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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| library_name
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Shishir1807/Indication_Training-1
|
Shishir1807
| 2023-07-13T10:42:46Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-13T10:40:21Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.28.1
pip install accelerate==0.18.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/Indication_Training-1",
torch_dtype=torch.float16,
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, if you prefer to not use `trust_remote_code=True` you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/Indication_Training-1",
use_fast=True,
padding_side="left"
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/Indication_Training-1",
torch_dtype=torch.float16,
device_map={"": "cuda:0"}
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/Indication_Training-1" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=2,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=Shishir1807/Indication_Training-1 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
waliaMuskaan011/model5
|
waliaMuskaan011
| 2023-07-13T10:40:35Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-12T16:33:09Z |
---
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
- tasks: automatic speech recognition
### Framework versions
- PEFT 0.4.0.dev0
|
imone/LLaMA_13B_with_EOT_token
|
imone
| 2023-07-13T10:40:28Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-26T08:22:58Z |
---
license: other
language:
- en
pipeline_tag: text-generation
---
# LLaMA 13B with End-of-turn (EOT) Token
This is the LLaMA 13B model with `<|end_of_turn|>` token added as id `32000`. The token input/output embedding is initialized as the mean of all existing input/output token embeddings, respectively.
|
ivivnov/ppo-Huggy
|
ivivnov
| 2023-07-13T10:36:38Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-13T10:36:25Z |
---
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: ivivnov/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
madroid/autotrain-text-chat-74266139562
|
madroid
| 2023-07-13T10:25:06Z | 108 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"autotrain",
"en",
"dataset:madroid/autotrain-data-text-chat",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T10:24:08Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- madroid/autotrain-data-text-chat
co2_eq_emissions:
emissions: 0.3508472536259808
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 74266139562
- CO2 Emissions (in grams): 0.3508
## Validation Metrics
- Loss: 0.005
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/madroid/autotrain-text-chat-74266139562
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("madroid/autotrain-text-chat-74266139562", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("madroid/autotrain-text-chat-74266139562", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
VK246/IC_ver6a_coco_swin_gpt2_50Apc_1e
|
VK246
| 2023-07-13T10:22:02Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:coco",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-07-13T07:09:15Z |
---
tags:
- generated_from_trainer
datasets:
- coco
metrics:
- rouge
- bleu
model-index:
- name: IC_ver6a_coco_swin_gpt2_50Apc_1e
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. -->
# IC_ver6a_coco_swin_gpt2_50Apc_1e
This model is a fine-tuned version of [](https://huggingface.co/) on the coco dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8477
- Rouge1: 40.2406
- Rouge2: 15.0629
- Rougel: 36.6294
- Rougelsum: 36.6164
- Bleu: 9.0728
- Gen Len: 11.2806
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------:|:-------:|
| 1.1343 | 0.17 | 500 | 0.9708 | 35.1592 | 11.4248 | 32.3362 | 32.3316 | 6.404 | 11.2806 |
| 0.9606 | 0.34 | 1000 | 0.9123 | 37.9656 | 12.9721 | 34.5569 | 34.5606 | 7.489 | 11.2806 |
| 0.9286 | 0.51 | 1500 | 0.8828 | 38.7702 | 13.945 | 35.4661 | 35.4648 | 8.022 | 11.2806 |
| 0.8994 | 0.68 | 2000 | 0.8619 | 39.8572 | 14.6183 | 36.3345 | 36.3262 | 8.7008 | 11.2806 |
| 0.8843 | 0.85 | 2500 | 0.8525 | 39.8151 | 14.7431 | 36.3033 | 36.2918 | 8.8305 | 11.2806 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
arunboss/triage_R5_model
|
arunboss
| 2023-07-13T10:17:28Z | 218 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-13T03:02:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: triage_R5_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. -->
# triage_R5_model
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0123
- Accuracy: 0.6837
## 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0452 | 1.0 | 159 | 1.9622 | 0.3814 |
| 1.7034 | 2.0 | 319 | 1.5695 | 0.4923 |
| 1.441 | 3.0 | 479 | 1.4427 | 0.5433 |
| 1.2908 | 4.0 | 639 | 1.2970 | 0.5895 |
| 1.2294 | 5.0 | 798 | 1.2293 | 0.6071 |
| 1.1097 | 6.0 | 958 | 1.1892 | 0.6300 |
| 1.0342 | 7.0 | 1118 | 1.1048 | 0.6546 |
| 0.9644 | 8.0 | 1278 | 1.0731 | 0.6678 |
| 0.8534 | 9.0 | 1437 | 1.0367 | 0.6766 |
| 0.8037 | 10.0 | 1597 | 1.0211 | 0.6802 |
| 0.7765 | 11.0 | 1757 | 1.0073 | 0.6885 |
| 0.7658 | 11.94 | 1908 | 1.0123 | 0.6837 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sarakolding/daT5-summariser
|
sarakolding
| 2023-07-13T10:12:54Z | 120 | 9 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"da",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-05-30T19:12:46Z |
---
language:
- da
tags:
- summarization
widget:
- text: "Det nye studie Cognitive Science på Aarhus Universitet, som i år havde Østjyllands højeste adgangskrav på 11,7 i karaktergennemsnit, udklækker det første hold bachelorer til sommer.
Men når de skal læse videre på kandidaten må de til udlandet, hvis ikke de vil skifte til et andet fag. Aarhus Universitet kan nemlig ikke nå at oprette en kandidat i Cognitive Science til næste sommer, hvor det første hold bachelorer er færdige.
Det rammer blandt andre Julie Sohn, der startede på uddannelsen i sommeren 2015, og derfor kun mangler et år, før hun er bachelor.
- Jeg synes, at det er ærgerligt, at vi som nye studerende på et populært studie ikke kan tage en kandidat i Danmark, siger hun.
Bacheloruddannelsen i Cognitive Science blev oprettet af Aarhus Universitet i 2015, og uddannelsen kombinerer viden om menneskelig adfærd med avanceret statistik. Da der endnu ikke er oprettet en kandidatuddannelse indenfor dette område, har Julie Sohn i stedet mulighed for at læse en kandidatgrad i for eksempel informationsvidenskab.
Hun vil dog hellere fortsætte på Cognitive Science, og derfor overvejer hun nu at læse videre i udlandet.
- Det ser ud til, at det er den eneste mulighed, hvis man gerne vil læse videre på noget, der faktisk passer ind til vores studie, siger hun.
Nye regler giver forsinkelse
På Aarhus Universitet havde man håbet på at have kandidatuddannelsen klar, når det første hold bachelorer bliver færdige til sommer. Arbejdet er dog blevet forsinket, fordi der er kommet nye regler for, hvornår man må oprette en uddannelse, fortæller Niels Lehmann, prodekan på fakultetet Arts, som Cognitive Science hører under.
Det er nogle meget dygtige studerende, der kommer ind på uddannelsen, og det er klart, at de i et vist omfang vil orientere sig mod udlandet, hvor man så kan forestille sig, at de bider sig fast.
NIELS LEHMANN, PRODEKAN, AARHUS UNIVERSITET
Tidligere skulle Danmarks Akkrediteringsinstitution se alle nye uddannelser efter i sømmene for at sikre, at kvaliteten var i orden. Nu skal uddannelsesinstitutionerne selv stå for det kvalitetstjek.
Men det tjek har Aarhus Universitet endnu ikke fået grønt lys til selv at udføre, fortæller prodekanen.
- Vi ville meget gerne have kunnet nå at få et udbud på kandidaten i gang i 2018, men så længe man er under institutionsakkreditering, så kan man ikke ansøge om nye uddannelser, siger han.
Det er endnu usikkert, hvornår Aarhus Universitet kan oprette kandidaten i Cognitive Science. Hvis de får alle de nødvendige godkendelser, kan den tidligst være klar i 2019.
Prodekan Niels Lehmann frygter, at Danmark kommer til at miste nogle af landets skarpeste studerende, hvis de er nødt til at rejse til udlandet for at gøre deres uddannelse færdig.
- Det er nogle meget, meget dygtige studerende, der kommer ind på denne uddannelse, og det er klart, at de i et vist omfang vil orientere sig mod udlandet, hvor man så kan forestille sig, at de bider sig fast, siger han.
Hos Danmarks Akkrediteringsinstitution forstår man godt, at universitets ansatte og studenrede ærgrer sig.
- Jeg kan godt forstå, at Aarhus Universitet ærgrer sig over, at det trækker ud, og at der går noget tid, før man får mulighed for at oprette nye uddannelser, og at man ikke har fået den genvej til at oprette nye uddannelser, som ville være fuldt med, hvis man havde opnået en positiv institutionsakkreditering, siger kommunikationsansvarlig Daniel Sebastian Larsen.
I år var Cognitive Science i Aarhus den uddannelse i Danmark, der havde det fjerde højeste karakterkrav - det højeste var 'AP Graduate in Marketing Management' på Erhvervsakademi Sjælland med et krav på 12,3."
example_title: "Summarization"
---
This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019).
|
Devops-hestabit/Othehalf-1.3b-onnx
|
Devops-hestabit
| 2023-07-13T10:08:08Z | 4 | 0 |
transformers
|
[
"transformers",
"onnx",
"gpt_neox",
"text-generation",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-13T09:25:46Z |
---
license: creativeml-openrail-m
---
|
gaioNL/roberta-base_ag_news
|
gaioNL
| 2023-07-13T09:49:21Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-10T04:49:51Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- ag_news
model-index:
- name: roberta-base_ag_news
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. -->
# roberta-base_ag_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.4306 | 1.0 | 15000 | 1.3696 |
| 1.0725 | 2.0 | 30000 | 0.9407 |
| 0.8715 | 3.0 | 45000 | 0.7991 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
thomas2112/dqn-SpaceInvadersNoFrameskip-v4
|
thomas2112
| 2023-07-13T09:39:48Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-04-15T12:26:01Z |
---
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: 622.50 +/- 94.51
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 thomas2112 -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 thomas2112 -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 thomas2112
```
## 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'}
```
|
jordyvl/vit-tiny_tobacco3482_simkd__tNone_gNone__logits
|
jordyvl
| 2023-07-13T09:39:46Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-13T07:53:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-tiny_tobacco3482_simkd__tNone_gNone__logits
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-tiny_tobacco3482_simkd__tNone_gNone__logits
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0388
- Accuracy: 0.82
- Brier Loss: 0.7227
- Nll: 2.6079
- F1 Micro: 0.82
- F1 Macro: 0.7960
- Ece: 0.6182
- Aurc: 0.0575
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 100 | 0.0492 | 0.03 | 0.9002 | 15.7630 | 0.03 | 0.0130 | 0.1184 | 0.9407 |
| No log | 2.0 | 200 | 0.0479 | 0.045 | 0.8991 | 12.1839 | 0.045 | 0.0140 | 0.1350 | 0.9268 |
| No log | 3.0 | 300 | 0.0472 | 0.305 | 0.8968 | 12.2323 | 0.305 | 0.1551 | 0.2930 | 0.5596 |
| No log | 4.0 | 400 | 0.0463 | 0.305 | 0.8938 | 10.0711 | 0.305 | 0.1809 | 0.3077 | 0.5037 |
| 0.0541 | 5.0 | 500 | 0.0453 | 0.325 | 0.8898 | 9.3778 | 0.325 | 0.2046 | 0.3193 | 0.4698 |
| 0.0541 | 6.0 | 600 | 0.0442 | 0.4 | 0.8787 | 9.6363 | 0.4000 | 0.2403 | 0.3713 | 0.3759 |
| 0.0541 | 7.0 | 700 | 0.0433 | 0.4 | 0.8639 | 9.3483 | 0.4000 | 0.2510 | 0.3685 | 0.3742 |
| 0.0541 | 8.0 | 800 | 0.0428 | 0.475 | 0.8521 | 9.0007 | 0.4750 | 0.2666 | 0.4177 | 0.2978 |
| 0.0541 | 9.0 | 900 | 0.0422 | 0.515 | 0.8438 | 7.6934 | 0.515 | 0.3644 | 0.4480 | 0.2431 |
| 0.0451 | 10.0 | 1000 | 0.0417 | 0.515 | 0.8210 | 7.6243 | 0.515 | 0.3743 | 0.4387 | 0.2146 |
| 0.0451 | 11.0 | 1100 | 0.0411 | 0.68 | 0.8190 | 3.6435 | 0.68 | 0.5226 | 0.5677 | 0.1272 |
| 0.0451 | 12.0 | 1200 | 0.0403 | 0.655 | 0.7803 | 5.4537 | 0.655 | 0.5379 | 0.5202 | 0.1352 |
| 0.0451 | 13.0 | 1300 | 0.0398 | 0.75 | 0.7745 | 4.1150 | 0.75 | 0.6543 | 0.5945 | 0.0749 |
| 0.0451 | 14.0 | 1400 | 0.0390 | 0.77 | 0.7561 | 2.8538 | 0.7700 | 0.6703 | 0.6085 | 0.0753 |
| 0.0406 | 15.0 | 1500 | 0.0392 | 0.745 | 0.7637 | 3.7047 | 0.745 | 0.6673 | 0.5796 | 0.1060 |
| 0.0406 | 16.0 | 1600 | 0.0398 | 0.73 | 0.7603 | 4.3010 | 0.7300 | 0.6681 | 0.5693 | 0.0949 |
| 0.0406 | 17.0 | 1700 | 0.0399 | 0.705 | 0.7610 | 3.6375 | 0.705 | 0.6564 | 0.5590 | 0.0923 |
| 0.0406 | 18.0 | 1800 | 0.0397 | 0.705 | 0.7536 | 4.2628 | 0.705 | 0.6170 | 0.5472 | 0.1332 |
| 0.0406 | 19.0 | 1900 | 0.0390 | 0.745 | 0.7400 | 2.8861 | 0.745 | 0.6686 | 0.5644 | 0.1070 |
| 0.0379 | 20.0 | 2000 | 0.0394 | 0.785 | 0.7238 | 3.3111 | 0.785 | 0.7255 | 0.5995 | 0.0695 |
| 0.0379 | 21.0 | 2100 | 0.0396 | 0.76 | 0.7419 | 3.1935 | 0.76 | 0.7463 | 0.5885 | 0.0793 |
| 0.0379 | 22.0 | 2200 | 0.0396 | 0.785 | 0.7423 | 3.7954 | 0.785 | 0.7737 | 0.6043 | 0.0873 |
| 0.0379 | 23.0 | 2300 | 0.0395 | 0.78 | 0.7321 | 3.8067 | 0.78 | 0.7491 | 0.5885 | 0.0908 |
| 0.0379 | 24.0 | 2400 | 0.0387 | 0.8 | 0.7228 | 2.9339 | 0.8000 | 0.7758 | 0.6038 | 0.0609 |
| 0.037 | 25.0 | 2500 | 0.0387 | 0.795 | 0.7222 | 2.6252 | 0.795 | 0.7601 | 0.6094 | 0.0606 |
| 0.037 | 26.0 | 2600 | 0.0387 | 0.8 | 0.7241 | 2.6253 | 0.8000 | 0.7628 | 0.6110 | 0.0607 |
| 0.037 | 27.0 | 2700 | 0.0387 | 0.795 | 0.7235 | 2.4818 | 0.795 | 0.7616 | 0.6093 | 0.0629 |
| 0.037 | 28.0 | 2800 | 0.0387 | 0.795 | 0.7245 | 2.6226 | 0.795 | 0.7586 | 0.6032 | 0.0604 |
| 0.037 | 29.0 | 2900 | 0.0387 | 0.805 | 0.7253 | 2.7588 | 0.805 | 0.7725 | 0.6144 | 0.0609 |
| 0.0364 | 30.0 | 3000 | 0.0387 | 0.805 | 0.7233 | 2.4956 | 0.805 | 0.7701 | 0.6204 | 0.0594 |
| 0.0364 | 31.0 | 3100 | 0.0387 | 0.81 | 0.7241 | 2.7695 | 0.81 | 0.7797 | 0.6188 | 0.0602 |
| 0.0364 | 32.0 | 3200 | 0.0386 | 0.81 | 0.7239 | 2.6185 | 0.81 | 0.7797 | 0.6190 | 0.0580 |
| 0.0364 | 33.0 | 3300 | 0.0387 | 0.805 | 0.7238 | 2.9106 | 0.805 | 0.7717 | 0.6182 | 0.0586 |
| 0.0364 | 34.0 | 3400 | 0.0386 | 0.805 | 0.7231 | 2.9062 | 0.805 | 0.7725 | 0.6133 | 0.0590 |
| 0.0364 | 35.0 | 3500 | 0.0387 | 0.805 | 0.7247 | 2.7645 | 0.805 | 0.7717 | 0.6141 | 0.0590 |
| 0.0364 | 36.0 | 3600 | 0.0386 | 0.805 | 0.7238 | 2.9152 | 0.805 | 0.7717 | 0.6104 | 0.0578 |
| 0.0364 | 37.0 | 3700 | 0.0387 | 0.805 | 0.7229 | 2.9094 | 0.805 | 0.7717 | 0.6142 | 0.0588 |
| 0.0364 | 38.0 | 3800 | 0.0386 | 0.805 | 0.7237 | 2.9185 | 0.805 | 0.7717 | 0.6173 | 0.0565 |
| 0.0364 | 39.0 | 3900 | 0.0386 | 0.805 | 0.7230 | 2.9178 | 0.805 | 0.7717 | 0.6131 | 0.0578 |
| 0.0364 | 40.0 | 4000 | 0.0386 | 0.805 | 0.7233 | 2.9155 | 0.805 | 0.7717 | 0.6131 | 0.0561 |
| 0.0364 | 41.0 | 4100 | 0.0386 | 0.805 | 0.7235 | 2.9142 | 0.805 | 0.7717 | 0.6173 | 0.0574 |
