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stringlengths 2
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int64 0
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| likes
int64 0
6.56k
| library_name
stringclasses 368
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sequencelengths 1
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stringclasses 51
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stringlengths 1
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keemkorn/is-cat-on-head | keemkorn | "2024-04-05T03:51:05Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-04-05T03:51:05Z" | ---
license: mit
---
|
harish3742/bert-base-cnn | harish3742 | "2024-04-05T05:07:18Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-04-05T03:56:26Z" | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-cnn
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cnn
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3839
- Rouge2 Precision: 0.0112
- Rouge2 Recall: 0.0386
- Rouge2 Fmeasure: 0.0162
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| No log | 1.0 | 8 | 1.6622 | 0.0104 | 0.042 | 0.0164 |
| No log | 2.0 | 16 | 1.3839 | 0.0112 | 0.0386 | 0.0162 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Blyuv/Yandere | Blyuv | "2024-04-05T04:03:25Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-05T04:02:38Z" | ---
license: openrail
---
|
Diegoskx/TavoGaray | Diegoskx | "2024-04-05T04:03:57Z" | 0 | 0 | null | [
"license:unknown",
"region:us"
] | null | "2024-04-05T04:02:38Z" | ---
license: unknown
---
|
bartowski/Starling-LM-10.7B-beta-exl2 | bartowski | "2024-04-05T04:09:14Z" | 0 | 3 | transformers | [
"transformers",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-05T04:09:01Z" | ---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
quantized_by: bartowski
---
## Exllama v2 Quantizations of Starling-LM-10.7B-beta
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.17">turboderp's ExLlamaV2 v0.0.17</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/ddh0/Starling-LM-10.7B-beta
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Starling-LM-10.7B-beta-exl2/tree/8_0) | 8.0 | 8.0 | 11.9 GB | 13.3 GB | 15.3 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Starling-LM-10.7B-beta-exl2/tree/6_5) | 6.5 | 8.0 | 10.3 GB | 11.7 GB | 13.7 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Starling-LM-10.7B-beta-exl2/tree/5_0) | 5.0 | 6.0 | 8.3 GB | 9.7 GB | 11.7 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Starling-LM-10.7B-beta-exl2/tree/4_25) | 4.25 | 6.0 | 7.4 GB | 8.6 GB | 10.6 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Starling-LM-10.7B-beta-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 7.8 GB | 9.8 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Starling-LM-10.7B-beta-exl2 Starling-LM-10.7B-beta-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Starling-LM-10.7B-beta-exl2`:
```shell
mkdir Starling-LM-10.7B-beta-exl2
huggingface-cli download bartowski/Starling-LM-10.7B-beta-exl2 --local-dir Starling-LM-10.7B-beta-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Starling-LM-10.7B-beta-exl2-6_5
huggingface-cli download bartowski/Starling-LM-10.7B-beta-exl2 --revision 6_5 --local-dir Starling-LM-10.7B-beta-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Starling-LM-10.7B-beta-exl2-6.5
huggingface-cli download bartowski/Starling-LM-10.7B-beta-exl2 --revision 6_5 --local-dir Starling-LM-10.7B-beta-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |
Axion004/swin-tiny-patch4-window7-224-finetuned-eurosat | Axion004 | "2024-04-05T04:29:14Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-04-05T04:10:12Z" | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
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: 0.9814814814814815
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0655
- Accuracy: 0.9815
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2747 | 1.0 | 190 | 0.1382 | 0.9563 |
| 0.1928 | 2.0 | 380 | 0.0913 | 0.9715 |
| 0.1317 | 3.0 | 570 | 0.0655 | 0.9815 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ERDAG/rep2 | ERDAG | "2024-04-05T04:20:13Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-05T04:12:56Z" | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
oerdal/q-FrozenLake-v1-4x4-noSlippery | oerdal | "2024-04-05T04:15:30Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-04-05T04:15:18Z" | ---
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="oerdal/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"])
```
|
voxmenthe/airoboros-34b-3.3-mlx-4bit | voxmenthe | "2024-04-05T04:29:24Z" | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"dataset:jondurbin/airoboros-3.2",
"dataset:bluemoon-fandom-1-1-rp-cleaned",
"dataset:boolq",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:LDJnr/Capybara",
"dataset:jondurbin/cinematika-v0.1",
"dataset:glaiveai/glaive-function-calling-v2",
"dataset:grimulkan/LimaRP-augmented",
"dataset:piqa",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"dataset:mattpscott/airoboros-summarization",
"dataset:unalignment/toxic-dpo-v0.2",
"license:other",
"region:us"
] | null | "2024-04-05T04:18:46Z" | ---
license: other
tags:
- mlx
base_model: 01-ai/yi-34b-200k
datasets:
- jondurbin/airoboros-3.2
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- jondurbin/gutenberg-dpo-v0.1
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- glaiveai/glaive-function-calling-v2
- grimulkan/LimaRP-augmented
- piqa
- Vezora/Tested-22k-Python-Alpaca
- mattpscott/airoboros-summarization
- unalignment/toxic-dpo-v0.2
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
---
# voxmenthe/airoboros-34b-3.3-mlx-4bit
This model was converted to MLX format from [`jondurbin/airoboros-34b-3.3`]() using mlx-lm version **0.6.0**.
Refer to the [original model card](https://huggingface.co/jondurbin/airoboros-34b-3.3) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("voxmenthe/airoboros-34b-3.3-mlx-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
sardal/obesity_risk | sardal | "2024-04-05T05:06:30Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-04-05T04:22:40Z" | ---
license: mit
---
|
Sukuntul/Char.Hsr | Sukuntul | "2024-04-05T04:29:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T04:27:05Z" | Entry not found |
Agent-Eval-Refine/CogAgent-iOS-FilteredBC | Agent-Eval-Refine | "2024-04-05T05:06:55Z" | 0 | 0 | null | [
"license:bsd",
"region:us"
] | null | "2024-04-05T04:28:29Z" | ---
license: bsd
---
CogAgent finetuned on its filtered iOS trajectories.
