modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-04 06:26:56
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 538
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
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
|
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
...
```
|
Abinaya/opt-1.3b-lora-summary
|
Abinaya
| 2023-07-06T07:35:05Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-06T06:35:55Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
```
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "Abinaya/opt-1.3-b-lora"
config = PeftConfig.from_pretrained("Abinaya/opt-1.3b-lora-summary")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b")
model = PeftModel.from_pretrained(model, "Abinaya/opt-1.3b-lora-summary")
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
```
## For inference to get summary
```
batch = tokenizer("Natural language processing is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data", return_tensors='pt')
with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch, max_new_tokens=50)
print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
```
|
Word2vec/nlpl_222
|
Word2vec
| 2023-07-06T07:31:04Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_November_2021",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T08:01:35Z |
---
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 199807 corresponding to 2717675616 tokens from the dataset `English_Wikipedia_Dump_of_November_2021`.
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 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_222", 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/20/222.zip
|
Word2vec/nlpl_220
|
Word2vec
| 2023-07-06T07:30:44Z | 0 | 0 | null |
[
"word2vec",
"rus",
"dataset:Russian_National_Corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T08:01:16Z |
---
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 249333 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 10 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_220", 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/20/220.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
|
Word2vec/nlpl_205
|
Word2vec
| 2023-07-06T07:29:34Z | 0 | 0 | null |
[
"word2vec",
"pol",
"dataset:Polish_CommonCrawl_Dump_of_December_2019",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-05T08:04:52Z |
---
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 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_205", 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/20/205.zip
|
afaan00733/refference_filtering
|
afaan00733
| 2023-07-06T07:28:03Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-06T07:15:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: refference_filtering
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. -->
# refference_filtering
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: 0.3518
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.6560 | 0.8947 |
| No log | 2.0 | 4 | 0.6103 | 1.0 |
| No log | 3.0 | 6 | 0.5545 | 1.0 |
| No log | 4.0 | 8 | 0.4951 | 0.9474 |
| No log | 5.0 | 10 | 0.4457 | 1.0 |
| No log | 6.0 | 12 | 0.4127 | 1.0 |
| No log | 7.0 | 14 | 0.3894 | 1.0 |
| No log | 8.0 | 16 | 0.3705 | 1.0 |
| No log | 9.0 | 18 | 0.3577 | 1.0 |
| No log | 10.0 | 20 | 0.3518 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
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
|
digiplay/Zevinemix_v1.0
|
digiplay
| 2023-07-06T07:24:33Z | 255 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-06T04:38:41Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/103015?modelVersionId=110251
Sample image I made :
Original Author's DEMO images :
|
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
---
|
youyougu/test-01
|
youyougu
| 2023-07-06T07:06:18Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-06T06:53:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: test-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-01
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
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
|
JennnDexter/pokemon-lora
|
JennnDexter
| 2023-07-06T06:44:42Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"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
| 2023-06-12T06:24:16Z |
---
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 - JennnDexter/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
NasimB/gpt2-concat-aochildes-16plus6k
|
NasimB
| 2023-07-06T06:39:38Z | 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
| 2023-07-06T04:47:18Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-aochildes-16plus6k
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-16plus6k
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: 3.1978
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7265 | 0.3 | 500 | 5.6481 |
| 5.3801 | 0.59 | 1000 | 5.2065 |
| 5.0346 | 0.89 | 1500 | 4.9518 |
| 4.7589 | 1.19 | 2000 | 4.8123 |
| 4.6003 | 1.48 | 2500 | 4.6915 |
| 4.4941 | 1.78 | 3000 | 4.5806 |
| 4.3447 | 2.07 | 3500 | 4.5155 |
| 4.1761 | 2.37 | 4000 | 4.4640 |
| 4.1351 | 2.67 | 4500 | 4.4014 |
| 4.1043 | 2.96 | 5000 | 4.3576 |
| 3.8639 | 3.26 | 5500 | 4.3597 |
| 3.8432 | 3.56 | 6000 | 4.3266 |
| 3.8118 | 3.85 | 6500 | 4.2913 |
| 3.6736 | 4.15 | 7000 | 4.2957 |
| 3.5472 | 4.45 | 7500 | 4.2920 |
| 3.5398 | 4.74 | 8000 | 4.2794 |
| 3.507 | 5.04 | 8500 | 4.2806 |
| 3.3499 | 5.33 | 9000 | 4.2855 |
| 3.3504 | 5.63 | 9500 | 4.2851 |
| 3.3498 | 5.93 | 10000 | 4.2849 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
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
|
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-chat-v1
|
Waterhorse
| 2023-07-06T06:20:40Z | 124 | 10 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"dataset:Waterhorse/chess_data",
"dataset:anon8231489123/ShareGPT_Vicuna_unfiltered",
"dataset:OpenAssistant/oasst1",
"dataset:vicgalle/alpaca-gpt4",
"arxiv:2306.09200",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-03T21:18:08Z |
---
license: apache-2.0
language:
- en
datasets:
- Waterhorse/chess_data
- anon8231489123/ShareGPT_Vicuna_unfiltered
- OpenAssistant/oasst1
- vicgalle/alpaca-gpt4
---
# Chessgpt-Chat-v1
Chessgpt-Chat-v1 is the sft-tuned model of Chessgpt-Base-v1.
- 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-chat-v1")
model = AutoModelForCausalLM.from_pretrained("Waterhorse/chessgpt-chat-v1", torch_dtype=torch.float16)
model = model.to('cuda:0')
# infer
# Conversation between two
prompt = "A friendly, helpful chat between some humans.<|endoftext|>Human 0: 1.e4 c5, what is the name of this opening?<|endoftext|>Human 1:"
# Conversation between more than two
#prompt = "A friendly, helpful chat between some humans.<|endoftext|>Human 0: 1.e4 c5, what is the name of this opening?<|endoftext|>Human 1: Sicilian defense.<|endoftext|>Human 2:"
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-chat-v1` is mainly for research on large language model, especially for those research about policy learning and language modeling.
#### Out-of-Scope Use
`chessgpt-chat-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-chat-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}
}
```
|
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
|
saintzeno/a2c-PandaReachDense-v3
|
saintzeno
| 2023-07-06T06:10:45Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T05:52:59Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
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
|
Ryukijano/whisper-small-dv
|
Ryukijano
| 2023-07-06T05:36:17Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"dataset:mozilla-foundation/common_voice_13_0",
"license:mit",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-05T06:25:50Z |
---
license: mit
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
---
---
# Whisper Small DV Model
## Model Description
The `whisper-small-dv` model is an advanced Automatic Speech Recognition (ASR) model, trained on the extensive [Mozilla Common Voice 13.0](https://commonvoice.mozilla.org/en/datasets) dataset. This model is capable of transcribing spoken language into written text with high accuracy, making it a valuable tool for a wide range of applications, from transcription services to voice assistants.