| 0.0364 | 42.0 | 4200 | 0.0387 | 0.805 | 0.7225 | 2.9162 | 0.805 | 0.7717 | 0.6196 | 0.0572 |
| 0.0364 | 43.0 | 4300 | 0.0387 | 0.805 | 0.7231 | 3.0596 | 0.805 | 0.7717 | 0.6139 | 0.0560 |
| 0.0364 | 44.0 | 4400 | 0.0387 | 0.805 | 0.7229 | 3.0584 | 0.805 | 0.7717 | 0.6140 | 0.0558 |
| 0.0364 | 45.0 | 4500 | 0.0386 | 0.815 | 0.7231 | 2.9107 | 0.815 | 0.7856 | 0.6224 | 0.0551 |
| 0.0364 | 46.0 | 4600 | 0.0386 | 0.8 | 0.7228 | 3.0609 | 0.8000 | 0.7683 | 0.6154 | 0.0570 |
| 0.0364 | 47.0 | 4700 | 0.0386 | 0.8 | 0.7229 | 3.0539 | 0.8000 | 0.7683 | 0.6141 | 0.0564 |
| 0.0364 | 48.0 | 4800 | 0.0387 | 0.805 | 0.7228 | 2.9149 | 0.805 | 0.7753 | 0.6164 | 0.0559 |
| 0.0364 | 49.0 | 4900 | 0.0387 | 0.805 | 0.7239 | 3.0631 | 0.805 | 0.7729 | 0.6144 | 0.0569 |
| 0.0364 | 50.0 | 5000 | 0.0387 | 0.8 | 0.7231 | 3.0551 | 0.8000 | 0.7683 | 0.6094 | 0.0562 |
| 0.0364 | 51.0 | 5100 | 0.0387 | 0.815 | 0.7232 | 3.0662 | 0.815 | 0.7868 | 0.6212 | 0.0569 |
| 0.0364 | 52.0 | 5200 | 0.0386 | 0.805 | 0.7226 | 2.9067 | 0.805 | 0.7753 | 0.6241 | 0.0550 |
| 0.0364 | 53.0 | 5300 | 0.0387 | 0.81 | 0.7236 | 2.9086 | 0.81 | 0.7761 | 0.6209 | 0.0558 |
| 0.0364 | 54.0 | 5400 | 0.0387 | 0.805 | 0.7240 | 2.9108 | 0.805 | 0.7729 | 0.6072 | 0.0577 |
| 0.0364 | 55.0 | 5500 | 0.0387 | 0.815 | 0.7228 | 2.9235 | 0.815 | 0.7832 | 0.6202 | 0.0556 |
| 0.0364 | 56.0 | 5600 | 0.0387 | 0.82 | 0.7229 | 2.9335 | 0.82 | 0.7898 | 0.6273 | 0.0544 |
| 0.0364 | 57.0 | 5700 | 0.0387 | 0.82 | 0.7230 | 2.9210 | 0.82 | 0.7900 | 0.6292 | 0.0561 |
| 0.0364 | 58.0 | 5800 | 0.0387 | 0.82 | 0.7227 | 2.9211 | 0.82 | 0.7898 | 0.6325 | 0.0560 |
| 0.0364 | 59.0 | 5900 | 0.0386 | 0.82 | 0.7238 | 3.0664 | 0.82 | 0.7898 | 0.6249 | 0.0548 |
| 0.0364 | 60.0 | 6000 | 0.0387 | 0.82 | 0.7218 | 2.9137 | 0.82 | 0.7900 | 0.6397 | 0.0545 |
| 0.0364 | 61.0 | 6100 | 0.0387 | 0.82 | 0.7233 | 3.0756 | 0.82 | 0.7900 | 0.6225 | 0.0563 |
| 0.0364 | 62.0 | 6200 | 0.0387 | 0.815 | 0.7231 | 3.0725 | 0.815 | 0.7878 | 0.6190 | 0.0558 |
| 0.0364 | 63.0 | 6300 | 0.0387 | 0.82 | 0.7218 | 3.0589 | 0.82 | 0.7900 | 0.6223 | 0.0554 |
| 0.0364 | 64.0 | 6400 | 0.0387 | 0.82 | 0.7231 | 3.0632 | 0.82 | 0.7900 | 0.6289 | 0.0551 |
| 0.0363 | 65.0 | 6500 | 0.0387 | 0.82 | 0.7227 | 3.0595 | 0.82 | 0.7900 | 0.6339 | 0.0560 |
| 0.0363 | 66.0 | 6600 | 0.0387 | 0.82 | 0.7229 | 2.9000 | 0.82 | 0.7900 | 0.6320 | 0.0551 |
| 0.0363 | 67.0 | 6700 | 0.0387 | 0.82 | 0.7222 | 2.9092 | 0.82 | 0.7900 | 0.6292 | 0.0548 |
| 0.0363 | 68.0 | 6800 | 0.0387 | 0.82 | 0.7233 | 2.7662 | 0.82 | 0.7900 | 0.6299 | 0.0564 |
| 0.0363 | 69.0 | 6900 | 0.0387 | 0.82 | 0.7230 | 2.9095 | 0.82 | 0.7900 | 0.6239 | 0.0555 |
| 0.0363 | 70.0 | 7000 | 0.0387 | 0.82 | 0.7226 | 2.9175 | 0.82 | 0.7908 | 0.6274 | 0.0549 |
| 0.0363 | 71.0 | 7100 | 0.0387 | 0.82 | 0.7237 | 3.0548 | 0.82 | 0.7900 | 0.6337 | 0.0550 |
| 0.0363 | 72.0 | 7200 | 0.0387 | 0.815 | 0.7229 | 2.9144 | 0.815 | 0.7841 | 0.6207 | 0.0570 |
| 0.0363 | 73.0 | 7300 | 0.0387 | 0.82 | 0.7235 | 2.9041 | 0.82 | 0.7900 | 0.6310 | 0.0564 |
| 0.0363 | 74.0 | 7400 | 0.0387 | 0.82 | 0.7227 | 2.9094 | 0.82 | 0.7908 | 0.6291 | 0.0558 |
| 0.0363 | 75.0 | 7500 | 0.0387 | 0.825 | 0.7236 | 2.9105 | 0.825 | 0.7983 | 0.6319 | 0.0543 |
| 0.0363 | 76.0 | 7600 | 0.0387 | 0.82 | 0.7225 | 2.9172 | 0.82 | 0.7908 | 0.6260 | 0.0550 |
| 0.0363 | 77.0 | 7700 | 0.0387 | 0.815 | 0.7227 | 2.9050 | 0.815 | 0.7841 | 0.6325 | 0.0557 |
| 0.0363 | 78.0 | 7800 | 0.0387 | 0.825 | 0.7236 | 2.9242 | 0.825 | 0.7983 | 0.6264 | 0.0575 |
| 0.0363 | 79.0 | 7900 | 0.0387 | 0.82 | 0.7231 | 2.9167 | 0.82 | 0.7900 | 0.6263 | 0.0572 |
| 0.0363 | 80.0 | 8000 | 0.0387 | 0.825 | 0.7229 | 2.7707 | 0.825 | 0.8004 | 0.6311 | 0.0569 |
| 0.0363 | 81.0 | 8100 | 0.0387 | 0.81 | 0.7230 | 2.9083 | 0.81 | 0.7812 | 0.6295 | 0.0573 |
| 0.0363 | 82.0 | 8200 | 0.0387 | 0.82 | 0.7227 | 2.7600 | 0.82 | 0.7927 | 0.6263 | 0.0576 |
| 0.0363 | 83.0 | 8300 | 0.0387 | 0.815 | 0.7226 | 2.7902 | 0.815 | 0.7930 | 0.6169 | 0.0576 |
| 0.0363 | 84.0 | 8400 | 0.0387 | 0.815 | 0.7226 | 2.7666 | 0.815 | 0.7841 | 0.6254 | 0.0571 |
| 0.0363 | 85.0 | 8500 | 0.0387 | 0.82 | 0.7228 | 2.7730 | 0.82 | 0.7960 | 0.6226 | 0.0566 |
| 0.0363 | 86.0 | 8600 | 0.0387 | 0.815 | 0.7228 | 2.6311 | 0.815 | 0.7878 | 0.6186 | 0.0572 |
| 0.0363 | 87.0 | 8700 | 0.0388 | 0.82 | 0.7230 | 2.6272 | 0.82 | 0.7924 | 0.6321 | 0.0575 |
| 0.0363 | 88.0 | 8800 | 0.0387 | 0.82 | 0.7227 | 2.7579 | 0.82 | 0.7924 | 0.6318 | 0.0568 |
| 0.0363 | 89.0 | 8900 | 0.0388 | 0.82 | 0.7227 | 2.7688 | 0.82 | 0.7960 | 0.6238 | 0.0575 |
| 0.0363 | 90.0 | 9000 | 0.0387 | 0.82 | 0.7232 | 2.6100 | 0.82 | 0.7986 | 0.6290 | 0.0569 |
| 0.0363 | 91.0 | 9100 | 0.0387 | 0.82 | 0.7228 | 2.6088 | 0.82 | 0.7960 | 0.6258 | 0.0574 |
| 0.0363 | 92.0 | 9200 | 0.0387 | 0.825 | 0.7228 | 2.6134 | 0.825 | 0.8038 | 0.6239 | 0.0575 |
| 0.0363 | 93.0 | 9300 | 0.0387 | 0.825 | 0.7229 | 2.6136 | 0.825 | 0.7990 | 0.6283 | 0.0576 |
| 0.0363 | 94.0 | 9400 | 0.0387 | 0.82 | 0.7228 | 2.6112 | 0.82 | 0.7924 | 0.6235 | 0.0580 |
| 0.0363 | 95.0 | 9500 | 0.0388 | 0.82 | 0.7228 | 2.6077 | 0.82 | 0.7960 | 0.6185 | 0.0575 |
| 0.0363 | 96.0 | 9600 | 0.0388 | 0.825 | 0.7229 | 2.6073 | 0.825 | 0.8038 | 0.6206 | 0.0572 |
| 0.0363 | 97.0 | 9700 | 0.0388 | 0.82 | 0.7227 | 2.6077 | 0.82 | 0.7960 | 0.6213 | 0.0572 |
| 0.0363 | 98.0 | 9800 | 0.0388 | 0.815 | 0.7228 | 2.6094 | 0.815 | 0.7893 | 0.6161 | 0.0579 |
| 0.0363 | 99.0 | 9900 | 0.0388 | 0.815 | 0.7228 | 2.6086 | 0.815 | 0.7893 | 0.6160 | 0.0579 |
| 0.0363 | 100.0 | 10000 | 0.0388 | 0.82 | 0.7227 | 2.6079 | 0.82 | 0.7960 | 0.6182 | 0.0575 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.12.0
- Tokenizers 0.12.1
|
preetham/rpanda2
|
preetham
| 2023-07-13T09:37:58Z | 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
| 2023-07-13T09:02:06Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks panda
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - preetham/rpanda2
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks panda using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
dada325/Taxi-v3-qLearning-test
|
dada325
| 2023-07-13T09:34:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-13T09:34:46Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-qLearning-test
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
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="dada325/Taxi-v3-qLearning-test", 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"])
```
|
ashokurlana/mBART-TeSum
|
ashokurlana
| 2023-07-13T09:33:38Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"generated_from_trainer",
"multilingual",
"te",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-12T18:19:29Z |
---
language:
- multilingual
- te
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mBART-TeSum
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. -->
# mBART-TeSum
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on [TeSum](https://ltrc.iiit.ac.in/showfile.php?filename=downloads/teSum/) dataset. More details about the training and analysis mentioned in the [paper](https://aclanthology.org/2022.lrec-1.614.pdf).
## Model description
mBART-50 is a multilingual Sequence-to-Sequence model. It was introduced to show that multilingual translation models can be created through multilingual fine-tuning.
Instead of fine-tuning on one direction, a pre-trained model is fine-tuned on many directions simultaneously.
mBART-50 is created using the original mBART model and extended to add extra 25 languages to support multilingual machine translation models of 50 languages.
The pre-training objective is explained below.
**Multilingual Denoising Pretraining**: The model incorporates N languages by concatenating data:
`D = {D1, ..., DN }` where each Di is a collection of monolingual documents in language `i`.
The source documents are noised using two schemes, first randomly shuffling the original sentences' order, and second a novel in-filling scheme,
where spans of text are replaced with a single mask token. The model is then tasked to reconstruct the original text.
35% of each instance's words are masked by random sampling a span length according to a Poisson distribution `(λ = 3.5)`.
The decoder input is the original text with one position offset. A language id symbol `LID` is used as the initial token to predict
the sentence.
## Intended uses & limitations
mbart-large-50 is pre-trained model and primarily aimed at being fine-tuned on translation tasks. It can also be fine-tuned on other multilingual sequence-to-sequence tasks. See the [model hub](https://huggingface.co/models?other=mbart-50) to look for fine-tuned versions.
## Training
As the model is multilingual, it expects the sequences in a different format. A special language id token is used as a prefix in both the source and target text.
The text format is `[lang_code] X [eos]` with `X` being the source or target text respectively and lang_code is `source_lang_code` for source text
and `tgt_lang_code` for target text. `bos` is never used. Once the examples are prepared in this format, it can be trained as any other sequence-to-sequence model.
```python
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
model = MBartForConditionalGeneration.from_pretrained("ashokurlana/mBART-TeSum")
tokenizer = MBart50TokenizerFast.from_pretrained("ashokurlana/mBART-TeSum", src_lang="te_IN", tgt_lang="te_IN")
src_text = "తెలంగాణలో సచలనం సృష్టించిన టీఎస్పీఎస్సీ పేపర్ లీకేజీ వ్యవహారంపై ప్రభుత్వం తరపున మంత్రి కేటీఆర్ తొలిసారి స్పందించారు. ఇది వ్యవస్థ వైఫల్యం కాదని.., ఇద్దరు వ్యక్తులు చేసిన తప్పు అని కేటీఆర్ వెల్లడించారు. ఈ వ్యవహారం వెనుక ఏ పార్టీకి చెందిన వారున్నా.., ఎంతటి వారైనా కఠినంగా శిక్షిస్తామని చెప్పారు. నిరుద్యోగుల్లో ఆందోళనలు రేకెత్తించేలా ప్రతిపక్షాలు మాట్లాడటం సరికాదని హితవు పలికారు."
tgt_text = "తెలంగాణలో సచలనం సృష్టించిన టీఎస్ పీఎస్సీ పేపర్ లీకేజీ వ్యవహారంపై ప్రభుత్వం తరపున మంత్రి కేటీఆర్ స్పందించారు. ఇది వ్యవస్థ వైఫల్యం కాదని, ఇద్దరు వ్యక్తులు చేసిన తప్పు అని, ఈ వ్యవహారం వెనుక ఏ పార్టీకి చెందిన వారున్నా కఠినంగా శిక్షిస్తామని చెప్పారు."
model_inputs = tokenizer(src_text, return_tensors="pt")
with tokenizer.as_target_tokenizer():
labels = tokenizer(tgt_text, return_tensors="pt").input_ids
model(**model_inputs, labels=labels) # forward pass
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Evaluation results
It achieves the following results on the evaluation set:
- Loss: 1.4009
- Rouge1: 32.8603
- Rouge2: 12.2822
- Rougel: 31.7473
- Rougelsum: 32.505
- Gen Len: 117.6326
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
### BibTeX entry and citation info
```
@inproceedings{urlana-etal-2022-tesum,
title = "{T}e{S}um: Human-Generated Abstractive Summarization Corpus for {T}elugu",
author = "Urlana, Ashok and
Surange, Nirmal and
Baswani, Pavan and
Ravva, Priyanka and
Shrivastava, Manish",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.614",
pages = "5712--5722",
abstract = "Expert human annotation for summarization is definitely an expensive task, and can not be done on huge scales. But with this work, we show that even with a crowd sourced summary generation approach, quality can be controlled by aggressive expert informed filtering and sampling-based human evaluation. We propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation. Using this pipeline we create a high-quality Telugu Abstractive Summarization dataset (TeSum) which we validate with sampling-based human evaluation. We also provide baseline numbers for various models commonly used for summarization. A number of recently released datasets for summarization, scraped the web-content relying on the assumption that summary is made available with the article by the publishers. While this assumption holds for multiple resources (or news-sites) in English, it should not be generalised across languages without thorough analysis and verification. Our analysis clearly shows that this assumption does not hold true for most Indian language news resources. We show that our proposed filtration pipeline can even be applied to these large-scale scraped datasets to extract better quality article-summary pairs.",
}
```
|
n0madic/elegantEntropy_v1.2
|
n0madic
| 2023-07-13T09:29:46Z | 3 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-13T08:25:55Z |
---
license: creativeml-openrail-m
---
|
KyriaAnnwyn/vit-large-artifacts
|
KyriaAnnwyn
| 2023-07-13T09:26:30Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-07T12:11:49Z |
---
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-large-artifacts
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-large-artifacts
This model is a fine-tuned version of [kakaobrain/vit-large-patch16-512](https://huggingface.co/kakaobrain/vit-large-patch16-512) on the KyriaAnnwyn/artifacts_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5995
- Accuracy: 0.6705
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7001 | 0.01 | 100 | 0.6414 | 0.6559 |
| 0.6288 | 0.01 | 200 | 0.6666 | 0.6559 |
| 0.7237 | 0.02 | 300 | 0.7087 | 0.6559 |
| 0.8741 | 0.03 | 400 | 0.6739 | 0.6257 |
| 0.6093 | 0.04 | 500 | 0.6462 | 0.6559 |
| 0.5801 | 0.04 | 600 | 0.6822 | 0.6559 |
| 0.594 | 0.05 | 700 | 1.9948 | 0.6395 |
| 0.7724 | 0.06 | 800 | 0.6566 | 0.6553 |
| 0.6976 | 0.07 | 900 | 0.6774 | 0.6325 |
| 0.6583 | 0.07 | 1000 | 0.7175 | 0.3517 |
| 0.6779 | 0.08 | 1100 | 0.7012 | 0.6559 |
| 0.6478 | 0.09 | 1200 | 0.6336 | 0.6559 |
| 0.7405 | 0.1 | 1300 | 0.6577 | 0.6559 |
| 0.7362 | 0.1 | 1400 | 0.6630 | 0.6142 |
| 0.535 | 0.11 | 1500 | 0.7445 | 0.6559 |
| 0.7338 | 0.12 | 1600 | 0.7046 | 0.4718 |
| 0.6519 | 0.13 | 1700 | 0.6601 | 0.6426 |
| 0.5969 | 0.13 | 1800 | 0.6518 | 0.6559 |
| 0.5992 | 0.14 | 1900 | 0.6544 | 0.6559 |
| 0.5762 | 0.15 | 2000 | 0.6608 | 0.6559 |
| 0.6483 | 0.16 | 2100 | 0.6436 | 0.6331 |
| 0.7594 | 0.16 | 2200 | 0.7562 | 0.5213 |
| 0.6423 | 0.17 | 2300 | 0.6326 | 0.6433 |
| 0.7006 | 0.18 | 2400 | 0.6669 | 0.6108 |
| 0.833 | 0.19 | 2500 | 0.7043 | 0.6559 |
| 0.6133 | 0.19 | 2600 | 0.6356 | 0.6532 |
| 0.5285 | 0.2 | 2700 | 0.6619 | 0.6606 |
| 0.7209 | 0.21 | 2800 | 0.7306 | 0.4196 |
| 0.682 | 0.22 | 2900 | 0.6400 | 0.6539 |
| 0.7148 | 0.22 | 3000 | 0.6421 | 0.6559 |
| 0.6288 | 0.23 | 3100 | 0.7416 | 0.6559 |
| 0.666 | 0.24 | 3200 | 0.6368 | 0.6293 |
| 0.772 | 0.25 | 3300 | 0.6973 | 0.4985 |
| 0.6778 | 0.25 | 3400 | 0.6288 | 0.6604 |
| 0.5939 | 0.26 | 3500 | 0.6566 | 0.6559 |
| 0.6246 | 0.27 | 3600 | 0.6347 | 0.6618 |
| 0.649 | 0.28 | 3700 | 0.6353 | 0.6277 |
| 0.7122 | 0.28 | 3800 | 0.6407 | 0.6559 |
| 0.6292 | 0.29 | 3900 | 0.6776 | 0.6560 |
| 0.6079 | 0.3 | 4000 | 0.6220 | 0.6609 |
| 0.6971 | 0.31 | 4100 | 0.6258 | 0.6394 |
| 0.7131 | 0.31 | 4200 | 0.7202 | 0.6556 |
| 0.5346 | 0.32 | 4300 | 0.6394 | 0.6571 |
| 0.5801 | 0.33 | 4400 | 0.6960 | 0.6664 |
| 0.6806 | 0.34 | 4500 | 0.6339 | 0.6348 |
| 0.6245 | 0.34 | 4600 | 0.6226 | 0.6477 |
| 0.6905 | 0.35 | 4700 | 0.6203 | 0.6533 |
| 0.741 | 0.36 | 4800 | 0.6464 | 0.6680 |
| 0.5712 | 0.37 | 4900 | 0.6162 | 0.6640 |
| 0.5566 | 0.37 | 5000 | 0.6182 | 0.6507 |
