasyNegative，推荐使用高清修复。
EasyNegative recommended. hire.fix recommended.
## ReedMix_A
偏厚涂画风的模型。脸部画得比较好，背景需要较多tag才能画得比较细致。**容易出nsfw**。
Impasto-like model. Good at face, but many tags needed for detailed background. **Prone to nsfw**
## ReedMix_A_light
A模型画风明亮版，擅长画人物细节，缺点同A。
Brighter version of model A, good at figure details while same shortages with A.
## ReddMix_A_flat
测试模型，A模型2D版，线条会有点怪。
Beta model, 2D version of A, it may produce weird lines.
## ReedMix_B
比A的背景细节更多，但脸部略不如A。
More details in background than A, while a bit worse at face.
## ReedMix_C
以counterfeit 2.5为基础融合的模型。
Model based on counterfeit 2.5.
## ReedMix_P
偏平涂画风的模型，全身图肢体不易崩，背景细节也较多。
Flat-color model, good at anatomy and detailed background.
## ReedMix_P_light
P模型画风明亮版，比原版“蜡笔味”更少，但背景细节略逊于原版。
Brighter version of model P, less "pastel style" while inferior to origin model on background details.
### Acknowledgement
感谢这些作者发布的好用的模型
* 7th_anime_v3_A (https://huggingface.co/syaimu/7th_Layer)
* pastel-mix (https://huggingface.co/andite/pastel-mix)
* counterfeit-v2.5 (https://huggingface.co/gsdf/Counterfeit-V2.5)
* PileDream (https://civitai.com/models/20255)
* canisterMix-1 (https://civitai.com/models/94009/canistermix-1)
|
bernita/Test-1
|
bernita
| 2023-07-25T07:27:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-25T07:27:18Z |
---
license: creativeml-openrail-m
---
|
Vithika/llama2-qlora-finetunined-code-text
|
Vithika
| 2023-07-25T07:25:35Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T07:25:18Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
YarramsettiNaresh/poca-SoccerTwos
|
YarramsettiNaresh
| 2023-07-25T07:16:49Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-07-25T07:16:49Z |
---
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: YarramsettiNaresh/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Adarshagupta/BabyDragon
|
Adarshagupta
| 2023-07-25T07:13:53Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-07-25T07:13:53Z |
---
license: bigscience-openrail-m
---
|
shashank-mugiwara/thor
|
shashank-mugiwara
| 2023-07-25T06:41:22Z | 151 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"gpt",
"llm",
"large language model",
"thor service",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-24T22:06:56Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- thor service
inference: false
---
# Model Card
## Summary
- Base model: [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.30.2
pip install einops==0.6.1
pip install accelerate==0.20.3
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="shashank-mugiwara/thor",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"What is thor service?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("What is thor service?")["prompt_text"])
```
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"shashank-mugiwara/thor",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"shashank-mugiwara/thor",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "shashank-mugiwara/thor" # either local folder or huggingface model name
prompt = "<|prompt|>What is thor service?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
|
CloudBik/office-365-tenant-to-tenant-migration
|
CloudBik
| 2023-07-25T06:40:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-25T06:21:25Z |
Microsoft 365 or Office 365 Tenant to Tenant Migration is a procedure to migrate user mailboxes from one tenant to another tenant in Microsoft Office 365. This Migration can be performed manually or with the help of third-party migration services or tools. While Manual migration process saves your money, but third-party migration services can save your valuable time. Manual process is worth if you are migrating small number of users as it makes you perform multiple tasks that are time consuming. However, for migrating large number of mailboxes, one should consider third party migration services as they are efficient with no chances of error. It is totally depends on the user which one they prefer to use. I am sharing an informative article on tenant to tenant migration process so that you can learn and perform it yourself. It contains all the steps and the informations required to complete the migration process.
Read More: https://www.cloudbik.com/resources/blog/tenant-to-tenant-migration-office-365/
|
trillionmonster/Baichuan-13B-Chat-8bit
|
trillionmonster
| 2023-07-25T06:29:35Z | 15 | 9 |
transformers
|
[
"transformers",
"pytorch",
"baichuan",
"text-generation",
"custom_code",
"zh",
"en",
"autotrain_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] |
text-generation
| 2023-07-20T05:50:26Z |
---
language:
- zh
- en
pipeline_tag: text-generation
inference: false
---
原项目见 [https://huggingface.co/baichuan-inc/Baichuan-13B-Chat]
改动点：将原模型量化为8bit 保存为2GB大小的切片。
## 使用方式(int8)
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
tokenizer = AutoTokenizer.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit", device_map="auto", trust_remote_code=True)
model.generation_config = GenerationConfig.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit")
messages = []
messages.append({"role": "user", "content": "世界上第二高的山峰是哪座"})
response = model.chat(tokenizer, messages)
print(response)
```
如需使用 int4 量化 (Similarly, to use int4 quantization):
```python
model = AutoModelForCausalLM.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit", device_map="auto",load_in_4bit=True,trust_remote_code=True)
```
|
Chiahc/distilgpt2Lora
|
Chiahc
| 2023-07-25T06:26:33Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T06:26:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
CloudBik/Migrate-from-Google-Workspace-to-Office-365
|
CloudBik
| 2023-07-25T06:26:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-25T06:11:37Z |
Microsoft Office 365 offers variety of applications. It includes some applications like Word, Excel, Outlook, PowerPoint etc. In PowerPoint you can easily create amazing presentations like in 3d form, 2d or many more. Using this you can easily present your model effectively. If you are using Google Workspace, you should consider moving to Microsoft 365 to get access to the daily use applications and much advanced collaboration tools. If you are familiar with Microsoft products like word, excel, etc then it will be easy to get used to the Microsoft Office 365 applications. Some find it difficult to use but once you gets familiar with it, you can increase your productivity and collaboration between teams. Morever, it offers advanced security, so you do not need to worry about the data loss. Check out the below article on how to migrate from Google Workspace to Office 365 to read and perform the complete manual steps.