## Training
The model was trained using the PyTorch framework and the Transformers library. Training metrics and visualizations can be viewed on TensorBoard.
## Performance
The model's performance was evaluated on a held-out test set. The evaluation metrics and results can be found in the "Eval Results" section.
## Usage
The model can be used for any ASR task. To use the model, you can load it using the Transformers library:
```python
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Load the model
model = Wav2Vec2ForCTC.from_pretrained("Ryukijano/whisper-small-dv")
processor = Wav2Vec2Processor.from_pretrained("Ryukijano/whisper-small-dv")
# Use the model for ASR
inputs = processor("path_to_audio_file", return_tensors="pt", padding=True)
logits = model(inputs.input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0])
```
## License
This model is released under the MIT license.
---
P
|
eigenscribe/etzHayim
|
eigenscribe
| 2023-07-06T05:34:59Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-06T05:33:49Z |
---
license: creativeml-openrail-m
---
|
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
|
tuanio/WhisperCTC
|
tuanio
| 2023-07-06T05:06:09Z | 0 | 1 | null |
[
"summarization",
"dataset:mozilla-foundation/common_voice_13_0",
"arxiv:1910.09700",
"region:us"
] |
summarization
| 2023-07-06T04:55:16Z |
---
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
pipeline_tag: summarization
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
```python
class WhisperCTC(nn.Module):
def __init__(
self,
encoder_id: str = "tuanio/whisper-encoder.tiny.en",
dropout: float = 0.1,
vocab_size: int = 47,
):
super().__init__()
self.encoder = WhisperEncoder.from_pretrained(encoder_id)
print("Freezing Whisper Encoder...")
self.encoder._freeze_parameters()
print("Freezed!")
self.lm_head = nn.Sequential(
nn.SiLU(),
nn.Dropout(dropout),
nn.Linear(self.encoder.config.d_model, vocab_size),
)
nn.init.kaiming_uniform_(
self.lm_head[-1].weight, mode="fan_in", nonlinearity="relu"
)
def forward(self, feat: Tensor, attn_mask: Tensor):
enc = self.encoder(
input_features=feat, attention_mask=attn_mask
).last_hidden_state
logits = self.lm_head(enc)
log_probs = nn.functional.log_softmax(logits, dim=-1)
return log_probs
```
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
- IndictTTS: https://www.kaggle.com/datasets/tuannguyenvananh/indictts-english
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
```yaml
data_cfg:
dataset:
processor:
feat_extractor_id: ${model_cfg.model.encoder_id}
tokenizer_id: ${model_cfg.tokenizer_id}
path:
base:
indict_tts: ../IndicTTS
cv: ../
train:
- train_data/indict_tts_train.jsonl
# - train_data/cv_train.jsonl
test:
- train_data/indict_tts_test.jsonl
# - train_data/cv_test.jsonl
dev:
- train_data/indict_tts_dev.jsonl
# - train_data/cv_dev.jsonl
dataloader:
batch_size: 46
num_workers: 8
pin_memory: True
model_cfg:
tokenizer_id: tuanio/wav2vec2-phoneme-ipa-ctc
model:
dropout: 0.1
encoder_id: tuanio/whisper-encoder.medium.en
optim:
lr: 1.25e-05
betas: [0.9, 0.998]
weight_decay: 0.01
scheduler:
name: linear
total_steps: -1
warmup_ratio: 0.05
interval: step
frequency: 1
trainer_cfg:
log:
wandb: True
logger_wandb:
project: aped_indian-lish
name: whisper-medium-indict-tts-only-from-epoch1
log_model: all
arguments:
accelerator: gpu
devices: -1
max_epochs: 10
log_every_n_steps: 1
enable_checkpointing: True
accumulate_grad_batches: 2
inference_mode: True
gradient_clip_val: 5.0
check_val_every_n_epoch: 1
val_check_interval: null
experiment_cfg:
train: True
valid: True
test: True
ckpt:
resume_ckpt: True
ckpt_path: ckpt/medium.epoch3.ckpt
```
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
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 👀
|
whiteDandelion/swin-tiny-patch4-window7-224-finetuned-eurosat
|
whiteDandelion
| 2023-07-06T05:01:12Z | 228 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-06T04:12:49Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9805
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [andupets/real-estate-image-classification](https://huggingface.co/andupets/real-estate-image-classification) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0613
- Accuracy: 0.9805
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.089 | 0.99 | 140 | 0.1050 | 0.9635 |
| 0.0565 | 2.0 | 281 | 0.0760 | 0.9725 |
| 0.0421 | 2.98 | 420 | 0.0613 | 0.9805 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
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
---
|
mazeinmouse/a2c-AntBulletEnv-v0
|
mazeinmouse
| 2023-07-06T04:34:47Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T04:33:37Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1651.08 +/- 126.30
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
headflame02/AchaxV4
|
headflame02
| 2023-07-06T04:30:16Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-06T04:29:24Z |
---
license: creativeml-openrail-m
---
|
NasimB/gpt2-concat-cbt-rarity-2k-p3k
|
NasimB
| 2023-07-06T04:28:43Z | 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
| 2023-07-06T02:13:04Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-cbt-rarity-2k-p3k
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-2k-p3k
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: 3.0083
## 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: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7186 | 0.29 | 500 | 5.6281 |
| 5.3685 | 0.58 | 1000 | 5.1947 |
| 5.0278 | 0.87 | 1500 | 4.9465 |
| 4.7459 | 1.17 | 2000 | 4.8014 |
| 4.5838 | 1.46 | 2500 | 4.6757 |
| 4.4777 | 1.75 | 3000 | 4.5664 |
| 4.3633 | 2.04 | 3500 | 4.4935 |
| 4.1601 | 2.33 | 4000 | 4.4512 |
| 4.1388 | 2.62 | 4500 | 4.3967 |
| 4.1004 | 2.91 | 5000 | 4.3434 |
| 3.9085 | 3.21 | 5500 | 4.3385 |
| 3.8559 | 3.5 | 6000 | 4.3100 |
| 3.8409 | 3.79 | 6500 | 4.2772 |
| 3.7507 | 4.08 | 7000 | 4.2758 |
| 3.5677 | 4.37 | 7500 | 4.2717 |
| 3.5771 | 4.66 | 8000 | 4.2566 |
| 3.5653 | 4.95 | 8500 | 4.2354 |
| 3.3565 | 5.24 | 9000 | 4.2632 |
| 3.3184 | 5.54 | 9500 | 4.2598 |
| 3.3222 | 5.83 | 10000 | 4.2510 |
| 3.2596 | 6.12 | 10500 | 4.2621 |
| 3.1718 | 6.41 | 11000 | 4.2643 |
| 3.1656 | 6.7 | 11500 | 4.2647 |
| 3.1666 | 6.99 | 12000 | 4.2645 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
aroot/eng-mya-wsample.43a
|
aroot
| 2023-07-06T04:28:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T04:06:12Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-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-mya-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: 1.8306
- Bleu: 4.6779
## 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
|
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 👀
|
aroot/eng-mya-wsample.32a
|
aroot
| 2023-07-06T04:23:10Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T04:01:01Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-wsample.32a
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-wsample.32a
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: 1.8284
- Bleu: 4.7194
## 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
|
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**
|
lovelyxs/PPO-LunarLander-v2
|
lovelyxs
| 2023-07-06T04:11:32Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T03:54:28Z |
---
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: 265.53 +/- 16.26
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
...