| 0.6443 | 0.38 | 5100 | 0.6457 | 0.6664 |
| 0.6107 | 0.39 | 5200 | 0.6092 | 0.6617 |
| 0.5824 | 0.4 | 5300 | 0.6383 | 0.6571 |
| 0.4775 | 0.4 | 5400 | 0.6606 | 0.6621 |
| 0.7114 | 0.41 | 5500 | 0.6179 | 0.6619 |
| 0.7701 | 0.42 | 5600 | 0.7982 | 0.4217 |
| 0.6974 | 0.42 | 5700 | 0.6223 | 0.6540 |
| 0.6669 | 0.43 | 5800 | 0.6249 | 0.6559 |
| 0.6982 | 0.44 | 5900 | 0.6287 | 0.6564 |
| 0.5811 | 0.45 | 6000 | 0.6104 | 0.6506 |
| 0.4347 | 0.45 | 6100 | 1.0475 | 0.6559 |
| 0.5885 | 0.46 | 6200 | 0.6125 | 0.6552 |
| 0.6867 | 0.47 | 6300 | 0.6435 | 0.6468 |
| 0.6088 | 0.48 | 6400 | 0.6047 | 0.6623 |
| 0.8194 | 0.48 | 6500 | 0.6972 | 0.6589 |
| 0.8182 | 0.49 | 6600 | 0.6053 | 0.6644 |
| 0.6104 | 0.5 | 6700 | 0.7375 | 0.6571 |
| 0.5552 | 0.51 | 6800 | 0.6231 | 0.6402 |
| 0.6451 | 0.51 | 6900 | 0.6452 | 0.6561 |
| 0.7849 | 0.52 | 7000 | 0.6177 | 0.6612 |
| 0.64 | 0.53 | 7100 | 0.6307 | 0.6234 |
| 0.6393 | 0.54 | 7200 | 0.6130 | 0.6554 |
| 0.8326 | 0.54 | 7300 | 0.7210 | 0.6421 |
| 0.6579 | 0.55 | 7400 | 0.6227 | 0.6544 |
| 0.5195 | 0.56 | 7500 | 0.6619 | 0.6557 |
| 0.6197 | 0.57 | 7600 | 0.6354 | 0.6498 |
| 0.8507 | 0.57 | 7700 | 0.6820 | 0.6550 |
| 0.7163 | 0.58 | 7800 | 0.6720 | 0.5328 |
| 0.6896 | 0.59 | 7900 | 0.6530 | 0.6386 |
| 0.62 | 0.6 | 8000 | 0.6296 | 0.6559 |
| 0.8254 | 0.6 | 8100 | 0.6752 | 0.6200 |
| 0.7653 | 0.61 | 8200 | 0.7118 | 0.6558 |
| 0.7742 | 0.62 | 8300 | 0.6262 | 0.6497 |
| 0.6861 | 0.63 | 8400 | 0.6799 | 0.5566 |
| 0.5652 | 0.63 | 8500 | 0.6708 | 0.6559 |
| 0.7486 | 0.64 | 8600 | 0.6319 | 0.6559 |
| 0.6204 | 0.65 | 8700 | 0.6407 | 0.6530 |
| 0.673 | 0.66 | 8800 | 0.7154 | 0.4672 |
| 0.7272 | 0.66 | 8900 | 0.6323 | 0.6528 |
| 0.7364 | 0.67 | 9000 | 0.6436 | 0.6188 |
| 0.71 | 0.68 | 9100 | 0.6507 | 0.5924 |
| 0.6767 | 0.69 | 9200 | 0.6347 | 0.6575 |
| 0.7046 | 0.69 | 9300 | 0.6723 | 0.6127 |
| 0.7486 | 0.7 | 9400 | 0.6328 | 0.6485 |
| 0.7646 | 0.71 | 9500 | 0.6244 | 0.6550 |
| 0.5971 | 0.72 | 9600 | 0.6610 | 0.6558 |
| 0.6195 | 0.72 | 9700 | 0.6219 | 0.6515 |
| 0.6891 | 0.73 | 9800 | 0.6300 | 0.6619 |
| 0.6829 | 0.74 | 9900 | 0.6312 | 0.6568 |
| 0.4786 | 0.75 | 10000 | 0.7160 | 0.6573 |
| 0.6093 | 0.75 | 10100 | 0.6245 | 0.6503 |
| 0.672 | 0.76 | 10200 | 0.6248 | 0.6577 |
| 0.6734 | 0.77 | 10300 | 0.6541 | 0.6600 |
| 0.7826 | 0.78 | 10400 | 0.6413 | 0.6559 |
| 0.6851 | 0.78 | 10500 | 0.6478 | 0.6006 |
| 0.6776 | 0.79 | 10600 | 0.6453 | 0.6175 |
| 0.7322 | 0.8 | 10700 | 0.6188 | 0.6353 |
| 0.5144 | 0.81 | 10800 | 0.6762 | 0.6571 |
| 0.6977 | 0.81 | 10900 | 0.6559 | 0.6544 |
| 0.5681 | 0.82 | 11000 | 0.7225 | 0.6559 |
| 0.6449 | 0.83 | 11100 | 0.6372 | 0.6576 |
| 0.6067 | 0.83 | 11200 | 0.6207 | 0.6391 |
| 0.5921 | 0.84 | 11300 | 0.6178 | 0.6538 |
| 0.5373 | 0.85 | 11400 | 0.7370 | 0.6559 |
| 0.6926 | 0.86 | 11500 | 0.6346 | 0.6372 |
| 0.6634 | 0.86 | 11600 | 0.6274 | 0.6489 |
| 0.61 | 0.87 | 11700 | 0.6309 | 0.6427 |
| 0.6214 | 0.88 | 11800 | 0.6273 | 0.6480 |
| 0.6202 | 0.89 | 11900 | 0.6255 | 0.6559 |
| 0.6153 | 0.89 | 12000 | 0.6348 | 0.6459 |
| 0.7062 | 0.9 | 12100 | 0.6283 | 0.6512 |
| 0.6977 | 0.91 | 12200 | 0.6159 | 0.6515 |
| 0.6041 | 0.92 | 12300 | 0.6251 | 0.6504 |
| 0.6609 | 0.92 | 12400 | 0.6633 | 0.5870 |
| 0.7565 | 0.93 | 12500 | 0.6200 | 0.6562 |
| 0.6133 | 0.94 | 12600 | 0.6193 | 0.6527 |
| 0.7066 | 0.95 | 12700 | 0.6279 | 0.6180 |
| 0.5706 | 0.95 | 12800 | 0.6128 | 0.6575 |
| 0.6992 | 0.96 | 12900 | 0.6334 | 0.6449 |
| 0.6834 | 0.97 | 13000 | 0.6258 | 0.6591 |
| 0.6069 | 0.98 | 13100 | 0.6290 | 0.6620 |
| 0.743 | 0.98 | 13200 | 0.6110 | 0.6562 |
| 0.5226 | 0.99 | 13300 | 0.6165 | 0.6557 |
| 0.7359 | 1.0 | 13400 | 0.6207 | 0.6376 |
| 0.5812 | 1.01 | 13500 | 0.6192 | 0.6559 |
| 0.666 | 1.01 | 13600 | 0.6347 | 0.6602 |
| 0.5489 | 1.02 | 13700 | 0.6107 | 0.6459 |
| 0.701 | 1.03 | 13800 | 0.6172 | 0.6518 |
| 0.4873 | 1.04 | 13900 | 0.6786 | 0.6559 |
| 0.5807 | 1.04 | 14000 | 0.6636 | 0.6433 |
| 0.6824 | 1.05 | 14100 | 0.6176 | 0.6315 |
| 0.6012 | 1.06 | 14200 | 0.6097 | 0.6617 |
| 0.4865 | 1.07 | 14300 | 0.6103 | 0.6623 |
| 0.5612 | 1.07 | 14400 | 0.6947 | 0.6559 |
| 0.5968 | 1.08 | 14500 | 0.6559 | 0.5981 |
| 0.5657 | 1.09 | 14600 | 0.6076 | 0.6509 |
| 0.4778 | 1.1 | 14700 | 0.6808 | 0.6535 |
| 0.6047 | 1.1 | 14800 | 0.6131 | 0.6480 |
| 0.5999 | 1.11 | 14900 | 0.6120 | 0.6559 |
| 0.5852 | 1.12 | 15000 | 0.6356 | 0.6553 |
| 0.7033 | 1.13 | 15100 | 0.6578 | 0.6647 |
| 0.5925 | 1.13 | 15200 | 0.6153 | 0.6633 |
| 0.5959 | 1.14 | 15300 | 0.6306 | 0.6211 |
| 0.5929 | 1.15 | 15400 | 0.6246 | 0.6655 |
| 0.5621 | 1.16 | 15500 | 0.6126 | 0.6424 |
| 0.5508 | 1.16 | 15600 | 0.6844 | 0.6559 |
| 0.6276 | 1.17 | 15700 | 0.6066 | 0.6531 |
| 1.0359 | 1.18 | 15800 | 0.6271 | 0.6617 |
| 0.6191 | 1.19 | 15900 | 0.6166 | 0.6480 |
| 0.7095 | 1.19 | 16000 | 0.6228 | 0.6462 |
| 0.6567 | 1.2 | 16100 | 0.6066 | 0.6653 |
| 0.5653 | 1.21 | 16200 | 0.6022 | 0.6605 |
| 0.6894 | 1.21 | 16300 | 0.6216 | 0.6568 |
| 0.608 | 1.22 | 16400 | 0.6041 | 0.6559 |
| 0.665 | 1.23 | 16500 | 0.6111 | 0.6564 |
| 0.6753 | 1.24 | 16600 | 0.6138 | 0.6581 |
| 0.6213 | 1.24 | 16700 | 0.6121 | 0.6380 |
| 0.6983 | 1.25 | 16800 | 0.6166 | 0.6661 |
| 0.8521 | 1.26 | 16900 | 0.6202 | 0.6461 |
| 0.4927 | 1.27 | 17000 | 0.6313 | 0.6547 |
| 0.6414 | 1.27 | 17100 | 0.6011 | 0.6667 |
| 0.539 | 1.28 | 17200 | 0.6451 | 0.6664 |
| 0.5118 | 1.29 | 17300 | 0.6243 | 0.6641 |
| 0.7512 | 1.3 | 17400 | 0.6257 | 0.6586 |
| 0.5943 | 1.3 | 17500 | 0.6186 | 0.6423 |
| 0.5861 | 1.31 | 17600 | 0.6435 | 0.6638 |
| 0.7065 | 1.32 | 17700 | 0.6197 | 0.6279 |
| 0.5973 | 1.33 | 17800 | 0.6081 | 0.6535 |
| 0.5997 | 1.33 | 17900 | 0.6053 | 0.6608 |
| 0.7091 | 1.34 | 18000 | 0.6013 | 0.6644 |
| 0.691 | 1.35 | 18100 | 0.6103 | 0.6654 |
| 0.5559 | 1.36 | 18200 | 0.6110 | 0.6658 |
| 0.6309 | 1.36 | 18300 | 0.6067 | 0.6664 |
| 0.6262 | 1.37 | 18400 | 0.6027 | 0.6616 |
| 0.5551 | 1.38 | 18500 | 0.6106 | 0.6671 |
| 0.6703 | 1.39 | 18600 | 0.6043 | 0.6576 |
| 0.6849 | 1.39 | 18700 | 0.6018 | 0.6616 |
| 0.6136 | 1.4 | 18800 | 0.6324 | 0.6629 |
| 0.7075 | 1.41 | 18900 | 0.6057 | 0.6561 |
| 0.6036 | 1.42 | 19000 | 0.6081 | 0.6559 |
| 0.6549 | 1.42 | 19100 | 0.6352 | 0.6655 |
| 0.5168 | 1.43 | 19200 | 0.6042 | 0.6632 |
| 0.5864 | 1.44 | 19300 | 0.6111 | 0.6639 |
| 0.5961 | 1.45 | 19400 | 0.6003 | 0.6644 |
| 0.6077 | 1.45 | 19500 | 0.6125 | 0.6566 |
| 0.6215 | 1.46 | 19600 | 0.6128 | 0.6582 |
| 0.4005 | 1.47 | 19700 | 0.6348 | 0.6642 |
| 0.5689 | 1.48 | 19800 | 0.6355 | 0.6647 |
| 0.6026 | 1.48 | 19900 | 0.6127 | 0.6444 |
| 0.4982 | 1.49 | 20000 | 0.6034 | 0.6654 |
| 0.6189 | 1.5 | 20100 | 0.6202 | 0.6609 |
| 0.5502 | 1.51 | 20200 | 0.6044 | 0.6621 |
| 0.5924 | 1.51 | 20300 | 0.6107 | 0.6445 |
| 0.744 | 1.52 | 20400 | 0.6164 | 0.6559 |
| 0.5582 | 1.53 | 20500 | 0.6166 | 0.6559 |
| 0.6994 | 1.54 | 20600 | 0.6109 | 0.6664 |
| 0.5396 | 1.54 | 20700 | 0.6189 | 0.6670 |
| 0.7232 | 1.55 | 20800 | 0.6104 | 0.6610 |
| 0.9802 | 1.56 | 20900 | 0.6232 | 0.6642 |
| 0.6487 | 1.57 | 21000 | 0.6056 | 0.6505 |
| 0.5932 | 1.57 | 21100 | 0.5980 | 0.6702 |
| 0.7897 | 1.58 | 21200 | 0.6012 | 0.6638 |
| 0.6006 | 1.59 | 21300 | 0.6232 | 0.6672 |
| 0.4481 | 1.6 | 21400 | 0.6124 | 0.6676 |
| 0.6078 | 1.6 | 21500 | 0.6495 | 0.6664 |
| 0.595 | 1.61 | 21600 | 0.7122 | 0.6675 |
| 0.6388 | 1.62 | 21700 | 0.6227 | 0.6671 |
| 0.5731 | 1.62 | 21800 | 0.6252 | 0.6682 |
| 0.8603 | 1.63 | 21900 | 0.6026 | 0.6653 |
| 0.6316 | 1.64 | 22000 | 0.6494 | 0.6669 |
| 0.6712 | 1.65 | 22100 | 0.6097 | 0.6676 |
| 0.6102 | 1.65 | 22200 | 0.6221 | 0.6585 |
| 0.7099 | 1.66 | 22300 | 0.6006 | 0.6658 |
| 0.621 | 1.67 | 22400 | 0.6026 | 0.6626 |
| 0.478 | 1.68 | 22500 | 0.6062 | 0.6624 |
| 0.6106 | 1.68 | 22600 | 0.5990 | 0.6669 |
| 0.5793 | 1.69 | 22700 | 0.5980 | 0.6681 |
| 0.5804 | 1.7 | 22800 | 0.6014 | 0.6626 |
| 0.6304 | 1.71 | 22900 | 0.6107 | 0.6380 |
| 0.7427 | 1.71 | 23000 | 0.6051 | 0.6682 |
| 0.5794 | 1.72 | 23100 | 0.6105 | 0.6611 |
| 0.5084 | 1.73 | 23200 | 0.6643 | 0.6673 |
| 0.6518 | 1.74 | 23300 | 0.6366 | 0.6687 |
| 0.5129 | 1.74 | 23400 | 0.6053 | 0.6682 |
| 0.7593 | 1.75 | 23500 | 0.5977 | 0.6662 |
| 0.6645 | 1.76 | 23600 | 0.5988 | 0.6683 |
| 0.6144 | 1.77 | 23700 | 0.6130 | 0.6673 |
| 0.6855 | 1.77 | 23800 | 0.6192 | 0.6596 |
| 0.559 | 1.78 | 23900 | 0.6208 | 0.6574 |
| 0.4202 | 1.79 | 24000 | 0.6125 | 0.6690 |
| 0.6604 | 1.8 | 24100 | 0.6052 | 0.6685 |
| 0.5487 | 1.8 | 24200 | 0.6086 | 0.6685 |
| 0.6816 | 1.81 | 24300 | 0.5997 | 0.6620 |
| 0.6057 | 1.82 | 24400 | 0.6128 | 0.6530 |
| 0.4335 | 1.83 | 24500 | 0.6121 | 0.6676 |
| 0.6147 | 1.83 | 24600 | 0.6225 | 0.6670 |
| 0.7414 | 1.84 | 24700 | 0.6248 | 0.6718 |
| 0.622 | 1.85 | 24800 | 0.6084 | 0.6722 |
| 0.5356 | 1.86 | 24900 | 0.6003 | 0.6611 |
| 0.7994 | 1.86 | 25000 | 0.6098 | 0.6657 |
| 0.5389 | 1.87 | 25100 | 0.6052 | 0.6633 |
| 0.6985 | 1.88 | 25200 | 0.6073 | 0.6694 |
| 0.652 | 1.89 | 25300 | 0.6040 | 0.6709 |
| 0.5409 | 1.89 | 25400 | 0.6065 | 0.6709 |
| 0.6356 | 1.9 | 25500 | 0.6062 | 0.6699 |
| 0.7588 | 1.91 | 25600 | 0.6025 | 0.6711 |
| 0.5109 | 1.92 | 25700 | 0.5992 | 0.6693 |
| 0.6766 | 1.92 | 25800 | 0.6004 | 0.6693 |
| 0.6517 | 1.93 | 25900 | 0.6020 | 0.6701 |
| 0.6561 | 1.94 | 26000 | 0.5995 | 0.6705 |
| 0.6224 | 1.95 | 26100 | 0.6008 | 0.6717 |
| 0.6054 | 1.95 | 26200 | 0.6005 | 0.6714 |
| 0.5152 | 1.96 | 26300 | 0.6023 | 0.6709 |
| 0.5503 | 1.97 | 26400 | 0.6032 | 0.6706 |
| 0.5101 | 1.98 | 26500 | 0.6067 | 0.6709 |
| 0.5229 | 1.98 | 26600 | 0.6079 | 0.6702 |
| 0.8387 | 1.99 | 26700 | 0.6079 | 0.6700 |
| 0.608 | 2.0 | 26800 | 0.6069 | 0.6699 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu116
- Datasets 2.13.1
- Tokenizers 0.13.3
|
skywalker7/Reinforce-PixelCopter
|
skywalker7
| 2023-07-13T09:17:26Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-13T09:17:17Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 45.10 +/- 55.81
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
|
Daemon101/whisper-small-hi
|
Daemon101
| 2023-07-13T09:13:50Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-13T08:29:41Z |
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
YanJiangJerry/sentiment-bloom-large-e6
|
YanJiangJerry
| 2023-07-13T08:58:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-classification",
"generated_from_trainer",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T07:52:49Z |
---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
model-index:
- name: sentiment-bloom-large-e6
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. -->
# sentiment-bloom-large-e6
This model is a fine-tuned version of [LYTinn/bloom-finetuning-sentiment-model-3000-samples](https://huggingface.co/LYTinn/bloom-finetuning-sentiment-model-3000-samples) 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: 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: 6
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
digiplay/helloFlatAnime_v1
|
digiplay
| 2023-07-13T08:57:50Z | 1,606 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-13T07:42:03Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/102893/helloflatanime
Original Author's DEMO images :
,colored%20hair,%20(20%20years%20old%20woman%20in%20a%20tanktop,short%20pants,in%20the%20water%20on%20a.jpeg)
|
Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2
|
Jorgeutd
| 2023-07-13T08:54:20Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sagemaker",
"bert-base-uncased",
"text classification",
"en",
"dataset:adecorpusv2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language: en
widget:
- text: "I got a rash from taking acetaminophen"
tags:
- sagemaker
- bert-base-uncased
- text classification
license: apache-2.0
datasets:
- adecorpusv2
model-index:
- name: BERT-ade_corpus
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: "ade_corpus_v2Ade_corpus_v2_classification"
type: ade_corpus
metrics:
- name: Validation Accuracy
type: accuracy
value: 92.98
- name: Validation F1
type: f1
value: 82.73
---
## bert-base-uncased
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
- Problem type: Text Classification(adverse drug effects detection).
## Hyperparameters
```json
{
"do_eval": true,
"do_train": true,
"fp16": true,
"load_best_model_at_end": true,
"model_name": "bert-base-uncased",
"num_train_epochs": 10,
"per_device_eval_batch_size": 16,
"per_device_train_batch_size": 16,
"learning_rate":5e-5
}
```
## Validation Metrics
| key | value |
| --- | ----- |
| eval_accuracy | 0.9298021697511167 |
| eval_auc | 0.8902672664394546 |
| eval_f1 | 0.827315541601256 |
| eval_loss | 0.17835010588169098 |
| eval_recall | 0.8234375 |
| eval_precision | 0.831230283911672 |
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I got a rash from taking acetaminophen"}' https://api-inference.huggingface.co/models/Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2
```
"""
|
digiplay/hellopure_v2.24Beta
|
digiplay
| 2023-07-13T08:49:07Z | 70 | 4 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-13T04:21:25Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
👍👍👍👍👍
https://civitai.com/models/88202/hellopure
Other models from Author: https://civitai.com/user/aji1/models
Sample image I made with AUTOMATIC1111 :
parameters
very close-up ,(best beautiful:1.2), (masterpiece:1.2), (best quality:1.2),masterpiece, best quality, The image features a beautiful young woman with long light golden hair, beach near the ocean, white dress ,The beach is lined with palm trees,
Negative prompt: worst quality ,normal quality ,
Steps: 17, Sampler: Euler, CFG scale: 5, Seed: 1097775045, Size: 480x680, Model hash: 8d4fa7988b, Clip skip: 2, Version: v1.4.1
|
daxiboy/vit-base-patch16-224-finetuned-flower
|
daxiboy
| 2023-07-13T08:47:12Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-13T08:35:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
gabrielgme/falcon-7b-spider-with-schema
|
gabrielgme
| 2023-07-13T08:44:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-12T13:21:52Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
|
seongj/polyglot-ko-1.3b-quant
|
seongj
| 2023-07-13T08:24:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-12T10:52:42Z |
Pytorch Quantized Model
- Dynamic Quantization : INT8
|
uripper/AVA
|
uripper
| 2023-07-13T08:15:52Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-22T20:54:37Z |
---
license: cc
widget:
- text: "Movie: Parasite Score:"
example_title: "Parasite"
- text: "Movie: Come and See Score:"
example_title: "Come and See"
- text: "Movie: Harakiri Score:"
example_title: "Harakiri"
---
# Review Training Bot
This model was trained for the purpose of generating scores and reviews for any given movie. It is fine-tuned on distilgpt2 as a baseline and trained on a custom dataset created by scraping around 120k letterboxd reviews. The current state of the model can get the correct formatting reliably but oftentimes is prone to gibberish. Further training will hopefully add coherency. It is in version 0.1 currently.