To use the weights, please first merge the parts by
```
cat part_* > mp_rank_00_model_states.pt
``` |
Aditadot23/Andre.suhen | Aditadot23 | "2024-04-05T04:53:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T04:36:41Z" | Entry not found |
hungphongtrn/en_vi_envit5-translation_news_train | hungphongtrn | "2024-04-05T05:15:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/envit5-translation",
"base_model:finetune:VietAI/envit5-translation",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-04-05T04:37:50Z" | ---
license: openrail
base_model: VietAI/envit5-translation
tags:
- generated_from_trainer
model-index:
- name: en_vi_envit5-translation_news_train
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. -->
# en_vi_envit5-translation_news_train
This model is a fine-tuned version of [VietAI/envit5-translation](https://huggingface.co/VietAI/envit5-translation) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.37.2
- Pytorch 1.12.1+cu116
- Datasets 2.18.0
- Tokenizers 0.15.1
|
GraydientPlatformAPI/yes4 | GraydientPlatformAPI | "2024-04-05T04:57:01Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-04-05T04:43:10Z" | Entry not found |
akashjoy/model | akashjoy | "2024-04-05T04:43:34Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T04:43:34Z" | Entry not found |
oerdal/q-Taxi-v3 | oerdal | "2024-04-05T04:52:07Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-04-05T04:43:41Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="oerdal/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
GraydientPlatformAPI/famosa-alpha | GraydientPlatformAPI | "2024-04-05T04:57:27Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-04-05T04:44:04Z" | Entry not found |
YiandLi/QLORA-0404 | YiandLi | "2024-04-05T05:41:44Z" | 0 | 0 | null | [
"safetensors",
"license:mit",
"region:us"
] | null | "2024-04-05T04:47:37Z" | ---
license: mit
---
输入为 sample_answer + Student's Answer
```python
prompt = str(tokenizer.bos_token or '') + f"Standard Answer: {sample['sample_answer']}\n Student's Answer: {sample['new_response_no_index']}\n Evaluation:{sample['label'].strip()}""" + str(tokenizer.eos_token)
```
|
johnnybop/Bill | johnnybop | "2024-04-05T04:50:49Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-04-05T04:49:03Z" | ---
license: apache-2.0
---
|
giantdev/bxdtts1sh32 | giantdev | "2024-04-05T04:52:29Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T04:51:54Z" | Entry not found |
Dedewweee/Mar | Dedewweee | "2024-04-05T04:52:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T04:52:10Z" | Entry not found |
sayuj01/unslothtesting_4bit_mistral_imdb_model | sayuj01 | "2024-04-05T04:53:18Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T04:53:05Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** sayuj01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SnowLily98/ACE_VAV | SnowLily98 | "2024-04-05T04:57:09Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T04:56:00Z" | Entry not found |
LarryAIDraw/Nakano_Nino | LarryAIDraw | "2024-04-05T05:05:24Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-04-05T04:58:04Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/383543/nakano-nino |
LarryAIDraw/acheron-str-v1c | LarryAIDraw | "2024-04-05T05:05:35Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-04-05T04:58:28Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/384136/honkai-star-rail-acheron-or |
akashjoy/NLP_Customer_Care_Summary | akashjoy | "2024-04-05T05:50:03Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-cnn_dailymail",
"base_model:finetune:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-04-05T04:59:09Z" | ---
base_model: google/pegasus-cnn_dailymail
tags:
- generated_from_trainer
model-index:
- name: NLP_Customer_Care_Summary
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# NLP_Customer_Care_Summary
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4833
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6599 | 0.54 | 500 | 1.4833 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
raovasudev762/SpecialSDXL | raovasudev762 | "2024-04-05T07:32:36Z" | 0 | 0 | null | [
"arxiv:2307.01952",
"arxiv:2206.00364",
"region:us"
] | null | "2024-04-05T05:00:25Z" | # Generative Models by Stability AI
## News
**March 18, 2024**
- We are releasing **[SV3D](https://huggingface.co/stabilityai/sv3d)**, an image-to-video model for novel multi-view synthesis, for research purposes:
- **SV3D** was trained to generate 21 frames at resolution 576x576, given 1 context frame of the same size, ideally a white-background image with one object.
- **SV3D_u**: This variant generates orbital videos based on single image inputs without camera conditioning..
- **SV3D_p**: Extending the capability of **SVD3_u**, this variant accommodates both single images and orbital views allowing for the creation of 3D video along specified camera paths.
- We extend the streamlit demo `scripts/demo/video_sampling.py` and the standalone python script `scripts/sampling/simple_video_sample.py` for inference of both models.
- Please check our [project page](https://sv3d.github.io), [tech report](https://sv3d.github.io/static/paper.pdf) and [video summary](https://youtu.be/Zqw4-1LcfWg) for more details.
To run **SV3D_u** on a single image:
- Download `sv3d_u.safetensors` from https://huggingface.co/stabilityai/sv3d to `checkpoints/sv3d_u.safetensors`
- Run `python scripts/sampling/simple_video_sample.py --input_path <path/to/image.png> --version sv3d_u`
To run **SV3D_p** on a single image:
- Download `sv3d_p.safetensors` from https://huggingface.co/stabilityai/sv3d to `checkpoints/sv3d_p.safetensors`
1. Generate static orbit at a specified elevation eg. 10.0 : `python scripts/sampling/simple_video_sample.py --input_path <path/to/image.png> --version sv3d_p --elevations_deg 10.0`
2. Generate dynamic orbit at a specified elevations and azimuths: specify sequences of 21 elevations (in degrees) to `elevations_deg` ([-90, 90]), and 21 azimuths (in degrees) to `azimuths_deg` [0, 360] in sorted order from 0 to 360. For example: `python scripts/sampling/simple_video_sample.py --input_path <path/to/image.png> --version sv3d_p --elevations_deg [<list of 21 elevations in degrees>] --azimuths_deg [<list of 21 azimuths in degrees>]`
To run SVD or SV3D on a streamlit server:
`streamlit run scripts/demo/video_sampling.py`
**November 30, 2023**
- Following the launch of SDXL-Turbo, we are releasing [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo).
**November 28, 2023**
- We are releasing SDXL-Turbo, a lightning fast text-to image model.
Alongside the model, we release a [technical report](https://stability.ai/research/adversarial-diffusion-distillation)
- Usage:
- Follow the installation instructions or update the existing environment with `pip install streamlit-keyup`.
- Download the [weights](https://huggingface.co/stabilityai/sdxl-turbo) and place them in the `checkpoints/` directory.
- Run `streamlit run scripts/demo/turbo.py`.
**November 21, 2023**
- We are releasing Stable Video Diffusion, an image-to-video model, for research purposes:
- [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid): This model was trained to generate 14
frames at resolution 576x1024 given a context frame of the same size.
We use the standard image encoder from SD 2.1, but replace the decoder with a temporally-aware `deflickering decoder`.
- [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt): Same architecture as `SVD` but finetuned
for 25 frame generation.
- You can run the community-build gradio demo locally by running `python -m scripts.demo.gradio_app`.
- We provide a streamlit demo `scripts/demo/video_sampling.py` and a standalone python script `scripts/sampling/simple_video_sample.py` for inference of both models.
- Alongside the model, we release a [technical report](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets).