```
|
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
---
|
squeeze-ai-lab/sq-xgen-7b-8k-inst-w3-s45
|
squeeze-ai-lab
| 2023-07-06T03:56:32Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"region:us"
] | null | 2023-07-06T03:47:03Z |
**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 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:** 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
---
|
digiplay/CoffeeMix_v1
|
digiplay
| 2023-07-06T03:55:09Z | 307 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-06T02:17:13Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/40630?modelVersionId=45847
Sample image I made :
Original Author's DEMO images :
|
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
|
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
...
```
|
MWaleed/q-Taxi-v3
|
MWaleed
| 2023-07-06T03:23:27Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T03:23:24Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="MWaleed/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
squeeze-ai-lab/sq-xgen-7b-8k-inst-w3-s0
|
squeeze-ai-lab
| 2023-07-06T03:15:42Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"region:us"
] | null | 2023-07-05T23:32:13Z |
**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 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:** 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
---
|
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
---
|
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.
|
TanimHasan/LLaMA-NUBI-v2
|
TanimHasan
| 2023-07-06T02:02:44Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-06T02:02:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
roemmele/falcon-7b-loss-score
|
roemmele
| 2023-07-06T01:46:31Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWebModel",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-05T22:16:33Z |
This is a fork of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b), with a custom endpoint handler (handler.py) that returns the model loss score of a given input text.
|
Huggingfly/Reinforce-Cartpole-v1
|
Huggingfly
| 2023-07-06T01:38:51Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T01:38:41Z |
---
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
|
jsjung00/ppo-LunarLander-v2
|
jsjung00
| 2023-07-06T01:20:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T01:20:07Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -636.93 +/- 286.95
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
...
```
|
YIMMYCRUZ/vit-model-ojas
|
YIMMYCRUZ
| 2023-07-06T01:14:59Z | 72 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2023-07-05T03:17:25Z |
---
license: apache-2.0
tags:
- image-segmentation
- generated_from_trainer
metrics:
- accuracy
widget:
- src: https://i.ibb.co/NL52HmG/sana.png
example_title: Healthy
- src: https://i.ibb.co/P44CL1q/marchita.png
example_title: Bean Rust
model-index:
- name: vit-model-ojas
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-model-ojas
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0099
- Accuracy: 1.0
## Model description
You can manage to segment the images of plant leaves to be able to know if they are healthy or withered.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1457 | 3.85 | 500 | 0.0099 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
anujsahani01/finetuned_Mbart_mr_en
|
anujsahani01
| 2023-07-06T01:08:06Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-05T17:34:56Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: finetuned_Mbart_mr_en
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_Mbart_mr_en
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) 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: 10000
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dmatekenya/whisper-small_finetuned_sw_chich
|
dmatekenya
| 2023-07-06T00:54:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-05T20:02:12Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small_finetuned_sw_chich
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small_finetuned_sw_chich
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7430
- Wer: 80.1992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0324 | 4.39 | 500 | 1.5624 | 84.6754 |
| 0.0151 | 8.77 | 1000 | 1.6639 | 82.4073 |
| 0.0099 | 13.16 | 1500 | 1.7377 | 78.8912 |
| 0.0081 | 17.54 | 2000 | 1.7430 | 80.1992 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
chaudha7/LLMs
|
chaudha7
| 2023-07-06T00:51:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-17T21:15:35Z |
### Model Description
This is a fine-tuned Bloom-7b model. It has been trained on a dummy dataset for question answering purposes. It is not very useful for the general public.
I wanted to get an idea of the hugging face model and dataset pipeline.
Do check out https://huggingface.co/chaudha7/DiaryFlow
- **Developed by:** Aashay Chaudhari
|
chaudha7/DiaryFlow
|
chaudha7
| 2023-07-06T00:49:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-06T00:43:32Z |
### Model Description
This is a fine-tuned Bloom-7b model. It was a demo project which I wanted to try to alleviate the seriousness and rapid pace around the "LLM" usecases.
This model has been trained on a custom chatGPT-created dataset (https://huggingface.co/datasets/chaudha7/Diary-Entry-To-Rap) which turns diary entries into short rap songs with a motivational ending. Just something fun for the users.
### Developed by: Aashay Chaudhari
## Training procedure
The following `bits and bytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
### Results
### Input:
Dear Diary,
Oh boy, today was such an incredible day! I woke up with a burst of excitement in my heart because it was my birthday! Double digits, baby! Mom and Dad surprised
me with a brand new bike, just like the one I've been dreaming about for ages. We went for a ride in the park, and I felt like I was flying!
My friends came over for a super fun party, and we played games, danced, and laughed until our tummies hurt. Grandma baked the most delicious chocolate cake,
and I blew out all the candles with one big breath. The best part? I got some awesome presents, like a shiny diary (just like you, Diary!), art supplies, and
a book about adventures. I can't wait to read it tonight!
Tonight, as I snuggle under my cozy blanket, I can't help but feel so grateful for the love and joy that surrounds me. Being 10 feels amazing, Diary.
I can't wait to see what other incredible adventures await me this year!
Until tomorrow,
Emily
### Output:
(Verse)
Today's my birthday,
feeling so alive,
Double digits, yo,
I'm ready to strive.
Got a brand-new bike,
flying through the air,
Riding in the park,
without a single care.
Friends came over,
we had a blast,
Playing games, dancing,
memories that'll last.
Grandma's chocolate cake,
candles all aglow,
Blowing them out,
a wish ready to grow.
Shiny diary, art supplies, and books,
Unleashing my creativity, no limits, no hooks.
Grateful for love and joy, can't help but smile,
Ten years old, embracing life's every mile.
(Motivational Ending)
No matter the age, dreams light up the way,
Believe in yourself, make the most of today.
Adventures await, with each step you take,
Keep shining, keep growing, there's no limit to what you'll make.
|
CalamitousVisibility/UK_Energy_Industry_Complaints_Identifier_ver1
|
CalamitousVisibility
| 2023-07-06T00:28:38Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-05T22:24:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: UK_Energy_Industry_Complaints_Identifier_ver1
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. -->
# UK_Energy_Industry_Complaints_Identifier_ver1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a balanced dataset consisting of 17,620
publicy available customer reviews of various domestic energy suppliers in the United Kingdom.
It achieves the following results on the evaluation set:
- Loss: 0.3369
- Accuracy: 0.9561
- F1: [0.95594347 0.95621041]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.11.0
|
momomomomomo/Rotten_Tomato_Classfier
|
momomomomomo
| 2023-07-05T23:58:12Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-05T21:06:06Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: momomomomomo/Rotten_Tomato_Classfier
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. -->
# momomomomomo/Rotten_Tomato_Classfier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5703
- Validation Loss: 0.6171
- Train Accuracy: 0.7131
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 189675, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6738 | 0.6373 | 0.7018 | 0 |
| 0.5703 | 0.6171 | 0.7131 | 1 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
gagan3012/Qalam_onnx
|
gagan3012
| 2023-07-05T23:44:16Z | 10 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"ar",
"license:apache-2.0",
"region:us"
] |
image-to-text
| 2023-07-05T22:41:49Z |
---
license: apache-2.0
language:
- ar
metrics:
- wer
library_name: transformers.js
pipeline_tag: image-to-text
---
|
eluzhnica/mpt-7b-instruct-peft-compatible
|
eluzhnica
| 2023-07-05T23:35:23Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mpt",
"text-generation",
"Composer",
"MosaicML",
"llm-foundry",
"custom_code",
"dataset:mosaicml/dolly_hhrlhf",
"arxiv:2205.14135",
"arxiv:2108.12409",
"arxiv:2010.04245",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-05T23:14:18Z |
---
license: cc-by-sa-3.0
datasets:
- mosaicml/dolly_hhrlhf
tags:
- Composer
- MosaicML
- llm-foundry
inference: false
---
# MPT-7B-Instruct
This is the MPT-7B-Instruct but with added support to finetune using peft (tested with qlora). It is not finetuned further, the weights are the same as the original MPT-7B-Instruct.
I have not traced through the whole huggingface stack to see if this is working correctly but it does finetune with qlora and outputs are reasonable.
Inspired by implementations here https://huggingface.co/cekal/mpt-7b-peft-compatible/commits/main
https://huggingface.co/mosaicml/mpt-7b/discussions/42.
The original description for MosaicML team below:
MPT-7B-Instruct is a model for short-form instruction following.
It is built by finetuning [MPT-7B](https://huggingface.co/mosaicml/mpt-7b) on a [dataset](https://huggingface.co/datasets/sam-mosaic/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets.
* License: _CC-By-SA-3.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct)
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
May 5, 2023
## Model License
CC-By-SA-3.0
## Documentation
* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)!
### Example Question/Instruction
**Longboi24**:
> What is a quoll?
**MPT-7B-Instruct**:
>A Quoll (pronounced “cool”) is one of Australia’s native carnivorous marsupial mammals, which are also known as macropods or wallabies in other parts around Asia and South America
## How to Use
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom model architecture that is not yet part of the `transformers` package.
It includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-instruct',
trust_remote_code=True
)
```
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision:
```python
import torch
import transformers
name = 'mosaicml/mpt-7b-instruct'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton'
config.init_device = 'cuda:0' # For fast initialization directly on GPU!
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
torch_dtype=torch.bfloat16, # Load model weights in bfloat16
trust_remote_code=True
)
```
Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
```python
import transformers
name = 'mosaicml/mpt-7b-instruct'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
trust_remote_code=True
)
```
This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
```
The model can then be used, for example, within a text-generation pipeline.
Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html).
```python
from transformers import pipeline
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
with torch.autocast('cuda', dtype=torch.bfloat16):
print(
pipe('Here is a recipe for vegan banana bread:\n',
max_new_tokens=100,
do_sample=True,
use_cache=True))
```
### Formatting
This model was trained on data formatted in the dolly-15k format:
```python
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY,
)
example = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? Explain before answering."
fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example)
```
In the above example, `fmt_ex` is ready to be tokenized and sent through the model.
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 6.7B |
|n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | 2048 |
## PreTraining Data
For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b).
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
### Training Configuration
This model was trained on 8 A100-40GBs for about 2.3 hours using the [MosaicML Platform](https://www.mosaicml.com/platform).
The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-7B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-Instruct was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## Acknowledgements
This model was finetuned by Sam Havens and the MosaicML NLP team
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b).
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
```
|
asenella/mmnist_MoPoEconfig_resnet_seed_0_ratio_0_c
|
asenella
| 2023-07-05T23:16:00Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-06-04T21:11:40Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
hopkins/eng-mya-simcse.near2.4440
|
hopkins
| 2023-07-05T22:49:46Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-05T22:28:28Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse.near2.4440
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.near2.4440
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: 1.8502
- Bleu: 4.8797
## 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
|
hopkins/eng-mya-simcse.dev2.4440
|
hopkins
| 2023-07-05T22:46:19Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-05T22:24:42Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse.dev2.4440
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.dev2.4440
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: 1.8287
- Bleu: 4.8012
## 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
|
DawidL/ppo-LunarLander-v2
|
DawidL
| 2023-07-05T22:15:49Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-05T22:15:32Z |
---
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: 251.01 +/- 17.80
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
...
```
|
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-07-05_g05
|
jordyvl
| 2023-07-05T22:13:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-05T20:03:36Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-07-05_g05
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-07-05_g05
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1631
- Accuracy: 0.72
- Exit 0 Accuracy: 0.1125
- Exit 1 Accuracy: 0.155
- Exit 2 Accuracy: 0.3325
- Exit 3 Accuracy: 0.3225
- Exit 4 Accuracy: 0.105
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 288
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 0.72 | 2 | 2.7600 | 0.1075 | 0.075 | 0.0675 | 0.0925 | 0.0625 | 0.0625 |
| No log | 1.72 | 4 | 2.7312 | 0.1125 | 0.07 | 0.065 | 0.12 | 0.0625 | 0.0625 |
| No log | 2.72 | 6 | 2.6924 | 0.1325 | 0.075 | 0.06 | 0.1175 | 0.0625 | 0.0625 |
| No log | 3.72 | 8 | 2.6597 | 0.1675 | 0.0775 | 0.055 | 0.125 | 0.0625 | 0.0625 |
| No log | 4.72 | 10 | 2.6138 | 0.2025 | 0.0825 | 0.0575 | 0.12 | 0.0625 | 0.0625 |
| No log | 5.72 | 12 | 2.5640 | 0.215 | 0.0875 | 0.08 | 0.11 | 0.0625 | 0.0625 |
| No log | 6.72 | 14 | 2.5403 | 0.22 | 0.09 | 0.08 | 0.12 | 0.0625 | 0.0625 |
| No log | 7.72 | 16 | 2.5207 | 0.2275 | 0.09 | 0.0925 | 0.12 | 0.0625 | 0.0625 |
| No log | 8.72 | 18 | 2.4860 | 0.27 | 0.0975 | 0.0975 | 0.115 | 0.0625 | 0.0625 |
| No log | 9.72 | 20 | 2.4397 | 0.295 | 0.1 | 0.1075 | 0.13 | 0.0625 | 0.0625 |
| No log | 10.72 | 22 | 2.4044 | 0.3 | 0.095 | 0.12 | 0.1475 | 0.0625 | 0.0625 |
| No log | 11.72 | 24 | 2.3671 | 0.3075 | 0.1025 | 0.1175 | 0.1475 | 0.065 | 0.0625 |
| No log | 12.72 | 26 | 2.3178 | 0.3175 | 0.105 | 0.115 | 0.145 | 0.0775 | 0.0625 |
| No log | 13.72 | 28 | 2.2514 | 0.355 | 0.105 | 0.1225 | 0.155 | 0.11 | 0.0625 |
| No log | 14.72 | 30 | 2.2030 | 0.3775 | 0.1125 | 0.125 | 0.195 | 0.115 | 0.065 |
| No log | 15.72 | 32 | 2.1831 | 0.3725 | 0.1075 | 0.13 | 0.225 | 0.1075 | 0.065 |
| No log | 16.72 | 34 | 2.1417 | 0.3675 | 0.115 | 0.1375 | 0.2375 | 0.1075 | 0.065 |
| No log | 17.72 | 36 | 2.0688 | 0.3975 | 0.1075 | 0.1375 | 0.255 | 0.115 | 0.07 |
| No log | 18.72 | 38 | 2.0252 | 0.4075 | 0.115 | 0.14 | 0.26 | 0.1225 | 0.0825 |
| No log | 19.72 | 40 | 1.9896 | 0.4275 | 0.115 | 0.14 | 0.265 | 0.125 | 0.0925 |
| No log | 20.72 | 42 | 1.9344 | 0.4675 | 0.11 | 0.14 | 0.2675 | 0.11 | 0.095 |
| No log | 21.72 | 44 | 1.8826 | 0.48 | 0.11 | 0.1375 | 0.2625 | 0.1175 | 0.095 |
| No log | 22.72 | 46 | 1.8459 | 0.505 | 0.11 | 0.1375 | 0.2525 | 0.1125 | 0.095 |
| No log | 23.72 | 48 | 1.8152 | 0.5375 | 0.11 | 0.14 | 0.275 | 0.12 | 0.0975 |
| No log | 24.72 | 50 | 1.7909 | 0.535 | 0.11 | 0.1425 | 0.2975 | 0.135 | 0.1025 |
| No log | 25.72 | 52 | 1.7339 | 0.5575 | 0.1075 | 0.145 | 0.3 | 0.13 | 0.0975 |
| No log | 26.72 | 54 | 1.6912 | 0.56 | 0.1125 | 0.145 | 0.295 | 0.14 | 0.1025 |
| No log | 27.72 | 56 | 1.6601 | 0.575 | 0.115 | 0.1475 | 0.3025 | 0.1425 | 0.1025 |
| No log | 28.72 | 58 | 1.6302 | 0.585 | 0.115 | 0.1475 | 0.295 | 0.145 | 0.1 |
| No log | 29.72 | 60 | 1.5808 | 0.585 | 0.1125 | 0.1475 | 0.3 | 0.155 | 0.1025 |
| No log | 30.72 | 62 | 1.5408 | 0.6 | 0.115 | 0.1475 | 0.3025 | 0.175 | 0.1 |
| No log | 31.72 | 64 | 1.5289 | 0.605 | 0.115 | 0.145 | 0.3 | 0.18 | 0.0975 |
| No log | 32.72 | 66 | 1.5030 | 0.6125 | 0.115 | 0.145 | 0.2975 | 0.18 | 0.1 |
| No log | 33.72 | 68 | 1.4653 | 0.635 | 0.115 | 0.145 | 0.3 | 0.185 | 0.1 |
| No log | 34.72 | 70 | 1.4342 | 0.6325 | 0.1175 | 0.145 | 0.295 | 0.21 | 0.0975 |
| No log | 35.72 | 72 | 1.4088 | 0.64 | 0.115 | 0.1475 | 0.2975 | 0.2175 | 0.095 |
| No log | 36.72 | 74 | 1.3848 | 0.6375 | 0.1175 | 0.1475 | 0.3075 | 0.2175 | 0.095 |
| No log | 37.72 | 76 | 1.3533 | 0.6775 | 0.12 | 0.1475 | 0.315 | 0.2475 | 0.095 |
| No log | 38.72 | 78 | 1.3349 | 0.68 | 0.1175 | 0.1475 | 0.3125 | 0.2525 | 0.095 |
| No log | 39.72 | 80 | 1.3140 | 0.665 | 0.115 | 0.1475 | 0.325 | 0.255 | 0.0975 |
| No log | 40.72 | 82 | 1.3001 | 0.6825 | 0.115 | 0.1475 | 0.325 | 0.265 | 0.0975 |
| No log | 41.72 | 84 | 1.2824 | 0.695 | 0.115 | 0.1475 | 0.32 | 0.2625 | 0.1 |
| No log | 42.72 | 86 | 1.2740 | 0.7 | 0.115 | 0.1525 | 0.3275 | 0.265 | 0.1 |
| No log | 43.72 | 88 | 1.2538 | 0.7 | 0.115 | 0.1525 | 0.33 | 0.2675 | 0.1 |
| No log | 44.72 | 90 | 1.2348 | 0.6925 | 0.1125 | 0.1525 | 0.33 | 0.29 | 0.1025 |
| No log | 45.72 | 92 | 1.2253 | 0.705 | 0.1125 | 0.1525 | 0.3325 | 0.29 | 0.105 |
| No log | 46.72 | 94 | 1.2225 | 0.7025 | 0.1125 | 0.1525 | 0.335 | 0.2925 | 0.105 |
| No log | 47.72 | 96 | 1.2153 | 0.7075 | 0.1125 | 0.1525 | 0.3375 | 0.295 | 0.105 |
| No log | 48.72 | 98 | 1.1988 | 0.725 | 0.1125 | 0.1525 | 0.3325 | 0.3025 | 0.105 |
| No log | 49.72 | 100 | 1.1897 | 0.725 | 0.1125 | 0.1525 | 0.3325 | 0.31 | 0.105 |
| No log | 50.72 | 102 | 1.1835 | 0.7225 | 0.1125 | 0.1525 | 0.33 | 0.315 | 0.1025 |
| No log | 51.72 | 104 | 1.1834 | 0.72 | 0.1125 | 0.1525 | 0.335 | 0.3175 | 0.1025 |
| No log | 52.72 | 106 | 1.1767 | 0.7275 | 0.1125 | 0.1525 | 0.335 | 0.305 | 0.105 |
| No log | 53.72 | 108 | 1.1726 | 0.7225 | 0.1125 | 0.1525 | 0.335 | 0.31 | 0.105 |
| No log | 54.72 | 110 | 1.1696 | 0.7175 | 0.1125 | 0.1525 | 0.335 | 0.31 | 0.105 |
| No log | 55.72 | 112 | 1.1673 | 0.7125 | 0.1125 | 0.155 | 0.3325 | 0.3125 | 0.105 |
| No log | 56.72 | 114 | 1.1653 | 0.7175 | 0.1125 | 0.155 | 0.3325 | 0.32 | 0.105 |
| No log | 57.72 | 116 | 1.1638 | 0.72 | 0.1125 | 0.155 | 0.33 | 0.325 | 0.105 |
| No log | 58.72 | 118 | 1.1633 | 0.72 | 0.1125 | 0.155 | 0.33 | 0.3225 | 0.105 |
| No log | 59.72 | 120 | 1.1631 | 0.72 | 0.1125 | 0.155 | 0.3325 | 0.3225 | 0.105 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
TheSupremeTaco/Taxi-v3
|
TheSupremeTaco
| 2023-07-05T22:11:34Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-05T22:11:31Z |
---
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: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="TheSupremeTaco/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"])
```
|
LiviaQi/trained_model
|
LiviaQi
| 2023-07-05T22:10:22Z | 188 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-07-05T21:06:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: trained_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. -->
# trained_model
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
asenella/mmnist_MMVAEPlusconfig_resnet_seed_0_ratio_0_c
|
asenella
| 2023-07-05T22:07:37Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-07-05T22:07:20Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
josero23/irrut
|
josero23
| 2023-07-05T21:55:44Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-05T21:42:44Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### irrut Dreambooth model trained by josero23 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:
|
newconew/speecht5_finetuned_voxpopuli_nl
|
newconew
| 2023-07-05T21:55:25Z | 80 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-07-05T19:33:24Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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_voxpopuli_nl
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: 0.4612
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5194 | 4.3 | 1000 | 0.4806 |
| 0.494 | 8.61 | 2000 | 0.4670 |
| 0.4929 | 12.91 | 3000 | 0.4642 |
| 0.4914 | 17.21 | 4000 | 0.4612 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hopkins/eng-fra-simcse.near2.4440
|
hopkins
| 2023-07-05T21:32:35Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-05T21:12:42Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse.near2.4440
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.near2.4440
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: 1.1372
- Bleu: 33.0232
## 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
|
hopkins/eng-fra-simcse.dev2.4440
|
hopkins
| 2023-07-05T21:32:34Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-05T21:12:42Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse.dev2.4440
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.dev2.4440
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: 1.1146
- Bleu: 33.6862
## 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
|
KevinQuijano/model
|
KevinQuijano
| 2023-07-05T21:12:27Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-05T14:32:19Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - KevinQuijano/model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. 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.
DreamBooth for the text encoder was enabled: False.
|
joydragon/Reinforce-Pixelcopter-PLE-v2
|
joydragon
| 2023-07-05T20:50:19Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-05T20:50:15Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.00 +/- 28.73
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
|
joydragon/Reinforce-Pixelcopter-PLE-v1
|
joydragon
| 2023-07-05T20:49:56Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-05T19:14:28Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 39.00 +/- 36.85
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
|
choward/csv
|
choward
| 2023-07-05T20:46:13Z | 0 | 0 | null |
[
"text-generation",
"region:us"
] |
text-generation
| 2023-07-05T20:42:22Z |
---
pipeline_tag: text-generation
---
|
egarciamartin/poca-SoccerTwos
|
egarciamartin
| 2023-07-05T20:40:50Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-07-05T20:40:07Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: egarciamartin/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dhiruHF/falcon7b-FT-DocQA-v2
|
dhiruHF
| 2023-07-05T20:39:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-05T20:39:10Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
vinson099/DatasetModel
|
vinson099
| 2023-07-05T20:34:01Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-05T18:00:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: DatasetModel
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: flower_photos
split: train[:500]
args: flower_photos
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DatasetModel
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6457
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 6 | 1.2651 | 0.99 |
| 1.3875 | 1.92 | 12 | 0.7931 | 1.0 |
| 1.3875 | 2.88 | 18 | 0.6457 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
SaffalPoosh/falcon_7B_instruct_safetensors
|
SaffalPoosh
| 2023-07-05T20:27:23Z | 16 | 0 |
transformers
|
[
"transformers",
"safetensors",
"RefinedWebModel",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-05T20:13:30Z |
Converted using oobabooga script to safetensors to test the TGI LLM inference engine
|
durdana/alpaca7B-lora
|
durdana
| 2023-07-05T20:25:35Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-05T20:25:31Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
jcm-art/hf_image_classification_tuning_pipeline
|
jcm-art
| 2023-07-05T20:14:07Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-05T19:35:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: hf_image_classification_tuning_pipeline
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.903
---
<!-- 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. -->
# hf_image_classification_tuning_pipeline
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5764
- Accuracy: 0.903