Read More: https://www.cloudbik.com/resources/blog/google-workspace-to-microsoft-365-migration/
|
NasimB/guten-log-rarity
|
NasimB
| 2023-07-25T05:57:48Z | 5 | 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
| 2023-07-25T02:08:37Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: guten-log-rarity
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. -->
# guten-log-rarity
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: 4.1085
## 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: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3399 | 0.29 | 500 | 5.3371 |
| 5.0398 | 0.58 | 1000 | 4.9192 |
| 4.6956 | 0.87 | 1500 | 4.6871 |
| 4.4399 | 1.16 | 2000 | 4.5482 |
| 4.2944 | 1.46 | 2500 | 4.4250 |
| 4.1926 | 1.75 | 3000 | 4.3221 |
| 4.0782 | 2.04 | 3500 | 4.2542 |
| 3.8913 | 2.33 | 4000 | 4.2078 |
| 3.8644 | 2.62 | 4500 | 4.1535 |
| 3.8283 | 2.91 | 5000 | 4.1029 |
| 3.6399 | 3.2 | 5500 | 4.1006 |
| 3.5829 | 3.49 | 6000 | 4.0681 |
| 3.5631 | 3.79 | 6500 | 4.0411 |
| 3.4868 | 4.08 | 7000 | 4.0380 |
| 3.3087 | 4.37 | 7500 | 4.0326 |
| 3.3097 | 4.66 | 8000 | 4.0188 |
| 3.3016 | 4.95 | 8500 | 4.0055 |
| 3.1531 | 5.24 | 9000 | 4.0191 |
| 3.1275 | 5.53 | 9500 | 4.0183 |
| 3.126 | 5.82 | 10000 | 4.0171 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Chiahc/my_awesome_eli5_clm-model
|
Chiahc
| 2023-07-25T05:39:54Z | 224 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-25T05:07:05Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
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: 3.7420
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8685 | 1.0 | 1145 | 3.7625 |
| 3.7736 | 2.0 | 2290 | 3.7448 |
| 3.7339 | 3.0 | 3435 | 3.7420 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
annazhong/vit-base-patch16-224-finetuned-foveated-features
|
annazhong
| 2023-07-25T05:39:17Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-25T05:30:44Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-foveated-features
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. -->
# vit-base-patch16-224-finetuned-foveated-features
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1242
- Accuracy: 0.4595
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 150
- eval_batch_size: 150
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 600
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 1.2615 | 0.1622 |
| No log | 2.0 | 2 | 1.2910 | 0.3514 |
| No log | 3.0 | 3 | 1.1242 | 0.4595 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
edures/Reinforce-v1
|
edures
| 2023-07-25T05:35:34Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T05:35:23Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-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
|
pankajgharai/my_awesome_food_model
|
pankajgharai
| 2023-07-25T05:33:49Z | 216 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-25T05:23:08Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.892
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5995
- Accuracy: 0.892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6508 | 0.99 | 62 | 2.5037 | 0.82 |
| 1.8322 | 2.0 | 125 | 1.7732 | 0.875 |
| 1.5648 | 2.98 | 186 | 1.5995 | 0.892 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
davidrrobinson/BioLingual
|
davidrrobinson
| 2023-07-25T05:31:41Z | 1,056 | 4 |
transformers
|
[
"transformers",
"pytorch",
"clap",
"feature-extraction",
"dataset:davidrrobinson/AnimalSpeak",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-07-24T01:15:23Z |
---
datasets:
- davidrrobinson/AnimalSpeak
---
# Model card for BioLingual
Model card for BioLingual: Transferable Models for bioacoustics with Human Language Supervision
An audio-text model for bioacoustics based on contrastive language-audio pretraining.
# Usage
You can use this model for bioacoustic zero shot audio classification, or for fine-tuning on bioacoustic tasks.