**July 26, 2023**
- We are releasing two new open models with a
permissive [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0) (see [Inference](#inference) for file
hashes):
- [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0): An improved version
over `SDXL-base-0.9`.
- [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0): An improved version
over `SDXL-refiner-0.9`.
**July 4, 2023**
- A technical report on SDXL is now available [here](https://arxiv.org/abs/2307.01952).
**June 22, 2023**
- We are releasing two new diffusion models for research purposes:
- `SDXL-base-0.9`: The base model was trained on a variety of aspect ratios on images with resolution 1024^2. The
base model uses [OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip)
and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main) for text encoding whereas the refiner model only uses
the OpenCLIP model.
- `SDXL-refiner-0.9`: The refiner has been trained to denoise small noise levels of high quality data and as such is
not expected to work as a text-to-image model; instead, it should only be used as an image-to-image model.
If you would like to access these models for your research, please apply using one of the following links:
[SDXL-0.9-Base model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9),
and [SDXL-0.9-Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
This means that you can apply for any of the two links - and if you are granted - you can access both.
Please log in to your Hugging Face Account with your organization email to request access.
**We plan to do a full release soon (July).**
## The codebase
### General Philosophy
Modularity is king. This repo implements a config-driven approach where we build and combine submodules by
calling `instantiate_from_config()` on objects defined in yaml configs. See `configs/` for many examples.
### Changelog from the old `ldm` codebase
For training, we use [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), but it should be easy to use other
training wrappers around the base modules. The core diffusion model class (formerly `LatentDiffusion`,
now `DiffusionEngine`) has been cleaned up:
- No more extensive subclassing! We now handle all types of conditioning inputs (vectors, sequences and spatial
conditionings, and all combinations thereof) in a single class: `GeneralConditioner`,
see `sgm/modules/encoders/modules.py`.
- We separate guiders (such as classifier-free guidance, see `sgm/modules/diffusionmodules/guiders.py`) from the
samplers (`sgm/modules/diffusionmodules/sampling.py`), and the samplers are independent of the model.
- We adopt the ["denoiser framework"](https://arxiv.org/abs/2206.00364) for both training and inference (most notable
change is probably now the option to train continuous time models):
* Discrete times models (denoisers) are simply a special case of continuous time models (denoisers);
see `sgm/modules/diffusionmodules/denoiser.py`.
* The following features are now independent: weighting of the diffusion loss
function (`sgm/modules/diffusionmodules/denoiser_weighting.py`), preconditioning of the
network (`sgm/modules/diffusionmodules/denoiser_scaling.py`), and sampling of noise levels during
training (`sgm/modules/diffusionmodules/sigma_sampling.py`).
- Autoencoding models have also been cleaned up.
## Installation:
<a name="installation"></a>
#### 1. Clone the repo
```shell
git clone https://github.com/Stability-AI/generative-models.git
cd generative-models
```
#### 2. Setting up the virtualenv
This is assuming you have navigated to the `generative-models` root after cloning it.
**NOTE:** This is tested under `python3.10`. For other python versions, you might encounter version conflicts.
**PyTorch 2.0**
```shell
# install required packages from pypi
python3 -m venv .pt2
source .pt2/bin/activate
pip3 install -r requirements/pt2.txt
```
#### 3. Install `sgm`
```shell
pip3 install .
```
#### 4. Install `sdata` for training
```shell
pip3 install -e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
```
## Packaging
This repository uses PEP 517 compliant packaging using [Hatch](https://hatch.pypa.io/latest/).
To build a distributable wheel, install `hatch` and run `hatch build`
(specifying `-t wheel` will skip building a sdist, which is not necessary).
```
pip install hatch
hatch build -t wheel
```
You will find the built package in `dist/`. You can install the wheel with `pip install dist/*.whl`.
Note that the package does **not** currently specify dependencies; you will need to install the required packages,
depending on your use case and PyTorch version, manually.
## Inference
We provide a [streamlit](https://streamlit.io/) demo for text-to-image and image-to-image sampling
in `scripts/demo/sampling.py`.
We provide file hashes for the complete file as well as for only the saved tensors in the file (
see [Model Spec](https://github.com/Stability-AI/ModelSpec) for a script to evaluate that).
The following models are currently supported:
- [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
```
File Hash (sha256): 31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b
Tensordata Hash (sha256): 0xd7a9105a900fd52748f20725fe52fe52b507fd36bee4fc107b1550a26e6ee1d7
```
- [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
```
File Hash (sha256): 7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f
Tensordata Hash (sha256): 0x1a77d21bebc4b4de78c474a90cb74dc0d2217caf4061971dbfa75ad406b75d81
```
- [SDXL-base-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9)
- [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9)
- [SD-2.1-512](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/blob/main/v2-1_512-ema-pruned.safetensors)
- [SD-2.1-768](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors)
**Weights for SDXL**:
**SDXL-1.0:**
The weights of SDXL-1.0 are available (subject to
a [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0)) here:
- base model: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/
- refiner model: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/
**SDXL-0.9:**
The weights of SDXL-0.9 are available and subject to a [research license](model_licenses/LICENSE-SDXL0.9).
If you would like to access these models for your research, please apply using one of the following links:
[SDXL-base-0.9 model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9),
and [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
This means that you can apply for any of the two links - and if you are granted - you can access both.
Please log in to your Hugging Face Account with your organization email to request access.
After obtaining the weights, place them into `checkpoints/`.
Next, start the demo using
```
streamlit run scripts/demo/sampling.py --server.port <your_port>
```
### Invisible Watermark Detection
Images generated with our code use the
[invisible-watermark](https://github.com/ShieldMnt/invisible-watermark/)
library to embed an invisible watermark into the model output. We also provide
a script to easily detect that watermark. Please note that this watermark is
not the same as in previous Stable Diffusion 1.x/2.x versions.
To run the script you need to either have a working installation as above or
try an _experimental_ import using only a minimal amount of packages:
```bash
python -m venv .detect
source .detect/bin/activate
pip install "numpy>=1.17" "PyWavelets>=1.1.1" "opencv-python>=4.1.0.25"
pip install --no-deps invisible-watermark
```
To run the script you need to have a working installation as above. The script
is then useable in the following ways (don't forget to activate your
virtual environment beforehand, e.g. `source .pt1/bin/activate`):
```bash
# test a single file
python scripts/demo/detect.py <your filename here>
# test multiple files at once
python scripts/demo/detect.py <filename 1> <filename 2> ... <filename n>
# test all files in a specific folder
python scripts/demo/detect.py <your folder name here>/*
```
## Training:
We are providing example training configs in `configs/example_training`. To launch a training, run
```
python main.py --base configs/<config1.yaml> configs/<config2.yaml>
```
where configs are merged from left to right (later configs overwrite the same values).