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7113 | 0.99 | 62 | 2.4840 | 0.849 |
| 1.8024 | 2.0 | 125 | 1.7298 | 0.891 |
| 1.5532 | 2.98 | 186 | 1.5764 | 0.903 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-07-05_weighted
|
jordyvl
| 2023-07-05T20:02:58Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-05T17:53:13Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-07-05_weighted
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-07-05_weighted
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0783
- Accuracy: 0.71
- Exit 0 Accuracy: 0.115
- Exit 1 Accuracy: 0.1575
- Exit 2 Accuracy: 0.185
- Exit 3 Accuracy: 0.0875
- Exit 4 Accuracy: 0.0625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 288
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 0.72 | 2 | 2.7602 | 0.1125 | 0.0925 | 0.0675 | 0.0875 | 0.0625 | 0.0625 |
| No log | 1.72 | 4 | 2.7309 | 0.115 | 0.1175 | 0.0675 | 0.1075 | 0.0625 | 0.0625 |
| No log | 2.72 | 6 | 2.6967 | 0.1325 | 0.095 | 0.06 | 0.1175 | 0.0625 | 0.0625 |
| No log | 3.72 | 8 | 2.6631 | 0.17 | 0.085 | 0.0575 | 0.1275 | 0.0625 | 0.0625 |
| No log | 4.72 | 10 | 2.6242 | 0.205 | 0.085 | 0.0575 | 0.1225 | 0.0625 | 0.0625 |
| No log | 5.72 | 12 | 2.5736 | 0.2175 | 0.0875 | 0.0825 | 0.12 | 0.0625 | 0.0625 |
| No log | 6.72 | 14 | 2.5410 | 0.215 | 0.09 | 0.08 | 0.12 | 0.0625 | 0.0625 |
| No log | 7.72 | 16 | 2.5229 | 0.2325 | 0.1 | 0.0925 | 0.13 | 0.0625 | 0.0625 |
| No log | 8.72 | 18 | 2.4841 | 0.2525 | 0.1 | 0.1 | 0.1325 | 0.0625 | 0.0625 |
| No log | 9.72 | 20 | 2.4382 | 0.29 | 0.1 | 0.1025 | 0.1325 | 0.0625 | 0.0625 |
| No log | 10.72 | 22 | 2.3823 | 0.3 | 0.1 | 0.1275 | 0.1325 | 0.0625 | 0.0625 |
| No log | 11.72 | 24 | 2.3389 | 0.3275 | 0.1 | 0.1175 | 0.1225 | 0.0625 | 0.0625 |
| No log | 12.72 | 26 | 2.3002 | 0.35 | 0.0975 | 0.12 | 0.1225 | 0.0625 | 0.0625 |
| No log | 13.72 | 28 | 2.2421 | 0.36 | 0.0975 | 0.125 | 0.1275 | 0.0625 | 0.0625 |
| No log | 14.72 | 30 | 2.2026 | 0.3575 | 0.1025 | 0.13 | 0.125 | 0.0625 | 0.0625 |
| No log | 15.72 | 32 | 2.1712 | 0.375 | 0.105 | 0.1375 | 0.125 | 0.0625 | 0.0625 |
| No log | 16.72 | 34 | 2.0999 | 0.4075 | 0.1 | 0.145 | 0.125 | 0.0625 | 0.0625 |
| No log | 17.72 | 36 | 2.0414 | 0.4225 | 0.1025 | 0.145 | 0.1275 | 0.0625 | 0.0625 |
| No log | 18.72 | 38 | 1.9981 | 0.4375 | 0.0975 | 0.1425 | 0.13 | 0.0625 | 0.0625 |
| No log | 19.72 | 40 | 1.9369 | 0.4575 | 0.1025 | 0.14 | 0.1425 | 0.0625 | 0.0625 |
| No log | 20.72 | 42 | 1.8903 | 0.4975 | 0.1025 | 0.14 | 0.145 | 0.0625 | 0.0625 |
| No log | 21.72 | 44 | 1.8242 | 0.525 | 0.1025 | 0.1425 | 0.15 | 0.0625 | 0.0625 |
| No log | 22.72 | 46 | 1.7520 | 0.5325 | 0.11 | 0.1475 | 0.1475 | 0.0625 | 0.0625 |
| No log | 23.72 | 48 | 1.7203 | 0.5525 | 0.1125 | 0.1475 | 0.1525 | 0.0625 | 0.0625 |
| No log | 24.72 | 50 | 1.6753 | 0.565 | 0.1125 | 0.1475 | 0.155 | 0.0625 | 0.0625 |
| No log | 25.72 | 52 | 1.6245 | 0.575 | 0.1125 | 0.1475 | 0.155 | 0.0625 | 0.0625 |
| No log | 26.72 | 54 | 1.5832 | 0.61 | 0.11 | 0.15 | 0.1525 | 0.0625 | 0.0625 |
| No log | 27.72 | 56 | 1.5404 | 0.61 | 0.11 | 0.1475 | 0.155 | 0.0625 | 0.0625 |
| No log | 28.72 | 58 | 1.4958 | 0.6125 | 0.11 | 0.1475 | 0.1575 | 0.0625 | 0.0625 |
| No log | 29.72 | 60 | 1.4613 | 0.6325 | 0.11 | 0.1475 | 0.1575 | 0.0625 | 0.0625 |
| No log | 30.72 | 62 | 1.4479 | 0.63 | 0.11 | 0.1525 | 0.16 | 0.0625 | 0.0625 |
| No log | 31.72 | 64 | 1.4101 | 0.64 | 0.1125 | 0.1525 | 0.165 | 0.0625 | 0.0625 |
| No log | 32.72 | 66 | 1.3699 | 0.655 | 0.1125 | 0.1525 | 0.1675 | 0.0625 | 0.0625 |
| No log | 33.72 | 68 | 1.3427 | 0.6725 | 0.115 | 0.1525 | 0.165 | 0.0625 | 0.0625 |
| No log | 34.72 | 70 | 1.3161 | 0.6825 | 0.115 | 0.1525 | 0.1625 | 0.0625 | 0.0625 |
| No log | 35.72 | 72 | 1.2896 | 0.7025 | 0.115 | 0.1525 | 0.1675 | 0.0625 | 0.0625 |
| No log | 36.72 | 74 | 1.2720 | 0.705 | 0.11 | 0.1525 | 0.185 | 0.0625 | 0.0625 |
| No log | 37.72 | 76 | 1.2471 | 0.71 | 0.11 | 0.1525 | 0.1775 | 0.0625 | 0.0625 |
| No log | 38.72 | 78 | 1.2307 | 0.71 | 0.11 | 0.155 | 0.1775 | 0.0625 | 0.0625 |
| No log | 39.72 | 80 | 1.2174 | 0.7175 | 0.1125 | 0.155 | 0.1825 | 0.0625 | 0.0625 |
| No log | 40.72 | 82 | 1.1991 | 0.705 | 0.1125 | 0.1525 | 0.1775 | 0.0625 | 0.0625 |
| No log | 41.72 | 84 | 1.1867 | 0.71 | 0.1175 | 0.1525 | 0.18 | 0.065 | 0.0625 |
| No log | 42.72 | 86 | 1.1764 | 0.7025 | 0.115 | 0.1525 | 0.18 | 0.0675 | 0.0625 |
| No log | 43.72 | 88 | 1.1601 | 0.715 | 0.115 | 0.1525 | 0.1825 | 0.0725 | 0.0625 |
| No log | 44.72 | 90 | 1.1410 | 0.7175 | 0.115 | 0.1525 | 0.18 | 0.075 | 0.0625 |
| No log | 45.72 | 92 | 1.1408 | 0.71 | 0.115 | 0.155 | 0.1825 | 0.075 | 0.0625 |
| No log | 46.72 | 94 | 1.1443 | 0.7075 | 0.115 | 0.155 | 0.1825 | 0.0775 | 0.0625 |
| No log | 47.72 | 96 | 1.1364 | 0.705 | 0.115 | 0.155 | 0.1775 | 0.0825 | 0.0625 |
| No log | 48.72 | 98 | 1.1251 | 0.71 | 0.115 | 0.155 | 0.175 | 0.085 | 0.0625 |
| No log | 49.72 | 100 | 1.1113 | 0.7175 | 0.115 | 0.155 | 0.1775 | 0.085 | 0.0625 |
| No log | 50.72 | 102 | 1.1040 | 0.7175 | 0.115 | 0.155 | 0.18 | 0.0875 | 0.0625 |
| No log | 51.72 | 104 | 1.0972 | 0.715 | 0.115 | 0.155 | 0.18 | 0.0875 | 0.0625 |
| No log | 52.72 | 106 | 1.0938 | 0.7175 | 0.115 | 0.1575 | 0.1825 | 0.0875 | 0.0625 |
| No log | 53.72 | 108 | 1.0931 | 0.71 | 0.115 | 0.1575 | 0.185 | 0.0875 | 0.0625 |
| No log | 54.72 | 110 | 1.0887 | 0.7075 | 0.115 | 0.1575 | 0.185 | 0.0875 | 0.0625 |
| No log | 55.72 | 112 | 1.0865 | 0.7125 | 0.115 | 0.1575 | 0.1875 | 0.0875 | 0.0625 |
| No log | 56.72 | 114 | 1.0828 | 0.7125 | 0.115 | 0.1575 | 0.1875 | 0.0875 | 0.0625 |
| No log | 57.72 | 116 | 1.0801 | 0.7075 | 0.115 | 0.1575 | 0.1875 | 0.0875 | 0.0625 |