# Uses
## Perform zero-shot audio classification
### Using `pipeline`
```python
from datasets import load_dataset
from transformers import pipeline
dataset = load_dataset("ashraq/esc50")
audio = dataset["train"]["audio"][-1]["array"]
audio_classifier = pipeline(task="zero-shot-audio-classification", model="davidrrobinson/BioLingual")
output = audio_classifier(audio, candidate_labels=["Sound of a sperm whale", "Sound of a sea lion"])
print(output)
>>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}]
```
## Run the model:
You can also get the audio and text embeddings using `ClapModel`
### Run the model on CPU:
```python
from datasets import load_dataset
from transformers import ClapModel, ClapProcessor
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio_sample = librispeech_dummy[0]
model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
processor = ClapProcessor.from_pretrained("laion/clap-htsat-unfused")
inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt")
audio_embed = model.get_audio_features(**inputs)
```
### Run the model on GPU:
```python
from datasets import load_dataset
from transformers import ClapModel, ClapProcessor
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio_sample = librispeech_dummy[0]
model = ClapModel.from_pretrained("laion/clap-htsat-unfused").to(0)
processor = ClapProcessor.from_pretrained("laion/clap-htsat-unfused")
inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0)
audio_embed = model.get_audio_features(**inputs)
|
luoyt99/testllama
|
luoyt99
| 2023-07-25T05:29:51Z | 0 | 0 | null |
[
"dataset:nyanko7/LLaMA-65B",
"license:bsd",
"region:us"
] | null | 2023-07-25T05:28:22Z |
---
license: bsd
datasets:
- nyanko7/LLaMA-65B
---
|
vasevarad/roberta_dissonance_detector
|
vasevarad
| 2023-07-25T05:27:35Z | 0 | 1 | null |
[
"pytorch",
"arxiv:2305.02459",
"license:cc-by-3.0",
"region:us"
] | null | 2023-07-24T18:12:36Z |
---
license: cc-by-3.0
---
The SOTA model for Dissonance Detection from the paper [Transfer and Active Learning for Dissonance Detection: Addressing the Rare Class Challenge](https://arxiv.org/abs/2305.02459).
RoBERTA-base finetuned on [Dissonance Twitter Dataset](https://github.com/humanlab/dissonance-twitter-dataset), collected from annotating tweets for within-person dissonance.
## Dataset Annotation details
Tweets were parsed into discourse units, and marked as Belief (Thought or Action) or Other, and pairs of beliefs within the same tweet were relayed to annotators for Dissonance annotation.
The annotations were conducted on a sheet in the following **dissonance-first** format.
The annotators used the following flowchart as a more detailed guide to determining the Dissonance, Consonance and Neither/Other classes:
## Citation
If you use this dataset, please cite the associated paper:
```
@inproceedings{varadarajan2023transfer,
title={Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge},
author={Varadarajan, Vasudha and Juhng, Swanie and Mahwish, Syeda and Liu, Xiaoran and Luby, Jonah and Luhmann, Christian and Schwartz, H Andrew},
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Long Papers)",
month = july,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
abstract = "While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.",
}
```
|
jensg/distilhubert-finetuned-gtzan
|
jensg
| 2023-07-25T05:05:06Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-24T09:09:40Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.83
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5991
- Accuracy: 0.83
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1211 | 1.0 | 57 | 1.9967 | 0.4 |
| 1.6311 | 2.0 | 114 | 1.5599 | 0.58 |
| 1.2082 | 3.0 | 171 | 1.2194 | 0.72 |
| 1.1853 | 4.0 | 228 | 1.0276 | 0.75 |
| 0.7278 | 5.0 | 285 | 0.9232 | 0.78 |
| 0.6999 | 6.0 | 342 | 0.7392 | 0.82 |
| 0.4983 | 7.0 | 399 | 0.6779 | 0.84 |
| 0.5142 | 8.0 | 456 | 0.6483 | 0.83 |
| 0.417 | 9.0 | 513 | 0.6554 | 0.82 |
| 0.3725 | 10.0 | 570 | 0.5991 | 0.83 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
howardchen123/alpaca-lora-llama-sentiment
|
howardchen123
| 2023-07-25T05:01:16Z | 0 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-24T02:51:45Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
dafc/llama2-qlora-finetunined-french
|
dafc
| 2023-07-25T04:49:33Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T04:49:15Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
sukiee/qlora-koalpaca-polyglot-5.8b-callcenter
|
sukiee
| 2023-07-25T04:41:27Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-25T03:04:53Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
EXrRor3/Cartpole-v1
|
EXrRor3
| 2023-07-25T04:32:28Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-25T04:32:19Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: 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
|
HuyenNguyen/results
|
HuyenNguyen
| 2023-07-25T04:31:00Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"region:us"
] | null | 2023-07-25T03:25:20Z |
---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
annazhong/vit-base-patch16-224-finetuned-original-images
|
annazhong
| 2023-07-25T04:26:00Z | 166 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-25T03:31:42Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-original-images
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. -->
# vit-base-patch16-224-finetuned-original-images
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1367
- Accuracy: 0.4865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 150
- eval_batch_size: 150
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 600
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 1.4730 | 0.2703 |
| No log | 2.0 | 2 | 1.1367 | 0.4865 |
| No log | 3.0 | 3 | 0.9924 | 0.4324 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
intuol/SuperBlockBros
|
intuol
| 2023-07-25T04:24:08Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-07-25T04:18:34Z |
---
license: openrail
---
# SuperBlockBros (Object Show YouTuber)
## Data
- 600 Epochs
- RVC v2
- MangioCrepe
|
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