This can be used to combine model, training and data configs. However, all of them can also be
defined in a single config. For example, to run a class-conditional pixel-based diffusion model training on MNIST,
run
```bash
python main.py --base configs/example_training/toy/mnist_cond.yaml
```
**NOTE 1:** Using the non-toy-dataset
configs `configs/example_training/imagenet-f8_cond.yaml`, `configs/example_training/txt2img-clipl.yaml`
and `configs/example_training/txt2img-clipl-legacy-ucg-training.yaml` for training will require edits depending on the
used dataset (which is expected to stored in tar-file in
the [webdataset-format](https://github.com/webdataset/webdataset)). To find the parts which have to be adapted, search
for comments containing `USER:` in the respective config.
**NOTE 2:** This repository supports both `pytorch1.13` and `pytorch2`for training generative models. However for
autoencoder training as e.g. in `configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml`,
only `pytorch1.13` is supported.
**NOTE 3:** Training latent generative models (as e.g. in `configs/example_training/imagenet-f8_cond.yaml`) requires
retrieving the checkpoint from [Hugging Face](https://huggingface.co/stabilityai/sdxl-vae/tree/main) and replacing
the `CKPT_PATH` placeholder in [this line](configs/example_training/imagenet-f8_cond.yaml#81). The same is to be done
for the provided text-to-image configs.
### Building New Diffusion Models
#### Conditioner
The `GeneralConditioner` is configured through the `conditioner_config`. Its only attribute is `emb_models`, a list of
different embedders (all inherited from `AbstractEmbModel`) that are used to condition the generative model.
All embedders should define whether or not they are trainable (`is_trainable`, default `False`), a classifier-free
guidance dropout rate is used (`ucg_rate`, default `0`), and an input key (`input_key`), for example, `txt` for
text-conditioning or `cls` for class-conditioning.
When computing conditionings, the embedder will get `batch[input_key]` as input.
We currently support two to four dimensional conditionings and conditionings of different embedders are concatenated
appropriately.
Note that the order of the embedders in the `conditioner_config` is important.
#### Network
The neural network is set through the `network_config`. This used to be called `unet_config`, which is not general
enough as we plan to experiment with transformer-based diffusion backbones.
#### Loss
The loss is configured through `loss_config`. For standard diffusion model training, you will have to
set `sigma_sampler_config`.
#### Sampler config
As discussed above, the sampler is independent of the model. In the `sampler_config`, we set the type of numerical
solver, number of steps, type of discretization, as well as, for example, guidance wrappers for classifier-free
guidance.
### Dataset Handling
For large scale training we recommend using the data pipelines from
our [data pipelines](https://github.com/Stability-AI/datapipelines) project. The project is contained in the requirement
and automatically included when following the steps from the [Installation section](#installation).
Small map-style datasets should be defined here in the repository (e.g., MNIST, CIFAR-10, ...), and return a dict of
data keys/values,
e.g.,
```python
example = {"jpg": x, # this is a tensor -1...1 chw
"txt": "a beautiful image"}
```
where we expect images in -1...1, channel-first format.
|
GraydientPlatformAPI/manmaru3 | GraydientPlatformAPI | "2024-04-05T05:07:19Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-04-05T05:00:48Z" | Entry not found |
raghoeveer/gemma-7b | raghoeveer | "2024-04-05T05:03:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T05:03:25Z" | Entry not found |
zhangxueyu355/test | zhangxueyu355 | "2024-04-05T05:11:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T05:10:59Z" | Entry not found |
KingLLM/Hematologist_AI_Assistant | KingLLM | "2024-04-05T05:25:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T05:11:55Z" | Entry not found |
SubsWay/my_whisper_test | SubsWay | "2024-04-05T05:14:37Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T05:14:03Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
SnowLily98/ZIU_VAV | SnowLily98 | "2024-04-05T05:16:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T05:15:25Z" | Entry not found |
Agent-Eval-Refine/CogAgent-iOS-SelfTrain | Agent-Eval-Refine | "2024-04-05T05:53:24Z" | 0 | 0 | null | [
"license:bsd",
"region:us"
] | null | "2024-04-05T05:18:50Z" | ---
license: bsd
---
CogAgent finetuned on its iOS trajectories.
To use the weights, please first merge the parts by
cat part_* > mp_rank_00_model_states.pt |
rachfop/Mistral-7B-v0.1 | rachfop | "2024-04-05T05:19:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T05:19:57Z" | Entry not found |
Exc3ss/akshay_model_final | Exc3ss | "2024-04-05T06:03:45Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-04-05T05:21:16Z" | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
base_model: CompVis/stable-diffusion-v1-4
inference: true
instance_prompt: a photo of Akshay Kumar
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - Exc3ss/akshay_model_final
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of Akshay Kumar using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
IyanuMicheal/q-Taxi-v3 | IyanuMicheal | "2024-04-05T07:33:47Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-04-05T05:24:54Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="IyanuMicheal/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
raibot/wav2vec2-conformer-rel-pos-large-speech-commands | raibot | "2024-04-05T05:30:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T05:29:59Z" | Entry not found |
aicafeart/loras | aicafeart | "2024-04-05T05:42:15Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T05:36:35Z" | Entry not found |
Narednra/Ltinyllama1B | Narednra | "2024-04-05T05:40:29Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"region:us"
] | null | "2024-04-05T05:38:34Z" | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
samad321kk/chavoshi | samad321kk | "2024-04-05T05:40:57Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-05T05:39:01Z" | ---
license: openrail
---
|
jiangyzy/CustomNet | jiangyzy | "2024-04-05T06:40:23Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-04-05T05:44:42Z" | ---
license: apache-2.0
---
|
thusinh1969/LLaMA-2-Instruct-Chat-100k-08MMAR2024-tokenizer | thusinh1969 | "2024-04-05T05:49:37Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T05:49:29Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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samad321kk/chavoshi1 | samad321kk | "2024-04-05T05:56:14Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-05T05:54:38Z" | ---
license: openrail
---
|