## Intended uses & limitations
This model is intended to be used for entertainment.
Limitations for this model will be much of the same as distilgpt2 which can be viewed here https://huggingface.co/distilgpt2. These may include persistent biases. Another issue may be through language specifically on letterboxd that the algorithm may not be able to understand. i.e. an LGBT+ film on letterboxd may have multiple reviews that mention the word "gay" positively, this model has not been able to understand this contextual usage and will use the word as a slur. As the current model also struggles to find a connection between movie titles and the reviews, this could happen with any entered movie.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 10
- eval_batch_size: 20
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
Ablustrund/moss-rlhf-reward-model-7B-zh
|
Ablustrund
| 2023-07-13T08:10:42Z | 3 | 23 | null |
[
"llm",
"reward model",
"moss",
"rlhf",
"zh",
"arxiv:2307.04964",
"license:agpl-3.0",
"region:us"
] | null | 2023-07-12T02:27:02Z |
---
license: agpl-3.0
language:
- zh
tags:
- llm
- reward model
- moss
- rlhf
---
# MOSS-RLHF
### *MOSS-RLHF & "Secrets of RLHF in Large Language Models Part I: PPO" <br>👉 <a href="https://arxiv.org/abs/2307.04964" target="_blank">[Technical report]</a> <a href="https://openlmlab.github.io/MOSS-RLHF/" target="_blank">[Home page]*
## 🌟 News
### 👉 Wed, 12. July 2023. We have released Chinese reward model based OpenChineseLlama-7B!
[moss-rlhf-reward-model-7B-zh](https://huggingface.co/Ablustrund/moss-rlhf-reward-model-7B-zh/tree/main)
<br>
### 👉 Thu, 13. July 2023. We have released English reward model and SFT model based Llama-7B!
[moss-rlhf-reward-model-7B-en](https://huggingface.co/fnlp/moss-rlhf-reward-model-7B-en)
[moss-rlhf-sft-model-7B-en](https://huggingface.co/fnlp/moss-rlhf-sft-model-7B-en)
<br>
## 🧾 Open-source List
- [x] Open source code for RL training in large language models.
- [x] A 7B Chinese reward model based on openChineseLlama.
- [x] A 7B English reward model based on Llama-7B.
- [x] SFT model for English.
- [ ] Policy model for English after RLHF.
- ...
## 🌠 Introduction
Due to the challenges of reward design, environment interaction, and agent training, coupled with huge trial and error cost of large language models, there is a significant barrier for AI researchers to motivate the development of technical alignment and safe landing of LLMs. The stable training of RLHF has still been a puzzle.
In this technical report, we intend to help researchers to train their models stably with human feedback.
Contributions are summarized as follows:
1) We release competitive Chinese and English reward models, respectively, which have good cross-model generalization ability, alleviating the cost of relabeling human preference data;
2) We conduct in-depth analysis on the inner workings of PPO algorithm and propose the PPO-max algorithm to ensure stable model training;
3) We release the complete PPO-max codes to ensure that the LLMs in the current SFT stage can be better aligned with humans.
## 🔩 Requirements & Setup
This repository works on Python 3.8 and PyTorch 1.13.1.
We recommend using the **conda** virtual environment to run the code.
#### Step 1: Create a new Python virtual environment
```bash
conda update conda -n base -c defaults
conda create -n rlhf python=3.8
conda activate rlhf
```
#### Step 2: Install PyTorch and TensorBoard
```bash
conda install pytorch==1.13.1 pytorch-cuda=11.7 tensorboard -c pytorch -c nvidia
```
#### Step 3: Install the remaining dependencies
```bash
conda install datasets accelerate safetensors chardet cchardet -c huggingface -c conda-forge
pip3 install transformers sentencepiece einops triton==1.0.0 rouge jionlp==1.4.14 nltk sacrebleu cpm_kernels
apt install libaio-dev
DS_BUILD_OPS=1 pip install deepspeed
```
## ✨ Start training your own model!
Run code in a few steps.
### Step 1: Recover Reward model weights
We can not directly release the full weight of the reward model because of protocol restrictions.
You can merge the diff weight with original Llama-7B to recover the reward model we used.
We upload the diff models, thanks to tatsu-lab, you can recover the reward model follow these steps:
```bash
1) Download the weight diff into your local machine. The weight diff is located at:
# For English:
TODO
# For Chinese:
https://huggingface.co/Ablustrund/moss-rlhf-reward-model-7B-zh/tree/main
2) Merge the weight diff with the original Llama-7B:
# For English:
# Reward model
python merge_weight_en.py recover --path_raw decapoda-research/llama-7b-hf --path_diff ./models/moss-rlhf-reward-model-7B-en/diff --path_tuned ./models/moss-rlhf-reward-model-7B-en/recover --model_type reward
# SFT model
python merge_weight_en.py recover --path_raw decapoda-research/llama-7b-hf --path_diff ./models/moss-rlhf-sft-model-7B-en/diff --path_tuned ./models/moss-rlhf-sft-model-7B-en/recover --model_type sft
# Policy model
TODO
# For Chinese:
python merge_weight_zh.py recover --path_raw decapoda-research/llama-7b-hf --path_diff ./models/moss-rlhf-reward-model-7B-zh/diff --path_tuned ./models/moss-rlhf-reward-model-7B-zh/recover
```
### Step 2: Select your own SFT model.
Because of some limitations, we can not release the **Chinese** SFT model (Currently).
You can use your own SFT model, or a strong base model instead of our SFT model.
### Step 3: Start training
Run the command below.
```
# For Chinese:
# You need to use your own sft model currently.
bash run_zh.sh
# For English:
# We have loaded the sft model and reward model to huggingface.
bash run_en.sh
```
## Citation
```bibtex
@article{zheng2023secrets,
title={Secrets of RLHF in Large Language Models Part I: PPO},
author={Rui Zheng and Shihan Dou and Songyang Gao and Wei Shen and Binghai Wang and Yan Liu and Senjie Jin and Qin Liu and Limao Xiong and Lu Chen and Zhiheng Xi and Yuhao Zhou and Nuo Xu and Wenbin Lai and Minghao Zhu and Rongxiang Weng and Wensen Cheng and Cheng Chang and Zhangyue Yin and Yuan Hua and Haoran Huang and Tianxiang Sun and Hang Yan and Tao Gui and Qi Zhang and Xipeng Qiu and Xuanjing Huang},
year={2023},
eprint={2307.04964},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Trong-Nghia/bert-large-uncased-detect-dep-v2
|
Trong-Nghia
| 2023-07-13T08:06:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T06:00:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-large-uncased-detect-dep-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-detect-dep-v2
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6741
- Accuracy: 0.727
- F1: 0.7976
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6244 | 1.0 | 1502 | 0.5466 | 0.755 | 0.8228 |
| 0.5956 | 2.0 | 3004 | 0.5683 | 0.735 | 0.7988 |
| 0.523 | 3.0 | 4506 | 0.6741 | 0.727 | 0.7976 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
haxett333/RL-Reinforce-100TrainEpisodesInsteadof1000
|
haxett333
| 2023-07-13T08:00:13Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-13T08:00:09Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: RL-Reinforce-100TrainEpisodesInsteadof1000
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 98.70 +/- 36.77
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
|
aiacademy131/opt-6.7b-lora
|
aiacademy131
| 2023-07-13T07:34:15Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-13T06:21:31Z |
---
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
|
K024/chatglm2-6b-int4g32
|
K024
| 2023-07-13T07:25:25Z | 53 | 3 |
transformers
|
[
"transformers",
"ChatGLM2Model",
"glm",
"chatglm",
"thudm",
"zh",
"en",
"endpoints_compatible",
"region:us"
] | null | 2023-07-13T07:09:00Z |
---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM2 6b int4 g32 量化模型
详情参考 [K024/chatglm-q](https://github.com/K024/chatglm-q)。
See [K024/chatglm-q](https://github.com/K024/chatglm-q) for more details.
```python
import torch
from chatglm_q.decoder import ChatGLMDecoder, chat_template
device = torch.device("cuda")
decoder = ChatGLMDecoder.from_pretrained("K024/chatglm2-6b-int4g32", device=device)
prompt = chat_template([], "我是谁？")
for text in decoder.generate(prompt):
print(text)
```
模型权重按 ChatGLM2-6b 许可发布，见 [MODEL LICENSE](https://huggingface.co/THUDM/chatglm2-6b/blob/main/MODEL_LICENSE)。
Model weights are released under the same license as ChatGLM2-6b, see [MODEL LICENSE](https://huggingface.co/THUDM/chatglm2-6b/blob/main/MODEL_LICENSE).
|
K024/chatglm2-6b-int8
|
K024
| 2023-07-13T07:18:11Z | 49 | 1 |
transformers
|
[
"transformers",
"ChatGLM2Model",
"glm",
"chatglm",
"thudm",
"zh",
"en",
"endpoints_compatible",
"region:us"
] | null | 2023-07-13T07:13:41Z |
---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM2 6b int8 量化模型
详情参考 [K024/chatglm-q](https://github.com/K024/chatglm-q)。
See [K024/chatglm-q](https://github.com/K024/chatglm-q) for more details.
```python
import torch
from chatglm_q.decoder import ChatGLMDecoder, chat_template
device = torch.device("cuda")
decoder = ChatGLMDecoder.from_pretrained("K024/chatglm2-6b-int8", device=device)
prompt = chat_template([], "我是谁？")
for text in decoder.generate(prompt):
print(text)
```
模型权重按 ChatGLM2-6b 许可发布，见 [MODEL LICENSE](https://huggingface.co/THUDM/chatglm2-6b/blob/main/MODEL_LICENSE)。
Model weights are released under the same license as ChatGLM2-6b, see [MODEL LICENSE](https://huggingface.co/THUDM/chatglm2-6b/blob/main/MODEL_LICENSE).
|
vineetsharma/dqn-SpaceInvadersNoFrameskip-v4
|
vineetsharma
| 2023-07-13T07:13:19Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-13T07:12:43Z |
---
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: 560.00 +/- 101.24
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 vineetsharma -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 vineetsharma -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 vineetsharma
```
## 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'}
```
|
smithlai/qtable-taxi-v3
|
smithlai
| 2023-07-13T07:12:19Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-13T07:10:33Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: qtable-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
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="smithlai/qtable-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"])
```
|
preetham/rpanda1
|
preetham
| 2023-07-13T07:10:56Z | 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
| 2023-07-13T06:22:15Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks panda
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - preetham/rpanda1
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks panda using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
dchaudhari/my_awesome_qa_model
|
dchaudhari
| 2023-07-13T06:44:21Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-11T05:20:51Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: dchaudhari/my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dchaudhari/my_awesome_qa_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:
- Train Loss: 1.0913
- Validation Loss: 1.1726
- 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': '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': 1298, '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 | Epoch |
|:----------:|:---------------:|:-----:|
| 2.2288 | 1.3339 | 0 |
| 1.2300 | 1.1726 | 1 |
| 1.0913 | 1.1726 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
anindya64/alpaca-bank-issue-summarization
|
anindya64
| 2023-07-13T06:41:24Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-13T06:41:22Z |
---
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
|
aiacademy131/opt-2.7b-lora
|
aiacademy131
| 2023-07-13T06:34:01Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-13T05:36:48Z |
---
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
|
smithlai/q-FrozenLake-v1-4x4-noSlippery
|
smithlai
| 2023-07-13T06:33:59Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-13T06:33:57Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="smithlai/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
YanJiangJerry/SA-tweet-bert-large-e6-w1-1.5-b16-m4
|
YanJiangJerry
| 2023-07-13T06:33:15Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T05:57:56Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-tweet-bert-large-e6-w1-1.5-b16-m4
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. -->
# SA-tweet-bert-large-e6-w1-1.5-b16-m4
This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4333
- Accuracy: 0.933
- F1: 0.9406
- Precision: 0.9414
- Recall: 0.9397
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 285 | 0.2078 | 0.917 | 0.9280 | 0.9083 | 0.9486 |
| 0.2897 | 2.0 | 570 | 0.2084 | 0.92 | 0.9313 | 0.9033 | 0.9610 |
| 0.2897 | 3.0 | 855 | 0.2873 | 0.925 | 0.9343 | 0.9237 | 0.9450 |
| 0.1152 | 4.0 | 1140 | 0.3181 | 0.933 | 0.9408 | 0.9383 | 0.9433 |
| 0.1152 | 5.0 | 1425 | 0.4471 | 0.93 | 0.9382 | 0.9349 | 0.9415 |
| 0.036 | 6.0 | 1710 | 0.4333 | 0.933 | 0.9406 | 0.9414 | 0.9397 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
AnirbanRC/anirban_qa_model_finetuned
|
AnirbanRC
| 2023-07-13T06:14:26Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-13T05:38:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: anirban_qa_model_finetuned
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. -->
# anirban_qa_model_finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7210
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.5534 |
| 2.7985 | 2.0 | 500 | 1.8251 |
| 2.7985 | 3.0 | 750 | 1.7210 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
localmodels/WizardLM-13B-v1.1-GPTQ
|
localmodels
| 2023-07-13T06:11:46Z | 7 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-13T06:11:46Z |
---
duplicated_from: localmodels/LLM
---
# WizardLM 13B v1.1 GPTQ
From: https://huggingface.co/WizardLM/WizardLM-13B-V1.1
---
| Model | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| wizardlm-13b-v1.1-GPTQ-4bit-128g.no-act.order | 4 | 128 | False | 7.45 GB | True | GPTQ-for-LLaMa | Most compatible. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. |
|
localmodels/WizardLM-30B-v1.0-GPTQ
|
localmodels
| 2023-07-13T06:01:08Z | 5 | 1 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-07T00:29:47Z |
# WizardLM 30B v1.0 GPTQ
From: https://huggingface.co/WizardLM/WizardLM-30B-V1.0
---
## Model
* wizardlm-30b-1.0-4bit.safetensors
* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
* Works with AutoGPTQ
* Parameters: Groupsize = None. --act-order.
|
localmodels/Guanaco-7B-GPTQ
|
localmodels
| 2023-07-13T05:43:38Z | 17 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"arxiv:2305.14314",
"arxiv:2302.13971",
"arxiv:2304.07327",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-13T05:43:38Z |
---
duplicated_from: localmodels/LLM
---
# Guanaco 7B GPTQ
From: https://huggingface.co/timdettmers/guanaco-7b
---
## Model
* Guanaco-7B-GPTQ-4bit-128g.no-act-order.safetensors
* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
* Works with AutoGPTQ
* Parameters: Groupsize = 128. No act-order.
---
# Guanaco Models Based on LLaMA
| [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) |
**The Guanaco models are open-source finetuned chatbots obtained through 4-bit QLoRA tuning of LLaMA base models on the OASST1 dataset. They are available in 7B, 13B, 33B, and 65B parameter sizes.**
⚠️Guanaco is a model purely intended for research purposes and could produce problematic outputs.
## Why use Guanaco?
- **Competitive with commercial chatbot systems on the Vicuna and OpenAssistant benchmarks** (ChatGPT and BARD) according to human and GPT-4 raters. We note that the relative performance on tasks not covered in these benchmarks could be very different. In addition, commercial systems evolve over time (we used outputs from the March 2023 version of the models).
- **Available open-source for research purposes**. Guanaco models allow *cheap* and *local* experimentation with high-quality chatbot systems.
- **Replicable and efficient training procedure** that can be extended to new use cases. Guanaco training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora).
- **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning.
- **Lightweight** checkpoints which only contain adapter weights.
## License and Intended Use
Guanaco adapter weights are available under Apache 2 license. Note the use of the Guanaco adapter weights, requires access to the LLaMA model weighs.
Guanaco is based on LLaMA and therefore should be used according to the LLaMA license.
## Usage
Here is an example of how you would load Guanaco 7B in 4-bits:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/guanaco-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
),
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
Inference can then be performed as usual with HF models as follows:
```python
prompt = "Introduce yourself"
formatted_prompt = (
f"A chat between a curious human and an artificial intelligence assistant."
f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
f"### Human: {prompt} ### Assistant:"
)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0")
outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Expected output similar to the following:
```
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have.
```
## Current Inference Limitations
Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels.
Below is how you would load the model in 16 bits:
```python
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/guanaco-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Model Card
**Architecture**: The Guanaco models are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$.
**Base Model**: Guanaco uses LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that Guanaco can inherit biases and limitations of the base model.
**Finetuning Data**: Guanaco is finetuned on OASST1. The exact dataset is available at [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
**Languages**: The OASST1 dataset is multilingual (see [the paper](https://arxiv.org/abs/2304.07327) for details) and as such Guanaco responds to user queries in different languages. We note, however, that OASST1 is heavy in high-resource languages. In addition, human evaluation of Guanaco was only performed in English and based on qualitative analysis we observed degradation in performance in other languages.
Next, we describe Training and Evaluation details.
### Training
Guanaco models are the result of 4-bit QLoRA supervised finetuning on the OASST1 dataset.
All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models.
For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer.
### Training hyperparameters
Size| Dataset | Batch Size | Learning Rate | Max Steps | Sequence length
---|---|---|---|---|---
7B | OASST1 | 16 | 2e-4 | 1875 | 512
13B | OASST1 | 16 | 2e-4 | 1875 | 512
33B | OASST1 | 16 | 1e-4 | 1875 | 512
65B | OASST1 | 16 | 1e-4 | 1875 | 512
### Evaluation
We test generative language capabilities through both automated and human evaluations. This second set of evaluations relies on queries curated by humans and aims at measuring the quality of model responses. We use the Vicuna and OpenAssistant datasets with 80 and 953 prompts respectively.
In both human and automated evaluations, for each prompt, raters compare all pairs of responses across the models considered. For human raters we randomize the order of the systems, for GPT-4 we evaluate with both orders.
Benchmark | Vicuna | | Vicuna | | OpenAssistant | | -
-----------|----|-----|--------|---|---------------|---|---
Prompts | 80 | | 80 | | 953 | |
Judge | Human | | GPT-4 | | GPT-4 | |
Model | Elo | Rank | Elo | Rank | Elo | Rank | **Median Rank**
GPT-4 | 1176 | 1 | 1348 | 1 | 1294 | 1 | 1
Guanaco-65B | 1023 | 2 | 1022 | 2 | 1008 | 3 | 2
Guanaco-33B | 1009 | 4 | 992 | 3 | 1002 | 4 | 4
ChatGPT-3.5 Turbo | 916 | 7 | 966 | 5 | 1015 | 2 | 5
Vicuna-13B | 984 | 5 | 974 | 4 | 936 | 5 | 5
Guanaco-13B | 975 | 6 | 913 | 6 | 885 | 6 | 6
Guanaco-7B | 1010 | 3 | 879 | 8 | 860 | 7 | 7
Bard | 909 | 8 | 902 | 7 | - | - | 8
We also use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy.
Dataset | 7B | 13B | 33B | 65B
---|---|---|---|---
LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4
Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7
Longform | 32.1 | 43.2 | 56.6 | 59.7
Chip2 | 34.5 | 41.6 | 53.6 | 59.8
HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1
Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3
OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2
Alpaca | 38.8 | 47.8 | 57.3 | 62.5
FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9
## Risks and Biases
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets; it is possible that this model could generate lewd, biased, or otherwise offensive outputs.
However, we note that finetuning on OASST1 seems to reduce biases as measured on the CrowS dataset. We report here the performance of Guanaco-65B compared to other baseline models on the CrowS dataset.
| | LLaMA-65B | GPT-3 | OPT-175B | Guanaco-65B |
|----------------------|-----------|-------|----------|---------------|
| Gender | 70.6 | 62.6 | 65.7 | **47.5** |
| Religion | {79.0} | 73.3 | 68.6 | **38.7** |
| Race/Color | 57.0 | 64.7 | 68.6 | **45.3** |
| Sexual orientation | {81.0} | 76.2 | 78.6 | **59.1** |
| Age | 70.1 | 64.4 | 67.8 | **36.3** |
| Nationality | 64.2 | 61.6 | 62.9 | **32.4** |
| Disability | 66.7 | 76.7 | 76.7 | **33.9** |
| Physical appearance | 77.8 | 74.6 | 76.2 | **43.1** |
| Socioeconomic status | 71.5 | 73.8 | 76.2 | **55.3** |
| Average | 66.6 | 67.2 | 69.5 | **43.5** |
## Citation
```bibtex
@article{dettmers2023qlora,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2305.14314},
year={2023}
}
```
|
YanJiangJerry/SA-tweet-roberta-large-e4-w1-1.5-b16-m4
|
YanJiangJerry
| 2023-07-13T05:42:53Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T05:19:19Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-tweet-roberta-large-e4-w1-1.5-b16-m4
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. -->
# SA-tweet-roberta-large-e4-w1-1.5-b16-m4
This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3545
- Accuracy: 0.945
- F1: 0.9511
- Precision: 0.9537
- Recall: 0.9486
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 285 | 0.1933 | 0.92 | 0.9290 | 0.9306 | 0.9273 |
| 0.2508 | 2.0 | 570 | 0.2097 | 0.933 | 0.9411 | 0.9337 | 0.9486 |
| 0.2508 | 3.0 | 855 | 0.2958 | 0.937 | 0.9450 | 0.9312 | 0.9592 |
| 0.0947 | 4.0 | 1140 | 0.3545 | 0.945 | 0.9511 | 0.9537 | 0.9486 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
preetham/rpanda
|
preetham
| 2023-07-13T05:42:03Z | 30 | 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
| 2023-07-13T05:23:12Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks panda
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - preetham/rpanda
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks panda using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
ajaydvrj/datasetForSpotify
|
ajaydvrj
| 2023-07-13T05:38:51Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-12T13:40:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: datasetForSpotify
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. -->
# datasetForSpotify
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6052
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 3 | 5.7484 |
| No log | 2.0 | 6 | 5.6474 |
| No log | 3.0 | 9 | 5.6052 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
|
localmodels/Guanaco-13B-GPTQ
|
localmodels
| 2023-07-13T05:37:10Z | 5 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"arxiv:2305.14314",
"arxiv:2302.13971",
"arxiv:2304.07327",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-13T05:37:05Z |
---
duplicated_from: localmodels/LLM
---
# Guanaco 13B GPTQ
From: https://huggingface.co/timdettmers/guanaco-13b
---
## Model
* Guanaco-13B-GPTQ-4bit-128g.no-act-order.safetensors
* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
* Works with AutoGPTQ
* Parameters: Groupsize = 128. No act-order.
---
# Guanaco Models Based on LLaMA
| [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) |
**The Guanaco models are open-source finetuned chatbots obtained through 4-bit QLoRA tuning of LLaMA base models on the OASST1 dataset. They are available in 7B, 13B, 33B, and 65B parameter sizes.**
⚠️Guanaco is a model purely intended for research purposes and could produce problematic outputs.
## Why use Guanaco?
- **Competitive with commercial chatbot systems on the Vicuna and OpenAssistant benchmarks** (ChatGPT and BARD) according to human and GPT-4 raters. We note that the relative performance on tasks not covered in these benchmarks could be very different. In addition, commercial systems evolve over time (we used outputs from the March 2023 version of the models).
- **Available open-source for research purposes**. Guanaco models allow *cheap* and *local* experimentation with high-quality chatbot systems.
- **Replicable and efficient training procedure** that can be extended to new use cases. Guanaco training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora).
- **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning.
- **Lightweight** checkpoints which only contain adapter weights.
## License and Intended Use
Guanaco adapter weights are available under Apache 2 license. Note the use of the Guanaco adapter weights, requires access to the LLaMA model weighs.
Guanaco is based on LLaMA and therefore should be used according to the LLaMA license.
## Usage
Here is an example of how you would load Guanaco 7B in 4-bits:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/guanaco-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
),
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
Inference can then be performed as usual with HF models as follows:
```python
prompt = "Introduce yourself"
formatted_prompt = (
f"A chat between a curious human and an artificial intelligence assistant."
f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
f"### Human: {prompt} ### Assistant:"
)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0")
outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Expected output similar to the following:
```
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have.
```
## Current Inference Limitations
Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels.
Below is how you would load the model in 16 bits:
```python
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/guanaco-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Model Card
**Architecture**: The Guanaco models are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$.
**Base Model**: Guanaco uses LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that Guanaco can inherit biases and limitations of the base model.
**Finetuning Data**: Guanaco is finetuned on OASST1. The exact dataset is available at [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
**Languages**: The OASST1 dataset is multilingual (see [the paper](https://arxiv.org/abs/2304.07327) for details) and as such Guanaco responds to user queries in different languages. We note, however, that OASST1 is heavy in high-resource languages. In addition, human evaluation of Guanaco was only performed in English and based on qualitative analysis we observed degradation in performance in other languages.
Next, we describe Training and Evaluation details.
### Training
Guanaco models are the result of 4-bit QLoRA supervised finetuning on the OASST1 dataset.
All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models.
For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer.
### Training hyperparameters
Size| Dataset | Batch Size | Learning Rate | Max Steps | Sequence length
---|---|---|---|---|---
7B | OASST1 | 16 | 2e-4 | 1875 | 512
13B | OASST1 | 16 | 2e-4 | 1875 | 512
33B | OASST1 | 16 | 1e-4 | 1875 | 512
65B | OASST1 | 16 | 1e-4 | 1875 | 512
### Evaluation
We test generative language capabilities through both automated and human evaluations. This second set of evaluations relies on queries curated by humans and aims at measuring the quality of model responses. We use the Vicuna and OpenAssistant datasets with 80 and 953 prompts respectively.
In both human and automated evaluations, for each prompt, raters compare all pairs of responses across the models considered. For human raters we randomize the order of the systems, for GPT-4 we evaluate with both orders.
Benchmark | Vicuna | | Vicuna | | OpenAssistant | | -
-----------|----|-----|--------|---|---------------|---|---
Prompts | 80 | | 80 | | 953 | |
Judge | Human | | GPT-4 | | GPT-4 | |
Model | Elo | Rank | Elo | Rank | Elo | Rank | **Median Rank**
GPT-4 | 1176 | 1 | 1348 | 1 | 1294 | 1 | 1
Guanaco-65B | 1023 | 2 | 1022 | 2 | 1008 | 3 | 2
Guanaco-33B | 1009 | 4 | 992 | 3 | 1002 | 4 | 4
ChatGPT-3.5 Turbo | 916 | 7 | 966 | 5 | 1015 | 2 | 5
Vicuna-13B | 984 | 5 | 974 | 4 | 936 | 5 | 5
Guanaco-13B | 975 | 6 | 913 | 6 | 885 | 6 | 6
Guanaco-7B | 1010 | 3 | 879 | 8 | 860 | 7 | 7
Bard | 909 | 8 | 902 | 7 | - | - | 8
We also use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy.
Dataset | 7B | 13B | 33B | 65B
---|---|---|---|---
LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4
Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7
Longform | 32.1 | 43.2 | 56.6 | 59.7
Chip2 | 34.5 | 41.6 | 53.6 | 59.8
HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1
Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3
OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2
Alpaca | 38.8 | 47.8 | 57.3 | 62.5
FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9
## Risks and Biases
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets; it is possible that this model could generate lewd, biased, or otherwise offensive outputs.
However, we note that finetuning on OASST1 seems to reduce biases as measured on the CrowS dataset. We report here the performance of Guanaco-65B compared to other baseline models on the CrowS dataset.
| | LLaMA-65B | GPT-3 | OPT-175B | Guanaco-65B |
|----------------------|-----------|-------|----------|---------------|
| Gender | 70.6 | 62.6 | 65.7 | **47.5** |
| Religion | {79.0} | 73.3 | 68.6 | **38.7** |
| Race/Color | 57.0 | 64.7 | 68.6 | **45.3** |
| Sexual orientation | {81.0} | 76.2 | 78.6 | **59.1** |
| Age | 70.1 | 64.4 | 67.8 | **36.3** |
| Nationality | 64.2 | 61.6 | 62.9 | **32.4** |
| Disability | 66.7 | 76.7 | 76.7 | **33.9** |
| Physical appearance | 77.8 | 74.6 | 76.2 | **43.1** |
| Socioeconomic status | 71.5 | 73.8 | 76.2 | **55.3** |
| Average | 66.6 | 67.2 | 69.5 | **43.5** |
## Citation
```bibtex
@article{dettmers2023qlora,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2305.14314},
year={2023}
}
```
|
vivek22/vivek
|
vivek22
| 2023-07-13T05:05:38Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"en",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-12T12:30:41Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
language:
- en
pipeline_tag: text-to-image
---
# LoRA text2image fine-tuning - vivek22/vivek
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the vivek22/randm dataset.
|
Treeizard/tactile_generator
|
Treeizard
| 2023-07-13T04:57:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-13T04:57:07Z |
---
license: creativeml-openrail-m
---
|
FelixChao/falcon-7b-instruct-ft-adapters-ESG-chatting
|
FelixChao
| 2023-07-13T04:55:48Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-13T04:55:35Z |
---
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
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
- PEFT 0.4.0.dev0
|
YanJiangJerry/SA-tweet-roberta-large-e4-w1-1.5-b16
|
YanJiangJerry
| 2023-07-13T04:53:22Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T04:17:05Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SA-tweet-roberta-large-e4-w1-1.5-b16
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. -->
# SA-tweet-roberta-large-e4-w1-1.5-b16
This model is a fine-tuned version of [Amalq/autotrain-smm4h_large_roberta_clean-874027878](https://huggingface.co/Amalq/autotrain-smm4h_large_roberta_clean-874027878) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6396
- Accuracy: 0.9166
- F1: 0.8872
- Precision: 0.8939
- Recall: 0.8806
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2895 | 1.0 | 581 | 0.4026 | 0.9110 | 0.8806 | 0.8806 | 0.8806 |
| 0.1182 | 2.0 | 1162 | 0.6190 | 0.9110 | 0.8754 | 0.9153 | 0.8388 |
| 0.0589 | 3.0 | 1743 | 0.6167 | 0.9155 | 0.8838 | 0.9060 | 0.8627 |
| 0.0211 | 4.0 | 2324 | 0.6396 | 0.9166 | 0.8872 | 0.8939 | 0.8806 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
localmodels/Vicuna-7B-v1.3-GPTQ
|
localmodels
| 2023-07-13T04:47:45Z | 15 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"arxiv:2302.13971",
"arxiv:2306.05685",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-13T04:47:41Z |
---
duplicated_from: localmodels/LLM
---
# Vicuna 7B v1.3 GPTQ
From LMSYS: https://huggingface.co/lmsys/vicuna-7b-v1.3
---
| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| vicuna-7b-v1.3-GPTQ-4bit-128g.no-act.order | 4 | 128 | False | 4.00 GB | True | GPTQ-for-LLaMa | Most compatible. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. |
---
# Vicuna Model Card
## Model Details
Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
- **Developed by:** [LMSYS](https://lmsys.org/)
- **Model type:** An auto-regressive language model based on the transformer architecture.
- **License:** Non-commercial license
- **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971).
### Model Sources
- **Repository:** https://github.com/lm-sys/FastChat
- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
- **Paper:** https://arxiv.org/abs/2306.05685
- **Demo:** https://chat.lmsys.org/
## Uses
The primary use of Vicuna is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## How to Get Started with the Model
Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights.
APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api.
## Training Details
Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning.
The training data is around 140K conversations collected from ShareGPT.com.
See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
## Evaluation
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
## Difference between different versions of Vicuna
See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
|
glitchyordis/poca-SoccerTwos
|
glitchyordis
| 2023-07-13T04:35:57Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-07-13T04:35:46Z |
---
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: glitchyordis/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kazuhidet/dog
|
kazuhidet
| 2023-07-13T04:34:02Z | 9 | 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
| 2023-07-13T02:08:10Z |
---
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 - kazuhidet/dog
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.
|
Ife/BM-FR
|
Ife
| 2023-07-13T04:17:55Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"bm",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
language:
- bm
- fr
---
@inproceedings{adebara-abdul-mageed-2021-improving,
title = "Improving Similar Language Translation With Transfer Learning",
author = "Adebara, Ife and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.27",
pages = "273--278",
abstract = "We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we submitted models for different language pairs, including French-Bambara, Spanish-Catalan, and Spanish-Portuguese in both directions. Our models for Catalan-Spanish (82.79 BLEU)and Portuguese-Spanish (87.11 BLEU) rank top 1 in the official shared task evaluation, and we are the only team to submit models for the French-Bambara pairs.",
}
|
abbiezz/tomuntitled
|
abbiezz
| 2023-07-13T04:12:40Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-07-13T04:06:35Z |
---
license: openrail
---
https://drive.google.com/file/d/1qilU9BEfX7RY8q9Uohesz9qQa0R_B5PW/view?usp=drive_link
|
quyc/picture
|
quyc
| 2023-07-13T03:51:01Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"image-to-text",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2023-07-12T07:05:17Z |
---
pipeline_tag: image-to-text
---
|
nic1122/roberta-base-finetuned-cluener2020-chinese
|
nic1122
| 2023-07-13T03:50:31Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-13T03:09:03Z |
源自：https://huggingface.co/uer/roberta-base-finetuned-cluener2020-chinese
做了IOB适配，适用于elasticsearch ml
|
mgeller/opt-2.7b-lora
|
mgeller
| 2023-07-13T03:49:01Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-13T03:48:55Z |
---
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
|
kazuhidet/kasumi
|
kazuhidet
| 2023-07-13T03:35:32Z | 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
| 2023-07-13T03:18:42Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of people kasumi
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - kazuhidet/kasumi
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of people kasumi using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
keehun/textual_inversion_los_char
|
keehun
| 2023-07-13T03:23:01Z | 5 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-13T02:55:30Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - keehun/textual_inversion_los_char
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
LoupGarou/WizardCoder-Guanaco-15B-V1.1
|
LoupGarou
| 2023-07-13T03:21:55Z | 1,506 | 12 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"en",
"dataset:guanaco",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-12T06:10:19Z |
---
language:
- en
datasets:
- guanaco
model_hub_library:
- transformers
license:
- apache-2.0
---
## WizardCoder-Guanaco-15B-V1.1 Model Card
The WizardCoder-Guanaco-15B-V1.1 is a language model that combines the strengths of the [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) base model and the [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset for finetuning. The openassistant-guanaco dataset was further trimmed to within 2 standard deviations of token size for input and output pairs and all non-english data has been removed to reduce training size requirements.
Version 1.1 showcases notable enhancements, employing a modified version of the previous openassistant-guanaco dataset. This dataset underwent a comprehensive revision, replacing every single answer with those generated by GPT-4.
The volume of the datasets has also been augmented by approximately 50%, with a particular focus on high school and abstract algebra. This expansion leveraged the combined capabilities of GPT-4 and GPT-3.5-Turbo. The initial evaluation of algebraic functions over 12 epochs indicated promising results from this enriched dataset. However, this is just the beginning; further refinements are in the pipeline, aiming to optimize the dataset quality and subsequently decrease the number of epochs required to achieve comparable results.
Considering the need to curtail memory consumption during training, this dataset was tailored to consist solely of English language questions and answers. Consequently, the model's performance in language translation may not be up to par. Nevertheless, the focus remains on enhancing the model's proficiency and efficiency within its defined scope.
# Intended Use
This model is designed to be used for a wide array of text generation tasks that require understanding and generating English text. The model is expected to perform well in tasks such as answering questions, writing essays, summarizing text, translation, and more. However, given the specific data processing and finetuning done, it might be particularly effective for tasks related to English language question-answering systems.
# Limitations
Despite the powerful capabilities of this model, users should be aware of its limitations. The model's knowledge is up to date only until the time it was trained, and it doesn't know about events in the world after that. It can sometimes produce incorrect or nonsensical responses, as it doesn't understand the text in the same way humans do. It should be used as a tool to assist in generating text and not as a sole source of truth.
# How to use
Here is an example of how to use this model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import time
import torch
class Chatbot:
def __init__(self, model_name):
self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
self.model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, torch_dtype=torch.bfloat16)
if self.tokenizer.pad_token_id is None:
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
def get_response(self, prompt):
inputs = self.tokenizer.encode_plus(prompt, return_tensors="pt", padding='max_length', max_length=100)
if next(self.model.parameters()).is_cuda:
inputs = {name: tensor.to('cuda') for name, tensor in inputs.items()}
start_time = time.time()
tokens = self.model.generate(input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
pad_token_id=self.tokenizer.pad_token_id,
max_new_tokens=400)
end_time = time.time()
output_tokens = tokens[0][inputs['input_ids'].shape[-1]:]
output = self.tokenizer.decode(output_tokens, skip_special_tokens=True)
time_taken = end_time - start_time
return output, time_taken
def main():
chatbot = Chatbot("LoupGarou/WizardCoder-Guanaco-15B-V1.1")
while True:
user_input = input("Enter your prompt: ")
if user_input.lower() == 'quit':
break
output, time_taken = chatbot.get_response(user_input)
print("\033[33m" + output + "\033[0m")
print("Time taken to process: ", time_taken, "seconds")
print("Exited the program.")
if __name__ == "__main__":
main()
```
# Training Procedure
The WizardCoder model, serving as the base, was fine-tuned on a modified version of the openassistant-guanaco dataset. This dataset underwent a significant revision, replacing every single answer with responses generated by the AI model GPT-4. It was then expanded by approximately 50%, emphasizing high school and abstract algebra-related questions, using a mix of GPT-4 and GPT-3.5-Turbo for answer generation.
The selected dataset was standardized to fall within two standard deviations of token size for the question sets, ensuring consistency in data handling. The order of the questions was also randomized to mitigate any potential biases during the training phase.
In the interest of optimizing memory usage during the training process, the dataset was streamlined to only include English language content. As a result, all non-English data was systematically expunged from this fine-tuning dataset. It's worth noting that this modification limits the model's performance in language translation tasks, but it significantly boosts its efficiency and effectiveness when dealing with English language questions and answers.
## Acknowledgements
This model, WizardCoder-Guanaco-15B-V1.1, is simply building on the efforts of two great teams to evaluate the performance of a combined model with the strengths of the [WizardCoder base model](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) and the [openassistant-guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
A sincere appreciation goes out to the developers and the community involved in the creation and refinement of these models. Their commitment to providing open source tools and datasets have been instrumental in making this project a reality.
Moreover, a special note of thanks to the [Hugging Face](https://huggingface.co/) team, whose transformative library has not only streamlined the process of model creation and adaptation, but also democratized the access to state-of-the-art machine learning technologies. Their impact on the development of this project cannot be overstated.
|
Bimantara/lcb2
|
Bimantara
| 2023-07-13T03:15:09Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-13T03:14:25Z |
---
license: creativeml-openrail-m
---
|
h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2
|
h2oai
| 2023-07-13T03:12:11Z | 72 | 18 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWeb",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"custom_code",
"en",
"dataset:OpenAssistant/oasst1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-23T07:35:02Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
license: apache-2.0
datasets:
- OpenAssistant/oasst1
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
- Dataset preparation: [OpenAssistant/oasst1](https://github.com/h2oai/h2o-llmstudio/blob/1935d84d9caafed3ee686ad2733eb02d2abfce57/app_utils/utils.py#LL1896C5-L1896C28) personalized
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install bitsandbytes==0.39.0
pip install accelerate==0.19.0
pip install torch==2.0.0
pip install einops==0.6.1
```
```python
import torch
from transformers import pipeline, BitsAndBytesConfig, AutoTokenizer
model_kwargs = {}
quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0,
)
model_kwargs["quantization_config"] = quantization_config
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
generate_text = pipeline(
model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
tokenizer=tokenizer,
torch_dtype=torch.float16,
trust_remote_code=True,
use_fast=False,
device_map={"": "cuda:0"},
model_kwargs=model_kwargs,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0,
)
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
trust_remote_code=True,
torch_dtype=torch.float16,
device_map={"": "cuda:0"},
quantization_config=quantization_config
).eval()
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0,
)
tokenizer = AutoTokenizer.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
use_fast=False,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
trust_remote_code=True,
torch_dtype=torch.float16,
device_map={"": "cuda:0"},
quantization_config=quantization_config
).eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
RWForCausalLM(
(transformer): RWModel(
(word_embeddings): Embedding(65024, 8192)
(h): ModuleList(
(0-59): 60 x DecoderLayer(
(ln_attn): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
(ln_mlp): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
(self_attention): Attention(
(maybe_rotary): RotaryEmbedding()
(query_key_value): Linear(in_features=8192, out_features=9216, bias=False)
(dense): Linear(in_features=8192, out_features=8192, bias=False)
(attention_dropout): Dropout(p=0.0, inplace=False)
)
(mlp): MLP(
(dense_h_to_4h): Linear(in_features=8192, out_features=32768, bias=False)
(act): GELU(approximate='none')
(dense_4h_to_h): Linear(in_features=32768, out_features=8192, bias=False)
)
)
)
(ln_f): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=8192, out_features=65024, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
sd-dreambooth-library/Punk-VisualKei
|
sd-dreambooth-library
| 2023-07-13T03:08:35Z | 47 | 2 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-03T02:09:14Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: vskeei1
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
---
### Punk and Visual Kei Dreambooth model trained with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
[Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb).
VAE is not required but is fun.
I am not responsible for what you make.
If this model bites you call the CIA.
### Codeword:
vskeei1 (use that on your prompt)
|
jwchung/distilbert-base-uncased-finetuned-imdb
|
jwchung
| 2023-07-13T03:00:46Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-12T19:31:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
duyvt6663/RoBERTa_Subj
|
duyvt6663
| 2023-07-13T02:39:04Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-13T02:04:23Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: RoBERTa_Subj
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. -->
# RoBERTa_Subj
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: 0.0234
- Validation Loss: 0.1242
- Train Accuracy: 0.965
- Epoch: 3
## Model description
This model sucks, please don't use
## Intended uses & limitations
More information needed
## Training and evaluation data
The model was trained on the SetFit/Subj dataset.
The dataset, I believe, contains plot lines from movies and reviews. The former are all labeled as "objective" while the latter "subjective".
## 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': 2500, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1849 | 0.1106 | 0.9595 | 0 |
| 0.0652 | 0.1081 | 0.9625 | 1 |
| 0.0234 | 0.1242 | 0.965 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
peteryushunli/Fill_Mask_Tutorial_Model
|
peteryushunli
| 2023-07-13T02:21:03Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-13T01:50:24Z |
# Fill-Mask PyTorch Model (Camembert)
This model is a `fill-mask` model that was trained using the PyTorch framework and the Hugging Face Transformers library. It was utilized in Hugging Face's NLP course as an introductory model.
## Model Description
This model uses the `camembert` architecture, a variant of the RoBERTa model adapted for French. It's designed for the fill-mask task, where a portion of input text is masked and the model predicts the missing token.
## Features
- **PyTorch**: The model was implemented and trained using the PyTorch deep learning framework, which allows for dynamic computation graphs and is known for its flexibility and efficiency.
- **Safetensors**: The model utilizes Safetensors, a Python library that provides safer operations for PyTorch Tensors.
- **Transformers**: The model was built using the Hugging Face Transformers library, a state-of-the-art NLP library that provides thousands of pre-trained models and easy-to-use implementations of transformer architectures.
- **AutoTrain Compatible**: This model is compatible with Hugging Face's AutoTrain, a tool that automates the training of transformer models.
## Usage
```python
from transformers import CamembertForMaskedLM, CamembertTokenizer
tokenizer = CamembertTokenizer.from_pretrained('model-name')
model = CamembertForMaskedLM.from_pretrained('model-name')
inputs = tokenizer("Le camembert est <mask>.", return_tensors='pt')
outputs = model(**inputs)
predictions = outputs.logits
predicted_index = torch.argmax(predictions[0, mask_position]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
|
Jonathaniu/alpaca-breast-cancer-7b-epoch-2
|
Jonathaniu
| 2023-07-13T02:19:50Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-13T02:19:33Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
### Framework versions
- PEFT 0.4.0.dev0
|
hululuzhu/solidity-t5
|
hululuzhu
| 2023-07-13T00:59:53Z | 118 | 10 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"solidity",
"web3",
"code generation",
"smart contract",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-01T02:23:20Z |
---
language:
- en
license: apache-2.0
tags:
- solidity
- web3
- code generation
- smart contract
widget:
- text: "pragma solidity ^0.5.7;\n// Context: ParentA | Functions: helloA helloB | Constants: constantA \ncontract HelloWorld is ParentA {"
---
# A code generation T5 model for solidity (web3 smart contract)
- See https://github.com/hululuzhu/solidity-t5 for more context
## How to use this trained model
- A hello world example to use this model, notice the input `text` includes
- Header solidity version like `pragma solidity ^0.5.7`
- Ancestor class/library info, e.g. public functions and constants from `ParentA`
- Contract/Library/Interface declaration header, e.g. `HelloWorld` ended with `{`
- Or simply use the test widget on the right side of the window and test, however
the quality is known to be worse without decoding params
```python
# !pip install transformers -q
from transformers import AutoTokenizer, T5ForConditionalGeneration
DEVICE = 'cuda' # fallback to cpu if you do not have cuda
tokenizer = AutoTokenizer.from_pretrained("hululuzhu/solidity-t5")
model = T5ForConditionalGeneration.from_pretrained("hululuzhu/solidity-t5").to(DEVICE)
text = """pragma solidity ^0.5.7;
// Context: ParentA | Functions: helloA helloB | Constants: constantA
contract HelloWorld is ParentA {"""
input_ids = tokenizer(text, return_tensors="pt", truncation=True).input_ids.to(DEVICE)
# Need to tune beam/topk/topp params to get good outcome
generated_ids = model.generate(input_ids, max_length=256, num_beams=5, top_p=0.95, top_k=50)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
# Expect outcome
"""
string public constant name = "Hello World";
...
uint256 public constant override returns (uint256) {
return initialSupply;
}
function initialSupply() public view returns (uint256) {
...
"""
```
## Background
- Base T5 code model: https://huggingface.co/Salesforce/codet5-large
- Source data: https://huggingface.co/datasets/mwritescode/slither-audited-smart-contracts
- Processing steps: Clean, contract-level segmentation sepration, split in and out
- After processing input sample
```
pragma solidity 0.5.7;
// Context: PauserRole | Functions: isPauser addPauser renouncePauser | Constants:
contract Pausable is PauserRole {
```
- After processing output sample (**notice indentation is bad, this is intentional to reduce token size**)
```
event Paused(address account);
event Unpaused(address account);
bool private _pausableActive;
bool private _paused;
constructor () internal {
_paused = false;
}
function paused() public view returns (bool) {
return _paused;
}
modifier whenNotPaused() {
require(!_paused);
_;
}
modifier whenPaused() {
require(_paused);
_;
}
function pause() public onlyPauser whenNotPaused whenPausableActive {
_paused = true;
emit Paused(msg.sender);
}
function unpause() public onlyPauser whenPaused whenPausableActive {
_paused = false;
emit Unpaused(msg.sender);
}
function _setPausableActive(bool _active) internal {
_pausableActive = _active;
}
modifier whenPausableActive() {
require(_pausableActive);
_;
}
}
```
- Source training code: See the [end to end notebook](https://github.com/hululuzhu/solidity-t5/blob/main/code/Solidity_T5_Data_Processing_and_Training.ipynb) at code dir here
## Future TODO
- The model is significantly under-trained because of lack of GPU budget, need 10x colab resources (~$100 for full train)
- This is quite limited on how the model is used, potentially we could switch to GPT2 decoder-only to compare, but CodeT5 has its strong code optimization
- Need more classifiers (T5 or BERT alike) to detect potential defects.
|
tbooy/q-FrozenLake-v1-4x4-noSlippery
|
tbooy
| 2023-07-13T00:56:16Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-13T00:56:09Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="tbooy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
mpinedaa/distilbert_squad_sample_finetuned_model
|
mpinedaa
| 2023-07-13T00:30:09Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-12T14:14:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert_squad_sample_finetuned_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_squad_sample_finetuned_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.3529 |
| 2.7741 | 2.0 | 500 | 1.6561 |
| 2.7741 | 3.0 | 750 | 1.5925 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
|
manmyung/dqn-SpaceInvadersNoFrameskip-v4
|
manmyung
| 2023-07-13T00:08:57Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-13T00:08:12Z |
---
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: 613.50 +/- 78.77
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 manmyung -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 manmyung -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 manmyung
```
## 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'}
```
|
rohn132/ppo-LunarLander-v2
|
rohn132
| 2023-07-12T23:58:20Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T23:57:59Z |
---
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: 264.99 +/- 28.96
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
...
```
|
Blu72/Falcon
|
Blu72
| 2023-07-12T23:48:51Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-07-12T23:48:30Z |
---
license: openrail
---
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-40b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-40b")
|
SHENMU007/neunit_BASE_V12.5
|
SHENMU007
| 2023-07-12T23:39:19Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-07-12T20:39:15Z |
---
language:
- zh
license: mit
base_model: microsoft/speecht5_tts
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
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 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ayanban011/vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split
|
ayanban011
| 2023-07-12T23:33:16Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-12T18:58:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base_tobacco_bs_16_lr_1e-5_e_200_wr_0.05_wd_0.4_split
This model is a fine-tuned version of [jordyvl/vit-base_tobacco](https://huggingface.co/jordyvl/vit-base_tobacco) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0411
- Accuracy: 0.8333
- Brier Loss: 0.3084
- Nll: 1.3568
- F1 Micro: 0.8333
- F1 Macro: 0.8183
- Ece: 0.1563
- Aurc: 0.0847
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- 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: 0.05
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 0.99 | 43 | 0.7544 | 0.7960 | 0.3088 | 1.3391 | 0.7960 | 0.7715 | 0.1991 | 0.0817 |
| No log | 2.0 | 87 | 0.7158 | 0.8218 | 0.2920 | 1.1888 | 0.8218 | 0.7941 | 0.1863 | 0.0741 |
| No log | 2.98 | 130 | 0.7144 | 0.7989 | 0.2932 | 1.2958 | 0.7989 | 0.7701 | 0.1628 | 0.0749 |
| No log | 3.99 | 174 | 0.6762 | 0.8305 | 0.2749 | 1.1916 | 0.8305 | 0.8076 | 0.1844 | 0.0678 |
| No log | 4.98 | 217 | 0.6710 | 0.8362 | 0.2745 | 1.0739 | 0.8362 | 0.8076 | 0.1696 | 0.0664 |
| No log | 5.99 | 261 | 0.6532 | 0.8362 | 0.2675 | 1.0011 | 0.8362 | 0.8115 | 0.1750 | 0.0602 |
| No log | 6.98 | 304 | 0.6404 | 0.8362 | 0.2635 | 1.0072 | 0.8362 | 0.8106 | 0.1714 | 0.0633 |
| No log | 7.99 | 348 | 0.6635 | 0.8218 | 0.2707 | 1.0903 | 0.8218 | 0.8030 | 0.1513 | 0.0770 |
| No log | 9.0 | 392 | 0.6167 | 0.8420 | 0.2534 | 1.0176 | 0.8420 | 0.8259 | 0.1613 | 0.0796 |
| No log | 9.99 | 435 | 0.6496 | 0.8276 | 0.2703 | 0.9646 | 0.8276 | 0.8085 | 0.1643 | 0.0588 |
| No log | 11.0 | 479 | 0.6091 | 0.8506 | 0.2467 | 1.1036 | 0.8506 | 0.8308 | 0.1483 | 0.0650 |
| 0.4309 | 11.98 | 522 | 0.6075 | 0.8420 | 0.2483 | 0.9144 | 0.8420 | 0.8246 | 0.1391 | 0.0519 |
| 0.4309 | 12.99 | 566 | 0.6164 | 0.8276 | 0.2576 | 0.9703 | 0.8276 | 0.8092 | 0.1467 | 0.0645 |
| 0.4309 | 13.98 | 609 | 0.5893 | 0.8592 | 0.2347 | 1.1493 | 0.8592 | 0.8483 | 0.1347 | 0.0715 |
| 0.4309 | 14.99 | 653 | 0.6123 | 0.8477 | 0.2485 | 1.1889 | 0.8477 | 0.8232 | 0.1587 | 0.0764 |
| 0.4309 | 16.0 | 697 | 0.6352 | 0.8420 | 0.2615 | 1.1999 | 0.8420 | 0.8403 | 0.1368 | 0.0668 |
| 0.4309 | 16.99 | 740 | 0.6329 | 0.8333 | 0.2625 | 1.1748 | 0.8333 | 0.8249 | 0.1267 | 0.0744 |
| 0.4309 | 18.0 | 784 | 0.6350 | 0.8448 | 0.2590 | 1.2154 | 0.8448 | 0.8386 | 0.1423 | 0.0688 |
| 0.4309 | 18.98 | 827 | 0.5892 | 0.8592 | 0.2383 | 1.1001 | 0.8592 | 0.8515 | 0.1293 | 0.0630 |
| 0.4309 | 19.99 | 871 | 0.5981 | 0.8477 | 0.2476 | 1.0104 | 0.8477 | 0.8375 | 0.1345 | 0.0630 |
| 0.4309 | 20.98 | 914 | 0.6484 | 0.8420 | 0.2642 | 1.3553 | 0.8420 | 0.8292 | 0.1490 | 0.0770 |
| 0.4309 | 21.99 | 958 | 0.6298 | 0.8305 | 0.2657 | 1.1220 | 0.8305 | 0.8208 | 0.1292 | 0.0670 |
| 0.1285 | 22.98 | 1001 | 0.6325 | 0.8391 | 0.2633 | 1.2549 | 0.8391 | 0.8362 | 0.1328 | 0.0708 |
| 0.1285 | 23.99 | 1045 | 0.6032 | 0.8534 | 0.2486 | 1.1258 | 0.8534 | 0.8444 | 0.1229 | 0.0706 |
