modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 00:42:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 553
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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|---|---|---|---|---|---|---|---|---|---|
Srikanthr2/whisper-medium-sanskasr-37000-V1
|
Srikanthr2
| 2023-07-06T12:10:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"sa",
"dataset:addy88/sanskrit-asr-84",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-21T06:37:43Z |
---
language:
- sa
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- addy88/sanskrit-asr-84
model-index:
- name: whisper-medium-sanskasr-37000-V1-upload
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-sanskasr-37000-V1-upload
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the addy88/sanskrit-asr-84 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
anujsahani01/finetuned_AI4Bharat_en_mr
|
anujsahani01
| 2023-07-06T11:55:30Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-06T01:54:41Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: finetuned_AI4Bharat_en_mr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_AI4Bharat_en_mr
This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 8000
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
HasinMDG/UVC-Deberta-baseline
|
HasinMDG
| 2023-07-06T11:55:28Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"deberta-v2",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-07-06T11:55:11Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/UVC-Deberta-baseline
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("HasinMDG/UVC-Deberta-baseline")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
tom192180/distilbert-base-uncased-finetuned-squad
|
tom192180
| 2023-07-06T11:49:51Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-06T09:37:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 57 | 3.5390 |
| No log | 2.0 | 114 | 3.2458 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Binaryy/blender-bot-distill-finetuned
|
Binaryy
| 2023-07-06T11:36:26Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"blenderbot",
"text2text-generation",
"code",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-17T16:39:36Z |
---
license: apache-2.0
language:
- en
pipeline_tag: conversational
tags:
- code
---
|
papahawk/gpt2-1.5b
|
papahawk
| 2023-07-06T11:19:11Z | 206 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"onnx",
"safetensors",
"gpt2",
"text-generation",
"pyTtorch",
"tensorflow",
"en",
"dataset:gpt-2-output-dataset",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-05T22:17:24Z |
---
language:
- en
tags:
- text-generation
- pyTtorch
- tensorflow
- transformers
datasets:
- gpt-2-output-dataset
license: mit
---
<h1 style='text-align: center '>GPT2-1.5b LLM</h1>
<h2 style='text-align: center '><em>Fork of OpenAI/GPT2-1.5b</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://alt-web.xyz/images/rainbow.png" alt="Rainbow Solutions" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# gpt2-1.5b
Code and models from the paper ["Language Models are Unsupervised Multitask Learners"](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf).
You can read about GPT-2 and its staged release in our [original blog post](https://blog.openai.com/better-language-models/), [6 month follow-up post](https://openai.com/blog/gpt-2-6-month-follow-up/), and [final post](https://www.openai.com/blog/gpt-2-1-5b-release/).
We have also [released a dataset](https://github.com/openai/gpt-2-output-dataset) for researchers to study their behaviors.
<sup>*</sup> *Note that our original parameter counts were wrong due to an error (in our previous blog posts and paper). Thus you may have seen small referred to as 117M and medium referred to as 345M.*
## Usage
This repository is meant to be a starting point for researchers and engineers to experiment with GPT-2.
For basic information, see our [model card](./model_card.md).
### Some caveats
- GPT-2 models' robustness and worst case behaviors are not well-understood. As with any machine-learned model, carefully evaluate GPT-2 for your use case, especially if used without fine-tuning or in safety-critical applications where reliability is important.
- The dataset our GPT-2 models were trained on contains many texts with [biases](https://twitter.com/TomerUllman/status/1101485289720242177) and factual inaccuracies, and thus GPT-2 models are likely to be biased and inaccurate as well.
- To avoid having samples mistaken as human-written, we recommend clearly labeling samples as synthetic before wide dissemination. Our models are often incoherent or inaccurate in subtle ways, which takes more than a quick read for a human to notice.
### Work with us
Please [let us know](mailto:languagequestions@openai.com) if you’re doing interesting research with or working on applications of GPT-2! We’re especially interested in hearing from and potentially working with those who are studying
- Potential malicious use cases and defenses against them (e.g. the detectability of synthetic text)
- The extent of problematic content (e.g. bias) being baked into the models and effective mitigations
## Development
See [DEVELOPERS.md](./DEVELOPERS.md)
## Contributors
See [CONTRIBUTORS.md](./CONTRIBUTORS.md)
## Citation
Please use the following bibtex entry:
```
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
## Future work
We may release code for evaluating the models on various benchmarks.
We are still considering release of the larger models.
## License
[Modified MIT](./LICENSE)
|
rohanbalkondekar/QnA-with-context
|
rohanbalkondekar
| 2023-07-06T11:13:08Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-06T11:06:40Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.30.1
pip install accelerate==0.20.3
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="BeRohan/QnA-with-context",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"BeRohan/QnA-with-context",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"BeRohan/QnA-with-context",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "BeRohan/QnA-with-context" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=BeRohan/QnA-with-context --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
jayavibhav/t5-small-finetuned-xsum
|
jayavibhav
| 2023-07-06T11:06:12Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-06T07:13:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.2871
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4784
- Rouge1: 28.2871
- Rouge2: 7.7216
- Rougel: 22.2416
- Rougelsum: 22.237
- Gen Len: 18.8267
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7171 | 1.0 | 12753 | 2.4784 | 28.2871 | 7.7216 | 22.2416 | 22.237 | 18.8267 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
irfan62622/Taxi-v3
|
irfan62622
| 2023-07-06T11:01:51Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T11:01:11Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="irfan62622/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"])
```
|
Zain6699/intent-classifier-call_to_action
|
Zain6699
| 2023-07-06T11:00:48Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-06T10:59:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: intent-classifier-call_to_action
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. -->
# intent-classifier-call_to_action
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0810
- Accuracy: 0.9875
- F1: 0.9639
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
cerindam30/tugas_akhir
|
cerindam30
| 2023-07-06T10:56:16Z | 30 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-02T08:20:21Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: tugas_akhir
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. -->
# tugas_akhir
This model is a fine-tuned version of [indobenchmark/indobart-v2](https://huggingface.co/indobenchmark/indobart-v2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- label_smoothing_factor: 0.1
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Zain6699/intent-classifier-flattery
|
Zain6699
| 2023-07-06T10:56:16Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-06T10:54:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: intent-classifier-flattery
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. -->
# intent-classifier-flattery
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0434
- Accuracy: 0.9917
- F1: 0.9747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
nikolamilosevic/distil_bert_uncased-finetuned-relations
|
nikolamilosevic
| 2023-07-06T10:55:05Z | 152 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-14T11:08:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
model-index:
- name: distil_bert_uncased-finetuned-relations
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. -->
# distil_bert_uncased-finetuned-relations
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4191
- Accuracy: 0.8866
- Prec: 0.8771
- Recall: 0.8866
- F1: 0.8808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Prec | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|
| 1.1823 | 1.0 | 232 | 0.5940 | 0.8413 | 0.8273 | 0.8413 | 0.8224 |
| 0.4591 | 2.0 | 464 | 0.4600 | 0.8607 | 0.8539 | 0.8607 | 0.8555 |
| 0.3106 | 3.0 | 696 | 0.4160 | 0.8812 | 0.8763 | 0.8812 | 0.8785 |
| 0.246 | 4.0 | 928 | 0.4113 | 0.8834 | 0.8766 | 0.8834 | 0.8796 |
| 0.2013 | 5.0 | 1160 | 0.4191 | 0.8866 | 0.8771 | 0.8866 | 0.8808 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.13.0.dev20220614
- Datasets 2.2.2
- Tokenizers 0.11.6
|
linlinlin/peft-fine-tuning
|
linlinlin
| 2023-07-06T10:54:57Z | 0 | 0 | null |
[
"pytorch",
"tensorboard",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-07-06T10:31:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: peft-fine-tuning
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. -->
# peft-fine-tuning
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50
### Training results
### Framework versions
- Transformers 4.27.2
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
linlinlin/full-fine-tuning
|
linlinlin
| 2023-07-06T10:53:14Z | 180 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-06T10:22:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: full-fine-tuning
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. -->
# full-fine-tuning
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50
### Training results
### Framework versions
- Transformers 4.27.2
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
mpetrikov/dqn-unit3-SpaceInvadersNoFrameskip-v4
|
mpetrikov
| 2023-07-06T10:45:49Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T10:45:14Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 568.00 +/- 121.35
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mpetrikov -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mpetrikov -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mpetrikov
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Hardi13/Revier
|
Hardi13
| 2023-07-06T10:44:53Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-07-06T10:44:53Z |
---
license: bigscience-openrail-m
---
|
isaachong127/gpt2_chinese_with_personal_qqchat_data
|
isaachong127
| 2023-07-06T10:43:00Z | 131 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-05T20:01:00Z |
---
license: apache-2.0
language:
- zh
library_name: transformers
---
# intro
1. 1.38G的中文私人QQ群聊天记录语料
2. 1400万个tokens
3. 一张3060显卡训练17小时
个人首次尝试训练人工智能模型，学习训练GPT2模型，仅供参考。
交互结果仅供参考，本模型不对结果的合法性和合理性做保证，
# Link
[从头开始训练因果语言模型](https://huggingface.co/course/zh-CN/chapter7/6?fw=pt)
# infer code
```python
from transformers import GPT2LMHeadModel, AutoTokenizer
model_name_or_path = "isaachong127/gpt2_chinese_with_personal_qqchat_data"#"checkpoint-16000"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained(model_name_or_path, pad_token_id=tokenizer.eos_token_id)
```
```python
txt = """\
今天
"""
# encode context the generation is conditioned on
input_ids = tokenizer.encode(txt, return_tensors='pt')
# set no_repeat_ngram_size to 2
beam_output = model.generate(
input_ids,
max_length=100,
num_beams=5,
no_repeat_ngram_size=2,
early_stopping=True
)
print("Output:\n" + 50 * '-')
print(tokenizer.decode(beam_output[0], skip_special_tokens=True))
```
```bash
Output:
----------------------------------------------------------------------------------------------------
今天 已 经 是 你 的 第 667 次 签 到 啦 ～ 纱 雾 酱 对 乃 的 好 感 度 [ + 10 ] 2021 年 ， 要 加 油 哦 ~ ','签 到 ','@ \ u202e
```
|
bofenghuang/asr-wav2vec2-ctc-french
|
bofenghuang
| 2023-07-06T10:34:26Z | 429 | 12 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"CTC",
"Wav2vec2",
"fr",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:facebook/multilingual_librispeech",
"dataset:facebook/voxpopuli",
"dataset:gigant/african_accented_french",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-25T15:33:14Z |
---
license: apache-2.0
language: fr
library_name: transformers
thumbnail: null
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
- CTC
- Wav2vec2
datasets:
- common_voice
- mozilla-foundation/common_voice_11_0
- facebook/multilingual_librispeech
- facebook/voxpopuli
- gigant/african_accented_french
metrics:
- wer
model-index:
- name: Fine-tuned wav2vec2-FR-7K-large model for ASR in French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
args: fr
metrics:
- name: Test WER
type: wer
value: 11.44
- name: Test WER (+LM)
type: wer
value: 9.66
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech (MLS)
type: facebook/multilingual_librispeech
args: french
metrics:
- name: Test WER
type: wer
value: 5.93
- name: Test WER (+LM)
type: wer
value: 5.13
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: VoxPopuli
type: facebook/voxpopuli
args: fr
metrics:
- name: Test WER
type: wer
value: 9.33
- name: Test WER (+LM)
type: wer
value: 8.51
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: African Accented French
type: gigant/african_accented_french
args: fr
metrics:
- name: Test WER
type: wer
value: 16.22
- name: Test WER (+LM)
type: wer
value: 15.39
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 16.56
- name: Test WER (+LM)
type: wer
value: 12.96
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Fleurs
type: google/fleurs
args: fr_fr
metrics:
- name: Test WER
type: wer
value: 10.10
- name: Test WER (+LM)
type: wer
value: 8.84
---
# Fine-tuned wav2vec2-FR-7K-large model for ASR in French
<style>
img {
display: inline;
}
</style>
This model is a fine-tuned version of [LeBenchmark/wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large), trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and validation splits of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Multilingual TEDx](http://www.openslr.org/100), [MediaSpeech](https://www.openslr.org/108), and [African Accented French](https://huggingface.co/datasets/gigant/african_accented_french). When using the model make sure that your speech input is also sampled at 16Khz.
## Usage
1. To use on a local audio file with the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2ProcessorWithLM
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = AutoModelForCTC.from_pretrained("bhuang/asr-wav2vec2-french").to(device)
processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained("bhuang/asr-wav2vec2-french")
model_sample_rate = processor_with_lm.feature_extractor.sampling_rate
wav_path = "example.wav" # path to your audio file
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != model_sample_rate:
resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate)
waveform = resampler(waveform)
# normalize
input_dict = processor_with_lm(waveform, sampling_rate=model_sample_rate, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to(device)).logits
predicted_sentence = processor_with_lm.batch_decode(logits.cpu().numpy()).text[0]
```
2. To use on a local audio file without the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2Processor
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = AutoModelForCTC.from_pretrained("bhuang/asr-wav2vec2-french").to(device)
processor = Wav2Vec2Processor.from_pretrained("bhuang/asr-wav2vec2-french")
model_sample_rate = processor.feature_extractor.sampling_rate
wav_path = "example.wav" # path to your audio file
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != model_sample_rate:
resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate)
waveform = resampler(waveform)
# normalize
input_dict = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to(device)).logits
# decode
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentence = processor.batch_decode(predicted_ids)[0]
```
## Evaluation
1. To evaluate on `mozilla-foundation/common_voice_11_0`
```bash
python eval.py \
--model_id "bhuang/asr-wav2vec2-french" \
--dataset "mozilla-foundation/common_voice_11_0" \
--config "fr" \
--split "test" \
--log_outputs \
--outdir "outputs/results_mozilla-foundatio_common_voice_11_0_with_lm"
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py \
--model_id "bhuang/asr-wav2vec2-french" \
--dataset "speech-recognition-community-v2/dev_data" \
--config "fr" \
--split "validation" \
--chunk_length_s 30.0 \
--stride_length_s 5.0 \
--log_outputs \
--outdir "outputs/results_speech-recognition-community-v2_dev_data_with_lm"
```
|
Zain6699/intent-classifier
|
Zain6699
| 2023-07-06T10:32:41Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-06T10:22:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: intent-classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# intent-classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0590
- Accuracy: 0.9854
- F1: 0.9586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
AAOBA/RND-PyamidsRND
|
AAOBA
| 2023-07-06T10:28:36Z | 17 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-07-06T10:27:59Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: chikoto/RND-PyamidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
soduhh/marian-finetuned-kde4-en-to-fr
|
soduhh
| 2023-07-06T10:26:33Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-05T14:32:51Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: soduhh/marian-finetuned-kde4-en-to-fr
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# soduhh/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6854
- Validation Loss: 0.8044
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0627 | 0.8795 | 0 |
| 0.7968 | 0.8213 | 1 |
| 0.6854 | 0.8044 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Tiru8055/rl_course_vizdoom_health_gathering_supreme
|
Tiru8055
| 2023-07-06T10:24:27Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T10:24:20Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.50 +/- 5.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Tiru8055/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
thirupathibandam/autotrain-phanik-gpt-neo-125m-self-72606138970
|
thirupathibandam
| 2023-07-06T10:01:36Z | 0 | 0 | null |
[
"autotrain",
"text-generation",
"dataset:thirupathibandam/autotrain-data-phanik-gpt-neo-125m-self",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-06T10:00:49Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
datasets:
- thirupathibandam/autotrain-data-phanik-gpt-neo-125m-self
co2_eq_emissions:
emissions: 0.03549660564532989
---
# Model Trained Using AutoTrain
- Problem type: Text Generation
- CO2 Emissions (in grams): 0.0355
## Validation Metrics
loss: 1.8581730127334595
|
Sourabh2/speecht5_finetuned_model
|
Sourabh2
| 2023-07-06T09:58:19Z | 73 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-07-06T07:36:01Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_model
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4585
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5238 | 4.3 | 1000 | 0.4791 |
| 0.4994 | 8.61 | 2000 | 0.4679 |
| 0.4914 | 12.91 | 3000 | 0.4599 |
| 0.4869 | 17.21 | 4000 | 0.4585 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
blanchefort/rubert-base-cased-sentiment-mokoron
|
blanchefort
| 2023-07-06T09:56:44Z | 129 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"sentiment",
"ru",
"dataset:RuTweetCorp",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- RuTweetCorp
---
# RuBERT for Sentiment Analysis of Tweets
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuTweetCorp](https://study.mokoron.com/).
## Labels
0: POSITIVE
1: NEGATIVE
## How to use
```python
import torch
from transformers import AutoModelForSequenceClassification
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained('blanchefort/rubert-base-cased-sentiment-mokoron')
model = AutoModelForSequenceClassification.from_pretrained('blanchefort/rubert-base-cased-sentiment-mokoron', return_dict=True)
@torch.no_grad()
def predict(text):
inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**inputs)
predicted = torch.nn.functional.softmax(outputs.logits, dim=1)
predicted = torch.argmax(predicted, dim=1).numpy()
return predicted
```
## Dataset used for model training
**[RuTweetCorp](https://study.mokoron.com/)**
> Рубцова Ю. Автоматическое построение и анализ корпуса коротких текстов (постов микроблогов) для задачи разработки и тренировки тонового классификатора // Инженерия знаний и технологии семантического веба. – 2012. – Т. 1. – С. 109-116.
|
ketong3906/opus-mt-en-zh-finetuned-eng-to-chn
|
ketong3906
| 2023-07-06T09:53:13Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-06T09:50:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: opus-mt-en-zh-finetuned-eng-to-chn
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-zh-finetuned-eng-to-chn
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 1 | 6.2769 | 0.8101 | 73.625 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ddmlproject/cassianatuzzi
|
ddmlproject
| 2023-07-06T09:48:26Z | 30 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-06T09:44:16Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### cassianatuzzi Dreambooth model trained by ddmlproject with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
.jpg)
.jpg)
.jpg)
.jpeg)
.jpg)
.jpg)
.jpg)
.jpeg)
.jpg)
.jpg)
.jpg)
|
arham061/codeparrot-ds
|
arham061
| 2023-07-06T09:47:41Z | 127 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-06T09:36:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
NasimB/gpt2-concat-cbt-rarity-all-7k-p8k
|
NasimB
| 2023-07-06T09:41:44Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-06T07:38:30Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-cbt-rarity-all-7k-p8k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-concat-cbt-rarity-all-7k-p8k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7249 | 0.29 | 500 | 5.6400 |
| 5.3729 | 0.59 | 1000 | 5.2003 |
| 5.0283 | 0.88 | 1500 | 4.9502 |
| 4.7537 | 1.17 | 2000 | 4.8035 |
| 4.5903 | 1.47 | 2500 | 4.6765 |
| 4.4832 | 1.76 | 3000 | 4.5717 |
| 4.3484 | 2.05 | 3500 | 4.4930 |
| 4.1512 | 2.35 | 4000 | 4.4467 |
| 4.1329 | 2.64 | 4500 | 4.3805 |
| 4.091 | 2.93 | 5000 | 4.3309 |
| 3.8799 | 3.23 | 5500 | 4.3273 |
| 3.8248 | 3.52 | 6000 | 4.2923 |
| 3.8074 | 3.81 | 6500 | 4.2605 |
| 3.6914 | 4.11 | 7000 | 4.2581 |
| 3.534 | 4.4 | 7500 | 4.2538 |
| 3.5261 | 4.69 | 8000 | 4.2382 |
| 3.5255 | 4.99 | 8500 | 4.2256 |
| 3.351 | 5.28 | 9000 | 4.2383 |
| 3.3357 | 5.57 | 9500 | 4.2375 |
| 3.3375 | 5.87 | 10000 | 4.2364 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
nolanaatama/mrdcrvcv2400pchscrckdfl
|
nolanaatama
| 2023-07-06T09:40:59Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-06T09:37:34Z |
---
license: creativeml-openrail-m
---
|
RogerB/KinyaBERT-small-finetuned-kintweetsB
|
RogerB
| 2023-07-06T09:33:58Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-06T09:26:55Z |
---
tags:
- generated_from_trainer
model-index:
- name: KinyaBERT-small-finetuned-kintweetsB
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. -->
# KinyaBERT-small-finetuned-kintweetsB
This model is a fine-tuned version of [jean-paul/KinyaBERT-small](https://huggingface.co/jean-paul/KinyaBERT-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.3312 | 1.0 | 900 | 3.9289 |
| 4.0017 | 2.0 | 1800 | 3.8163 |
| 3.8861 | 3.0 | 2700 | 3.7473 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
squeeze-ai-lab/sq-opt-13b-w4-s0
|
squeeze-ai-lab
| 2023-07-06T09:29:03Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"arxiv:2205.01068",
"region:us"
] | null | 2023-07-06T08:38:38Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
4-bit quantized OPT 13B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
* **Base Model:** [OPT 13B](https://arxiv.org/abs/2205.01068)
* **Bitwidth:** 4-bit
* **Sparsity Level:** 0% (dense-only)
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
irfan62622/q-FrozenLake-v1-4x4-noSlippery
|
irfan62622
| 2023-07-06T09:29:01Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T09:28:58Z |
---
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="irfan62622/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"])
```
|
squeeze-ai-lab/sq-opt-13b-w3-s0
|
squeeze-ai-lab
| 2023-07-06T09:25:37Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"arxiv:2205.01068",
"region:us"
] | null | 2023-07-06T08:38:24Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
3-bit quantized OPT 13B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
* **Base Model:** [OPT 13B](https://arxiv.org/abs/2205.01068)
* **Bitwidth:** 3-bit
* **Sparsity Level:** 0% (dense-only)
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
ireneli1024/bigbird-pegasus-large-pubmed-plos-finetuned
|
ireneli1024
| 2023-07-06T09:18:37Z | 88 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"text-generation-inference",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-05T05:58:53Z |
---
license: other
language:
- en
metrics:
- rouge
tags:
- text-generation-inference
---
This is the finetuned model based on the [google/bigbird-pegasus-large-pubmed](https://huggingface.co/google/bigbird-pegasus-large-pubmed) model.
The data is from BioLaySumm 2023 [shared task 1](https://biolaysumm.org/#data).
|
HilbertS/rl_course_vizdoom_health_gathering_supreme
|
HilbertS
| 2023-07-06T09:16:47Z | 2 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-04T15:06:01Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.39 +/- 5.13
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r HilbertS/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Sekiraw/space_invaders
|
Sekiraw
| 2023-07-06T09:16:19Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-05T12:58:30Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 251.50 +/- 28.46
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Sekiraw -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Sekiraw -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Sekiraw
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 200000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
squeeze-ai-lab/sq-opt-6.7b-w4-s0
|
squeeze-ai-lab
| 2023-07-06T09:11:07Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"arxiv:2205.01068",
"region:us"
] | null | 2023-07-06T08:28:51Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
4-bit quantized OPT 6.7B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
* **Base Model:** [OPT 6.7B](https://arxiv.org/abs/2205.01068)
* **Bitwidth:** 4-bit
* **Sparsity Level:** 0% (dense-only)
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
NasimB/gpt2-concat-aochildes-length-16k-rarity-all-4k-1p2k
|
NasimB
| 2023-07-06T09:01:21Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-06T06:55:39Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-aochildes-length-16k-rarity-all-4k-1p2k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-concat-aochildes-length-16k-rarity-all-4k-1p2k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1849
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7383 | 0.3 | 500 | 5.6385 |
| 5.3727 | 0.59 | 1000 | 5.1984 |
| 5.0316 | 0.89 | 1500 | 4.9474 |
| 4.7489 | 1.18 | 2000 | 4.7974 |
| 4.5919 | 1.48 | 2500 | 4.6733 |
| 4.481 | 1.77 | 3000 | 4.5743 |
| 4.3448 | 2.07 | 3500 | 4.5035 |
| 4.1586 | 2.36 | 4000 | 4.4505 |
| 4.131 | 2.66 | 4500 | 4.3894 |
| 4.0922 | 2.95 | 5000 | 4.3352 |
| 3.8662 | 3.25 | 5500 | 4.3390 |
| 3.8273 | 3.54 | 6000 | 4.3014 |
| 3.8116 | 3.84 | 6500 | 4.2720 |
| 3.6686 | 4.13 | 7000 | 4.2734 |
| 3.5444 | 4.43 | 7500 | 4.2662 |
| 3.5274 | 4.73 | 8000 | 4.2522 |
| 3.5039 | 5.02 | 8500 | 4.2497 |
| 3.3378 | 5.32 | 9000 | 4.2560 |
| 3.336 | 5.61 | 9500 | 4.2548 |
| 3.3376 | 5.91 | 10000 | 4.2538 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
viceisi/identify-my-cat
|
viceisi
| 2023-07-06T08:54:29Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-06-28T15:18:19Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
aronmal/Reinforce-PixelCopterMLP
|
aronmal
| 2023-07-06T08:42:01Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T08:41:58Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopterMLP
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.60 +/- 14.97
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
smaciu/bee-wings-classifier
|
smaciu
| 2023-07-06T08:32:55Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-06-24T10:25:38Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
m-aliabbas1/q-FrozenLake-v1-4x4-noSlippery
|
m-aliabbas1
| 2023-07-06T08:31:44Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T08:31:42Z |
---
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="m-aliabbas1/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"])
```
|
CICLAB-Comillas/BARTSumpson
|
CICLAB-Comillas
| 2023-07-06T08:12:24Z | 106 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-05T08:49:23Z |
---
license: mit
language:
- es
---
|
symanto/mpnet-base-snli-mnli
|
symanto
| 2023-07-06T07:54:17Z | 136 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mpnet",
"text-classification",
"zero-shot-classification",
"en",
"dataset:SNLI",
"dataset:MNLI",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
datasets:
- SNLI
- MNLI
tags:
- zero-shot-classification
---
A cross-attention NLI model trained for zero-shot and few-shot text classification.
The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base), trained with the code from [here](https://github.com/facebookresearch/anli);
on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
Usage:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import numpy as np
model = AutoModelForSequenceClassification.from_pretrained("symanto/mpnet-base-snli-mnli")
tokenizer = AutoTokenizer.from_pretrained("symanto/mpnet-base-snli-mnli")
input_pairs = [("I like this pizza.", "The sentence is positive."), ("I like this pizza.", "The sentence is negative.")]
inputs = tokenizer(["</s></s>".join(input_pair) for input_pair in input_pairs], return_tensors="pt")
logits = model(**inputs).logits
probs = torch.softmax(logits, dim=1).tolist()
print("probs", probs)
np.testing.assert_almost_equal(probs, [[0.86, 0.14, 0.00], [0.16, 0.15, 0.69]], decimal=2)
```
|
aronmal/Reinforce-CartpoleMLP
|
aronmal
| 2023-07-06T07:53:32Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T07:53:23Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-MLP
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 464.00 +/- 91.98
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Technotech/opt-125m-4bit-128g
|
Technotech
| 2023-07-06T07:51:47Z | 5 | 1 |
transformers
|
[
"transformers",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"license:other",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-06-12T08:04:01Z |
---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
## OPT-125m-4bit-128g
OPT 125M, quantised to 4bit using AutoGPTQ, with groupsize 128g, no act order.
Good for testing AutoGPTQ with a small model download.
# Original Model Card
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf).
Content from **this** model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).
### How to use
You can use this model directly with a pipeline for text generation.
```python
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model="facebook/opt-125m")
>>> generator("Hello, I'm am conscious and")
[{'generated_text': 'Hello, I am conscious and aware of the fact that I am a woman. I am aware of'}]
```
By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-125m", do_sample=True)
>>> generator("Hello, I'm am conscious and")
[{'generated_text': 'Hello, I am conscious and active member of the Khaosan Group, a private, self'}]
```
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
xian79/Reinforce-CartPole-v1
|
xian79
| 2023-07-06T07:51:38Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T07:51:27Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Technotech/RedPajama-Base-3B-4bit-128g
|
Technotech
| 2023-07-06T07:49:49Z | 5 | 0 |
transformers
|
[
"transformers",
"gpt_neox",
"text-generation",
"gptq",
"en",
"dataset:togethercomputer/RedPajama-Data-1T",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-12T09:18:42Z |
---
license: apache-2.0
language:
- en
datasets:
- togethercomputer/RedPajama-Data-1T
tags:
- gptq
---
## RedPajama-Base-3B-4bit-128g
RedPajama 3B, quantised to 4bit with groupsize of 128, no act order.
# Original Model Card
# RedPajama-INCITE-Base-3B-v1
RedPajama-INCITE-Base-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
The training was done on 3,072 V100 GPUs provided as part of the INCITE 2023 project on Scalable Foundation Models for Transferrable Generalist AI, awarded to MILA, LAION, and EleutherAI in fall 2022, with support from the Oak Ridge Leadership Computing Facility (OLCF) and INCITE program.
- Base Model: [RedPajama-INCITE-Base-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1)
- Instruction-tuned Version: [RedPajama-INCITE-Instruct-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1)
- Chat Version: [RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1)
## Model Details
- **Developed by**: Together Computer.
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 2.8B parameter pretrained language model.
# Quick Start
Please note that the model requires `transformers` version >= 4.25.1.
## GPU Inference
This requires a GPU with 8GB memory.
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", torch_dtype=torch.float16)
model = model.to('cuda:0')
# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True,
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
a name that has been synonymous with the computer age since the 1950s. The British mathematician, logician, and cryptanalyst is widely regarded as the father of modern computing. His contributions to the development of the modern computer and the theory of computation have had a profound impact on the world we live in today.
Turing’s contributions to the development of the modern computer were made in the 1940s and 1950s. He is most famous for his work on the Turing machine, a theoretical model of a computing machine that was able to perform all the mathematical operations of a computer. Turing’s work on the...
"""
```
## GPU Inference in Int8
To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command:
```bash
pip install accelerate
pip install bitsandbytes
```
Then you can run inference with int8 as follows:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)
# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
the man who cracked the Enigma code during World War II, and who was later convicted of homosexual acts. He was a brilliant mathematician, and a visionary who foresaw the computer age....
"""
```
## CPU Inference
You can run inference on CPU as follows:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", torch_dtype=torch.bfloat16)
# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
a name that is synonymous with the history of computer science. As the man who invented the Turing machine, the mathematical model that defines the limits of what can be computed, Turing is credited with the invention of the modern computer. Turing was also a mathematician and logician, and his work in these fields led to the development of the field of artificial intelligence...
"""
```
Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference.
# Uses
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.
#### Out-of-Scope Use
`RedPajama-INCITE-Base-3B-v1` is a language model and may not perform well for other use cases outside of its intended scope.
For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society.
It is important to consider the limitations of the model and to only use it for its intended purpose.
#### Misuse and Malicious Use
`RedPajama-INCITE-Base-3B-v1` is designed for language modeling.
Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project.
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating fake news, misinformation, or propaganda
- Promoting hate speech, discrimination, or violence against individuals or groups
- Impersonating individuals or organizations without their consent
- Engaging in cyberbullying or harassment
- Defamatory content
- Spamming or scamming
- Sharing confidential or sensitive information without proper authorization
- Violating the terms of use of the model or the data used to train it
- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming
## Limitations
`RedPajama-INCITE-Base-3B-v1`, like other language models, has limitations that should be taken into consideration.
For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data.
We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.
## Training
**Training Data**
Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
**Training Procedure**
- **Hardware:** 256 nodes of 6xV100 (IBM Power9), on the OLCF Summit cluster
- **Optimizer:** Apex FusedAdam
- **Parallelism:** Pipeline parallel 6, tensor parallel 2
- **Gradient Accumulations**: 8 (global batch size 4M tokens)
- **Num of Tokens:** 800B Tokens
- **Learning rate:** 0.00016
## Benchmark
Please refer to our [blog post](https://together.xyz) for benchmark results.
## Community
Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
|
atrytone/MIReAD-Neuro-Contrastive
|
atrytone
| 2023-07-06T07:40:38Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-07-06T07:38:47Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 480 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 100,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Vtmpas/ppo-LunarLander-v2
|
Vtmpas
| 2023-07-06T07:36:16Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T07:35:49Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 240.43 +/- 16.07
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Word2vec/nlpl_224
|
Word2vec
| 2023-07-06T07:31:46Z | 0 | 0 | null |
[
"word2vec",
"ukr",
"dataset:Ukrainian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T08:02:16Z |
---
language: ukr
license: cc-by-4.0
tags:
- word2vec
datasets: Ukrainian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 99884 corresponding to 299668196 tokens from the dataset `Ukrainian_CoNLL17_corpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 10 and dimension of 200.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_224", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/224.zip
|
Word2vec/nlpl_223
|
Word2vec
| 2023-07-06T07:31:31Z | 0 | 1 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_November_2021",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T08:01:57Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_November_2021
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 199430 corresponding to 2717675616 tokens from the dataset `English_Wikipedia_Dump_of_November_2021`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 2 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_223", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/223.zip
|
Word2vec/nlpl_208
|
Word2vec
| 2023-07-06T07:30:26Z | 0 | 0 | null |
[
"word2vec",
"pol",
"dataset:Polish_CommonCrawl_Dump_of_December_2019",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T08:25:40Z |
---
language: pol
license: cc-by-4.0
tags:
- word2vec
datasets: Polish_CommonCrawl_Dump_of_December_2019
---
## Information
A word2vec model trained by Krzysztof Wolk (kwolk@pja.edu.pl) on a vocabulary of size 35193029 corresponding to 32565035188 tokens from the dataset `Polish_CommonCrawl_Dump_of_December_2019`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_208", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/208.zip
|
Word2vec/nlpl_207
|
Word2vec
| 2023-07-06T07:30:10Z | 0 | 0 | null |
[
"word2vec",
"pol",
"dataset:Polish_CommonCrawl_Dump_of_December_2019",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T09:08:03Z |
---
language: pol
license: cc-by-4.0
tags:
- word2vec
datasets: Polish_CommonCrawl_Dump_of_December_2019
---
## Information
A word2vec model trained by Krzysztof Wolk (kwolk@pja.edu.pl) on a vocabulary of size 35193029 corresponding to 32565035188 tokens from the dataset `Polish_CommonCrawl_Dump_of_December_2019`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_207", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/207.zip
|
NTQAI/pedestrian_gender_recognition
|
NTQAI
| 2023-07-06T07:29:58Z | 45,879 | 15 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"safetensors",
"beit",
"image-classification",
"vision",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-01-06T04:37:51Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: outputs
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9107332624867163
---
<!-- 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. -->
# outputs
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the [PETA dataset](http://mmlab.ie.cuhk.edu.hk/projects/PETA_files/Pedestrian%20Attribute%20Recognition%20At%20Far%20Distance.pdf) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2170
- Accuracy: 0.9107
## Model description
More information needed
#### How to use
You can use this model with Transformers *pipeline* .
```python
from transformers import pipeline
gender_classifier = pipeline(model="NTQAI/pedestrian_gender_recognition")
image_path = "abc.jpg"
results = gender_classifier(image_path)
print(results)
```
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5193 | 1.0 | 2000 | 0.3346 | 0.8533 |
| 0.337 | 2.0 | 4000 | 0.2892 | 0.8778 |
| 0.3771 | 3.0 | 6000 | 0.2493 | 0.8969 |
| 0.3819 | 4.0 | 8000 | 0.2275 | 0.9100 |
| 0.3581 | 5.0 | 10000 | 0.2170 | 0.9107 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).
|
Word2vec/nlpl_206
|
Word2vec
| 2023-07-06T07:29:52Z | 0 | 0 | null |
[
"word2vec",
"pol",
"dataset:Polish_CommonCrawl_Dump_of_December_2019",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T08:09:12Z |
---
language: pol
license: cc-by-4.0
tags:
- word2vec
datasets: Polish_CommonCrawl_Dump_of_December_2019
---
## Information
A word2vec model trained by Krzysztof Wolk (kwolk@pja.edu.pl) on a vocabulary of size 4885806 corresponding to 32565035188 tokens from the dataset `Polish_CommonCrawl_Dump_of_December_2019`.
The model is trained with the following properties: no lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_206", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/206.zip
|
NTQAI/pedestrian_age_recognition
|
NTQAI
| 2023-07-06T07:28:59Z | 110,387 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"beit",
"image-classification",
"vision",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-01-09T03:36:33Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: pedestrian_age_recognition_local
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: 0.8073394495412844
---
<!-- 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. -->
# pedestrian_age_recognition_local
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5004
- Accuracy: 0.8073
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8849 | 1.0 | 2008 | 0.7939 | 0.6807 |
| 0.9836 | 2.0 | 4016 | 0.6694 | 0.7336 |
| 0.8128 | 3.0 | 6024 | 0.5768 | 0.7668 |
| 0.7611 | 4.0 | 8032 | 0.5541 | 0.7833 |
| 0.6441 | 5.0 | 10040 | 0.5473 | 0.7773 |
| 0.5696 | 6.0 | 12048 | 0.5187 | 0.7971 |
| 0.6925 | 7.0 | 14056 | 0.5082 | 0.8038 |
| 0.5711 | 8.0 | 16064 | 0.5092 | 0.8098 |
| 0.7741 | 9.0 | 18072 | 0.5026 | 0.8020 |
| 0.5269 | 10.0 | 20080 | 0.5004 | 0.8073 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).
|
Word2vec/nlpl_200
|
Word2vec
| 2023-07-06T07:28:57Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_October_2019",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T07:56:11Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_October_2019
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 249212 corresponding to 3530685741 tokens from the dataset `English_Wikipedia_Dump_of_October_2019`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 3 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_200", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/200.zip
|
Word2vec/nlpl_186
|
Word2vec
| 2023-07-06T07:28:40Z | 0 | 0 | null |
[
"word2vec",
"rus",
"dataset:Taiga_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T07:55:53Z |
---
language: rus
license: cc-by-4.0
tags:
- word2vec
datasets: Taiga_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 249946 corresponding to 4867000000 tokens from the dataset `Taiga_corpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_186", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/186.zip
|
Word2vec/nlpl_184
|
Word2vec
| 2023-07-06T07:28:01Z | 0 | 0 | null |
[
"word2vec",
"rus",
"dataset:Russian_News",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T07:55:10Z |
---
language: rus
license: cc-by-4.0
tags:
- word2vec
datasets: Russian_News
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 249318 corresponding to 2550000000 tokens from the dataset `Russian_News`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_184", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/184.zip
|
Word2vec/nlpl_180
|
Word2vec
| 2023-07-06T07:27:01Z | 0 | 0 | null |
[
"word2vec",
"rus",
"dataset:Russian_National_Corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T07:54:19Z |
---
language: rus
license: cc-by-4.0
tags:
- word2vec
datasets: Russian_National_Corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 189193 corresponding to 270000000 tokens from the dataset `Russian_National_Corpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 20 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_180", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/180.zip
|
Bugsys0302/m416
|
Bugsys0302
| 2023-07-06T07:16:46Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-06T07:06:10Z |
---
license: creativeml-openrail-m
---
|
Bugsys0302/beltbr
|
Bugsys0302
| 2023-07-06T06:59:17Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-06T06:57:43Z |
---
license: creativeml-openrail-m
---
|
afaan00733/my_awesome_model
|
afaan00733
| 2023-07-06T06:56:30Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-04T21:18:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6546
- Accuracy: 0.4737
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.6732 | 0.4737 |
| No log | 2.0 | 4 | 0.6546 | 0.4737 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
rohanbalkondekar/spicy-caiman
|
rohanbalkondekar
| 2023-07-06T06:55:23Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-06T06:48:59Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.30.1
pip install accelerate==0.20.3
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="BeRohan/spicy-caiman",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"BeRohan/spicy-caiman",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"BeRohan/spicy-caiman",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "BeRohan/spicy-caiman" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=BeRohan/spicy-caiman --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
guaguale/path-to-save-model
|
guaguale
| 2023-07-06T06:50:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-05T09:49:11Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - guaguale/path-to-save-model
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
IliyanGochev/whisper-small-bg
|
IliyanGochev
| 2023-07-06T06:50:12Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"bg",
"dataset:mozilla-foundation/common_voice_13_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-05T08:04:03Z |
---
language:
- bg
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: whisper-small-bg
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_13_0 bg
type: mozilla-foundation/common_voice_13_0
config: bg
split: test
args: bg
metrics:
- name: Wer
type: wer
value: 44.67291341315287
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-bg
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_13_0 bg dataset.
It achieves the following results on the evaluation set:
- Loss: 9.0612
- Wer: 44.6729
## 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: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 4.9319 | 6.76 | 1000 | 10.0774 | 73.9892 |
| 2.6116 | 13.51 | 2000 | 11.4089 | 67.0484 |
| 0.9607 | 20.27 | 3000 | 11.8266 | 60.9448 |
| 0.3464 | 27.03 | 4000 | 9.9500 | 52.1213 |
| 0.0122 | 33.78 | 5000 | 9.0612 | 44.6729 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Bugsys0302/fmmstrb
|
Bugsys0302
| 2023-07-06T06:46:46Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-06T06:40:45Z |
---
license: creativeml-openrail-m
---
|
aroot/eng-mya-simcse_random
|
aroot
| 2023-07-06T06:36:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T06:14:10Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse_random
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. -->
# eng-mya-simcse_random
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8977
- Bleu: 4.1368
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-mya-simcse_central
|
aroot
| 2023-07-06T06:36:12Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T06:14:05Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse_central
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. -->
# eng-mya-simcse_central
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8980
- Bleu: 4.1973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
cherrue/RandomCrop_Rescale_epoch_3_learning_rate_5e_5_decay_0_01
|
cherrue
| 2023-07-06T06:30:06Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-06T05:35:06Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: cherrue/pricetag_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# cherrue/pricetag_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0546
- Validation Loss: 1.2226
- Train Accuracy: 0.3846
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1251, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.3379 | 1.2276 | 0.5128 | 0 |
| 1.1973 | 1.1561 | 0.4615 | 1 |
| 1.0546 | 1.2226 | 0.3846 | 2 |
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Waterhorse/chessgpt-base-v1
|
Waterhorse
| 2023-07-06T06:19:40Z | 83 | 6 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"dataset:Waterhorse/chess_data",
"arxiv:2306.09200",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-02T22:03:14Z |
---
license: apache-2.0
language:
- en
datasets:
- Waterhorse/chess_data
---
# Chessgpt-Base-3B-v1
Chessgpt-Base-v1 is the base model of Chessgpt.
- Base Model: [Chessgpt-base-v1](https://huggingface.co/Waterhorse/chessgpt-base-v1)
- Chat Version: [chessgpt-chat-v1](https://huggingface.co/Waterhorse/chessgpt-chat-v1)
Also, we are actively working on the development of the next-generation model, ChessGPT-V2. We welcome any contribution, especially on chess related dataset. For related matters, please contact xidong.feng.20@ucl.ac.uk.
## Model Details
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 2.8B parameter pretrained language model in Chess.
## GPU Inference
This requires a GPU with 8GB memory.
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("Waterhorse/chessgpt-base-v1")
model = AutoModelForCausalLM.from_pretrained("Waterhorse/chessgpt-base-v1", torch_dtype=torch.float16)
model = model.to('cuda:0')
# infer
# Conversation between two
prompt = "Q: 1.e4 c5, what is the name of this opening?A:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True,
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
```
# Uses
Excluded uses are described below.
### Direct Use
`chessgpt-base-v1` is mainly for research on large language model, especially for those research about policy learning and language modeling.
#### Out-of-Scope Use
`chessgpt-base-v1` is a language model trained on chess related data and may not perform well for other use cases beyond chess domain.
#### Bias, Risks, and Limitations
Just as with any language model, chessgpt-base-v1 carries inherent limitations that necessitate careful consideration. Specifically, it may occasionally generate responses that are irrelevant or incorrect, particularly when tasked with interpreting complex or ambiguous queries. Additionally, given that its training is rooted in online data, the model may inadvertently reflect and perpetuate common online stereotypes and biases.
# Evaluation
Please refer to our [paper](https://arxiv.org/abs/2306.09200) and [code](https://github.com/waterhorse1/ChessGPT)for benchmark results.
# Citation Information
```bash
@article{feng2023chessgpt,
title={ChessGPT: Bridging Policy Learning and Language Modeling},
author={Feng, Xidong and Luo, Yicheng and Wang, Ziyan and Tang, Hongrui and Yang, Mengyue and Shao, Kun and Mguni, David and Du, Yali and Wang, Jun},
journal={arXiv preprint arXiv:2306.09200},
year={2023}
}
```
|
sukritiverma/thumbs-up-tom_cruise
|
sukritiverma
| 2023-07-06T06:14:17Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-05T23:31:34Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - sukritiverma/thumbs-up-tom_cruise
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
yuuhan/roberta-base-rte-lora
|
yuuhan
| 2023-07-06T06:12:21Z | 6 | 0 |
peft
|
[
"peft",
"text-classification",
"en",
"dataset:SetFit/rte",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-07-06T06:03:00Z |
---
license: apache-2.0
datasets:
- SetFit/rte
language:
- en
metrics:
- accuracy
library_name: peft
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
Accuracy: 0.7328519855595668 on RTE
|
yuuhan/roberta-base-mnli-lora
|
yuuhan
| 2023-07-06T06:01:55Z | 0 | 0 |
peft
|
[
"peft",
"text-classification",
"en",
"dataset:SetFit/mnli",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-07-06T05:57:19Z |
---
license: apache-2.0
datasets:
- SetFit/mnli
language:
- en
metrics:
- accuracy
library_name: peft
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
Accurate: 0.8654100866021396 on glue/mnli
|
LarryAIDraw/sakurako
|
LarryAIDraw
| 2023-07-06T06:00:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-06T05:27:47Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/100652/sakurako-busujima-grand-blue
|
aroot/eng-guj-simcse_central
|
aroot
| 2023-07-06T05:52:24Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T05:29:33Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-simcse_central
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. -->
# eng-guj-simcse_central
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2829
- Bleu: 2.7255
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
mazeinmouse/a2c-PandaReachDense-v2
|
mazeinmouse
| 2023-07-06T05:32:52Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T05:29:58Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.88 +/- 0.45
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
insub/distilbert-base-uncased-finetuned-imdb
|
insub
| 2023-07-06T05:22:05Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-06T05:17:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
aroot/eng-fra-simcse_central
|
aroot
| 2023-07-06T05:13:08Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T04:53:14Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse_central
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. -->
# eng-fra-simcse_central
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1521
- Bleu: 31.5479
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ashmitg/model_lora
|
ashmitg
| 2023-07-06T05:11:34Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-04T22:28:40Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
AAOBA/ppo-PyramidsRND
|
AAOBA
| 2023-07-06T05:05:37Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-07-06T05:04:49Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: chikoto/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
squeeze-ai-lab/sq-xgen-7b-8k-base-w4-s45
|
squeeze-ai-lab
| 2023-07-06T04:47:33Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"region:us"
] | null | 2023-07-06T03:46:56Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
4-bit XGen-7B Base model with 8K sequence length quantized using SqueezeLLM.
More details on the quantization method can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
More detailed model descriptions can be found in the [link](https://huggingface.co/Salesforce/xgen-7b-8k-base).
* **Base Model:** [XGen-7B-8K-Base](https://huggingface.co/Salesforce/xgen-7b-8k-base) (by Salesforce AI Research)
* **Bitwidth:** 4-bit
* **Sparsity Level:** 0.45%
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
squeeze-ai-lab/sq-xgen-7b-8k-base-w3-s45
|
squeeze-ai-lab
| 2023-07-06T04:46:32Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"region:us"
] | null | 2023-07-06T03:46:53Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
3-bit XGen-7B Base model with 8K sequence length quantized using SqueezeLLM.
More details on the quantization method can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
More detailed model descriptions can be found in the [link](https://huggingface.co/Salesforce/xgen-7b-8k-base).
* **Base Model:** [XGen-7B-8K-Base](https://huggingface.co/Salesforce/xgen-7b-8k-base) (by Salesforce AI Research)
* **Bitwidth:** 3-bit
* **Sparsity Level:** 0.45%
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
KPrashanth/Reinforce_Agent_playing_Cartpole_v1
|
KPrashanth
| 2023-07-06T04:36:55Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T04:36:41Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce_Agent_playing_Cartpole_v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
omnitron/PPO-Huggy
|
omnitron
| 2023-07-06T04:23:24Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-06T04:22:59Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: omnitron/PPO-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ocisd4/openllama-zh-7B
|
ocisd4
| 2023-07-06T04:13:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-06T03:46:10Z |
```python
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
import transformers
tokenizer = LlamaTokenizer.from_pretrained(
'ocisd4/openllama-zh',
add_bos_token=False,
add_eos_token=False,
use_auth_token=True,
use_fast=False)
model = LlamaForCausalLM.from_pretrained('ocisd4/openllama-zh', device_map='auto',use_auth_token=True)
prompt = '關於華碩的傳說'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=256,
do_sample=True, top_k=40, top_p=0.95, temperature=0.7, repetition_penalty=1.08,
)
print(tokenizer.decode(generation_output[0]))
```
The is a 7B pretrain model, train from openllama pretrain weight, context size=2048
**keep updating new model**
|
dangvansam/whisper-base-vi
|
dangvansam
| 2023-07-06T04:09:35Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"vi",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-05T10:42:24Z |
---
language:
- vi
pipeline_tag: automatic-speech-recognition
---
|
squeeze-ai-lab/sq-xgen-7b-8k-inst-w4-s45
|
squeeze-ai-lab
| 2023-07-06T03:58:19Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"region:us"
] | null | 2023-07-06T03:47:10Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
4-bit XGen-7B instruction-tuned model (i.e. finetuned model on public domain instructional data) with 8K sequence length quantized using SqueezeLLM.
More details on the quantization method can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
More detailed model descriptions can be found in the [link](https://huggingface.co/Salesforce/xgen-7b-8k-inst).
* **Base Model:** [XGen-7B-8K-Inst](https://huggingface.co/Salesforce/xgen-7b-8k-inst) (by Salesforce AI Research)
* **Bitwidth:** 4-bit
* **Sparsity Level:** 0.45%
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
aroot/eng-guj-wsample.43a
|
aroot
| 2023-07-06T03:44:33Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T03:21:38Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-wsample.43a
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. -->
# eng-guj-wsample.43a
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2191
- Bleu: 2.9237
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ddoc/pta
|
ddoc
| 2023-07-06T03:42:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-06T03:41:49Z |
# stable-diffusion-webui-prompt-travel
Travel between prompts in the latent space to make pseudo-animation, extension script for AUTOMATIC1111/stable-diffusion-webui.
----
<p align="left">
<a href="https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel/commits"><img alt="Last Commit" src="https://img.shields.io/github/last-commit/Kahsolt/stable-diffusion-webui-prompt-travel"></a>
<a href="https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/Kahsolt/stable-diffusion-webui-prompt-travel"></a>
<a href="https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/Kahsolt/stable-diffusion-webui-prompt-travel"></a>
<a href="https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel/network"><img alt="GitHub forks" src="https://img.shields.io/github/forks/Kahsolt/stable-diffusion-webui-prompt-travel"></a>
<img alt="Language" src="https://img.shields.io/github/languages/top/Kahsolt/stable-diffusion-webui-prompt-travel">
<img alt="License" src="https://img.shields.io/github/license/Kahsolt/stable-diffusion-webui-prompt-travel">
<br/>
</p>
Try interpolating on the hidden vectors of conditioning prompt to make seemingly-continuous image sequence, or let's say a pseudo-animation. 😀
Not only prompts! We also support non-prompt conditions, read => [README_ext.md](README_ext.md) ~
⚠ 我们成立了插件反馈 QQ 群: 616795645 (赤狐屿)，欢迎出建议、意见、报告bug等 (w
⚠ We have a QQ chat group (616795645) now, any suggestions, discussions and bug reports are highly wellllcome!!
ℹ 实话不说，我想有可能通过这个来做ppt童话绘本<del>甚至本子</del>……
ℹ 聪明的用法：先手工盲搜两张好看的图 (只有prompt差异)，然后再尝试在其间 travel :lolipop:
⚠ Remeber to check "Always save all generated images" on in the settings tab, otherwise "upscaling" and "saving intermediate images" would not work.
⚠ 记得在设置页勾选 “总是保存所有生成的图片”，否则 上采样 与 保存中间图片 将无法工作。
### Change Log
⚪ Compatibility
The latest version `v3.0` is synced & tested with:
- [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui): version `v1.4.0`, tag [v1.4.0](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.4.0)
- [Mikubill/sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet): version `v1.1.229`, commit [eceeec7a7e](https://github.com/Mikubill/sd-webui-controlnet/commit/eceeec7a7e856867de56e26cae9f3e1076480344)
⚪ Features
- 2023/07/05: `v3.0` re-impl core with sd-webui `v1.4.0` callbacks; this new implementation will be slower, but more compatible with other extensions
- 2023/04/13: `v2.7` add RIFE to controlnet-travel, skip fusion (experimental)
- 2023/03/31: `v2.6` add a tkinter [GUI](#run-each-time) for postprocess toolchain
- 2023/03/30: `v2.5` add controlnet-travel script (experimental), interpolating between hint conditions **instead of prompts**, thx for the code base from [sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet)
- 2023/02/14: `v2.3` integrate basic function of [depth-image-io](https://github.com/AnonymousCervine/depth-image-io-for-SDWebui) for depth2img models
- 2023/01/27: `v2.2` add 'slerp' linear interpolation method
- 2023/01/22: `v2.1` add experimental 'replace' mode again, it's not smooth interpolation
- 2023/01/20: `v2.0` add optional external [post-processing pipeline](#post-processing-pipeline) to highly boost up smoothness, greate thx to [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) and [RIFE](https://github.com/nihui/rife-ncnn-vulkan)!!
- 2023/01/16: `v1.5` add upscale options (issue #12); add 'embryo' genesis, reproducing idea of [stable-diffusion-animation](https://replicate.com/andreasjansson/stable-diffusion-animation) except [FILM](https://github.com/google-research/frame-interpolation) support (issue #11)
- 2023/01/12: `v1.4` remove 'replace' & 'grad' mode support, due to webui's code change
- 2022/12/11: `v1.3` work in a more 'successive' way, idea borrowed from [deforum](https://github.com/deforum-art/deforum-for-automatic1111-webui) ('genesis' option)
- 2022/11/14: `v1.2` walk by substituting token embedding ('replace' mode)
- 2022/11/13: `v1.1` walk by optimizing condition ('grad' mode)
- 2022/11/10: `v1.0` interpolate linearly on condition/uncondition ('linear' mode)
⚪ Fixups
- 2023/07/05: sync sd-webui-controlnet to `v1.1.229`
- 2023/04/30: update controlnet core to `v1.1.116`
- 2023/03/29: `v2.4` bug fixes on script hook, now working correctly with extra networks & [sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet)
- 2023/01/31: keep up with webui's updates, (issue #14: `ImportError: cannot import name 'single_sample_to_image'`)
- 2023/01/28: keep up with webui's updates, extra-networks rework
- 2023/01/16: `v1.5` apply zero padding when condition length mismatch (issue #10: `RuntimeError: The size of tensor a (77) must match the size of tensor b (154) at non-singleton dimension 0`), typo in demo filename
- 2023/01/12: `v1.4` keep up with webui's updates (issue #9: `AttributeError: 'FrozenCLIPEmbedderWithCustomWords' object has no attribute 'process_text'`)
- 2022/12/13: `#bdd8bed` fixup no working when negative prompt is left empty (issue #6: `neg_prompts[-1] IndexError: List index out of range`)
- 2022/11/27: `v1.2-fix2` keep up with webui's updates (error `ImportError: FrozenCLIPEmbedderWithCustomWords`)
- 2022/11/20: `v1.2-fix1` keep up with webui's updates (error `AttributeError: p.all_negative_prompts[0]`)
⚠ this script will NOT probably support the schedule syntax (i.e.: `[prompt:prompt:number]`), because interpolate on mutable conditions requires sampler level tracing which is hard to maintain :(
⚠ this script will NOT probably work together with `hires.fix` due to some inner conceptual/logical conflict of `denoising_strength`, you can alternatively perform batch-upscale then batch-img2img.
### How it works?
- input **multiple lines** in the prompt/negative-prompt box, each line is called a **stage**
- generate images one by one, interpolating from one stage towards the next (batch configs are ignored)
- gradually change the digested inputs between prompts
- freeze all other settings (`steps`, `sampler`, `cfg factor`, `seed`, etc.)
- note that only the major `seed` will be forcely fixed through all processes, you can still set `subseed = -1` to allow more variances
- export a video!
- follow [post-processing pipeline](#post-processing-pipeline) to get much better result 👌
⚪ Txt2Img
| sampler \ genesis | fixed | successive | embryo |
| :-: | :-: | :-: | :-: |
| Eular a |  |  |  |
| DDIM |  |  |  |
⚪ Img2Img
| sampler \ genesis | fixed | successive | embryo |
| :-: | :-: | :-: | :-: |
| Eular a |  |  |  |
| DDIM |  |  |  |
post-processing pipeline (case `i2i-f-ddim`):
| w/o. post-processing | w/. post-processing |
| :-: | :-: |
|  |  |
other stuff:
| reference image for img2img | embryo image decoded <br/> case `i2i-e-euler_a` with `embryo_step=8` |
| :-: | :-: |
|  |  |
⚪ ControlNet support
| prompt-travel with ControlNet (depth) | controlnet-travel (depth) |
| :-: | :-: |
|  |  |
Example above run configure:
```text
Prompt:
(((masterpiece))), highres, ((boy)), child, cat ears, white hair, red eyes, yellow bell, red cloak, barefoot, angel, [flying], egyptian
((masterpiece)), highres, ((girl)), loli, cat ears, light blue hair, red eyes, magical wand, barefoot, [running]
Negative prompt:
(((nsfw))), ugly,duplicate,morbid,mutilated,tranny,trans,trannsexual,mutation,deformed,long neck,bad anatomy,bad proportions,extra arms,extra legs, disfigured,more than 2 nipples,malformed,mutated,hermaphrodite,out of frame,extra limbs,missing arms,missing legs,poorly drawn hands,poorty drawn face,mutation,poorly drawn,long body,multiple breasts,cloned face,gross proportions, mutated hands,bad hands,bad feet,long neck,missing limb,malformed limbs,malformed hands,fused fingers,too many fingers,extra fingers,missing fingers,extra digit,fewer digits,mutated hands and fingers,lowres,text,error,cropped,worst quality,low quality,normal quality,jpeg artifacts,signature,watermark,username,blurry,text font ufemale focus, poorly drawn, deformed, poorly drawn face, (extra leg:1.3), (extra fingers:1.2),out of frame
Steps: 15
CFG scale: 7
Clip skip: 1
Seed: 114514
Size: 512 x 512
Model hash: animefull-final-pruned.ckpt
Hypernet: (this is my secret :)
```
### Options
- prompt: (list of strings)
- negative prompt: (list of strings)
- input multiple lines of prompt text
- we call each line of prompt a stage, usually you need at least 2 lines of text to starts travel
- if len(positive_prompts) != len(negative_prompts), the shorter one's last item will be repeated to match the longer one
- mode: (categorical)
- `linear`: linear interpolation on condition/uncondition of CLIP output
- `replace`: gradually replace of CLIP output
- replace_dim: (categorical)
- `token`: per token-wise vector
- `channel`: per channel-wise vector
- `random`: per point-wise element
- replace_order: (categorical)
- `similiar`: from the most similiar first (L1 distance)
- `different`: from the most different first
- `random`: just randomly
- `embryo`: pre-denoise few steps, then hatch a set of image from the common embryo by linear interpolation
- steps: (int, list of int)
- number of images to interpolate between two stages
- if int, constant number of travel steps
- if list of int, length should match `len(stages)-1`, separate by comma, e.g.: `12, 24, 36`
- genesis: (categorical), the a prior for each image frame
- `fixed`: starts from pure noise in txt2img pipeline, or from the same ref-image given in img2img pipeline
- `successive`: starts from the last generated image (this will force txt2img turn to actually be img2img from the 2nd frame on)
- `embryo`: starts from the same half-denoised image, see [=> How does it work?](https://replicate.com/andreasjansson/stable-diffusion-animation#readme)
- (experimental) it only processes 2 lines of prompts, and does not interpolate on negative_prompt :(
- genesis_extra_params
- denoise_strength: (float), denoise strength in img2img pipelines (for `successive`)
- embryo_step: (int or float), steps to hatch the common embryo (for `embryo`)
- if >= 1, taken as step cout
- if < 1, taken as ratio of total step
- video_*
- fps: (float), FPS of video, set `0` to disable file saving
- fmt: (categorical), export video file format
- pad: (int), repeat beginning/ending frames, giving a in/out time
- pick: (string), cherry pick frames by [python slice syntax](https://www.pythoncentral.io/how-to-slice-listsarrays-and-tuples-in-python) before padding (e.g.: set `::2` to get only even frames, set `:-1` to drop last frame)
### Installation
Easiest way to install it is to:
1. Go to the "Extensions" tab in the webui, switch to the "Install from URL" tab
2. Paste https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel.git into "URL for extension's git repository" and click install
3. (Optional) You will need to restart the webui for dependencies to be installed or you won't be able to generate video files
Manual install:
1. Copy this repo folder to the 'extensions' folder of https://github.com/AUTOMATIC1111/stable-diffusion-webui
2. (Optional) Restart the webui
### Post-processing pipeline
There are still two steps away from a really smooth and high resolution animation, namely image **super-resolution** & video **frame interpolation** (see `third-party tools` below).
⚠ Media data processing is intrinsic resource-exhausting, and it's also not webui's work or duty, hence we separated it out. 😃
#### setup once
⚪ auto install (Windows)
- run `cd tools & install.cmd`
- trouble shooting
- if you got any file system access errors like `Access denied.`, try run it again until you see `Done!` without errors 😂
- if you got SSL errors about `curl schannel ... Unknown error ... certificate.`, the downloader not work due to some SSL security reasons, just turn to install manually...
- you will have four components: [Busybox](https://frippery.org/busybox/), [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan), [RIFE](https://github.com/nihui/rife-ncnn-vulkan) and [FFmpeg](https://ffmpeg.org/) installed under the [tools](tools) folder
⚪ manually install (Windows/Linux/Mac)
ℹ Understand the `tools` folder layout first => [tools/README.txt](tools/README.txt)
ℹ If you indeed wanna put the tools elsewhere, modify paths in [tools/link.cmd](tools/link.cmd) and run `cd tools & link.cmd` 😉
For Windows:
- download [Busybox](https://frippery.org/busybox/)
- download [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases) (e.g.: `realesrgan-ncnn-vulkan-20220424-windows.zip`)
- (optional) download interesting seperated model checkpoints (e.g.: `realesr-animevideov3.pth`)
- download [rife-ncnn-vulkan](https://github.com/nihui/rife-ncnn-vulkan/releases) bundle (e.g.: `rife-ncnn-vulkan-20221029-windows.zip `)
- download [FFmpeg](https://ffmpeg.org/download.html) binary (e.g.: `ffmpeg-release-full-shared.7z` or `ffmpeg-git-full.7z`)
For Linux/Mac:
- download [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases) and [rife-ncnn-vulkan](https://github.com/nihui/rife-ncnn-vulkan/releases), put them according to the `tools` folder layout, manually apply `chmod 755` to the executables
- `ffmpeg` can be easily found in your app store or package manager, run like `apt install ffmpeg`; DO NOT need to link it under `tools` folder
#### run each time
⚪ tkinter GUI (Windows/Linux/Mac)
For Windows:
- run `manager.cmd`, to start webui's python venv
- run the [DOSKEY](https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/doskey) `install` (only setup once)
- run the DOSKEY `run`
For Linux/Mac:
- run `../../venv/Scripts/activate`, to start webui's python venv
- run `pip install -r requirements.txt` (only setup once)
- run `python manager.py`
ℹ find usage help message in right click pop menu~
⚪ <del> cmd script (Windows) - deprecated </del>
- check params in [postprocess-config.cmd](postprocess-config.cmd)
- pick one way to start 😃
- run `postprocess.cmd path/to/<image_folder>` from command line
- drag & drop any image folder over `postprocess.cmd` icon
- once processing finished, the explorer will be auto lauched to locate the generated file named with `synth.mp4`
### Related Projects
⚪ extensions that inspired this repo
- sd-webui-controlnet (various image conditions): [https://github.com/Mikubill/sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet)
- depth-image-io (custom depth2img): [https://github.com/AnonymousCervine/depth-image-io-for-SDWebui](https://github.com/AnonymousCervine/depth-image-io-for-SDWebui)
- animator (img2img): [https://github.com/Animator-Anon/animator_extension](https://github.com/Animator-Anon/animator_extension)
- sd-webui-riffusion (music gen): [https://github.com/enlyth/sd-webui-riffusion](https://github.com/enlyth/sd-webui-riffusion)
- sd-animation (half denoise + FILM):
- Github: [https://github.com/andreasjansson/cog-stable-diffusion](https://github.com/andreasjansson/cog-stable-diffusion)
- Replicate: [https://replicate.com/andreasjansson/stable-diffusion-animation](https://replicate.com/andreasjansson/stable-diffusion-animation)
- deforum (img2img + depth model): [https://github.com/deforum-art/deforum-for-automatic1111-webui](https://github.com/deforum-art/deforum-for-automatic1111-webui)
- seed-travel (varying seed): [https://github.com/yownas/seed_travel](https://github.com/yownas/seed_travel)
⚪ third-party tools
- image super-resoultion
- ESRGAN:
- ESRGAN: [https://github.com/xinntao/ESRGAN](https://github.com/xinntao/ESRGAN)
- Real-ESRGAN: [https://github.com/xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)
- Real-ESRGAN-ncnn-vulkan (recommended): [https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)
- video frame interpolation
- FILM (recommended): [https://github.com/google-research/frame-interpolation](https://github.com/google-research/frame-interpolation)
- RIFE:
- ECCV2022-RIFE: [https://github.com/megvii-research/ECCV2022-RIFE](https://github.com/megvii-research/ECCV2022-RIFE)
- rife-ncnn-vulkan (recommended): [https://github.com/nihui/rife-ncnn-vulkan](https://github.com/nihui/rife-ncnn-vulkan)
- Squirrel-RIFE: [https://github.com/Justin62628/Squirrel-RIFE](https://github.com/Justin62628/Squirrel-RIFE)
- Practical-RIFE: [https://github.com/hzwer/Practical-RIFE](https://github.com/hzwer/Practical-RIFE)
- GNU tool-kits
- BusyBox: [https://www.busybox.net/](https://www.busybox.net/)
- BusyBox for Windows: [https://frippery.org/busybox/](https://frippery.org/busybox/)
- FFmpeg: [https://ffmpeg.org/](https://ffmpeg.org/)
⚪ my other experimental toy extensions
- vid2vid (video2video) [https://github.com/Kahsolt/stable-diffusion-webui-vid2vid](https://github.com/Kahsolt/stable-diffusion-webui-vid2vid)
- hires-fix-progressive (a progressive version of hires.fix): [https://github.com/Kahsolt/stable-diffusion-webui-hires-fix-progressive](https://github.com/Kahsolt/stable-diffusion-webui-hires-fix-progressive)
- sonar (k_diffuison samplers): [https://github.com/Kahsolt/stable-diffusion-webui-sonar](https://github.com/Kahsolt/stable-diffusion-webui-sonar)
- size-travel (kind of X-Y plot on image size): [https://github.com/Kahsolt/stable-diffusion-webui-size-travel](https://github.com/Kahsolt/stable-diffusion-webui-size-travel)
----
by Armit
2022/11/10
|
zhundred/ppo-LunarLander-v2
|
zhundred
| 2023-07-06T03:38:13Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T03:37:29Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.86 +/- 20.77
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BaoKien/deberta-base-finetuned-squad-v2
|
BaoKien
| 2023-07-06T03:22:36Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-06T01:19:43Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: deberta-base-finetuned-squad-v2
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. -->
# deberta-base-finetuned-squad-v2
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9221
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.753 | 1.0 | 8238 | 0.7286 |
| 0.5378 | 2.0 | 16476 | 0.7578 |
| 0.3881 | 3.0 | 24714 | 0.9221 |
### Performance
- 'exact': 81.84115219405373
- 'f1': 85.19125695340612
- 'total': 11873
- 'HasAns_exact': 80.24628879892038
- 'HasAns_f1': 86.95610556811602
- 'HasAns_total': 5928
- 'NoAns_exact': 83.43145500420522
- 'NoAns_f1': 83.43145500420522
- 'NoAns_total': 5945
- 'best_exact': 81.84115219405373
- 'best_exact_thresh': 0.9994916319847107
- 'best_f1': 85.19125695340657
- 'best_f1_thresh': 0.9994916319847107
- 'total_time_in_seconds': 294.34524957099984
- 'samples_per_second': 40.33698528277447
- 'latency_in_seconds': 0.024791143735450168
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
KJan05/rl-CartPole-v1-unit4
|
KJan05
| 2023-07-06T03:21:57Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T03:21:45Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: rl-CartPole-v1-unit4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
squeeze-ai-lab/sq-xgen-7b-8k-base-w4-s0
|
squeeze-ai-lab
| 2023-07-06T03:14:48Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"region:us"
] | null | 2023-07-05T23:31:51Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
4-bit XGen-7B Base model with 8K sequence length quantized using SqueezeLLM.
More details on the quantization method can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
More detailed model descriptions can be found in the [link](https://huggingface.co/Salesforce/xgen-7b-8k-base).
* **Base Model:** [XGen-7B-8K-Base](https://huggingface.co/Salesforce/xgen-7b-8k-base) (by Salesforce AI Research)
* **Bitwidth:** 4-bit
* **Sparsity Level:** 0% (dense-only)
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
squeeze-ai-lab/sq-xgen-7b-8k-base-w3-s0
|
squeeze-ai-lab
| 2023-07-06T03:14:31Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"region:us"
] | null | 2023-07-05T23:31:15Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
3-bit XGen-7B Base model with 8K sequence length quantized using SqueezeLLM.
More details on the quantization method can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
More detailed model descriptions can be found in the [link](https://huggingface.co/Salesforce/xgen-7b-8k-base).
* **Base Model:** [XGen-7B-8K-Base](https://huggingface.co/Salesforce/xgen-7b-8k-base) (by Salesforce AI Research)
* **Bitwidth:** 3-bit
* **Sparsity Level:** 0% (dense-only)
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
nimakha/ppo-Huggy
|
nimakha
| 2023-07-06T03:11:22Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-06T03:11:18Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: nimakha/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bellaaazzzzz/models_fill
|
Bellaaazzzzz
| 2023-07-06T02:41:19Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-06T02:35:57Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-Bellaaazzzzz/models_fill
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
Validation result of 1 round.
Validation result of 2 round.
|
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