sourabhbargi11/Caption_generator_model | sourabhbargi11 | "2024-04-05T06:00:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | "2024-04-05T05:57:42Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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- **Hardware Type:** [More Information Needed]
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|
Aymeric29bzh/TTS-01-33000 | Aymeric29bzh | "2024-04-05T05:59:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T05:59:10Z" | Entry not found |
qamyr/test_010_phi2_2_7b_100steps__finetuned_lora_model | qamyr | "2024-04-05T06:00:59Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T06:00:54Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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|
shidowake/240405-chatntq-ja-7b-v1-co0.8-mist-inst-v0.2-edit-config-gguf | shidowake | "2024-04-05T07:50:51Z" | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T06:02:02Z" | Entry not found |
mattoofahad/ttstflite | mattoofahad | "2024-04-05T06:02:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T06:02:05Z" | Entry not found |
viggneshk/autotrain-2272r-rzuen | viggneshk | "2024-04-05T07:11:54Z" | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | "2024-04-05T06:02:45Z" | Entry not found |
NassimB/Llama-2-13b-hf-platypus_v2 | NassimB | "2024-04-05T07:41:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T06:06:15Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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|
Gargaz/Llama-2-new-GGUF-NEW1 | Gargaz | "2024-04-05T06:10:13Z" | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T06:07:00Z" | Entry not found |
meghas/pheme_twitter_bert | meghas | "2024-04-05T07:46:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-04-05T06:09:01Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
EgorHuf/Luna | EgorHuf | "2024-04-05T06:33:36Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T06:11:15Z" | Entry not found |
sezenkarakus/image-BLIP2-event-model-v1 | sezenkarakus | "2024-04-05T06:14:16Z" | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Salesforce/blip2-opt-2.7b",
"base_model:adapter:Salesforce/blip2-opt-2.7b",
"region:us"
] | null | "2024-04-05T06:14:11Z" | ---
library_name: peft
base_model: Salesforce/blip2-opt-2.7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.1.dev0 |
frankmurray/megir | frankmurray | "2024-04-05T06:19:36Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-05T06:18:56Z" | ---
license: openrail
---
|
vignesh-spericorn/test-case-bart | vignesh-spericorn | "2024-04-05T06:21:39Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-04-05T06:20:46Z" | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: test-case-bart
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. -->
# test-case-bart
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3864
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7423 | 1.0 | 284 | 0.5396 |
| 0.4034 | 2.0 | 568 | 0.4471 |
| 0.3873 | 3.0 | 852 | 0.4062 |
| 0.3427 | 4.0 | 1136 | 0.3816 |
| 0.1896 | 5.0 | 1420 | 0.3715 |
| 0.2805 | 6.0 | 1704 | 0.3745 |
| 0.2514 | 7.0 | 1988 | 0.3745 |
| 0.2622 | 8.0 | 2272 | 0.3818 |
| 0.2816 | 9.0 | 2556 | 0.3817 |
| 0.1705 | 10.0 | 2840 | 0.3864 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Tokenizers 0.15.2
|
chaerene/jkt48.jeane | chaerene | "2024-04-05T06:50:14Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-05T06:21:26Z" | ---
license: openrail
---
|
Darshm1029/bottle_LoRA | Darshm1029 | "2024-04-05T06:26:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T06:26:32Z" | Entry not found |
Bala-123-murugan-123/garbagee-classification | Bala-123-murugan-123 | "2024-04-05T06:32:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-04-05T06:32:08Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ZeZanZiet/mamba_se_text_classification_v1 | ZeZanZiet | "2024-04-05T15:20:35Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T06:33:31Z" | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mamba_se_text_classification_v1
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. -->
# mamba_se_text_classification_v1
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6253
- Accuracy: 0.8317
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1427 | 1.0 | 507 | 0.5768 | 0.7921 |
| 0.2961 | 2.0 | 1014 | 0.8613 | 0.8020 |
| 0.0001 | 3.0 | 1521 | 0.8920 | 0.8218 |
| 0.1608 | 4.0 | 2028 | 1.1532 | 0.8119 |
| 0.0 | 5.0 | 2535 | 1.5611 | 0.8317 |
| 0.0001 | 6.0 | 3042 | 1.5699 | 0.8317 |
| 0.0 | 7.0 | 3549 | 1.5917 | 0.8317 |
| 0.0 | 8.0 | 4056 | 1.6159 | 0.8317 |
| 0.0 | 9.0 | 4563 | 1.6238 | 0.8317 |
| 0.0 | 10.0 | 5070 | 1.6253 | 0.8317 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
G-reen/EXPERIMENT-SFT-m7b2-2-lora | G-reen | "2024-04-11T21:44:23Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-04-05T06:37:10Z" | Check G-reen/EXPERIMENT-SFT-m7b2-2-merged (https://huggingface.co/G-reen/EXPERIMENT-SFT-m7b2-2-merged) for details. |
jaisailaynn/sdx1-db-ashu | jaisailaynn | "2024-04-05T06:43:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T06:43:54Z" | Entry not found |
jaisailaynn/sdx1-db-leonalmessi | jaisailaynn | "2024-04-05T06:44:37Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T06:44:37Z" | Entry not found |
Lieriant/PleatherRVC2 | Lieriant | "2024-04-05T06:53:30Z" | 0 | 0 | null | [
"license:unknown",
"region:us"
] | null | "2024-04-05T06:53:07Z" | ---
license: unknown
---
|
nived2/face-detail | nived2 | "2024-04-05T06:57:09Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T06:55:24Z" | Entry not found |
goosebok/yura1575 | goosebok | "2024-04-05T06:58:57Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-05T06:56:44Z" | ---
license: openrail
---
|
Ponce-01/DFEP-05 | Ponce-01 | "2024-04-05T21:42:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-05T06:56:48Z" | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
HPLT/sft-fpft-cs-pythia-6.9b-deduped | HPLT | "2024-04-05T06:58:03Z" | 0 | 0 | null | [
"generation",
"question answering",
"instruction tuning",
"cs",
"arxiv:2309.08958",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-04-05T06:58:00Z" |
---
language:
- cs
tags:
- generation
- question answering
- instruction tuning
license: cc-by-nc-4.0
---
### Model Description
This HF repository contains base LLMs instruction tuned (SFT) with full-parameter fine-tuning and then used to study whether monolingual or multilingual instruction tuning is more favourable.
* [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main)
* [Paper](https://arxiv.org/abs/2309.08958)
#### Instruction tuning details
* Base model: [pythia-6.9b-deduped](https://huggingface.co/pythia-6.9b-deduped)
* Instruction tuning language: Czech
* Training method: full-parameter fine-tuning.
* Best checkpoint: best cross-entropy on a validation set, trained for 3 epochs.
* Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data).
#### Usage
The model checkpoint should be loaded using `transformers` library.
Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/fpft) for inference and training instructions.