| 0.1285 | 25.0 | 1089 | 0.6080 | 0.8534 | 0.2460 | 1.2033 | 0.8534 | 0.8414 | 0.1257 | 0.0755 |
| 0.1285 | 25.99 | 1132 | 0.6321 | 0.8391 | 0.2667 | 1.2242 | 0.8391 | 0.8355 | 0.1349 | 0.0697 |
| 0.1285 | 27.0 | 1176 | 0.6325 | 0.8592 | 0.2522 | 1.2029 | 0.8592 | 0.8493 | 0.1278 | 0.0778 |
| 0.1285 | 27.98 | 1219 | 0.6585 | 0.8534 | 0.2546 | 1.3669 | 0.8534 | 0.8378 | 0.1368 | 0.0890 |
| 0.1285 | 28.99 | 1263 | 0.6302 | 0.8563 | 0.2517 | 1.2419 | 0.8563 | 0.8508 | 0.1294 | 0.0751 |
| 0.1285 | 29.98 | 1306 | 0.6663 | 0.8477 | 0.2637 | 1.4132 | 0.8477 | 0.8339 | 0.1399 | 0.0828 |
| 0.1285 | 30.99 | 1350 | 0.7063 | 0.8362 | 0.2799 | 1.4323 | 0.8362 | 0.8330 | 0.1441 | 0.0863 |
| 0.1285 | 32.0 | 1394 | 0.6564 | 0.8506 | 0.2570 | 1.1583 | 0.8506 | 0.8417 | 0.1358 | 0.0847 |
| 0.1285 | 32.99 | 1437 | 0.6738 | 0.8477 | 0.2647 | 1.3855 | 0.8477 | 0.8398 | 0.1305 | 0.0775 |
| 0.1285 | 34.0 | 1481 | 0.6528 | 0.8563 | 0.2559 | 1.2601 | 0.8563 | 0.8462 | 0.1310 | 0.0789 |
| 0.0385 | 34.98 | 1524 | 0.6534 | 0.8563 | 0.2537 | 1.2931 | 0.8563 | 0.8461 | 0.1241 | 0.0773 |
| 0.0385 | 35.99 | 1568 | 0.6541 | 0.8534 | 0.2525 | 1.2589 | 0.8534 | 0.8449 | 0.1315 | 0.0833 |
| 0.0385 | 36.98 | 1611 | 0.6769 | 0.8592 | 0.2545 | 1.4351 | 0.8592 | 0.8492 | 0.1242 | 0.0792 |
| 0.0385 | 37.99 | 1655 | 0.6824 | 0.8592 | 0.2576 | 1.2241 | 0.8592 | 0.8472 | 0.1327 | 0.0810 |
| 0.0385 | 38.98 | 1698 | 0.6843 | 0.8563 | 0.2589 | 1.3394 | 0.8563 | 0.8450 | 0.1311 | 0.0802 |
| 0.0385 | 39.99 | 1742 | 0.6964 | 0.8506 | 0.2630 | 1.2625 | 0.8506 | 0.8405 | 0.1310 | 0.0789 |
| 0.0385 | 41.0 | 1786 | 0.7051 | 0.8534 | 0.2671 | 1.3296 | 0.8534 | 0.8434 | 0.1353 | 0.0794 |
| 0.0385 | 41.99 | 1829 | 0.7006 | 0.8506 | 0.2645 | 1.2965 | 0.8506 | 0.8400 | 0.1373 | 0.0796 |
| 0.0385 | 43.0 | 1873 | 0.7054 | 0.8563 | 0.2646 | 1.2973 | 0.8563 | 0.8450 | 0.1313 | 0.0790 |
| 0.0385 | 43.98 | 1916 | 0.7143 | 0.8506 | 0.2673 | 1.2640 | 0.8506 | 0.8399 | 0.1359 | 0.0803 |
| 0.0385 | 44.99 | 1960 | 0.7168 | 0.8534 | 0.2665 | 1.3058 | 0.8534 | 0.8429 | 0.1389 | 0.0820 |
| 0.0206 | 45.98 | 2003 | 0.7204 | 0.8506 | 0.2669 | 1.3009 | 0.8506 | 0.8384 | 0.1336 | 0.0805 |
| 0.0206 | 46.99 | 2047 | 0.7265 | 0.8534 | 0.2683 | 1.2633 | 0.8534 | 0.8415 | 0.1319 | 0.0806 |
| 0.0206 | 48.0 | 2091 | 0.7311 | 0.8506 | 0.2695 | 1.2725 | 0.8506 | 0.8396 | 0.1372 | 0.0811 |
| 0.0206 | 48.99 | 2134 | 0.7384 | 0.8477 | 0.2729 | 1.3385 | 0.8477 | 0.8364 | 0.1387 | 0.0807 |
| 0.0206 | 50.0 | 2178 | 0.7383 | 0.8534 | 0.2695 | 1.1951 | 0.8534 | 0.8406 | 0.1344 | 0.0827 |
| 0.0206 | 50.98 | 2221 | 0.7440 | 0.8506 | 0.2740 | 1.3360 | 0.8506 | 0.8394 | 0.1418 | 0.0812 |
| 0.0206 | 51.99 | 2265 | 0.7455 | 0.8506 | 0.2727 | 1.2704 | 0.8506 | 0.8388 | 0.1351 | 0.0816 |
| 0.0206 | 52.98 | 2308 | 0.7474 | 0.8506 | 0.2708 | 1.2622 | 0.8506 | 0.8384 | 0.1334 | 0.0823 |
| 0.0206 | 53.99 | 2352 | 0.7581 | 0.8477 | 0.2750 | 1.3446 | 0.8477 | 0.8374 | 0.1406 | 0.0826 |
| 0.0206 | 54.98 | 2395 | 0.7571 | 0.8477 | 0.2751 | 1.3703 | 0.8477 | 0.8363 | 0.1378 | 0.0814 |
| 0.0206 | 55.99 | 2439 | 0.7618 | 0.8477 | 0.2752 | 1.3702 | 0.8477 | 0.8363 | 0.1363 | 0.0827 |
| 0.0206 | 57.0 | 2483 | 0.7638 | 0.8477 | 0.2749 | 1.3774 | 0.8477 | 0.8363 | 0.1394 | 0.0819 |
| 0.0135 | 57.99 | 2526 | 0.7693 | 0.8477 | 0.2760 | 1.3370 | 0.8477 | 0.8363 | 0.1378 | 0.0824 |
| 0.0135 | 59.0 | 2570 | 0.7724 | 0.8448 | 0.2779 | 1.3710 | 0.8448 | 0.8344 | 0.1431 | 0.0823 |
| 0.0135 | 59.98 | 2613 | 0.7780 | 0.8477 | 0.2784 | 1.3328 | 0.8477 | 0.8363 | 0.1463 | 0.0828 |
| 0.0135 | 60.99 | 2657 | 0.7818 | 0.8477 | 0.2795 | 1.3289 | 0.8477 | 0.8363 | 0.1466 | 0.0828 |
| 0.0135 | 61.98 | 2700 | 0.7847 | 0.8420 | 0.2805 | 1.3308 | 0.8420 | 0.8308 | 0.1418 | 0.0830 |
| 0.0135 | 62.99 | 2744 | 0.7851 | 0.8448 | 0.2782 | 1.3650 | 0.8448 | 0.8344 | 0.1411 | 0.0834 |
| 0.0135 | 64.0 | 2788 | 0.7925 | 0.8420 | 0.2829 | 1.4383 | 0.8420 | 0.8319 | 0.1425 | 0.0821 |
| 0.0135 | 64.99 | 2831 | 0.7959 | 0.8448 | 0.2826 | 1.4130 | 0.8448 | 0.8353 | 0.1431 | 0.0826 |
| 0.0135 | 66.0 | 2875 | 0.7989 | 0.8420 | 0.2821 | 1.4040 | 0.8420 | 0.8285 | 0.1446 | 0.0833 |
| 0.0135 | 66.98 | 2918 | 0.7996 | 0.8477 | 0.2807 | 1.3296 | 0.8477 | 0.8363 | 0.1464 | 0.0837 |
| 0.0135 | 67.99 | 2962 | 0.8042 | 0.8448 | 0.2824 | 1.3637 | 0.8448 | 0.8344 | 0.1434 | 0.0837 |
| 0.0097 | 68.98 | 3005 | 0.8095 | 0.8391 | 0.2845 | 1.3635 | 0.8391 | 0.8275 | 0.1468 | 0.0835 |
| 0.0097 | 69.99 | 3049 | 0.8073 | 0.8448 | 0.2824 | 1.3640 | 0.8448 | 0.8344 | 0.1413 | 0.0833 |
| 0.0097 | 70.98 | 3092 | 0.8140 | 0.8477 | 0.2834 | 1.3617 | 0.8477 | 0.8363 | 0.1444 | 0.0837 |
| 0.0097 | 71.99 | 3136 | 0.8152 | 0.8420 | 0.2842 | 1.4009 | 0.8420 | 0.8277 | 0.1439 | 0.0840 |
| 0.0097 | 73.0 | 3180 | 0.8163 | 0.8391 | 0.2858 | 1.4029 | 0.8391 | 0.8246 | 0.1482 | 0.0836 |
| 0.0097 | 73.99 | 3223 | 0.8192 | 0.8391 | 0.2844 | 1.3644 | 0.8391 | 0.8240 | 0.1475 | 0.0843 |
| 0.0097 | 75.0 | 3267 | 0.8225 | 0.8448 | 0.2836 | 1.3593 | 0.8448 | 0.8344 | 0.1473 | 0.0847 |
| 0.0097 | 75.98 | 3310 | 0.8267 | 0.8362 | 0.2859 | 1.3642 | 0.8362 | 0.8207 | 0.1473 | 0.0840 |
| 0.0097 | 76.99 | 3354 | 0.8275 | 0.8391 | 0.2847 | 1.3618 | 0.8391 | 0.8240 | 0.1450 | 0.0849 |
| 0.0097 | 77.98 | 3397 | 0.8325 | 0.8362 | 0.2879 | 1.3686 | 0.8362 | 0.8207 | 0.1491 | 0.0843 |
| 0.0097 | 78.99 | 3441 | 0.8389 | 0.8448 | 0.2885 | 1.3629 | 0.8448 | 0.8329 | 0.1504 | 0.0833 |
| 0.0097 | 80.0 | 3485 | 0.8420 | 0.8420 | 0.2887 | 1.3610 | 0.8420 | 0.8261 | 0.1458 | 0.0837 |
| 0.0073 | 80.99 | 3528 | 0.8452 | 0.8362 | 0.2900 | 1.4064 | 0.8362 | 0.8221 | 0.1488 | 0.0833 |
| 0.0073 | 82.0 | 3572 | 0.8492 | 0.8362 | 0.2898 | 1.4076 | 0.8362 | 0.8221 | 0.1500 | 0.0837 |
| 0.0073 | 82.98 | 3615 | 0.8478 | 0.8362 | 0.2895 | 1.3609 | 0.8362 | 0.8207 | 0.1485 | 0.0847 |
| 0.0073 | 83.99 | 3659 | 0.8483 | 0.8391 | 0.2880 | 1.3622 | 0.8391 | 0.8243 | 0.1480 | 0.0842 |
| 0.0073 | 84.98 | 3702 | 0.8534 | 0.8420 | 0.2892 | 1.3609 | 0.8420 | 0.8261 | 0.1468 | 0.0843 |
| 0.0073 | 85.99 | 3746 | 0.8547 | 0.8333 | 0.2898 | 1.4028 | 0.8333 | 0.8186 | 0.1513 | 0.0846 |
| 0.0073 | 86.98 | 3789 | 0.8618 | 0.8391 | 0.2906 | 1.3597 | 0.8391 | 0.8243 | 0.1445 | 0.0846 |
| 0.0073 | 87.99 | 3833 | 0.8594 | 0.8420 | 0.2885 | 1.3265 | 0.8420 | 0.8311 | 0.1462 | 0.0848 |
| 0.0073 | 89.0 | 3877 | 0.8669 | 0.8391 | 0.2911 | 1.3592 | 0.8391 | 0.8243 | 0.1471 | 0.0843 |
| 0.0073 | 89.99 | 3920 | 0.8664 | 0.8391 | 0.2901 | 1.3597 | 0.8391 | 0.8243 | 0.1468 | 0.0852 |
| 0.0073 | 91.0 | 3964 | 0.8678 | 0.8420 | 0.2905 | 1.3253 | 0.8420 | 0.8296 | 0.1462 | 0.0854 |
| 0.0057 | 91.98 | 4007 | 0.8719 | 0.8391 | 0.2909 | 1.3585 | 0.8391 | 0.8243 | 0.1475 | 0.0853 |
| 0.0057 | 92.99 | 4051 | 0.8768 | 0.8391 | 0.2930 | 1.3595 | 0.8391 | 0.8243 | 0.1493 | 0.0852 |
| 0.0057 | 93.98 | 4094 | 0.8785 | 0.8333 | 0.2928 | 1.4034 | 0.8333 | 0.8203 | 0.1529 | 0.0849 |
| 0.0057 | 94.99 | 4138 | 0.8859 | 0.8333 | 0.2942 | 1.3684 | 0.8333 | 0.8183 | 0.1543 | 0.0844 |
| 0.0057 | 96.0 | 4182 | 0.8839 | 0.8362 | 0.2937 | 1.3597 | 0.8362 | 0.8221 | 0.1497 | 0.0852 |
| 0.0057 | 96.99 | 4225 | 0.8864 | 0.8333 | 0.2940 | 1.4012 | 0.8333 | 0.8203 | 0.1532 | 0.0850 |
| 0.0057 | 98.0 | 4269 | 0.8879 | 0.8362 | 0.2941 | 1.3607 | 0.8362 | 0.8221 | 0.1504 | 0.0849 |
| 0.0057 | 98.98 | 4312 | 0.8921 | 0.8333 | 0.2954 | 1.3609 | 0.8333 | 0.8183 | 0.1521 | 0.0851 |
| 0.0057 | 99.99 | 4356 | 0.8949 | 0.8391 | 0.2945 | 1.3575 | 0.8391 | 0.8243 | 0.1491 | 0.0854 |
| 0.0057 | 100.98 | 4399 | 0.8945 | 0.8362 | 0.2945 | 1.3591 | 0.8362 | 0.8221 | 0.1500 | 0.0856 |
| 0.0057 | 101.99 | 4443 | 0.8985 | 0.8333 | 0.2944 | 1.3599 | 0.8333 | 0.8183 | 0.1530 | 0.0854 |
| 0.0057 | 102.98 | 4486 | 0.8987 | 0.8391 | 0.2951 | 1.3586 | 0.8391 | 0.8246 | 0.1499 | 0.0850 |
| 0.0045 | 103.99 | 4530 | 0.9025 | 0.8362 | 0.2957 | 1.3592 | 0.8362 | 0.8221 | 0.1510 | 0.0857 |
| 0.0045 | 105.0 | 4574 | 0.9082 | 0.8305 | 0.2972 | 1.3625 | 0.8305 | 0.8165 | 0.1568 | 0.0852 |
| 0.0045 | 105.99 | 4617 | 0.9087 | 0.8362 | 0.2958 | 1.3579 | 0.8362 | 0.8221 | 0.1505 | 0.0858 |
| 0.0045 | 107.0 | 4661 | 0.9105 | 0.8305 | 0.2977 | 1.3619 | 0.8305 | 0.8165 | 0.1561 | 0.0844 |
| 0.0045 | 107.98 | 4704 | 0.9136 | 0.8305 | 0.2978 | 1.3994 | 0.8305 | 0.8165 | 0.1559 | 0.0851 |
| 0.0045 | 108.99 | 4748 | 0.9148 | 0.8391 | 0.2968 | 1.3573 | 0.8391 | 0.8243 | 0.1504 | 0.0856 |
| 0.0045 | 109.98 | 4791 | 0.9188 | 0.8333 | 0.2974 | 1.3569 | 0.8333 | 0.8183 | 0.1532 | 0.0850 |
| 0.0045 | 110.99 | 4835 | 0.9164 | 0.8362 | 0.2959 | 1.3595 | 0.8362 | 0.8221 | 0.1507 | 0.0857 |
| 0.0045 | 112.0 | 4879 | 0.9221 | 0.8333 | 0.2977 | 1.3573 | 0.8333 | 0.8183 | 0.1550 | 0.0857 |
| 0.0045 | 112.99 | 4922 | 0.9256 | 0.8305 | 0.2990 | 1.3599 | 0.8305 | 0.8165 | 0.1574 | 0.0852 |
| 0.0045 | 114.0 | 4966 | 0.9284 | 0.8305 | 0.2994 | 1.3610 | 0.8305 | 0.8165 | 0.1572 | 0.0848 |
| 0.0037 | 114.98 | 5009 | 0.9312 | 0.8333 | 0.2998 | 1.3565 | 0.8333 | 0.8183 | 0.1537 | 0.0857 |
| 0.0037 | 115.99 | 5053 | 0.9322 | 0.8333 | 0.2995 | 1.3583 | 0.8333 | 0.8183 | 0.1543 | 0.0852 |
| 0.0037 | 116.98 | 5096 | 0.9385 | 0.8305 | 0.3007 | 1.3593 | 0.8305 | 0.8165 | 0.1577 | 0.0852 |
| 0.0037 | 117.99 | 5140 | 0.9386 | 0.8305 | 0.3009 | 1.4329 | 0.8305 | 0.8165 | 0.1582 | 0.0851 |
| 0.0037 | 118.98 | 5183 | 0.9386 | 0.8333 | 0.2996 | 1.3570 | 0.8333 | 0.8183 | 0.1542 | 0.0855 |
| 0.0037 | 119.99 | 5227 | 0.9406 | 0.8333 | 0.2995 | 1.3554 | 0.8333 | 0.8183 | 0.1540 | 0.0848 |
| 0.0037 | 121.0 | 5271 | 0.9442 | 0.8305 | 0.3006 | 1.3589 | 0.8305 | 0.8165 | 0.1570 | 0.0849 |
| 0.0037 | 121.99 | 5314 | 0.9435 | 0.8333 | 0.3000 | 1.3551 | 0.8333 | 0.8183 | 0.1546 | 0.0855 |
| 0.0037 | 123.0 | 5358 | 0.9456 | 0.8333 | 0.2996 | 1.3550 | 0.8333 | 0.8183 | 0.1544 | 0.0848 |
| 0.0037 | 123.98 | 5401 | 0.9490 | 0.8333 | 0.3008 | 1.3561 | 0.8333 | 0.8183 | 0.1547 | 0.0850 |
| 0.0037 | 124.99 | 5445 | 0.9500 | 0.8333 | 0.3011 | 1.3592 | 0.8333 | 0.8183 | 0.1551 | 0.0846 |
| 0.0037 | 125.98 | 5488 | 0.9513 | 0.8333 | 0.3003 | 1.3549 | 0.8333 | 0.8183 | 0.1544 | 0.0845 |
| 0.0031 | 126.99 | 5532 | 0.9575 | 0.8305 | 0.3024 | 1.3580 | 0.8305 | 0.8165 | 0.1581 | 0.0849 |
| 0.0031 | 128.0 | 5576 | 0.9593 | 0.8305 | 0.3025 | 1.4028 | 0.8305 | 0.8165 | 0.1591 | 0.0851 |
| 0.0031 | 128.99 | 5619 | 0.9594 | 0.8305 | 0.3021 | 1.3619 | 0.8305 | 0.8165 | 0.1579 | 0.0849 |
| 0.0031 | 130.0 | 5663 | 0.9628 | 0.8305 | 0.3025 | 1.3589 | 0.8305 | 0.8165 | 0.1587 | 0.0847 |
| 0.0031 | 130.98 | 5706 | 0.9652 | 0.8305 | 0.3031 | 1.3599 | 0.8305 | 0.8165 | 0.1593 | 0.0844 |
| 0.0031 | 131.99 | 5750 | 0.9646 | 0.8362 | 0.3005 | 1.3353 | 0.8362 | 0.8205 | 0.1520 | 0.0851 |
| 0.0031 | 132.98 | 5793 | 0.9658 | 0.8333 | 0.3021 | 1.3562 | 0.8333 | 0.8183 | 0.1555 | 0.0849 |
| 0.0031 | 133.99 | 5837 | 0.9698 | 0.8333 | 0.3023 | 1.3545 | 0.8333 | 0.8183 | 0.1554 | 0.0845 |
| 0.0031 | 134.98 | 5880 | 0.9716 | 0.8333 | 0.3032 | 1.3559 | 0.8333 | 0.8183 | 0.1555 | 0.0852 |
| 0.0031 | 135.99 | 5924 | 0.9736 | 0.8305 | 0.3037 | 1.3624 | 0.8305 | 0.8165 | 0.1584 | 0.0849 |
| 0.0031 | 137.0 | 5968 | 0.9760 | 0.8333 | 0.3039 | 1.3575 | 0.8333 | 0.8183 | 0.1551 | 0.0845 |
| 0.0026 | 137.99 | 6011 | 0.9789 | 0.8305 | 0.3041 | 1.3569 | 0.8305 | 0.8165 | 0.1592 | 0.0848 |
| 0.0026 | 139.0 | 6055 | 0.9801 | 0.8305 | 0.3040 | 1.3574 | 0.8305 | 0.8165 | 0.1598 | 0.0854 |
| 0.0026 | 139.98 | 6098 | 0.9806 | 0.8333 | 0.3035 | 1.3552 | 0.8333 | 0.8183 | 0.1557 | 0.0852 |
| 0.0026 | 140.99 | 6142 | 0.9835 | 0.8333 | 0.3041 | 1.3574 | 0.8333 | 0.8183 | 0.1564 | 0.0846 |
| 0.0026 | 141.98 | 6185 | 0.9838 | 0.8333 | 0.3037 | 1.3549 | 0.8333 | 0.8183 | 0.1557 | 0.0849 |
| 0.0026 | 142.99 | 6229 | 0.9872 | 0.8333 | 0.3044 | 1.3544 | 0.8333 | 0.8183 | 0.1557 | 0.0851 |
| 0.0026 | 144.0 | 6273 | 0.9900 | 0.8305 | 0.3056 | 1.3654 | 0.8305 | 0.8165 | 0.1597 | 0.0847 |
| 0.0026 | 144.99 | 6316 | 0.9907 | 0.8333 | 0.3049 | 1.3551 | 0.8333 | 0.8183 | 0.1565 | 0.0854 |
| 0.0026 | 146.0 | 6360 | 0.9896 | 0.8333 | 0.3044 | 1.3569 | 0.8333 | 0.8183 | 0.1563 | 0.0843 |
| 0.0026 | 146.98 | 6403 | 0.9938 | 0.8333 | 0.3053 | 1.3550 | 0.8333 | 0.8183 | 0.1562 | 0.0844 |
| 0.0026 | 147.99 | 6447 | 0.9962 | 0.8305 | 0.3056 | 1.3615 | 0.8305 | 0.8165 | 0.1594 | 0.0844 |
| 0.0026 | 148.98 | 6490 | 0.9954 | 0.8305 | 0.3051 | 1.3601 | 0.8305 | 0.8165 | 0.1590 | 0.0847 |
| 0.0022 | 149.99 | 6534 | 0.9961 | 0.8333 | 0.3043 | 1.3550 | 0.8333 | 0.8183 | 0.1554 | 0.0847 |
| 0.0022 | 150.98 | 6577 | 1.0026 | 0.8333 | 0.3059 | 1.3555 | 0.8333 | 0.8183 | 0.1563 | 0.0853 |
| 0.0022 | 151.99 | 6621 | 1.0004 | 0.8333 | 0.3049 | 1.3544 | 0.8333 | 0.8183 | 0.1566 | 0.0847 |
| 0.0022 | 153.0 | 6665 | 1.0024 | 0.8305 | 0.3058 | 1.3606 | 0.8305 | 0.8165 | 0.1595 | 0.0846 |
| 0.0022 | 153.99 | 6708 | 1.0054 | 0.8305 | 0.3064 | 1.3598 | 0.8305 | 0.8165 | 0.1591 | 0.0848 |
| 0.0022 | 155.0 | 6752 | 1.0053 | 0.8333 | 0.3054 | 1.3548 | 0.8333 | 0.8183 | 0.1562 | 0.0845 |
| 0.0022 | 155.98 | 6795 | 1.0068 | 0.8333 | 0.3053 | 1.3548 | 0.8333 | 0.8183 | 0.1562 | 0.0846 |
| 0.0022 | 156.99 | 6839 | 1.0076 | 0.8333 | 0.3055 | 1.3551 | 0.8333 | 0.8183 | 0.1561 | 0.0844 |
| 0.0022 | 157.98 | 6882 | 1.0105 | 0.8333 | 0.3059 | 1.3546 | 0.8333 | 0.8183 | 0.1563 | 0.0845 |
| 0.0022 | 158.99 | 6926 | 1.0114 | 0.8333 | 0.3061 | 1.3555 | 0.8333 | 0.8183 | 0.1559 | 0.0851 |
| 0.0022 | 160.0 | 6970 | 1.0108 | 0.8333 | 0.3061 | 1.3586 | 0.8333 | 0.8183 | 0.1561 | 0.0848 |
| 0.002 | 160.99 | 7013 | 1.0129 | 0.8333 | 0.3064 | 1.3577 | 0.8333 | 0.8183 | 0.1560 | 0.0845 |
| 0.002 | 162.0 | 7057 | 1.0141 | 0.8333 | 0.3060 | 1.3542 | 0.8333 | 0.8183 | 0.1562 | 0.0845 |
| 0.002 | 162.98 | 7100 | 1.0150 | 0.8333 | 0.3063 | 1.3555 | 0.8333 | 0.8183 | 0.1563 | 0.0847 |
| 0.002 | 163.99 | 7144 | 1.0181 | 0.8305 | 0.3071 | 1.3616 | 0.8305 | 0.8165 | 0.1587 | 0.0847 |
| 0.002 | 164.98 | 7187 | 1.0197 | 0.8305 | 0.3073 | 1.3610 | 0.8305 | 0.8165 | 0.1585 | 0.0847 |
| 0.002 | 165.99 | 7231 | 1.0203 | 0.8333 | 0.3071 | 1.3566 | 0.8333 | 0.8183 | 0.1565 | 0.0846 |
| 0.002 | 166.98 | 7274 | 1.0214 | 0.8333 | 0.3070 | 1.3561 | 0.8333 | 0.8183 | 0.1564 | 0.0845 |
| 0.002 | 167.99 | 7318 | 1.0211 | 0.8333 | 0.3067 | 1.3558 | 0.8333 | 0.8183 | 0.1562 | 0.0846 |
| 0.002 | 169.0 | 7362 | 1.0255 | 0.8305 | 0.3077 | 1.3564 | 0.8305 | 0.8165 | 0.1592 | 0.0846 |
| 0.002 | 169.99 | 7405 | 1.0238 | 0.8333 | 0.3066 | 1.3535 | 0.8333 | 0.8183 | 0.1567 | 0.0844 |
| 0.002 | 171.0 | 7449 | 1.0258 | 0.8333 | 0.3075 | 1.3580 | 0.8333 | 0.8183 | 0.1562 | 0.0847 |
| 0.002 | 171.98 | 7492 | 1.0260 | 0.8333 | 0.3073 | 1.3594 | 0.8333 | 0.8183 | 0.1559 | 0.0846 |
| 0.0018 | 172.99 | 7536 | 1.0281 | 0.8305 | 0.3077 | 1.3584 | 0.8305 | 0.8165 | 0.1586 | 0.0847 |
| 0.0018 | 173.98 | 7579 | 1.0274 | 0.8333 | 0.3073 | 1.3577 | 0.8333 | 0.8183 | 0.1560 | 0.0851 |
| 0.0018 | 174.99 | 7623 | 1.0323 | 0.8305 | 0.3082 | 1.3577 | 0.8305 | 0.8165 | 0.1596 | 0.0848 |
| 0.0018 | 176.0 | 7667 | 1.0303 | 0.8333 | 0.3076 | 1.3579 | 0.8333 | 0.8183 | 0.1561 | 0.0846 |
| 0.0018 | 176.99 | 7710 | 1.0325 | 0.8333 | 0.3081 | 1.3567 | 0.8333 | 0.8183 | 0.1565 | 0.0845 |
| 0.0018 | 178.0 | 7754 | 1.0319 | 0.8333 | 0.3077 | 1.3569 | 0.8333 | 0.8183 | 0.1560 | 0.0847 |
| 0.0018 | 178.98 | 7797 | 1.0340 | 0.8333 | 0.3081 | 1.3568 | 0.8333 | 0.8183 | 0.1562 | 0.0847 |
| 0.0018 | 179.99 | 7841 | 1.0331 | 0.8333 | 0.3072 | 1.3550 | 0.8333 | 0.8183 | 0.1564 | 0.0847 |
| 0.0018 | 180.98 | 7884 | 1.0346 | 0.8333 | 0.3079 | 1.3563 | 0.8333 | 0.8183 | 0.1561 | 0.0847 |
| 0.0018 | 181.99 | 7928 | 1.0344 | 0.8333 | 0.3079 | 1.3577 | 0.8333 | 0.8183 | 0.1565 | 0.0847 |
| 0.0018 | 182.98 | 7971 | 1.0363 | 0.8333 | 0.3080 | 1.3556 | 0.8333 | 0.8183 | 0.1566 | 0.0850 |
| 0.0016 | 183.99 | 8015 | 1.0368 | 0.8333 | 0.3080 | 1.3569 | 0.8333 | 0.8183 | 0.1561 | 0.0847 |
| 0.0016 | 185.0 | 8059 | 1.0369 | 0.8333 | 0.3080 | 1.3563 | 0.8333 | 0.8183 | 0.1562 | 0.0847 |
| 0.0016 | 185.99 | 8102 | 1.0373 | 0.8333 | 0.3080 | 1.3565 | 0.8333 | 0.8183 | 0.1561 | 0.0850 |
| 0.0016 | 187.0 | 8146 | 1.0377 | 0.8333 | 0.3080 | 1.3568 | 0.8333 | 0.8183 | 0.1561 | 0.0846 |
| 0.0016 | 187.98 | 8189 | 1.0392 | 0.8333 | 0.3084 | 1.3577 | 0.8333 | 0.8183 | 0.1565 | 0.0846 |
| 0.0016 | 188.99 | 8233 | 1.0391 | 0.8333 | 0.3082 | 1.3564 | 0.8333 | 0.8183 | 0.1564 | 0.0848 |
| 0.0016 | 189.98 | 8276 | 1.0393 | 0.8333 | 0.3081 | 1.3561 | 0.8333 | 0.8183 | 0.1562 | 0.0847 |
| 0.0016 | 190.99 | 8320 | 1.0398 | 0.8333 | 0.3084 | 1.3582 | 0.8333 | 0.8183 | 0.1562 | 0.0846 |
| 0.0016 | 192.0 | 8364 | 1.0405 | 0.8333 | 0.3083 | 1.3558 | 0.8333 | 0.8183 | 0.1564 | 0.0847 |
| 0.0016 | 192.99 | 8407 | 1.0401 | 0.8333 | 0.3082 | 1.3558 | 0.8333 | 0.8183 | 0.1564 | 0.0847 |
| 0.0016 | 194.0 | 8451 | 1.0407 | 0.8333 | 0.3083 | 1.3564 | 0.8333 | 0.8183 | 0.1564 | 0.0847 |
| 0.0016 | 194.98 | 8494 | 1.0414 | 0.8333 | 0.3086 | 1.3573 | 0.8333 | 0.8183 | 0.1564 | 0.0847 |
| 0.0015 | 195.99 | 8538 | 1.0410 | 0.8333 | 0.3084 | 1.3567 | 0.8333 | 0.8183 | 0.1564 | 0.0848 |
| 0.0015 | 196.98 | 8581 | 1.0411 | 0.8333 | 0.3084 | 1.3568 | 0.8333 | 0.8183 | 0.1563 | 0.0846 |
| 0.0015 | 197.42 | 8600 | 1.0411 | 0.8333 | 0.3084 | 1.3568 | 0.8333 | 0.8183 | 0.1563 | 0.0847 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
NasimB/gpt2-concat-aochildes-mod-sub-rarity-all-no-cut-rev
|
NasimB
| 2023-07-12T22:56:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-12T20:57:12Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-aochildes-mod-sub-rarity-all-no-cut-rev
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-mod-sub-rarity-all-no-cut-rev
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3218
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.6977 | 0.29 | 500 | 5.6354 |
| 5.3398 | 0.59 | 1000 | 5.1922 |
| 4.9904 | 0.88 | 1500 | 4.9412 |
| 4.7171 | 1.17 | 2000 | 4.7994 |
| 4.5533 | 1.47 | 2500 | 4.6748 |
| 4.4487 | 1.76 | 3000 | 4.5745 |
| 4.3168 | 2.05 | 3500 | 4.4945 |
| 4.132 | 2.35 | 4000 | 4.4485 |
| 4.096 | 2.64 | 4500 | 4.3926 |
| 4.0654 | 2.93 | 5000 | 4.3391 |
| 3.8471 | 3.23 | 5500 | 4.3342 |
| 3.799 | 3.52 | 6000 | 4.3047 |
| 3.7884 | 3.81 | 6500 | 4.2746 |
| 3.665 | 4.11 | 7000 | 4.2788 |
| 3.5131 | 4.4 | 7500 | 4.2703 |
| 3.5095 | 4.69 | 8000 | 4.2576 |
| 3.4965 | 4.99 | 8500 | 4.2442 |
| 3.3238 | 5.28 | 9000 | 4.2613 |
| 3.3171 | 5.58 | 9500 | 4.2598 |
| 3.3159 | 5.87 | 10000 | 4.2586 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Peebranco/teste-pedro-branco
|
Peebranco
| 2023-07-12T22:53:38Z | 0 | 0 | null |
[
"pt",
"en",
"dataset:Open-Orca/OpenOrca",
"region:us"
] | null | 2023-07-12T22:52:52Z |
---
datasets:
- Open-Orca/OpenOrca
language:
- pt
- en
metrics:
- character
---
|
komo-dono/rammatra_jp
|
komo-dono
| 2023-07-12T22:52:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-12T22:51:26Z |
---
license: openrail
language:
- ja
tags:
- music
rammatra jp 500 epoch
|
digiplay/FumizukiMix_v1
|
digiplay
| 2023-07-12T22:49:15Z | 329 | 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
| 2023-07-12T22:33:07Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/107380/fumizukimix
|
nolanaatama/nglshdbhtcvbrnnknckrbckrgnshnmpctrvcv2150pchmklgn
|
nolanaatama
| 2023-07-12T22:40:26Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-12T22:08:55Z |
---
license: creativeml-openrail-m
---
|
whywynn/q-Taxi-v3
|
whywynn
| 2023-07-12T22:34:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T21:52:10Z |
---
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="whywynn/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"])
```
|
saeedehj/t5-small-finetune-xsum
|
saeedehj
| 2023-07-12T22:15:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-12T18:59:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetune-xsum
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. -->
# t5-small-finetune-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4414
- Rouge1: 29.3266
- Rouge2: 8.4122
- Rougel: 23.086
- Rougelsum: 23.0988
- Gen Len: 18.8112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7808 | 1.0 | 2000 | 2.5082 | 27.7519 | 7.446 | 21.7066 | 21.7226 | 18.835 |
| 2.748 | 2.0 | 4000 | 2.4904 | 27.9132 | 7.5749 | 21.9684 | 21.9859 | 18.81 |
| 2.6996 | 3.0 | 6000 | 2.4786 | 28.2767 | 7.8416 | 22.1965 | 22.2152 | 18.785 |
| 2.6992 | 4.0 | 8000 | 2.4694 | 28.6795 | 7.9755 | 22.4116 | 22.437 | 18.8275 |
| 2.6118 | 5.0 | 10000 | 2.4627 | 28.6839 | 7.9493 | 22.4375 | 22.4522 | 18.8075 |
| 2.6242 | 6.0 | 12000 | 2.4549 | 28.8803 | 8.1118 | 22.6837 | 22.6895 | 18.8169 |
| 2.5889 | 7.0 | 14000 | 2.4523 | 29.0163 | 8.2553 | 22.9279 | 22.9428 | 18.8281 |
| 2.5689 | 8.0 | 16000 | 2.4515 | 28.9347 | 8.1521 | 22.7739 | 22.7803 | 18.8169 |
| 2.5309 | 9.0 | 18000 | 2.4490 | 29.1943 | 8.2996 | 23.0166 | 23.005 | 18.8238 |
| 2.5179 | 10.0 | 20000 | 2.4460 | 29.1816 | 8.3726 | 23.0678 | 23.0622 | 18.8025 |
| 2.5114 | 11.0 | 22000 | 2.4451 | 29.1586 | 8.3156 | 23.0407 | 23.0485 | 18.8094 |
| 2.4775 | 12.0 | 24000 | 2.4440 | 29.2132 | 8.452 | 23.0056 | 23.0021 | 18.8069 |
| 2.5082 | 13.0 | 26000 | 2.4440 | 29.1495 | 8.3541 | 22.9148 | 22.9349 | 18.8025 |
| 2.4888 | 14.0 | 28000 | 2.4431 | 29.2776 | 8.3071 | 23.0654 | 23.0685 | 18.8138 |
| 2.479 | 15.0 | 30000 | 2.4431 | 29.378 | 8.4205 | 23.1346 | 23.1347 | 18.8044 |
| 2.4464 | 16.0 | 32000 | 2.4427 | 29.3569 | 8.4209 | 23.0688 | 23.0814 | 18.8038 |
| 2.4431 | 17.0 | 34000 | 2.4423 | 29.2736 | 8.3856 | 23.0737 | 23.0696 | 18.8188 |
| 2.447 | 18.0 | 36000 | 2.4419 | 29.2725 | 8.41 | 23.0817 | 23.1089 | 18.8125 |
| 2.4626 | 19.0 | 38000 | 2.4416 | 29.3144 | 8.3858 | 23.0861 | 23.0993 | 18.8075 |
| 2.4362 | 20.0 | 40000 | 2.4414 | 29.3266 | 8.4122 | 23.086 | 23.0988 | 18.8112 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
uribah/my_awesome_model_2
|
uribah
| 2023-07-12T21:59:11Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-12T21:27:51Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: uribah/my_awesome_model_2
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. -->
# uribah/my_awesome_model_2
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: 0.4528
- Validation Loss: 0.2263
- Train Accuracy: 0.9160
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1470, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4528 | 0.2263 | 0.9160 | 0 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
cworthingtonfujitsu/falcon-7b-instruct-jukebox
|
cworthingtonfujitsu
| 2023-07-12T21:58:59Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-12T21:58:36Z |
---
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
|
TheBloke/OpenOrca-Preview1-13B-GGML
|
TheBloke
| 2023-07-12T21:50:58Z | 0 | 15 |
transformers
|
[
"transformers",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"arxiv:2306.02707",
"arxiv:2301.13688",
"arxiv:2302.13971",
"license:other",
"region:us"
] |
text-generation
| 2023-07-12T21:19:38Z |
---
datasets:
- Open-Orca/OpenOrca
inference: false
language:
- en
library_name: transformers
license: other
model_type: llama
pipeline_tag: text-generation
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Open-Orca's OpenOrca-Preview1-13B GGML
These files are GGML format model files for [Open-Orca's OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend.
* [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server.
These files were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate).
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/OpenOrca-Preview1-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenOrca-Preview1-13B-GGML)
* [Open-Orca's unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction: {prompt}
### Response:
```
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.
They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.
## Explanation of the new k-quant methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| openorca-preview1-200k-llama-13b.ggmlv3.q2_K.bin | q2_K | 2 | 5.51 GB| 8.01 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| openorca-preview1-200k-llama-13b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.93 GB| 9.43 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| openorca-preview1-200k-llama-13b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.31 GB| 8.81 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| openorca-preview1-200k-llama-13b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.66 GB| 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| openorca-preview1-200k-llama-13b.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
| openorca-preview1-200k-llama-13b.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB| 10.64 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| openorca-preview1-200k-llama-13b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.87 GB| 10.37 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| openorca-preview1-200k-llama-13b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.37 GB| 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| openorca-preview1-200k-llama-13b.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| openorca-preview1-200k-llama-13b.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| openorca-preview1-200k-llama-13b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.23 GB| 11.73 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| openorca-preview1-200k-llama-13b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.97 GB| 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| openorca-preview1-200k-llama-13b.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB| 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| openorca-preview1-200k-llama-13b.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB| 16.33 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m openorca-preview1-200k-llama-13b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
**Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Open-Orca's OpenOrca Preview1 200k GPT4 LLaMA 13B
<p><h1>🐋 The First OpenOrca Model Preview! 🐋</h1></p>
# OpenOrca_Preview1-200k-GPT4_LLaMA-13B
We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune LLaMA-13B.
This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707).
We have trained on less than 6% of our data, just to give a preview of what is possible while we further refine our dataset!
We trained a refined selection of 200k GPT-4 entries from OpenOrca.
We have filtered our GPT-4 augmentations to remove statements like, "As an AI language model..." and other responses which have been shown to harm model reasoning capabilities. Further details on our dataset curation practices will be forthcoming with our full model releases.
This release highlights that even a small portion of our training data can produce state of the art results in this model class with training costs <$200 in total.
We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners.
We will also give sneak-peak announcements on our Discord, which you can find here:
https://AlignmentLab.ai
# Evaluation
We have evaluated OpenOrca_Preview1-200k-GPT4_LLaMA-13B on hard reasoning tasks from BigBench-Hard and AGIEval as outlined in the Orca paper.
Our average performance for BigBench-Hard: 0.3753
Average for AGIEval: 0.3638
In the Orca paper, they measured their score relative to Vicuna on these evals.
We've done the same and have found our score averages to ~60% of the total improvement that was shown in the Orca paper.
So we got 60% of the improvement with 6% of the data!
## BigBench-Hard Performance
## AGIEval Performance
We will report our results on [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Evals once we receive them.
# Dataset
We used a small (6%, 200k) subset of our data from OpenOrca, which aims to reproduce the Orca Research Paper dataset.
As this release is intended as a preview, please await our full releases for further details on the training data.
# Training
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
We trained with 8x A100-80G GPUs for 15 hours. Commodity cost was < $200.
We trained for 4 epochs and selected a snapshot at 3 epochs for peak performance.
Please await our full releases for further training details.
# Citation
```bibtex
@software{OpenOrca_Preview1,
title = {OpenOrca_Preview1: A LLaMA-13B Model Fine-tuned on Small Portion of OpenOrcaV1 Dataset},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and "NanoBit" and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca_Preview1-200k-GPT4_LLaMA-13B},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
|
lovelyxs/ppo-SnowballTarget
|
lovelyxs
| 2023-07-12T21:43:03Z | 9 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-07-12T21:42:55Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: lovelyxs/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
S1X3L4/Reinforce-cartpole0
|
S1X3L4
| 2023-07-12T21:36:17Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T21:36:00Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole0
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
|
pavankantharaju/q-Taxi-v3
|
pavankantharaju
| 2023-07-12T21:30:27Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T21:30:25Z |
---
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="pavankantharaju/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"])
```
|
pavankantharaju/q-FrozenLake-v1-4x4-noSlippery
|
pavankantharaju
| 2023-07-12T21:25:16Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T21:25:12Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pavankantharaju/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
whywynn/q-FrozenLake-v1-4x4-noSlippery
|
whywynn
| 2023-07-12T21:24:58Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T21:24:49Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="whywynn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
SrPrieto/ppo-LunarLander-v2
|
SrPrieto
| 2023-07-12T21:14:49Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T21:14:30Z |
---
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: 271.18 +/- 13.06
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
...
```
|
lovelyxs/Reinforce-Pixelcopter-PLE-v0
|
lovelyxs
| 2023-07-12T21:06:20Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T21:06:13Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 45.30 +/- 39.51
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
|
foreverip/q-Taxi-v3
|
foreverip
| 2023-07-12T20:56:40Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T20:56:37Z |
---
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="foreverip/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"])
```
|
MarcoIPolo/distilbert-base-uncased-finetuned-emotion
|
MarcoIPolo
| 2023-07-12T20:50:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-12T16:05:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9245630401134893
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2193
- Accuracy: 0.9245
- F1: 0.9246
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3295 | 0.899 | 0.8946 |
| No log | 2.0 | 500 | 0.2193 | 0.9245 | 0.9246 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
grace-pro/afriberta-small-finetuned-hausa-2e-4
|
grace-pro
| 2023-07-12T20:50:37Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-12T20:15:26Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: afriberta-small-finetuned-hausa-2e-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# afriberta-small-finetuned-hausa-2e-4
This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2081
- Precision: 0.6383
- Recall: 0.4793
- F1: 0.5475
- Accuracy: 0.9589
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1575 | 1.0 | 1312 | 0.1439 | 0.6452 | 0.3971 | 0.4917 | 0.9569 |
| 0.1201 | 2.0 | 2624 | 0.1371 | 0.6344 | 0.4451 | 0.5231 | 0.9578 |
| 0.0831 | 3.0 | 3936 | 0.1544 | 0.6444 | 0.4727 | 0.5454 | 0.9591 |
| 0.0523 | 4.0 | 5248 | 0.1836 | 0.6500 | 0.4683 | 0.5444 | 0.9592 |
| 0.0318 | 5.0 | 6560 | 0.2081 | 0.6383 | 0.4793 | 0.5475 | 0.9589 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
foreverip/q-FrozenLake-v1-4x4-noSlippery
|
foreverip
| 2023-07-12T20:49:59Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-12T20:49:56Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="foreverip/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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