| No log | 58.72 | 118 | 1.0786 | 0.7125 | 0.115 | 0.1575 | 0.1875 | 0.0875 | 0.0625 |
| No log | 59.72 | 120 | 1.0783 | 0.71 | 0.115 | 0.1575 | 0.185 | 0.0875 | 0.0625 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BadreddineHug/donut-base-ocr6
|
BadreddineHug
| 2023-07-05T20:01:09Z | 72 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-07-05T19:35:16Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-ocr6
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. -->
# donut-base-ocr6
This model is a fine-tuned version of [BadreddineHug/donut-base-ocr4](https://huggingface.co/BadreddineHug/donut-base-ocr4) on the imagefolder 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: 0.002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
pszemraj/gpt2-medium-vaguely-human-dialogue
|
pszemraj
| 2023-07-05T19:57:49Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"gpt",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- text-generation
- gpt2
- gpt
license: mit
widget:
- text: |+
Do you like my new haircut?
person beta:
example_title: haircut
- text: |+
I love to learn new things.. are you willing to teach me something?
person beta:
example_title: teaching
- text: |+
What's your favorite animal? Mine is the dog?
person beta:
example_title: favorite
- text: |+
how much does it cost?
person beta:
example_title: money
inference:
parameters:
min_length: 2
max_length: 64
length_penalty: 0.6
no_repeat_ngram_size: 3
do_sample: true
top_p: 0.85
top_k: 10
repetition_penalty: 2.1
pipeline_tag: text-generation
---
<!-- 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. -->
# pszemraj/gpt2-medium-vaguely-human-dialogue
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on a parsed version of Wizard of Wikipedia. Because the batch size was so large, it learned a general understanding of words that makes sense together but does not specifically respond to anything - sort of like an alien learning to imitate human words to convince others that it is human.
It achieves the following results on the evaluation set:
- Loss: 4.3281
## Model description
- a decent example of what happens when your batch size is too large and the global optima does not reflect specific prompts / use cases.
## Intended uses & limitations
- there are no intended uses
## Training and evaluation data
- a parsed version of the wizard of Wikipedia dataset
## 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
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 34.991 | 1.0 | 837 | 14.8359 |
| 12.2881 | 2.0 | 1674 | 9.375 |
| 8.5071 | 3.0 | 2511 | 7.2148 |
| 7.6031 | 4.0 | 3348 | 6.1758 |
| 6.4808 | 5.0 | 4185 | 5.5820 |
| 5.8562 | 6.0 | 5022 | 5.0977 |
| 5.6094 | 7.0 | 5859 | 4.8203 |
| 5.2591 | 8.0 | 6696 | 4.5977 |
| 5.0031 | 9.0 | 7533 | 4.4219 |
| 4.8837 | 10.0 | 8370 | 4.3281 |
### Framework versions
- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.0
|
khushpreet/eyedisease
|
khushpreet
| 2023-07-05T19:51:05Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"medical",
"image-classification",
"arxiv:1910.09700",
"region:us"
] |
image-classification
| 2023-07-05T19:48:02Z |
---
metrics:
- accuracy
library_name: keras
pipeline_tag: image-classification
tags:
- medical
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
rsilg/dqn-SpaceInvadersNoFrameskip-v4
|
rsilg
| 2023-07-05T19:40:58Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-05T19:40:29Z |
---
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: 541.50 +/- 118.85
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 rsilg -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 rsilg -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 rsilg
```
## 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'}
```
|
aroot/wsample.43a
|
aroot
| 2023-07-05T19:38:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-05T18:34:22Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: 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. -->
# 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: 1.8306
- Bleu: 4.7146
## 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
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
|
aroot/wsample.32a
|
aroot
| 2023-07-05T19:38:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-05T18:34:12Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: wsample.32a
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. -->
# wsample.32a
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: 1.8284
- Bleu: 4.7412
## 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
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
|
Shezus/finetuning-sentiment-model-3000-samples
|
Shezus
| 2023-07-05T19:30:51Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-03T22:11:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8766666666666667
- name: F1
type: f1
value: 0.877076411960133
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
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: 0.3107
- Accuracy: 0.8767
- F1: 0.8771
## 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
|
BadreddineHug/donut-base-ocr4
|
BadreddineHug
| 2023-07-05T19:27:19Z | 74 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-07-05T18:38:04Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-ocr4
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. -->
# donut-base-ocr4
This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder 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: 0.002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
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