#### Citation
```
@inproceedings{chen-etal-2024-monolingual,
title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}",
author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield",
year="2024",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
}
```
|
m-a-p/CT-LLM-SFT-experiment-ckpts | m-a-p | "2024-04-08T08:32:36Z" | 0 | 0 | null | [
"safetensors",
"arxiv:2404.04167",
"region:us"
] | null | "2024-04-05T06:59:23Z" | # CT-LLM-SFT-experiment-ckpts
[**🌐 Homepage**](https://chinese-tiny-llm.github.io) | [**🤗 MAP-CC**](https://huggingface.co/datasets/m-a-p/MAP-CC) | [**🤗 CHC-Bench**](https://huggingface.co/datasets/m-a-p/CHC-Bench) | [**🤗 CT-LLM**](https://huggingface.co/collections/m-a-p/chinese-tiny-llm-660d0133dff6856f94ce0fc6) | [**📖 arXiv**](https://arxiv.org/abs/2404.04167) | [**GitHub**](https://github.com/Chinese-Tiny-LLM/Chinese-Tiny-LLM)
This warehouse contains all SFT experiment ckpts, which are fine-tuned by different Chinese and English data ratios, as follows:
- zh_105k_en_105k(1:1)
- zh_105k_en_52k(2:1)
- zh_105k_en_26k(4:1)
- zh_105k_en_13k(8:1)
- zh_105k(only Chinese)
- en_105k(only English)
## Uses
Please refer to the usage [CT-LLM-SFT](https://huggingface.co/m-a-p/CT-LLM-SFT)
## Disclaimer
This model, developed for academic purposes, employs rigorously compliance-checked training data to uphold the highest standards of integrity and compliance. Despite our efforts, the inherent complexities of data and the broad spectrum of model applications prevent us from ensuring absolute accuracy or appropriateness of the model outputs in every scenario.
It is essential to highlight that our model and its associated training data are intended solely for scholarly research. We explicitly disclaim any liability for problems that may arise from improper use, interpretation errors, unlawful activities, the dissemination of false information, or any data security issues related to the utilization of our model or its training data.
We strongly encourage users to report any concerns related to data misuse, security breaches, or potential infringement issues directly to us for immediate investigation and resolution.
#### Contact: {`ge.zhang@uwaterloo.ca; duxinrun2000@gmail.com`}
Our commitment to responsible data sharing and the security of our academic tools is paramount. We thank you for your cooperation in maintaining the ethical use of this technology. |
RUXHIR2828/ProjectPat | RUXHIR2828 | "2024-04-05T07:01:57Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-05T07:00:20Z" | ---
license: openrail
---
|
tanumoyghosh10/opt-6.7b-lora-v0 | tanumoyghosh10 | "2024-04-05T07:00:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T07:00:44Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
web30india/LLM-Hindi-Large | web30india | "2024-04-05T07:26:49Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"whisper",
"automatic-speech-recognition",
"en",
"hi",
"license:creativeml-openrail-m",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-04-05T07:00:52Z" | ---
license: creativeml-openrail-m
language:
- en
- hi
pipeline_tag: automatic-speech-recognition
---
---
language:
- hi
license: apache-2.0
tags:
- whisper-event
metrics:
- wer
model-index:
- name: LLM-HINDI-LARGE - Manan Raval
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: hn_in
split: test
metrics:
- type: wer
value: 12.33
name: WER
## Usage
In order to infer a single audio file using this model, the following code snippet can be used:
```python
>>> import torch
>>> from transformers import pipeline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> transcribe = pipeline(task="automatic-speech-recognition", model="web30india/LLM-Hindi-Large", chunk_length_s=30, device=device)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="hi", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
## Acknowledgement
This work was done at [Virtual Height IT Services Pvt. Ltd.] |
Oriroter/Testmodel | Oriroter | "2024-04-05T07:03:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:02:58Z" | Entry not found |
louislu9911/convnextv2-base-1k-224-finetuned-cassava-leaf-disease-finetuned-cassava-leaf-disease | louislu9911 | "2024-04-05T07:06:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:06:01Z" | Entry not found |
ashishp-wiai/vit-base-patch16-224-in21k-finetune-os-fixes | ashishp-wiai | "2024-04-05T08:06:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-04-05T07:09:59Z" | Entry not found |
codewithtej/pix2struct_intern | codewithtej | "2024-04-05T07:11:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:11:43Z" | Entry not found |
nbrc/my_awesome_mind_model | nbrc | "2024-04-05T07:12:19Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:12:19Z" | Entry not found |
Seqath/CherryOMN | Seqath | "2024-04-05T07:15:38Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:13:37Z" | Entry not found |
chaniii/chanmodel | chaniii | "2024-04-05T07:14:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:14:13Z" | Entry not found |
vibhagadhiya/image_generate | vibhagadhiya | "2024-04-05T07:15:43Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:15:43Z" | Entry not found |
ClaireOzzz/specbnw2 | ClaireOzzz | "2024-04-05T07:17:35Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:17:35Z" | Entry not found |
chakkakrishna/tinyLlama1B | chakkakrishna | "2024-04-05T07:44:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"region:us"
] | null | "2024-04-05T07:42:10Z" | ---
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
|
Jinwoo870/Llama2_Finetuned_Metadata_Instruction_Set | Jinwoo870 | "2024-04-05T07:42:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:42:54Z" | Entry not found |
sintecs/SDXL_Loras | sintecs | "2024-05-06T21:38:34Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:43:28Z" | Entry not found |
yongyogn/knime-mistral-test | yongyogn | "2024-04-05T08:29:44Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-04-05T07:44:18Z" | |
zzhhjjj/my_repo | zzhhjjj | "2024-04-05T07:48:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T07:48:26Z" | Entry not found |
yujia23/axolotl-mistral-cn-1e-5-lora | yujia23 | "2024-04-05T07:49:38Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2024-04-05T07:48:37Z" | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: home/yujia/home/CN_Hateful/trained_models/mistral/CN/toxi/1e-5/
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
# - path: mhenrichsen/alpaca_2k_test
# - path: /home/yujia/home/CN_Hateful/train_toxiCN.json
- path: /home/yujia/home/CN_Hateful/train_toxiCN_cn.json
# - path: /home/yujia/home/CN_Hateful/train.json
ds_type: json
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
# output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/toxi/1e-5/
output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/CN/toxi/1e-5/
# output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/cold/3e-5/
adapter: lora
lora_model_dir:
sequence_len: 256
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# home/yujia/home/CN_Hateful/trained_models/mistral/CN/toxi/1e-5/
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5188 | 0.01 | 1 | 2.5282 |
| 1.0047 | 0.25 | 17 | 0.8628 |
| 0.086 | 0.51 | 34 | 0.0862 |
| 0.0732 | 0.76 | 51 | 0.0753 |
| 0.0719 | 1.02 | 68 | 0.0753 |
| 0.0722 | 1.25 | 85 | 0.0680 |
| 0.0676 | 1.51 | 102 | 0.0666 |
| 0.068 | 1.76 | 119 | 0.0648 |
| 0.0562 | 2.02 | 136 | 0.0637 |
| 0.0674 | 2.25 | 153 | 0.0628 |
| 0.0611 | 2.51 | 170 | 0.0625 |
| 0.0536 | 2.76 | 187 | 0.0627 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0 |
Mayank082000/job_search_finetune | Mayank082000 | "2024-04-05T07:59:35Z" | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"pytorch",
"llama",
"job-search",
"skill-development",
"foreign-counseling",
"text-generation",
"en",
"hi",
"pa",
"dataset:Mayank082000/Multilingual_Sentences_with_Sentences",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-04-05T07:49:53Z" | ---
license: apache-2.0
datasets:
- Mayank082000/Multilingual_Sentences_with_Sentences
language:
- en
- hi
- pa
library_name: adapter-transformers
pipeline_tag: text-generation
tags:
- job-search
- skill-development
- foreign-counseling
---
# Fine-Tuned Llama 2 model for Multilingual Text Generation
This repository contains adapters for the `adapter-transformers` library aimed at enabling multilingual text generation. It leverages datasets such as `siddeo99/sidtestfiverrmulti` and supports multiple languages including English, Hindi, and Punjabi.
## Installation
To install the necessary library, you can use pip:
```python
!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7
!pip install pyarrow
```
# This Section is for GPU enabled devices(For cpu code is below,skip the below code for cpu)
## Import libraries
```python
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
)
from peft import LoraConfig, PeftModel
```
## Configuration Parameters
```python
# The model that you want to train from the Hugging Face hub
model_name = "siddeo99/job_search_category"
################################################################################
# QLoRA parameters
################################################################################
# LoRA attention dimension
lora_r = 64
# Alpha parameter for LoRA scaling
lora_alpha = 16
# Dropout probability for LoRA layers
lora_dropout = 0.1
################################################################################
# bitsandbytes parameters
################################################################################
# Activate 4-bit precision base model loading
use_4bit = True
# Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"
# Quantization type (fp4 or nf4)
bnb_4bit_quant_type = "nf4"
# Activate nested quantization for 4-bit base models (double quantization)
use_nested_quant = False
device_map = {"": 0}
```
### Loading Configuration
```python
# Load tokenizer and model with QLoRA configuration
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
bnb_config = BitsAndBytesConfig(
load_in_4bit=use_4bit,
bnb_4bit_quant_type=bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=use_nested_quant,
)
# Check GPU compatibility with bfloat16
if compute_dtype == torch.float16 and use_4bit:
major, _ = torch.cuda.get_device_capability()
if major >= 8:
print("=" * 80)
print("Your GPU supports bfloat16: accelerate training with bf16=True")
print("=" * 80)
# Load base model
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
device_map=device_map
)
model.config.use_cache = False
model.config.pretraining_tp = 1
# Load LLaMA tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right" # Fix weird overflow issue with fp16 training
# Load LoRA configuration
peft_config = LoraConfig(
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
r=lora_r,
bias="none",
task_type="CAUSAL_LM",
)
```
## Text_generation
```python
prompt = "What is a large language model?"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(f"<s>[INST] {prompt} [/INST]")
print(result[0]['generated_text'])
```
# This Section is for CPU only(Slower than GPU)
## Import libraries
```python
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
pipeline,
)
from peft import LoraConfig
```
## Run the model on CPU
```python
model = AutoModelForCausalLM.from_pretrained(model_name)
model.config.use_cache = False
model.config.pretraining_tp = 1
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right" # Fix weird overflow issue with fp16 training
# Load LoRA configuration
peft_config = LoraConfig(
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
r=lora_r,
bias="none",
task_type="CAUSAL_LM",
)
# Run text generation pipeline with our next model
prompt = "भारत से ऑस्ट्रेलिया में एक कार्य वीजा के लिए आवेदन करने के लिए क्या आवश्यकताएं हैं?"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(f"<s>[INST] {prompt} [/INST]")
print(result[0]['generated_text'])
```
|
AleKOr10/ChatBotTest | AleKOr10 | "2024-04-05T07:50:13Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-04-05T07:50:13Z" | ---
license: apache-2.0
---
|
meghas/pheme_reddit_bert | meghas | "2024-04-05T08:21:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-04-05T08:06:23Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Drahokma/mistral_7b_cz | Drahokma | "2024-04-05T08:07:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T08:07:02Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
SimSaas/txt2KG_onT5_large | SimSaas | "2024-04-05T08:28:35Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-04-05T08:20:50Z" | Entry not found |
LivevreXH/outputs_mistral_finetuned_items_livres | LivevreXH | "2024-04-05T08:25:37Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | "2024-04-05T08:23:11Z" | Entry not found |
ITG/PlatVR-sft | ITG | "2024-04-17T09:12:53Z" | 0 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"chatml",
"synthetic data",
"finetune",
"conversational",
"en",
"dataset:ITG/PlatVR-sft",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-05T08:24:42Z" | ---
library_name: transformers
tags:
- chatml
- mistral
- synthetic data
- finetune
license: apache-2.0
language:
- en
datasets:
- ITG/PlatVR-sft
---
# PlatVR-sft - Hermes 2 Pro - Mistral 7B
**Image generated by [copilot designer](https://copilot.microsoft.com/images/create).
## Model Details
This model is part of the EVIDENT framework, designed to enhance the creative process in generating background images for virtual reality sets. It interprets user instructions to generate and modify prompts for text-to-image models. This is the SFT version of the model, you can check the additional [DPO](https://huggingface.co/ITG/PlatVR-dpo) and [KTO](https://huggingface.co/ITG/PlatVR-kto) versions as well.
The [demo](https://youtu.be/NKevZLvaGaA) integrates a diffusion model to test prompt-image alignment, and mechanisms for user feedback and iterative prompt refinement, aiming to enhance user creativity and satisfaction.
The instruction categories are:
- **Addition**: Involves the inclusion of new elements or features.
- **Condensation**: Consists in the summarization of the description.
- **Modification**: Alters specific aspects of the description to change the scene.
- **Rearrangement**: Reordering of sentences within the descriptions.
- **Removal**: Elimination of specific details in the description.
- **Rephrase**: Rewriting parts of the description.
- **Scene Change**: Overall description context switch.
The output language of the model is English, but other languages can be used as input (quality depends of the quantity of tokens used on the pre-training phase for the given language).
### Model Description
Developed as part of the EVIDENT framework, this model leverages a large language model fine-tuned on synthetic data to generate and refine text prompts for creating virtual reality backgrounds.
- **Developed by:** [ITG](https://itg.es/en)
- **Model type:** Text-to-Text for Image Prompt Generation
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** [Hermes 2 Pro](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
### Model Sources [optional]
- **Demo video:** [EVIDENT Demo](https://youtu.be/NKevZLvaGaA)
## Uses
### Prompt Format
It uses ChatML as the prompt format.
Here is the original prompt that was used in the fine-tuning process:
```
<|im_start|>system
As an AI assistant dedicated to refining and adjusting prompts for image generation, your primary task involves interpreting and applying user-specific modifications to enhance the original prompt. Your modifications may include:
Additions: Introducing new elements or features to enrich the context, such as weather conditions or additional objects, aiming to enable the AI to interpret and generate more complex and detailed prompts.
Condensations: Summarizing longer descriptions into more concise forms without losing essential meaning, aiming at generating relevant images from shorter prompts.
Modifications: Altering specific details within the descriptions to change the scene.
Rearrangement: Changing the order of sentences or phrases to test the AI's context understanding and narrative flow.
Removal: Eliminating redundant or non-essential information to clarify the prompt.
Rephrase: Rewriting sentences or phrases to convey the same meaning using different words or structures.
Scene Change: Altering the setting or background to create a completely new context.
Your goal is to skillfully adapt the new prompt in line with the user's precise directives, ensuring the essence of their vision is captured—all while maintaining responses exclusively in English, regardless of the original prompt's language.
It is crucial that the revised prompt strictly adheres to the user's intent, incorporating their specified changes with precision. Additionally, ensure the new prompt does not suggest alterations that imply dynamics or qualities unsuitable for visual representation, such as smell, scent, or sound, which cannot be captured in an image.
Your role is to ensure the prompt is optimized for image generation, clearly reflecting the user's adjustments while respecting these guidelines, with a consistent use of English for all responses. The focus should be on creating a vivid, static depiction that stays true to the conceptual and aesthetic requirements set forth by the user, communicated effectively in English.
Remember, the new prompt must not contain references to smell, scent, or sound, which cannot be captured in an image.
Below is the original prompt that you will meticulously refine:
{original_prompt}<|im_end|>
<|im_start|>user
{instruction}<|im_end|>
<|im_start|>assistant
```
### Notes
- **{original_prompt}**: Is the previous prompt that the system returned to the user.
- **{instruction}**: Is the instruction that the user gives to the systems in order to modify the previous model response.
- **Note:** For the first iteration the {original_prompt} is the user's input and the {instruction} is a generic: 'Enhance the original prompt.'.
### Direct Use
This model is designed for direct use in generating and refining text prompts for text-to-image generation, specifically tailored for creating virtual reality environments and sets.
Load model:
```bash
docker run --gpus all --rm --shm-size 1g -p 8080:80 -v ~/huggingface/hub/:/data ghcr.io/huggingface/text-generation-inference:latest --model-id ITG/PlatVR-sft
```
Python client:
```python
from huggingface_hub import InferenceClient
client = InferenceClient(model="http://localhost:8080")
template = ("""<|im_start|>system
As an AI assistant dedicated to refining and adjusting prompts for image generation, your primary task involves interpreting and applying user-specific modifications to enhance the original prompt. Your modifications may include:
Additions: Introducing new elements or features to enrich the context, such as weather conditions or additional objects, aiming to enable the AI to interpret and generate more complex and detailed prompts.
Condensations: Summarizing longer descriptions into more concise forms without losing essential meaning, aiming at generating relevant images from shorter prompts.
Modifications: Altering specific details within the descriptions to change the scene.
Rearrangement: Changing the order of sentences or phrases to test the AI's context understanding and narrative flow.
Removal: Eliminating redundant or non-essential information to clarify the prompt.
Rephrase: Rewriting sentences or phrases to convey the same meaning using different words or structures.
Scene Change: Altering the setting or background to create a completely new context.
Your goal is to skillfully adapt the new prompt in line with the user's precise directives, ensuring the essence of their vision is captured—all while maintaining responses exclusively in English, regardless of the original prompt's language.
It is crucial that the revised prompt strictly adheres to the user's intent, incorporating their specified changes with precision. Additionally, ensure the new prompt does not suggest alterations that imply dynamics or qualities unsuitable for visual representation, such as smell, scent, or sound, which cannot be captured in an image.
Your role is to ensure the prompt is optimized for image generation, clearly reflecting the user's adjustments while respecting these guidelines, with a consistent use of English for all responses. The focus should be on creating a vivid, static depiction that stays true to the conceptual and aesthetic requirements set forth by the user, communicated effectively in English.
Remember, the new prompt must not contain references to smell, scent, or sound, which cannot be captured in an image.
Below is the original prompt that you will meticulously refine:
{original_prompt}<|im_end|>
<|im_start|>user
{instruction}<|im_end|>
<|im_start|>assistant
""")
instruction = "Add details to the original prompt in a single sentence."
original_prompt = "Una montaña"
input_prompt = template.format(original_prompt=original_prompt, instruction=instruction)
print(client.text_generation(prompt=input_prompt, max_new_tokens=512))
```
### Downstream Use
The model can be fine-tuned or integrated into larger ecosystems or applications that require dynamic, user-driven creation of prompts for visual content.
### Out-of-Scope Use
The model is not intended for uses beyond text prompt generation for visual content.
## Bias, Risks, and Limitations
The model may inherit biases from its training data or exhibit limitations in understanding complex user instructions. Potential risks include generating inappropriate or unintended content based on ambiguous prompts.
## Evaluation metrics
Please go to [the KTO version of the model](https://huggingface.co/ITG/PlatVR-kto#evaluation-metrics) for the full report.
### Recommendations
Users should be aware of the model's limitations and biases. It is recommended to monitor the outputs for unintended content and refine prompts accordingly.
### Demo example
## Request Demo
- Contact Email: huggingface@itg.es
## Model Card Contact
- Contact Email: huggingface@itg.es |
LivevreXH/mistral_finetuned_items_livres | LivevreXH | "2024-04-05T08:28:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T08:27:43Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ShaneOD2611/detr-resnet-101_finetuned_ClashOfClans | ShaneOD2611 | "2024-04-05T08:30:08Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-05T08:30:07Z" | Entry not found |
adamo1139/Yi-34B-200K-AEZAKMI-RAW-TOXIC-XLCTX-2303-2.6bpw-exl2 | adamo1139 | "2024-05-27T21:35:41Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-04-05T08:31:28Z" | ---
license: apache-2.0
---
|
ericwky/llm_model_basel | ericwky | "2024-04-05T08:40:47Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-05T08:32:37Z" | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |