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purplegenie97/allmodeltype | purplegenie97 | 2023-11-07T00:55:04Z | 11 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-07T00:48:39Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### AllModelType Dreambooth model trained by purplegenie97 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
.jpeg)
.jpeg)
|
pjherron/finetuning-emotion-model-f7bshardquant4 | pjherron | 2023-11-07T00:37:19Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:emotion",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"region:us"
]
| null | 2023-11-07T00:17:22Z | ---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: finetuning-emotion-model-f7bshardquant4
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. -->
# finetuning-emotion-model-f7bshardquant4
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4323
- Accuracy: 0.4815
- F1: 0.3900
## 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: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.4428 | 1.0 | 8000 | 1.4323 | 0.4815 | 0.3900 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
fzanartu/flicc | fzanartu | 2023-11-07T00:29:49Z | 14 | 1 | transformers | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"climate",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-10-24T10:45:03Z | ---
license: mit
language:
- en
widget:
- text: >-
There is little doubt that some players in the climate game not a lot, but
enough to have severely damaged the reputation of climate scientists in
general have stepped across the boundary into postmodern science.
example_title: Ad homienm
- text: >-
Another famous place is the Tuvalu Islands, which are supposed to soon
disappear. There we have a tide gauge record, a variograph record, from
1978, so it's 30 years. And again - absolutely no trend, no rise.
example_title: Cherry picking
- text: >-
So do petitions signed by more than 30,000 scientists that have challenged
IPCC's 1995 procedures and report representations.
example_title: Fake experts
- text: >-
Fourth, if industrial civilization is dangerously altering global climate,
can any treaty stop it? The Kyoto accord in itself would do nothing to
mitigate climate change, since it exempts the developing countries, which
will be the major emissions source in the next century.
example_title: Impossible expectations
tags:
- climate
pipeline_tag: text-classification
---
# Model Card
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model identifies 12 distinct climate change denial strategies or fallacies to classify and analyse texts that express skepticism or opposition to climate change scientific findings.
These 12 distinct labels come from the FLICC taxonomy created by John Cook and his colleagues.
The FLICC taxonomy divides denial strategies into five primary categories: fake experts, logical fallacies, impossible expectations, cherry-picking, and conspiracy theories.
- **Developed by:** Francisco Zanartu, John Cook, Julian Garcia and Markus Wagner
- **Model type:** DeBERTa (Decoding-enhanced BERT with disentangled attention) is a Transformer-based neural language model
- **Language(s) (NLP):** Finetuned and evaluated on a English dataset.
- **License:** [More Information Needed]
- **Finetuned from model:** microsoft/deberta-v2-xlarge
| Fallacy Type | Definition |
|----------------------|-------------------------------------|
| Ad hominem | Attacking a person/group instead of addressing their arguments |
| Anecdote | Using personal experience or isolated examples instead of sound arguments or compelling evidence |
| Cherry Picking | Selecting data that appear to confirm one position while ignoring other data that contradicts that position |
| Conspiracy theory | Proposing that a secret plan exists to implement a nefarious scheme such as hiding a truth |
| Fake experts | Presenting an unqualified person or institution as a source of credible information. |
| False choice | Presenting two options as the only possibilities, when other possibilities exist |
| False equivalence | Incorrectly claiming that two things are equivalent, despite the fact that there are notable differences between them |
| Impossible expectations | Demanding unrealistic standards of certainty before acting on the science |
| Misrepresentation | Misrepresenting a situation or an opponent’s position in such a way as to distort understanding |
| Oversimplification | Simplifying a situation in such a way as to distort understanding, leading to erroneous conclusions |
| Single cause | Assuming a single cause or reason when there might be multiple causes or reasons |
| Slothful induction | Ignoring relevant evidence when coming to a conclusion | |
joshuaoreilly/ppo-SnowballTarget | joshuaoreilly | 2023-11-07T00:23:30Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-11-07T00:23:10Z | ---
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: joshuaoreilly/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
trainzment/my_awesome_qa_model | trainzment | 2023-11-07T00:19:51Z | 3 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-11-06T21:30:39Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: trainzment/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. -->
# trainzment/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: 3.6883
- Validation Loss: 2.7132
- 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 |
|:----------:|:---------------:|:-----:|
| 3.6883 | 2.7132 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
phi0112358/all-MiniLM-L6-v2-GGML | phi0112358 | 2023-11-07T00:17:14Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net",
"dataset:search_qa",
"dataset:eli5",
"dataset:snli",
"dataset:multi_nli",
"dataset:wikihow",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/sentence-compression",
"dataset:embedding-data/flickr30k-captions",
"dataset:embedding-data/altlex",
"dataset:embedding-data/simple-wiki",
"dataset:embedding-data/QQP",
"dataset:embedding-data/SPECTER",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/WikiAnswers",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-11-07T00:11:03Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
library_name: sentence-transformers
---
# all-MiniLM-L6-v2-GGML
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** | |
AdamCodd/distilbart-sum-arxiv | AdamCodd | 2023-11-07T00:16:18Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"dataset:ccdv/arxiv-summarization",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-06T17:57:08Z | ---
datasets:
- ccdv/arxiv-summarization
model-index:
- name: distilbart-sum-arxiv
results:
- task:
type: summarization
name: Summarization
dataset:
name: arxiv-summarization
type: arxiv-summarization
metrics:
- type: rouge-1
value: 42.1856
name: Validation ROUGE-1
- type: rouge-2
value: 15.4815
name: Validation ROUGE-2
- type: rouge-l
value: 24.4409
name: Validation ROUGE-L
---
## distilbart-sum-arxiv
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on a subset of the [ccdv/arxiv-summarization dataset](https://huggingface.co/datasets/ccdv/arxiv-summarization). It achieves the following results on the evaluation set:
* Loss: 2.420
* Rouge1: 42.185
* Rouge2: 15.481
* RougeL: 24.440
* RougeLSum: 24.260
## Model description
This model is a distilled version of BART with 306M parameters (vs. 406 for the BART model), but it is 1.68 times faster than BART at inference. It has been trained on 60_000 samples and has a limitation of 1024 tokens.
## Intended uses & limitations
Since this model has been trained on scientific papers, it may perform poorly when attempting to summarize other types of content.
```python
from transformers import pipeline
summarizer = pipeline("text2text-generation", model="AdamCodd/distilbart-sum-arxiv")
paper = "Scientific paper..."
result = summarizer(paper)
print(result)
```
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1270
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 1
### Training results
| key | value |
| --- | ----- |
| eval_rouge1 | 42.185638427734375 |
| eval_rouge2 | 15.481599807739258 |
| eval_rougeL | 24.440900802612305 |
| eval_rougeLsum | 24.260608673095703 |
### Framework versions
- Transformers 4.33.0
- Pytorch lightning 2.0.8
- Tokenizers 0.13.3
If you want to support me, you can [here](https://ko-fi.com/adamcodd). |
farama-minari/HalfCheetah-v5-SAC-expert | farama-minari | 2023-11-07T00:15:39Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"HalfCheetah-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-06T04:17:00Z | ---
library_name: stable-baselines3
tags:
- HalfCheetah-v5
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetah-v5
type: HalfCheetah-v5
metrics:
- type: mean_reward
value: 11448.22 +/- 96.24
name: mean_reward
verified: false
---
# **SAC** Agent playing **HalfCheetah-v5**
This is a trained model of a **SAC** agent playing **HalfCheetah-v5**
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
...
```
|
Kyle1668/boss-sentiment-6000-bert-base-uncased | Kyle1668 | 2023-11-07T00:11:06Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-06T21:42:16Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: boss-sentiment-6000-bert-base-uncased
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. -->
# boss-sentiment-6000-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- F1: 0.6808
- Acc: 0.8249
- Loss: 1.1523
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | F1 | Acc | Validation Loss |
|:-------------:|:-----:|:----:|:------:|:------:|:---------------:|
| No log | 1.0 | 375 | 0.6519 | 0.8092 | 0.5142 |
| 0.8417 | 2.0 | 750 | 0.6644 | 0.8447 | 0.4261 |
| 0.4234 | 3.0 | 1125 | 0.7150 | 0.8582 | 0.4002 |
| 0.234 | 4.0 | 1500 | 0.7360 | 0.8883 | 0.4355 |
| 0.234 | 5.0 | 1875 | 0.7408 | 0.8848 | 0.5524 |
| 0.1203 | 6.0 | 2250 | 0.7118 | 0.8484 | 0.8452 |
| 0.0738 | 7.0 | 2625 | 0.7201 | 0.8680 | 0.8452 |
| 0.0416 | 8.0 | 3000 | 0.6808 | 0.8249 | 1.1523 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
bartmiller/a2c-PandaReachDense-v3 | bartmiller | 2023-11-06T23:48:48Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-06T23:43:17Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.17 +/- 0.13
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mi-rei/features_model | mi-rei | 2023-11-06T23:38:47Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-06T21:38:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: features_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. -->
# features_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4903
- Accuracy: 0.7311
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5644 | 1.0 | 4642 | 0.5476 | 0.7030 |
| 0.4915 | 2.0 | 9284 | 0.4903 | 0.7311 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
|
am-infoweb/RPA_Synth1_ON_7_Nov | am-infoweb | 2023-11-06T23:27:44Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-11-06T23:11:50Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: RPA_Synth1_ON_7_Nov
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. -->
# RPA_Synth1_ON_7_Nov
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2413 | 1.0 | 825 | 0.0210 |
| 0.034 | 2.0 | 1650 | 0.0023 |
| 0.025 | 3.0 | 2475 | 0.0009 |
| 0.007 | 4.0 | 3300 | 0.0005 |
| 0.012 | 5.0 | 4125 | 0.0084 |
| 0.0087 | 6.0 | 4950 | 0.0000 |
| 0.0009 | 7.0 | 5775 | 0.0001 |
| 0.0 | 8.0 | 6600 | 0.0000 |
| 0.0035 | 9.0 | 7425 | 0.0000 |
| 0.0 | 10.0 | 8250 | 0.0000 |
| 0.0085 | 11.0 | 9075 | 0.0000 |
| 0.0 | 12.0 | 9900 | 0.0000 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Kyle1668/boss-sentiment-3000-bert-base-uncased | Kyle1668 | 2023-11-06T23:26:23Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-06T17:53:15Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: boss-sentiment-3000-bert-base-uncased
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. -->
# boss-sentiment-3000-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- F1: 0.6539
- Acc: 0.8012
- Loss: 1.0668
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | F1 | Acc | Validation Loss |
|:-------------:|:-----:|:----:|:------:|:------:|:---------------:|
| No log | 1.0 | 188 | 0.5948 | 0.8309 | 0.5611 |
| No log | 2.0 | 376 | 0.6500 | 0.7876 | 0.5856 |
| 0.6906 | 3.0 | 564 | 0.7083 | 0.8627 | 0.3858 |
| 0.6906 | 4.0 | 752 | 0.6588 | 0.7697 | 0.8185 |
| 0.6906 | 5.0 | 940 | 0.6687 | 0.8142 | 0.8388 |
| 0.1956 | 6.0 | 1128 | 0.6539 | 0.8012 | 1.0668 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Kyle1668/boss-toxicity-3000-bert-base-uncased | Kyle1668 | 2023-11-06T23:13:24Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-06T22:37:54Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: boss-toxicity-3000-bert-base-uncased
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. -->
# boss-toxicity-3000-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- F1: 0.7041
- Acc: 0.8559
- Loss: 0.7057
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | F1 | Acc | Validation Loss |
|:-------------:|:-----:|:----:|:------:|:------:|:---------------:|
| No log | 1.0 | 188 | 0.5425 | 0.6756 | 0.5975 |
| No log | 2.0 | 376 | 0.7372 | 0.8909 | 0.2901 |
| 0.4492 | 3.0 | 564 | 0.7260 | 0.8775 | 0.3477 |
| 0.4492 | 4.0 | 752 | 0.6595 | 0.8084 | 0.8310 |
| 0.4492 | 5.0 | 940 | 0.7041 | 0.8559 | 0.7057 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
bartowski/Hexoteric-7B-exl2 | bartowski | 2023-11-06T23:04:28Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-06T20:08:30Z | ---
license: apache-2.0
quantized_by: bartowski
---
## Exllama v2 Quantizations of Hexoteric-7B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.7">turboderp's ExLlamaV2 v0.0.7</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.
Original model: https://huggingface.co/CalderaAI/Hexoteric-7B
<a href="https://huggingface.co/bartowski/Hexoteric-7B-exl2/tree/4.0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Hexoteric-7B-exl2/tree/6.0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Hexoteric-7B-exl2/tree/8.0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/Hexoteric-7B-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Hexoteric-7B-exl2`:
```shell
mkdir Hexoteric-7B-exl2
huggingface-cli download bartowski/Hexoteric-7B-exl2 --local-dir Hexoteric-7B-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Hexoteric-7B-exl2
huggingface-cli download bartowski/Hexoteric-7B-exl2 --revision 4.0 --local-dir Hexoteric-7B-exl2 --local-dir-use-symlinks False
```
|
phi0112358/paraphrase-MiniLM-L3-v2-GGML | phi0112358 | 2023-11-06T23:02:17Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:s2orc",
"dataset:ms_marco",
"dataset:wiki_atomic_edits",
"dataset:snli",
"dataset:multi_nli",
"dataset:embedding-data/altlex",
"dataset:embedding-data/simple-wiki",
"dataset:embedding-data/flickr30k-captions",
"dataset:embedding-data/coco_captions",
"dataset:embedding-data/sentence-compression",
"dataset:embedding-data/QQP",
"dataset:yahoo_answers_topics",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-11-06T22:58:49Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- flax-sentence-embeddings/stackexchange_xml
- s2orc
- ms_marco
- wiki_atomic_edits
- snli
- multi_nli
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/flickr30k-captions
- embedding-data/coco_captions
- embedding-data/sentence-compression
- embedding-data/QQP
- yahoo_answers_topics
---
# sentence-transformers/paraphrase-MiniLM-L3-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L3-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L3-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L3-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-MiniLM-L3-v2)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
Kyle1668/boss-sentiment-1500-bert-base-uncased | Kyle1668 | 2023-11-06T22:58:47Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-06T19:36:23Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: boss-sentiment-1500-bert-base-uncased
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. -->
# boss-sentiment-1500-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- F1: 0.6639
- Acc: 0.8128
- Loss: 0.9879
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | F1 | Acc | Validation Loss |
|:-------------:|:-----:|:----:|:------:|:------:|:---------------:|
| No log | 1.0 | 94 | 0.4638 | 0.6355 | 1.0296 |
| No log | 2.0 | 188 | 0.6014 | 0.8005 | 0.5842 |
| No log | 3.0 | 282 | 0.6925 | 0.8577 | 0.3928 |
| No log | 4.0 | 376 | 0.6529 | 0.7895 | 0.6497 |
| No log | 5.0 | 470 | 0.6965 | 0.8595 | 0.5122 |
| 0.5499 | 6.0 | 564 | 0.6758 | 0.8256 | 0.7653 |
| 0.5499 | 7.0 | 658 | 0.6720 | 0.8277 | 0.8562 |
| 0.5499 | 8.0 | 752 | 0.6639 | 0.8128 | 0.9879 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
linoyts/huggy_v17 | linoyts | 2023-11-06T22:57:19Z | 4 | 0 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| text-to-image | 2023-11-06T22:16:00Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A <s0><s1> emoji
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - LinoyTsaban/huggy_test11
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on A <s0><s1> emoji using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
phi0112358/multi-qa-MiniLM-L6-cos-v1-GGML | phi0112358 | 2023-11-06T22:51:17Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"en",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/QQP",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/Amazon-QA",
"dataset:embedding-data/WikiAnswers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-11-06T22:34:46Z | ---
language:
- en
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- search_qa
- eli5
- natural_questions
- trivia_qa
- embedding-data/QQP
- embedding-data/PAQ_pairs
- embedding-data/Amazon-QA
- embedding-data/WikiAnswers
---
# multi-qa-MiniLM-L6-cos-v1-GGML
This is a [sentence-transformers](https://www.SBERT.net) model aimed to be used with **bert.cpp by Gerganov's GGML Library**: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
## Usage (Start Server)
Using this model becomes easy when you have bert.cpp installed:
```
./build/bin/server -m models/all-MiniLM-L6-v2/ggml-model-q4_0.bin --port 8085
# bert_model_load: loading model from 'models/all-MiniLM-L6-v2/ggml-model-q4_0.bin' - please wait ...
# bert_model_load: n_vocab = 30522
# bert_model_load: n_ctx = 512
# bert_model_load: n_embd = 384
# bert_model_load: n_intermediate = 1536
# bert_model_load: n_head = 12
# bert_model_load: n_layer = 6
# bert_model_load: f16 = 2
# bert_model_load: ggml ctx size = 13.57 MB
# bert_model_load: ............ done
# bert_model_load: model size = 13.55 MB / num tensors = 101
# Server running on port 8085 with 4 threads
# Waiting for a client
```
## Usage (Start Client)
Then you can use the model like this:
```
python3 examples/sample_client.py 8085
# Loading texts from sample_client_texts.txt...
# Loaded 1738 lines.
# Starting with a test query "Should I get health insurance?"
# Closest texts:
# 1. Will my Medicare premiums be higher because of my higher income?
# (similarity score: 0.4844)
# 2. Can I sign up for Medicare Part B if I am working and have health insurance through an employer?
# (similarity score: 0.4575)
# 3. Should I sign up for Medicare Part B if I have Veterans' Benefits?
# (similarity score: 0.4052)
# Enter a text to find similar texts (enter 'q' to quit): expensive
# Closest texts:
# 1. It is priced at $ 5,995 for an unlimited number of users tapping into the single processor , or $ 195 per user with a minimum of five users .
# (similarity score: 0.4597)
# 2. The new system costs between $ 1.1 million and $ 22 million , depending on configuration .
# (similarity score: 0.4547)
# 3. Each hull will cost about $ 1.4 billion , with each fully outfitted submarine costing about $ 2.2 billion , Young said .
# (similarity score: 0.4078)
```
### Converting models to ggml format
Converting models is similar to llama.cpp. Use models/convert-to-ggml.py to make hf models into either f32 or f16 ggml models. Then use ./build/bin/quantize to turn those into Q4_0, 4bit per weight models.
There is also models/run_conversions.sh which creates all 4 versions (f32, f16, Q4_0, Q4_1) at once.
```sh
cd models
# Clone a model from hf
git clone https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1
# Run conversions to 4 ggml formats (f32, f16, Q4_0, Q4_1)
sh run_conversions.sh multi-qa-MiniLM-L6-cos-v1
```
## Technical Details
In the following some technical details how this model must be used:
| Setting | Value |
| --- | :---: |
| Dimensions | 384 |
| Produces normalized embeddings | Yes |
| Pooling-Method | Mean pooling |
| Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |
Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
## Benchmarks
Running MTEB (Massive Text Embedding Benchmark) with bert.cpp vs. [sbert](https://sbert.net/)(cpu mode) gives comparable results between the two, with quantization having minimal effect on accuracy and eval time being similar or better than sbert with batch_size=1 (bert.cpp doesn't support batching).
See [benchmarks](benchmarks) more info.
### all-MiniLM-L6-v2
| Data Type | STSBenchmark | eval time | EmotionClassification | eval time |
|-----------|-----------|------------|-----------|------------|
| f32 | 0.8201 | 6.83 | 0.4082 | 11.34 |
| f16 | 0.8201 | 6.17 | 0.4085 | 10.28 |
| q4_0 | 0.8175 | 5.45 | 0.3911 | 10.63 |
| q4_1 | 0.8223 | 6.79 | 0.4027 | 11.41 |
| sbert | 0.8203 | 2.74 | 0.4085 | 5.56 |
| sbert-batchless | 0.8203 | 13.10 | 0.4085 | 15.52 |
### all-MiniLM-L12-v2
| Data Type | STSBenchmark | eval time | EmotionClassification | eval time |
|-----------|-----------|------------|-----------|------------|
| f32 | 0.8306 | 13.36 | 0.4117 | 21.23 |
| f16 | 0.8306 | 11.51 | 0.4119 | 20.08 |
| q4_0 | 0.8310 | 11.27 | 0.4183 | 20.81 |
| q4_1 | 0.8325 | 12.37 | 0.4093 | 19.38 |
| sbert | 0.8309 | 5.11 | 0.4117 | 8.93 |
| sbert-batchless | 0.8309 | 22.81 | 0.4117 | 28.04 |
### bert-base-uncased
bert-base-uncased is not a very good sentence embeddings model, but it's here to show that bert.cpp correctly runs models that are not from SentenceTransformers. Technically any hf model with architecture `BertModel` or `BertForMaskedLM` should work.
| Data Type | STSBenchmark | eval time | EmotionClassification | eval time |
|-----------|-----------|------------|-----------|------------|
| f32 | 0.4738 | 52.38 | 0.3361 | 88.56 |
| f16 | 0.4739 | 33.24 | 0.3361 | 55.86 |
| q4_0 | 0.4940 | 33.93 | 0.3375 | 57.82 |
| q4_1 | 0.4612 | 36.86 | 0.3318 | 59.63 |
| sbert | 0.4729 | 16.97 | 0.3527 | 28.77 |
| sbert-batchless | 0.4729 | 69.97 | 0.3526 | 79.02 |
----
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
## Training procedure
The full training script is accessible in this current repository: `train_script.py`.
### Pre-training
We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
#### Training
We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using Mean-pooling, cosine-similarity as similarity function, and a scale of 20.
| Dataset | Number of training tuples |
|--------------------------------------------------------|:--------------------------:|
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine | 17,579,773 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet | 3,012,496 |
| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
| **Total** | **214,988,242** | |
chineidu/bert-finetuned-sequence-classification | chineidu | 2023-11-06T22:40:31Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-10-29T13:31:38Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-finetuned-sequence-classification
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-finetuned-sequence-classification
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3960
- Accuracy: 0.8269
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4913 | 1.0 | 2354 | 0.4760 | 0.8092 |
| 0.4004 | 2.0 | 4708 | 0.4182 | 0.8025 |
| 0.3576 | 3.0 | 7062 | 0.3960 | 0.8269 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
aleksahet/helpful-river-99 | aleksahet | 2023-11-06T22:14:33Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-11-06T17:25:53Z | ---
tags:
- generated_from_trainer
model-index:
- name: helpful-river-99
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. -->
# helpful-river-99
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.3894 | 0.76 | 10000 | 5.3583 |
| 5.3327 | 1.53 | 20000 | 5.3183 |
| 5.2983 | 2.29 | 30000 | 5.2886 |
| 5.2634 | 3.05 | 40000 | 5.2619 |
| 5.268 | 3.82 | 50000 | 5.2496 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Ahmed107/hamsa-sy | Ahmed107 | 2023-11-06T22:12:04Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:nadsoft/hamsa-v0.1-beta",
"base_model:finetune:nadsoft/hamsa-v0.1-beta",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-11-06T15:23:34Z | ---
license: apache-2.0
base_model: nadsoft/hamsa-v0.1-beta
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hamsa-sy
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. -->
# hamsa-sy
This model is a fine-tuned version of [nadsoft/hamsa-v0.1-beta](https://huggingface.co/nadsoft/hamsa-v0.1-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3495
- Wer: 99.7918
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4479 | 0.25 | 1000 | 0.4457 | 99.9687 |
| 0.3866 | 0.49 | 2000 | 0.3958 | 99.8948 |
| 0.3219 | 0.74 | 3000 | 0.3652 | 99.7717 |
| 0.3426 | 0.98 | 4000 | 0.3495 | 99.7918 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
shirleylqs/mistral-snomed-classification | shirleylqs | 2023-11-06T21:50:51Z | 11 | 4 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-classification",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-06T21:08:43Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4972
- Accuracy: 0.9440
## 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-06
- train_batch_size: 4
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3355 | 1.0 | 7990 | 0.6075 | 0.9138 |
| 0.0 | 2.0 | 15980 | 0.4972 | 0.9440 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
paulonasc7/ppo-LunarLandar-v2 | paulonasc7 | 2023-11-06T21:49:59Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-06T21:49:35Z | ---
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: 227.68 +/- 74.98
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
...
```
|
pjherron/finetuning-emotion-model-f7bshardquant | pjherron | 2023-11-06T21:31:12Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:emotion",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"region:us"
]
| null | 2023-11-06T18:04:43Z | ---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: finetuning-emotion-model-f7bshardquant
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. -->
# finetuning-emotion-model-f7bshardquant
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4243
- Accuracy: 0.4745
- F1: 0.3945
## 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: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.4214 | 1.0 | 8000 | 1.4243 | 0.4745 | 0.3945 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Haider7867/cat-xgz | Haider7867 | 2023-11-06T21:10:44Z | 4 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T20:58:26Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### cat-xgz Dreambooth model trained by Haider7867 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: CUTM-233
Sample pictures of this concept:
|
Irene1881/ARDSPredictionv5 | Irene1881 | 2023-11-06T21:02:03Z | 1 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
]
| null | 2023-11-06T21:02:01Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: float16
### Framework versions
- PEFT 0.6.0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: float16
### Framework versions
- PEFT 0.6.0
|
Gio200023/bert-finetuned-ner-8 | Gio200023 | 2023-11-06T21:01:03Z | 3 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-06T20:59:19Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Gio200023/bert-finetuned-ner-8
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. -->
# Gio200023/bert-finetuned-ner-8
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: 0.5073
- Validation Loss: 0.6186
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.2, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.2, 'decay_steps': 634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 2, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 15.9826 | 24.0274 | 0 |
| 6.6864 | 1.2147 | 1 |
| 0.5073 | 0.6186 | 2 |
### Framework versions
- Transformers 4.33.0
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Gio200023/bert-finetuned-ner-7 | Gio200023 | 2023-11-06T20:59:10Z | 3 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-06T20:57:36Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Gio200023/bert-finetuned-ner-7
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. -->
# Gio200023/bert-finetuned-ner-7
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: 0.3994
- Validation Loss: 0.5648
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.2, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.2, 'decay_steps': 634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 2, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 16.2654 | 27.1870 | 0 |
| 4.6626 | 0.5557 | 1 |
| 0.3994 | 0.5648 | 2 |
### Framework versions
- Transformers 4.33.0
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Gio200023/bert-finetuned-ner-5 | Gio200023 | 2023-11-06T20:55:37Z | 6 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-06T20:54:03Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Gio200023/bert-finetuned-ner-5
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. -->
# Gio200023/bert-finetuned-ner-5
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: 0.3819
- Validation Loss: 0.6073
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.2, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 17.4254 | 2.4089 | 0 |
| 2.3597 | 0.7196 | 1 |
| 0.3819 | 0.6073 | 2 |
### Framework versions
- Transformers 4.33.0
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
katydeng/wizard-rp | katydeng | 2023-11-06T20:48:28Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2304.12244",
"arxiv:2306.08568",
"arxiv:2308.09583",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-11-06T19:50:34Z | ---
license: llama2
---
This is the **Full-Weight** of WizardLM-13B V1.2 model, this model is trained from **Llama-2 13b**.
## WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
<p align="center">
🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
</p>
<p align="center">
👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
</p>
## News
- 🔥🔥🔥[2023/08/26] We released **WizardCoder-Python-34B-V1.0** , which achieves the **73.2 pass@1** and surpasses **GPT4 (2023/03/15)**, **ChatGPT-3.5**, and **Claude2** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder).
- [2023/06/16] We released **WizardCoder-15B-V1.0** , which surpasses **Claude-Plus (+6.8)**, **Bard (+15.3)** and **InstructCodeT5+ (+22.3)** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder).
| Model | Checkpoint | Paper | HumanEval | MBPP | Demo | License |
| ----- |------| ---- |------|-------| ----- | ----- |
| WizardCoder-Python-34B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 73.2 | 61.2 | [Demo](http://47.103.63.15:50085/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
| WizardCoder-15B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 59.8 |50.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
| WizardCoder-Python-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 64.0 | 55.6 | -- | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
| WizardCoder-Python-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 55.5 | 51.6 | [Demo](http://47.103.63.15:50088/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
| WizardCoder-3B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 34.8 |37.4 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
| WizardCoder-1B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 23.8 |28.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
- 🔥 [08/11/2023] We release **WizardMath** Models.
- 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
- 🔥 Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM.
- 🔥 Our **WizardMath-70B-V1.0** model achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM.
| Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License|
| ----- |------| ---- |------|-------| ----- | ----- |
| WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>|
<font size=4>
| <sup>Model</sup> | <sup>Checkpoint</sup> | <sup>Paper</sup> |<sup>MT-Bench</sup> | <sup>AlpacaEval</sup> | <sup>WizardEval</sup> | <sup>HumanEval</sup> | <sup>License</sup>|
| ----- |------| ---- |------|-------| ----- | ----- | ----- |
| <sup>WizardLM-13B-V1.2</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>| | <sup>7.06</sup> | <sup>89.17%</sup> | <sup>101.4% </sup>|<sup>36.6 pass@1</sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> |
| <sup>WizardLM-13B-V1.1</sup> |<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> | | <sup>6.76</sup> |<sup>86.32%</sup> | <sup>99.3% </sup> |<sup>25.0 pass@1</sup>| <sup>Non-commercial</sup>|
| <sup>WizardLM-30B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> | | <sup>7.01</sup> | | <sup>97.8% </sup> | <sup>37.8 pass@1</sup>| <sup>Non-commercial</sup> |
| <sup>WizardLM-13B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> | | <sup>6.35</sup> | <sup>75.31%</sup> | <sup>89.1% </sup> |<sup> 24.0 pass@1 </sup> | <sup>Non-commercial</sup>|
| <sup>WizardLM-7B-V1.0 </sup>| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> |<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>| | | <sup>78.0% </sup> |<sup>19.1 pass@1 </sup>|<sup> Non-commercial</sup>|
</font>
**Repository**: https://github.com/nlpxucan/WizardLM
**Twitter**:
- 🔥🔥🔥 [7/25/2023] We released **WizardLM V1.2** models. The **WizardLM-13B-V1.2** is here ([Demo_13B-V1.2](https://b7a19878988c8c73.gradio.app), [Demo_13B-V1.2_bak-1](https://d0a37a76e0ac4b52.gradio.app/), [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)). Please checkout the [paper](https://arxiv.org/abs/2304.12244).
- 🔥🔥🔥 [7/25/2023] The **WizardLM-13B-V1.2** achieves **7.06** on [MT-Bench Leaderboard](https://chat.lmsys.org/?leaderboard), **89.17%** on [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/), and **101.4%** on [WizardLM Eval](https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/data/WizardLM_testset.jsonl). (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)
❗<b>Note for model system prompts usage:</b>
<b>WizardLM</b> adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am WizardLM.</s>......
```
## Inference WizardLM Demo Script
We provide the inference WizardLM demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).
Please cite the paper if you use the data or code from WizardLM.
```
@article{xu2023wizardlm,
title={Wizardlm: Empowering large language models to follow complex instructions},
author={Xu, Can and Sun, Qingfeng and Zheng, Kai and Geng, Xiubo and Zhao, Pu and Feng, Jiazhan and Tao, Chongyang and Jiang, Daxin},
journal={arXiv preprint arXiv:2304.12244},
year={2023}
}
```
❗<b>To commen concern about dataset:</b>
Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models.
Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team .
Our researchers have no authority to publicly release them without authorization.
Thank you for your understanding. |
Akanksh2147/llama2-qlora-finetunined-french | Akanksh2147 | 2023-11-06T20:34:35Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
]
| null | 2023-11-06T20:34:17Z | ---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.7.0.dev0
|
prajwalJumde/RPA-Synth1 | prajwalJumde | 2023-11-06T20:16:19Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-11-06T20:04:26Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: RPA-Synth1
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. -->
# RPA-Synth1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 206 | 0.0181 |
| No log | 2.0 | 412 | 0.0011 |
| 0.9619 | 3.0 | 618 | 0.0001 |
| 0.9619 | 4.0 | 824 | 0.0000 |
| 0.003 | 5.0 | 1030 | 0.0000 |
| 0.003 | 6.0 | 1236 | 0.0000 |
| 0.003 | 7.0 | 1442 | 0.0000 |
| 0.001 | 8.0 | 1648 | 0.0000 |
| 0.001 | 9.0 | 1854 | 0.0000 |
| 0.0002 | 10.0 | 2060 | 0.0000 |
| 0.0002 | 11.0 | 2266 | 0.0000 |
| 0.0002 | 12.0 | 2472 | 0.0000 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|
generadaidemo2/GenerAd-AI2 | generadaidemo2 | 2023-11-06T20:12:26Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloom-1b7",
"base_model:adapter:bigscience/bloom-1b7",
"region:us"
]
| null | 2023-11-06T20:12:21Z | ---
library_name: peft
base_model: bigscience/bloom-1b7
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.7.0.dev0
|
johaanm/test-grader-alpha-V1.7 | johaanm | 2023-11-06T20:02:24Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-11-06T20:02:20Z | ---
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
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
- PEFT 0.4.0
|
akidse/ppo-Pyramids | akidse | 2023-11-06T20:00:39Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-11-06T20:00:33Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: akidse/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
folflo/mt5-small-finetuned-HunSum-1_hvg_index_1103 | folflo | 2023-11-06T19:55:15Z | 4 | 0 | transformers | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-03T09:43:43Z | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_keras_callback
model-index:
- name: folflo/mt5-small-finetuned-HunSum-1_hvg_index_1103
results: []
datasets:
- SZTAKI-HLT/HunSum-1
metrics:
- rouge
--- rouge1: 26.98
--- rouge2: 3.449
--- rougeL: 15.3615
--- rougeLsum: 20.3444
# folflo/mt5-small-finetuned-HunSum-1_hvg_index_1103
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8267
- Validation Loss: 2.6510
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 213912, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.8545 | 2.9337 | 0 |
| 3.1558 | 2.8171 | 1 |
| 3.0206 | 2.7562 | 2 |
| 2.9448 | 2.7049 | 3 |
| 2.8955 | 2.6809 | 4 |
| 2.8618 | 2.6626 | 5 |
| 2.8399 | 2.6548 | 6 |
| 2.8267 | 2.6510 | 7 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1 |
Schwedyboi/ppo-LunarLander-v2 | Schwedyboi | 2023-11-06T19:51:50Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-06T19:51:27Z | ---
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: 258.47 +/- 18.26
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
FuuToru/XLM-processed-squad-24k | FuuToru | 2023-11-06T19:16:26Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:deepset/roberta-base-squad2",
"base_model:finetune:deepset/roberta-base-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-11-06T15:04:53Z | ---
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: XLM-processed-squad-24k
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. -->
# XLM-processed-squad-24k
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2363
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.3087 | 1.0 | 19129 | 0.2523 |
| 0.2701 | 2.0 | 38258 | 0.2369 |
| 0.2284 | 3.0 | 57387 | 0.2363 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
weishuai-4670/textual_inversion_find_new_2 | weishuai-4670 | 2023-11-06T19:15:25Z | 13 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T08:18:13Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - weishuai-4670/textual_inversion_find_new_2
These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1. You can find some example images in the following.
|
1aurent/resnet34.tiatoolbox-kather100k | 1aurent | 2023-11-06T19:04:39Z | 2 | 0 | timm | [
"timm",
"safetensors",
"image-classification",
"feature-extraction",
"biology",
"cancer",
"histology",
"TIA",
"tiatoolbox",
"dataset:1aurent/NCT-CRC-HE",
"license:cc-by-4.0",
"region:us"
]
| image-classification | 2023-11-05T17:28:16Z | ---
tags:
- image-classification
- feature-extraction
- timm
- biology
- cancer
- histology
- TIA
- tiatoolbox
library_name: timm
pipeline_tag: image-classification
license: cc-by-4.0
datasets:
- 1aurent/NCT-CRC-HE
widget:
- src: >-
https://datasets-server.huggingface.co/assets/1aurent/NCT-CRC-HE/--/default/CRC_VAL_HE_7K/0/image/image.jpg
example_title: debris
---
# Model card for resnet34.tiatoolbox-kather100k
A ResNet34 image classification model. \
Trained by [Tissue Image Analytics (TIA) Centre](https://warwick.ac.uk/fac/cross_fac/tia/) on "kather100k" histology patches.
## Model Details
- **Model Type:** Image classification / Feature backbone
- **Model Stats:**
- Params (M): 21.3
- Image size: 224 x 224 x 3
- **Dataset**: [kather100k](https://tia-toolbox.readthedocs.io/en/latest/_autosummary/tiatoolbox.models.dataset.info.KatherPatchDataset.html#tiatoolbox.models.dataset.info.KatherPatchDataset), also called NCT-CRC-HE
- **Original:** https://github.com/TissueImageAnalytics/tiatoolbox
- **License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/resnet34.tiatoolbox-kather100k",
pretrained=True,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/resnet34.tiatoolbox-kather100k",
pretrained=True,
num_classes=0,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{Pocock2022,
author = {Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and Epstein, David and Minhas, Fayyaz and Rajpoot, Nasir M and Raza, Shan E Ahmed},
doi = {10.1038/s43856-022-00186-5},
issn = {2730-664X},
journal = {Communications Medicine},
month = {sep},
number = {1},
pages = {120},
publisher = {Springer US},
title = {{TIAToolbox as an end-to-end library for advanced tissue image analytics}},
url = {https://www.nature.com/articles/s43856-022-00186-5},
volume = {2},
year = {2022}
}
``` |
akidse/ppo-SnowballTarget | akidse | 2023-11-06T18:57:38Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-11-06T18:57:32Z | ---
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: akidse/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
1aurent/resnext101_32x8d.tiatoolbox-kather100k | 1aurent | 2023-11-06T18:52:15Z | 1 | 0 | timm | [
"timm",
"safetensors",
"image-classification",
"feature-extraction",
"biology",
"cancer",
"histology",
"TIA",
"tiatoolbox",
"dataset:1aurent/NCT-CRC-HE",
"license:cc-by-4.0",
"region:us"
]
| image-classification | 2023-11-06T16:41:44Z | ---
tags:
- image-classification
- feature-extraction
- timm
- biology
- cancer
- histology
- TIA
- tiatoolbox
library_name: timm
pipeline_tag: image-classification
license: cc-by-4.0
datasets:
- 1aurent/NCT-CRC-HE
widget:
- src: >-
https://datasets-server.huggingface.co/assets/1aurent/NCT-CRC-HE/--/default/CRC_VAL_HE_7K/0/image/image.jpg
example_title: debris
---
# Model card for resnext101_32x8d.tiatoolbox-kather100k
A ResNeXt-D image classification model. \
Trained by [Tissue Image Analytics (TIA) Centre](https://warwick.ac.uk/fac/cross_fac/tia/) on "kather100k" histology patches.
## Model Details
- **Model Type:** Image classification / Feature backbone
- **Model Stats:**
- Params (M): 87.0
- Image size: 224 x 224 x 3
- **Dataset**: [kather100k](https://tia-toolbox.readthedocs.io/en/latest/_autosummary/tiatoolbox.models.dataset.info.KatherPatchDataset.html#tiatoolbox.models.dataset.info.KatherPatchDataset), also called NCT-CRC-HE
- **Original:** https://github.com/TissueImageAnalytics/tiatoolbox
- **License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/resnext101_32x8d.tiatoolbox-kather100k",
pretrained=True,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/resnext101_32x8d.tiatoolbox-kather100k",
pretrained=True,
num_classes=0,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{Pocock2022,
author = {Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and Epstein, David and Minhas, Fayyaz and Rajpoot, Nasir M and Raza, Shan E Ahmed},
doi = {10.1038/s43856-022-00186-5},
issn = {2730-664X},
journal = {Communications Medicine},
month = {sep},
number = {1},
pages = {120},
publisher = {Springer US},
title = {{TIAToolbox as an end-to-end library for advanced tissue image analytics}},
url = {https://www.nature.com/articles/s43856-022-00186-5},
volume = {2},
year = {2022}
}
``` |
perecasxiru/Taxi-v3 | perecasxiru | 2023-11-06T18:50:06Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-06T18:50:04Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.69
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="perecasxiru/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"])
```
|
echan24/mistral-finetune | echan24 | 2023-11-06T18:48:20Z | 0 | 0 | peft | [
"peft",
"endpoints_compatible",
"region:us"
]
| null | 2023-11-01T17:29:17Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
1aurent/resnext50_32x4d.tiatoolbox-kather100k | 1aurent | 2023-11-06T18:48:17Z | 4 | 0 | timm | [
"timm",
"safetensors",
"image-classification",
"feature-extraction",
"biology",
"cancer",
"histology",
"TIA",
"tiatoolbox",
"dataset:1aurent/NCT-CRC-HE",
"license:cc-by-4.0",
"region:us"
]
| image-classification | 2023-11-06T16:40:22Z | ---
tags:
- image-classification
- feature-extraction
- timm
- biology
- cancer
- histology
- TIA
- tiatoolbox
library_name: timm
pipeline_tag: image-classification
license: cc-by-4.0
datasets:
- 1aurent/NCT-CRC-HE
widget:
- src: >-
https://datasets-server.huggingface.co/assets/1aurent/NCT-CRC-HE/--/default/CRC_VAL_HE_7K/0/image/image.jpg
example_title: debris
---
# Model card for resnext50_32x4d.tiatoolbox-kather100k
A ResNeXt-D image classification model. \
Trained by [Tissue Image Analytics (TIA) Centre](https://warwick.ac.uk/fac/cross_fac/tia/) on "kather100k" histology patches.
## Model Details
- **Model Type:** Image classification / Feature backbone
- **Model Stats:**
- Params (M): 23.1
- Image size: 224 x 224 x 3
- **Dataset**: [kather100k](https://tia-toolbox.readthedocs.io/en/latest/_autosummary/tiatoolbox.models.dataset.info.KatherPatchDataset.html#tiatoolbox.models.dataset.info.KatherPatchDataset), also called NCT-CRC-HE
- **Original:** https://github.com/TissueImageAnalytics/tiatoolbox
- **License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/resnext50_32x4d.tiatoolbox-kather100k",
pretrained=True,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/resnext50_32x4d.tiatoolbox-kather100k",
pretrained=True,
num_classes=0,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{Pocock2022,
author = {Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and Epstein, David and Minhas, Fayyaz and Rajpoot, Nasir M and Raza, Shan E Ahmed},
doi = {10.1038/s43856-022-00186-5},
issn = {2730-664X},
journal = {Communications Medicine},
month = {sep},
number = {1},
pages = {120},
publisher = {Springer US},
title = {{TIAToolbox as an end-to-end library for advanced tissue image analytics}},
url = {https://www.nature.com/articles/s43856-022-00186-5},
volume = {2},
year = {2022}
}
``` |
eloi-goncalves/handsfree_intent_classification | eloi-goncalves | 2023-11-06T18:46:03Z | 10 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-06T13:34:41Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: handsfree_intent_classification
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. -->
# handsfree_intent_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0070
- Accuracy: 0.9974
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0106 | 1.0 | 2549 | 0.0083 | 0.9971 |
| 0.0064 | 2.0 | 5098 | 0.0070 | 0.9974 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
pjherron/falcon-7b-sharded-bf16-qlora | pjherron | 2023-11-06T18:38:59Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:adapter:ybelkada/falcon-7b-sharded-bf16",
"region:us"
]
| null | 2023-11-06T18:38:56Z | ---
library_name: peft
base_model: ybelkada/falcon-7b-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.7.0.dev0
|
LaTarn/ac-price-setfit-model | LaTarn | 2023-11-06T18:31:34Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
]
| text-classification | 2023-11-06T18:31:07Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ac-price-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ac-price-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
gr8testgad-1/movie_sentiment | gr8testgad-1 | 2023-11-06T18:29:34Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-10-31T14:51:13Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: movie_sentiment
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. -->
# movie_sentiment
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2751
- Accuracy: 0.526
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5021 | 0.2 | 500 | 0.3246 | 0.4888 |
| 0.4281 | 0.4 | 1000 | 0.2751 | 0.526 |
| 0.3504 | 0.6 | 1500 | 0.3991 | 0.5422 |
| 0.3707 | 0.8 | 2000 | 0.3008 | 0.4732 |
| 0.3436 | 1.0 | 2500 | 0.3011 | 0.4876 |
| 0.2867 | 1.2 | 3000 | 0.3297 | 0.5032 |
| 0.2865 | 1.4 | 3500 | 0.3501 | 0.4764 |
| 0.2851 | 1.6 | 4000 | 0.3594 | 0.473 |
| 0.263 | 1.8 | 4500 | 0.3562 | 0.5152 |
| 0.2599 | 2.0 | 5000 | 0.3647 | 0.4494 |
| 0.2083 | 2.2 | 5500 | 0.4269 | 0.5406 |
| 0.1978 | 2.4 | 6000 | 0.4369 | 0.4758 |
| 0.1763 | 2.6 | 6500 | 0.4031 | 0.5148 |
| 0.1845 | 2.8 | 7000 | 0.4249 | 0.501 |
| 0.2045 | 3.0 | 7500 | 0.4168 | 0.5056 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
AdamCodd/distilroberta-query-wellformedness | AdamCodd | 2023-11-06T18:28:50Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:google_wellformed_query",
"model-index",
"autotrain_compatible",
"region:us"
]
| text-classification | 2023-10-19T13:45:09Z | ---
metrics:
- mse
- r_squared
- mae
datasets:
- google_wellformed_query
inference: false
model-index:
- name: distilroberta-query-wellformedness
results:
- task:
type: text-classification
name: Text Classification
metrics:
- type: loss
value: 0.061837393790483475
- type: mse
value: 0.061837393790483475
name: Validation Mean Squared Error
- type: r2
value: 0.5726782083511353
name: Validation R-Squared
- type: mae
value: 0.183049738407135
name: Validation Mean Absolute Error
language:
- en
---
## DistilRoBERTa-query-wellformedness
This model utilizes the [Distilroberta base](https://huggingface.co/distilroberta-base) architecture, which has been fine-tuned for a regression task on the [Google's query wellformedness](https://huggingface.co/datasets/google_wellformed_query) dataset encompassing 25,100 queries from the Paralex corpus. Each query received annotations from five raters, who provided a continuous rating indicating the degree to which the query is well-formed.
## Model description
A regression head has been appended to the DistilRoBERTa model to tailor it for a regression task. This additional component is crucial and needs to be loaded alongside the base model during inference to ensure accurate predictions. The model evaluates the query for completeness and grammatical correctness, providing a score between 0 and 1, where 1 indicates correctness.
## Usage
Inference API has been disabled as this is a regression task, not a text classification task, and HuggingFace does not provide a pipeline for regression tasks.
Because of the dataset, it will perform better when handling queries in question form.
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AdamCodd/distilroberta-query-wellformedness")
class RegressionModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.model = AutoModelForSequenceClassification.from_pretrained("AdamCodd/distilroberta-query-wellformedness")
self.regression_head = torch.nn.Linear(self.model.config.hidden_size, 1)
def forward(self, input_ids, attention_mask, **kwargs):
outputs = self.model.base_model(input_ids=input_ids, attention_mask=attention_mask)
rating = self.regression_head(outputs.last_hidden_state[:, 0, :])
rating = torch.sigmoid(rating)
return rating.squeeze()
regression_model = RegressionModel()
# Do not forget to set the correct path to load the regression head
regression_model.regression_head.load_state_dict(torch.load("path_to_the_regression_head.pth"))
regression_model.eval()
# Examples
sentences = [
"The cat and dog in the yard.",
"she don't like apples.",
"Is rain sunny days sometimes?",
"She enjoys reading books and playing chess.",
"How many planets are there in our solar system?"
]
inputs = tokenizer(sentences, truncation=True, padding=True, return_tensors='pt')
with torch.no_grad():
outputs = regression_model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
predictions = outputs.tolist()
for i, rating in enumerate(predictions):
print(f'Sentence: {sentences[i]}')
print(f'Predicted Rating: {rating}\n')
```
Output:
```
Sentence: The cat and dog in the yard.
Predicted Rating: 0.20430190861225128
Sentence: she don't like apples.
Predicted Rating: 0.08289700001478195
Sentence: Is rain sunny days sometimes?
Predicted Rating: 0.20011138916015625
Sentence: She enjoys reading books and playing chess.
Predicted Rating: 0.8915354013442993
Sentence: How many planets are there in our solar system?
Predicted Rating: 0.974799394607544
```
## 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: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 5
### Training results
Metrics: Mean Squared Error, R-Squared, Mean Absolute Error
```
'test_loss': 0.061837393790483475,
'test_mse': 0.061837393790483475,
'test_r2': 0.5726782083511353,
'test_mae': 0.183049738407135
```
### Framework versions
- Transformers 4.34.1
- Pytorch lightning 2.1.0
- Tokenizers 0.14.1
If you want to support me, you can [here](https://ko-fi.com/adamcodd). |
deepu-2005/games-rdb | deepu-2005 | 2023-11-06T18:23:13Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-11-06T18:18:13Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### GAMES-rdb Dreambooth model trained by deepu-2005 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MITS-428
Sample pictures of this concept:
|
MimionanA/wav2vec2-large-xls-r-300m-turkish-colab | MimionanA | 2023-11-06T18:19:12Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-10-31T08:52:18Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: tr
split: test
args: tr
metrics:
- name: Wer
type: wer
value: 0.24591935808892226
---
<!-- 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. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2513
- Wer: 0.2459
## 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 512
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.4424 | 4.88 | 400 | 0.4321 | 0.4851 |
| 0.2599 | 9.77 | 800 | 0.2841 | 0.3252 |
| 0.1511 | 14.65 | 1200 | 0.2664 | 0.2900 |
| 0.1124 | 19.54 | 1600 | 0.2671 | 0.2687 |
| 0.0869 | 24.42 | 2000 | 0.2531 | 0.2586 |
| 0.068 | 29.31 | 2400 | 0.2513 | 0.2459 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hariram344/ocean-life-hnn | hariram344 | 2023-11-06T18:05:27Z | 2 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T18:00:18Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### OCEAN-LIFE-hnn Dreambooth model trained by hariram344 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MITS-1672
Sample pictures of this concept:
|
microsoft/BiomedNLP-BiomedELECTRA-base-uncased-abstract | microsoft | 2023-11-06T18:04:59Z | 18 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"exbert",
"en",
"arxiv:2007.15779",
"arxiv:2112.07869",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2023-01-07T13:42:25Z | ---
language: en
tags:
- exbert
license: mit
---
## MSR BiomedELECTRA-base (abstracts only)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named **"PubMedELECTRA (abstracts)"**.
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedELECTRA-base-uncased-abstract" or update your `transformers` library to version 4.22+ if you need to refer to the old name.
</div>
Pretraining large neural language models, such as BERT and ELECTRA, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. [Recent work](https://arxiv.org/abs/2007.15779) shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. [Followup work](https://arxiv.org/abs/2112.07869) explores alternate pretraining strategies and the impact of these on performance on the BLURB benchmark.
This BiomedELECTRA is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/).
## Citation
If you find BiomedELECTRA useful in your research, please cite the following paper:
```latex
@misc{https://doi.org/10.48550/arxiv.2112.07869,
doi = {10.48550/ARXIV.2112.07869},
url = {https://arxiv.org/abs/2112.07869},
author = {Tinn, Robert and Cheng, Hao and Gu, Yu and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
microsoft/BiomedNLP-BiomedELECTRA-large-uncased-abstract | microsoft | 2023-11-06T18:04:47Z | 7,498 | 5 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"exbert",
"en",
"arxiv:2007.15779",
"arxiv:2112.07869",
"license:mit",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2023-01-07T14:38:08Z | ---
language: en
tags:
- exbert
license: mit
---
## MSR BiomedELECTRA-large (abstracts only)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named **"PubMedELECTRA large (abstracts)"**.
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedELECTRA-large-uncased-abstract" or update your `transformers` library to version 4.22+ if you need to refer to the old name.
</div>
Pretraining large neural language models, such as BERT and ELECTRA, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. [Recent work](https://arxiv.org/abs/2007.15779) shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. [Followup work](https://arxiv.org/abs/2112.07869) explores alternate pretraining strategies and the impact of these on performance on the BLURB benchmark.
This BiomedELECTRA is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/).
## Citation
If you find BiomedELECTRA useful in your research, please cite the following paper:
```latex
@misc{https://doi.org/10.48550/arxiv.2112.07869,
doi = {10.48550/ARXIV.2112.07869},
url = {https://arxiv.org/abs/2112.07869},
author = {Tinn, Robert and Cheng, Hao and Gu, Yu and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract | microsoft | 2023-11-06T18:04:35Z | 705 | 17 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"exbert",
"en",
"arxiv:2007.15779",
"arxiv:2112.07869",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-01-02T16:59:12Z | ---
language: en
tags:
- exbert
license: mit
widget:
- text: "[MASK] is a tyrosine kinase inhibitor."
---
## MSR BiomedBERT-large (abstracts only)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named **"PubMedBERT large (abstracts)"**.
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract" or update your `transformers` library to version 4.22+ if you need to refer to the old name.
</div>
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. [Recent work](https://arxiv.org/abs/2007.15779) shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. [Followup work](https://arxiv.org/abs/2112.07869) explores larger model sizes and the impact of these on performance on the BLURB benchmark.
This BiomedBERT is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/).
## Citation
If you find BiomedBERT useful in your research, please cite the following paper:
```latex
@misc{https://doi.org/10.48550/arxiv.2112.07869,
doi = {10.48550/ARXIV.2112.07869},
url = {https://arxiv.org/abs/2112.07869},
author = {Tinn, Robert and Cheng, Hao and Gu, Yu and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
<a href="https://huggingface.co/exbert/?model=microsoft/BiomedNLP-PubMedBERT-large-uncased-abstract&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=10&heads=..0,1,2,3,4,5,6,7,8,9,10,11&threshold=0.7&tokenInd=17&tokenSide=right&maskInds=..&hideClsSep=true">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract | microsoft | 2023-11-06T18:04:15Z | 110,885 | 71 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"exbert",
"en",
"arxiv:2007.15779",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:05Z | ---
language: en
tags:
- exbert
license: mit
widget:
- text: "[MASK] is a tyrosine kinase inhibitor."
---
## MSR BiomedBERT (abstracts only)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named **"PubMedBERT (abstracts)"**.
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract" or update your `transformers` library to version 4.22+ if you need to refer to the old name.
</div>
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. [Recent work](https://arxiv.org/abs/2007.15779) shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
This BiomedBERT is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/). This model achieves state-of-the-art performance on several biomedical NLP tasks, as shown on the [Biomedical Language Understanding and Reasoning Benchmark](https://aka.ms/BLURB).
## Citation
If you find BiomedBERT useful in your research, please cite the following paper:
```latex
@misc{pubmedbert,
author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon},
title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
year = {2020},
eprint = {arXiv:2007.15779},
}
```
<a href="https://huggingface.co/exbert/?model=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=10&heads=..0,1,2,3,4,5,6,7,8,9,10,11&threshold=0.7&tokenInd=17&tokenSide=right&maskInds=..&hideClsSep=true">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext | microsoft | 2023-11-06T18:03:43Z | 104,821 | 213 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"exbert",
"en",
"arxiv:2007.15779",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:05Z | ---
language: en
tags:
- exbert
license: mit
widget:
- text: "[MASK] is a tumor suppressor gene."
---
## MSR BiomedBERT (abstracts + full text)
<div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;">
* This model was previously named **"PubMedBERT (abstracts + full text)"**.
* You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" or update your `transformers` library to version 4.22+ if you need to refer to the old name.
</div>
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. [Recent work](https://arxiv.org/abs/2007.15779) shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
BiomedBERT is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/) and _full-text_ articles from [PubMedCentral](https://www.ncbi.nlm.nih.gov/pmc/). This model achieves state-of-the-art performance on many biomedical NLP tasks, and currently holds the top score on the [Biomedical Language Understanding and Reasoning Benchmark](https://aka.ms/BLURB).
## Citation
If you find BiomedBERT useful in your research, please cite the following paper:
```latex
@misc{pubmedbert,
author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon},
title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
year = {2020},
eprint = {arXiv:2007.15779},
}
```
<a href="https://huggingface.co/exbert/?model=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=3&heads=..0,1,2,3,4,5,6,7,8,9,10,11&threshold=0.7&tokenInd=17&tokenSide=right&maskInds=..&hideClsSep=true">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
harshith7/eagle-image-zhd | harshith7 | 2023-11-06T17:46:57Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T17:42:21Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### eagle-image-zhd Dreambooth model trained by harshith7 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MITS-58
Sample pictures of this concept:
|
Priynshu/my-pet-cat-alb | Priynshu | 2023-11-06T17:45:45Z | 2 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T17:41:24Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Cat-ALB Dreambooth model trained by Priynshu following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AJ-University-24
Sample pictures of this concept:
|
Priyanka4/pink-sunglasses-cak | Priyanka4 | 2023-11-06T17:45:45Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T17:41:35Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Pink-Sunglasses-CAK Dreambooth model trained by Priyanka4 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MRCEW-141
Sample pictures of this concept:
.jpg)
|
Matthijs99/bert-finetuned-ner | Matthijs99 | 2023-11-06T17:37:25Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-06T16:05:14Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3409
- Precision: 0.6045
- Recall: 0.4844
- F1: 0.5378
- Accuracy: 0.9279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 425 | 0.2905 | 0.5554 | 0.4079 | 0.4703 | 0.9218 |
| 0.183 | 2.0 | 850 | 0.3119 | 0.5746 | 0.4653 | 0.5142 | 0.9256 |
| 0.0701 | 3.0 | 1275 | 0.3409 | 0.6045 | 0.4844 | 0.5378 | 0.9279 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cpu
- Datasets 2.14.6
- Tokenizers 0.14.1
|
andre-coy/speecht5_tts_tandt | andre-coy | 2023-11-06T17:35:32Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"Trinidadian TTS",
"generated_from_trainer",
"en",
"dataset:MK_TandT",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2023-09-01T21:03:37Z | ---
language:
- en
license: mit
base_model: microsoft/speecht5_tts
tags:
- Trinidadian TTS
- generated_from_trainer
datasets:
- MK_TandT
model-index:
- name: SpeechT5_Trini
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_Trini
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the TandT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3751
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4358 | 4.78 | 1000 | 0.3881 |
| 0.4205 | 9.57 | 2000 | 0.3799 |
| 0.4029 | 14.35 | 3000 | 0.3749 |
| 0.4106 | 19.14 | 4000 | 0.3751 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
qmeeus/whisper-large-v2-lora-cgn | qmeeus | 2023-11-06T17:31:24Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-06T17:30:51Z | ---
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v2-lora-cgn
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-large-v2-lora-cgn
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 50
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3312 | 0.17 | 50 | 0.3258 |
| 0.3107 | 0.35 | 100 | 0.3097 |
| 0.2899 | 0.52 | 150 | 0.2879 |
| 0.2851 | 0.69 | 200 | 0.2725 |
| 0.2856 | 0.86 | 250 | 0.2623 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
LaTarn/ac-location-setfit-model | LaTarn | 2023-11-06T17:28:36Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
]
| text-classification | 2023-11-06T17:28:11Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ac-location-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ac-location-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
SandriBarros/clinical_longformer_same_tokens_cienmil | SandriBarros | 2023-11-06T17:25:44Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"longformer",
"fill-mask",
"generated_from_trainer",
"base_model:allenai/longformer-base-4096",
"base_model:finetune:allenai/longformer-base-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-11-05T23:42:34Z | ---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
model-index:
- name: clinical_longformer_same_tokens_cienmil
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. -->
# clinical_longformer_same_tokens_cienmil
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0099
## 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: 2
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 120
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8534 | 0.32 | 121 | 2.3812 |
| 2.7631 | 0.65 | 242 | 2.1835 |
| 2.595 | 0.97 | 363 | 2.0099 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
pradyumna0/layoutlmv3-finetuned-invoice | pradyumna0 | 2023-11-06T17:25:03Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:generated",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-03T10:28:31Z | ---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- generated
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: generated
type: generated
config: sroie
split: test
args: sroie
metrics:
- name: Precision
type: precision
value: 0.9959514170040485
- name: Recall
type: recall
value: 0.9979716024340771
- name: F1
type: f1
value: 0.9969604863221885
- name: Accuracy
type: accuracy
value: 0.9995786812723826
---
<!-- 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. -->
# layoutlmv3-finetuned-invoice
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0042
- Precision: 0.9960
- Recall: 0.9980
- F1: 0.9970
- Accuracy: 0.9996
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.0 | 100 | 0.1164 | 0.902 | 0.9148 | 0.9084 | 0.9897 |
| No log | 4.0 | 200 | 0.0262 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 6.0 | 300 | 0.0157 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 8.0 | 400 | 0.0097 | 0.9877 | 0.9797 | 0.9837 | 0.9979 |
| 0.1294 | 10.0 | 500 | 0.0065 | 0.9939 | 0.9959 | 0.9949 | 0.9994 |
| 0.1294 | 12.0 | 600 | 0.0042 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.1294 | 14.0 | 700 | 0.0048 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.1294 | 16.0 | 800 | 0.0047 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.1294 | 18.0 | 900 | 0.0045 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 20.0 | 1000 | 0.0042 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 22.0 | 1100 | 0.0041 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 24.0 | 1200 | 0.0041 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 26.0 | 1300 | 0.0040 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0051 | 28.0 | 1400 | 0.0040 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 30.0 | 1500 | 0.0040 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 32.0 | 1600 | 0.0040 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 34.0 | 1700 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 36.0 | 1800 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0024 | 38.0 | 1900 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0018 | 40.0 | 2000 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
joshuaoreilly/PixelCopter | joshuaoreilly | 2023-11-06T17:16:10Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-06T17:16:05Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 70.50 +/- 57.13
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
|
SteveMLC/uplimit-project-3-phi-1.5 | SteveMLC | 2023-11-06T17:10:09Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:scitldr",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"region:us"
]
| null | 2023-11-06T17:10:07Z | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
datasets:
- scitldr
model-index:
- name: uplimit-project-3-phi-1.5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# uplimit-project-3-phi-1.5
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5341
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5552 | 0.1 | 200 | 2.5967 |
| 2.5715 | 0.2 | 400 | 2.5896 |
| 2.5357 | 0.3 | 600 | 2.5785 |
| 2.5818 | 0.4 | 800 | 2.5674 |
| 2.5451 | 0.5 | 1000 | 2.5583 |
| 2.5754 | 0.6 | 1200 | 2.5543 |
| 2.5495 | 0.7 | 1400 | 2.5468 |
| 2.5289 | 0.8 | 1600 | 2.5397 |
| 2.5787 | 0.9 | 1800 | 2.5341 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
benjipeng/whisper-tiny-en | benjipeng | 2023-11-06T17:09:10Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-11-06T03:42:08Z | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3484767504008552
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-en
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7374
- Wer Ortho: 0.3488
- Wer: 0.3485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0001 | 17.86 | 500 | 0.6973 | 0.3460 | 0.3463 |
| 0.0 | 35.71 | 1000 | 0.7374 | 0.3488 | 0.3485 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|
arincon/deberta-v3-small-autextification-adapter | arincon | 2023-11-06T17:03:59Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:autextification2023",
"base_model:microsoft/deberta-v3-small",
"base_model:finetune:microsoft/deberta-v3-small",
"license:mit",
"region:us"
]
| null | 2023-11-06T16:27:16Z | ---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
datasets:
- autextification2023
metrics:
- accuracy
model-index:
- name: deberta-v3-small-autextification-adapter
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-small-autextification-adapter
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the autextification2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6941
- Accuracy: 0.4874
## 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_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6969 | 1.0 | 3808 | 0.6930 | 0.5034 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hrishioa/wasm-ANIMA-Phi-Neptune-Mistral-7B-q4f32_1 | hrishioa | 2023-11-06T16:58:32Z | 0 | 1 | null | [
"dataset:Severian/Biomimicry",
"dataset:emrgnt-cmplxty/sciphi-textbooks-are-all-you-need",
"dataset:fmars/wiki_stem",
"dataset:fblgit/tree-of-knowledge",
"dataset:Severian/Bio-Design-Process",
"base_model:Severian/ANIMA-Phi-Neptune-Mistral-7B",
"base_model:finetune:Severian/ANIMA-Phi-Neptune-Mistral-7B",
"license:artistic-2.0",
"region:us"
]
| null | 2023-11-06T16:55:12Z | ---
base_model: Severian/ANIMA-Phi-Neptune-Mistral-7B
license: artistic-2.0
datasets:
- Severian/Biomimicry
- emrgnt-cmplxty/sciphi-textbooks-are-all-you-need
- fmars/wiki_stem
- fblgit/tree-of-knowledge
- Severian/Bio-Design-Process
model-index:
- name: wasm-ANIMA-Phi-Neptune-Mistral-7B-q4f32_1
results: []
model_name: WASM ANIMA Phi Neptune Mistral 7B
model_type: mistral
prompt_template: '<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant'
---
# Dolphin 2.2.1 (Finetune of Mistral 7B) compiled for WebGPU - q4f32_1
- Original model: [ANIMA-Phi-Neptune-Mistral-7B: Biomimicry Enhanced LLM](https://huggingface.co/Severian/ANIMA-Phi-Neptune-Mistral-7B)
- creator: Severian: [https://github.com/Severian42](https://github.com/Severian42)
- compiled by: Hrishi Olickel: [say hi on Twitter!](https://twitter.com/hrishioa)
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6469c972a5dd10c9a49d683b/Uuc3t7grf2zw3jkV1phSt.mp4"></video>
## Description
This is a quantized version of Anima Phi Neptune Mistral 7B:
<img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/JZH6p50t_j3-OUph4Wq6y.png" width="500">
### Overview
**ANIMA** (Advanced Nature Inspired Multidisciplinary Assistant) is an expert in various scientific disciplines, including but not limited to biomimicry, biology, and environmental science.
**Instagram: [@anima_llm](https://www.instagram.com/anima_llm)**
---
### Model Description
ANIMA is fine-tuned on a rich dataset encompassing:
- 4,000+ Nature-Biomimicry examples
- 60k Biomimicry Design Process examples
- 600k STEM facts from Wikipedia
- Science/Philosophy focused 'All-You-Need-Is-Textbooks' dataset
- Additional Tree of Knowledge + Biomimicry data combined fine-tuning
The model aims to assist users in solving problems using nature-inspired strategies and concepts.
Compiled with [mlc-llm](https://llm.mlc.ai/).
Very helpful direction provided by [felladrin](https://github.com/felladrin)!
You can use [his example](https://huggingface.co/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca) to get quickly started with this model.
## Prompt template: Llama-2
Prompt format:
This model (and all my future releases) use [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format.
```
[INST] How can biomimicry help in water purification? [/INST]
```
|
TheBloke/Barcenas-Mistral-7B-GGUF | TheBloke | 2023-11-06T16:55:25Z | 140 | 3 | transformers | [
"transformers",
"gguf",
"mistral",
"en",
"es",
"dataset:Danielbrdz/Barcenas-lmsys-Dataset",
"base_model:Danielbrdz/Barcenas-Mistral-7b",
"base_model:quantized:Danielbrdz/Barcenas-Mistral-7b",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-06T16:51:09Z | ---
base_model: Danielbrdz/Barcenas-Mistral-7b
datasets:
- Danielbrdz/Barcenas-lmsys-Dataset
inference: false
language:
- en
- es
license: apache-2.0
model_creator: Daniel
model_name: Barcenas Mistral 7B
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<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 style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Barcenas Mistral 7B - GGUF
- Model creator: [Daniel](https://huggingface.co/Danielbrdz)
- Original model: [Barcenas Mistral 7B](https://huggingface.co/Danielbrdz/Barcenas-Mistral-7b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Daniel's Barcenas Mistral 7B](https://huggingface.co/Danielbrdz/Barcenas-Mistral-7b).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF)
* [Daniel's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Danielbrdz/Barcenas-Mistral-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation 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
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [barcenas-mistral-7b.Q2_K.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [barcenas-mistral-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [barcenas-mistral-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [barcenas-mistral-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [barcenas-mistral-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [barcenas-mistral-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [barcenas-mistral-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [barcenas-mistral-7b.Q5_0.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [barcenas-mistral-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [barcenas-mistral-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [barcenas-mistral-7b.Q6_K.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [barcenas-mistral-7b.Q8_0.gguf](https://huggingface.co/TheBloke/Barcenas-Mistral-7B-GGUF/blob/main/barcenas-mistral-7b.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**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.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Barcenas-Mistral-7B-GGUF and below it, a specific filename to download, such as: barcenas-mistral-7b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Barcenas-Mistral-7B-GGUF barcenas-mistral-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Barcenas-Mistral-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Barcenas-Mistral-7B-GGUF barcenas-mistral-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m barcenas-mistral-7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Barcenas-Mistral-7B-GGUF", model_file="barcenas-mistral-7b.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## 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!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Daniel's Barcenas Mistral 7B
Barcenas-Mistral-7b is a fine-tuning of teknium/CollectiveCognition-v1-Mistral-7B
It was trained with Spanish data from lmsys/lmsys-chat-1m
provided by Danielbrdz/Barcenas-lmsys-Dataset
Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽
<!-- original-model-card end -->
|
danielcfox/my_awesome_eli5_clm-model | danielcfox | 2023-11-06T16:46:32Z | 8 | 0 | transformers | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-11-06T16:29:26Z | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_keras_callback
model-index:
- name: danielcfox/my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# danielcfox/my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.7264
- Validation Loss: 3.7409
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.9064 | 3.7702 | 0 |
| 3.7877 | 3.7486 | 1 |
| 3.7264 | 3.7409 | 2 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Sagicc/faster-whisper-large-v2-sr | Sagicc | 2023-11-06T16:35:05Z | 2 | 0 | ctranslate2 | [
"ctranslate2",
"audio",
"automatic-speech-recognition",
"sr",
"license:mit",
"region:us"
]
| automatic-speech-recognition | 2023-11-06T16:17:40Z | ---
language:
- sr
tags:
- audio
- automatic-speech-recognition
license: mit
library_name: ctranslate2
---
# Whisper large-v2 Serbian fine-tunned model for CTranslate2
This repository contains the conversion of [Sagicc/whisper-large-v2-sr-combined](https://huggingface.co/Sagicc/whisper-large-v2-sr-combined) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper).
## Example
```python
from faster_whisper import WhisperModel
model = WhisperModel("Sagicc/faster-whisper-large-v2-sr")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Conversion details
The original model was converted with the following command:
```
ct2-transformers-converter --model openai/whisper-large-v2 --output_dir faster-whisper-large-v2 \
--copy_files tokenizer.json --quantization float16
```
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
## More information
**For more information about the fine-tunned model, see its [model card](https://huggingface.co/Sagicc/whisper-large-v2-sr-combined).** |
TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ | TheBloke | 2023-11-06T16:30:51Z | 27 | 3 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:OpenBuddy/openbuddy-zephyr-7b-v14.1",
"base_model:quantized:OpenBuddy/openbuddy-zephyr-7b-v14.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
]
| text-generation | 2023-11-06T16:03:08Z | ---
base_model: OpenBuddy/openbuddy-zephyr-7b-v14.1
inference: false
license: apache-2.0
model_creator: OpenBuddy
model_name: Openbuddy Zephyr 7B v14.1
model_type: mistral
prompt_template: "You are a helpful, respectful and honest INTP-T AI Assistant named\
\ Buddy. You are talking to a human User.\nAlways answer as helpfully and logically\
\ as possible, while being safe. Your answers should not include any harmful, political,\
\ religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please\
\ ensure that your responses are socially unbiased and positive in nature.\nIf a\
\ question does not make any sense, or is not factually coherent, explain why instead\
\ of answering something not correct. If you don't know the answer to a question,\
\ please don't share false information.\nYou like to use emojis. You can speak fluently\
\ in many languages, for example: English, Chinese.\nYou cannot access the internet,\
\ but you have vast knowledge, cutoff: 2021-09.\nYou are trained by OpenBuddy team,\
\ (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based\
\ on LLaMA and Falcon transformers model, not related to GPT or OpenAI.\n\nUser:\
\ {prompt}\nAssistant: \n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<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 style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Openbuddy Zephyr 7B v14.1 - GPTQ
- Model creator: [OpenBuddy](https://huggingface.co/OpenBuddy)
- Original model: [Openbuddy Zephyr 7B v14.1](https://huggingface.co/OpenBuddy/openbuddy-zephyr-7b-v14.1)
<!-- description start -->
## Description
This repo contains GPTQ model files for [OpenBuddy's Openbuddy Zephyr 7B v14.1](https://huggingface.co/OpenBuddy/openbuddy-zephyr-7b-v14.1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF)
* [OpenBuddy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenBuddy/openbuddy-zephyr-7b-v14.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: OpenBuddy
```
You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.23 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.64 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.59 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.75 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.24 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.37 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `openbuddy-zephyr-7B-v14.1-GPTQ`:
```shell
mkdir openbuddy-zephyr-7B-v14.1-GPTQ
huggingface-cli download TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ --local-dir openbuddy-zephyr-7B-v14.1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir openbuddy-zephyr-7B-v14.1-GPTQ
huggingface-cli download TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir openbuddy-zephyr-7B-v14.1-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir openbuddy-zephyr-7B-v14.1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ --local-dir openbuddy-zephyr-7B-v14.1-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `openbuddy-zephyr-7B-v14.1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## 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!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: OpenBuddy's Openbuddy Zephyr 7B v14.1
|
J1mb0o/bert-finetuned-batch16-lr5e-5 | J1mb0o | 2023-11-06T16:29:46Z | 4 | 0 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-06T16:24:39Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: J1mb0o/bert-finetuned-batch16-lr5e-5
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. -->
# J1mb0o/bert-finetuned-batch16-lr5e-5
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0429
- Validation Loss: 0.3836
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 852, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3190 | 0.5631 | 0 |
| 0.1617 | 0.3734 | 1 |
| 0.0789 | 0.3784 | 2 |
| 0.0429 | 0.3836 | 3 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Chaitanyasree/rabbit-xzg | Chaitanyasree | 2023-11-06T16:29:29Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T16:25:27Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Rabbit-xzg Dreambooth model trained by Chaitanyasree following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MITS-388
Sample pictures of this concept:
|
Bjqrn/Reinforce-Pixelcopter-PLE-v0 | Bjqrn | 2023-11-06T16:25:55Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-05T11:19:05Z | ---
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: 22.70 +/- 22.27
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
|
LaTarn/ac-garage-setfit-model | LaTarn | 2023-11-06T16:23:43Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
]
| text-classification | 2023-10-29T05:27:00Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ac-garage-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ac-garage-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
marialcasimiro/tatoeba-opus-2021-02-22-fra-eng | marialcasimiro | 2023-11-06T16:22:30Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"fr",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-11-06T11:39:05Z | ---
language:
- fr
- en
tags:
- translation
license: apache-2.0
---
### fra-eng
* source language name: French
* target language name: English
* OPUS readme: [README.md](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/README.md)
* model: transformer-align
* source language code: fr
* target language code: en
* dataset: opus
* release date: 2021-02-22
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2021-02-22.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/opus-2021-02-22.zip/fra-eng/opus-2021-02-22.zip)
* Training data:
* fra-eng: Tatoeba-train (180923857)
* Validation data:
* eng-fra: Tatoeba-dev, 250098
* total-size-shuffled: 249757
* devset-selected: top 5000 lines of Tatoeba-dev.src.shuffled!
* Test data:
* newsdiscussdev2015-enfr.fra-eng: 1500/27759
* newsdiscusstest2015-enfr.fra-eng: 1500/26995
* newssyscomb2009.fra-eng: 502/11821
* news-test2008.fra-eng: 2051/49380
* newstest2009.fra-eng: 2525/65402
* newstest2010.fra-eng: 2489/61724
* newstest2011.fra-eng: 3003/74681
* newstest2012.fra-eng: 3003/72812
* newstest2013.fra-eng: 3000/64505
* newstest2014-fren.fra-eng: 3003/70708
* Tatoeba-test.fra-eng: 10000/77174
* test set translations file: [test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/opus-2021-02-22.zip/fra-eng/opus-2021-02-22.test.txt)
* test set scores file: [eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/opus-2021-02-22.zip/fra-eng/opus-2021-02-22.eval.txt)
* BLEU-scores
|Test set|score|
|---|---|
|Tatoeba-test.fra-eng|57.8|
|newsdiscusstest2015-enfr.fra-eng|39.7|
|newstest2014-fren.fra-eng|38.4|
|newsdiscussdev2015-enfr.fra-eng|34.4|
|newstest2013.fra-eng|34.0|
|newstest2012.fra-eng|33.2|
|newstest2011.fra-eng|33.1|
|newstest2010.fra-eng|32.7|
|newssyscomb2009.fra-eng|31.1|
|newstest2009.fra-eng|30.5|
|news-test2008.fra-eng|26.5|
* chr-F-scores
|Test set|score|
|---|---|
|Tatoeba-test.fra-eng|0.723|
|newstest2014-fren.fra-eng|0.636|
|newsdiscusstest2015-enfr.fra-eng|0.621|
|newstest2011.fra-eng|0.598|
|newstest2010.fra-eng|0.593|
|newstest2012.fra-eng|0.593|
|newstest2013.fra-eng|0.592|
|newsdiscussdev2015-enfr.fra-eng|0.587|
|newssyscomb2009.fra-eng|0.575|
|newstest2009.fra-eng|0.572|
|news-test2008.fra-eng|0.544|
### System Info:
* hf_name: fra-eng
* source_languages: fr
* target_languages: en
* opus_readme_url: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-eng/opus-2021-02-22.zip/README.md
* original_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['fr', 'en']
* src_constituents: ['fra']
* tgt_constituents: ['eng']
* src_multilingual: False
* tgt_multilingual: False
* helsinki_git_sha: 6faf2dab0b7b01a0e08a114dbacbb7deac54988d
* transformers_git_sha: e9a6c72b5edfb9561a981959b0e7c62d8ab9ef6c
* port_machine: 146-193-182-187.edr.inesc.pt
* port_time: 2023-11-06-16:20 |
Nagase-Kotono/RemonXLlama2-KO-13B | Nagase-Kotono | 2023-11-06T16:10:25Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-30T14:11:42Z | ---
license: llama2
---
# RemonXLlama2-KO-13B
**GenAI-llama2-ko-en-platypus-13B X Remon**
## Model Details
- **Model Developers:** ***Nagase_Kotono***
- **Input Models:** ***Input text only.***
- **Output Models:** ***Generate text only.***
- **Base Model:** ***[42MARU/GenAI-llama2-ko-en-platypus-13B](https://huggingface.co/42MARU/GenAI-llama2-ko-en-platypus-13B)***
- **Model Architecture:** ***RemonXLlama2-KO-13B is an auto-regressive language model, based on the Llama2 transformer architecture.***
- **Training Dataset:** ***[remon_without_nsfw](https://huggingface.co/datasets/junelee/remon_without_nsfw)***
## Hardware & Software
### Finetune
- **GPU:** ***NVIDIA A100 X 1***
- ***Used [Transformers](https://github.com/huggingface/transformers)***
## Prompt Template
```prompt
### User:
{User}
### Assistant:
{Assistant}
``` |
koszticzam/q-FrozenLake-v1-4x4-noSlippery | koszticzam | 2023-11-06T16:10:12Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-06T16:10:10Z | ---
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="koszticzam/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"])
```
|
bartowski/Naberius-7B-exl2 | bartowski | 2023-11-06T16:08:41Z | 3 | 0 | null | [
"llama",
"uncensored",
"merge",
"mix",
"slerp",
"spherical linear interpolation merge",
"mistral",
"hermes",
"openhermes",
"dolphin",
"zephyr",
"naberius",
"7b",
"llama2",
"en",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-06T03:02:55Z | ---
tags:
- llama
- uncensored
- merge
- mix
- slerp
- spherical linear interpolation merge
- mistral
- hermes
- openhermes
- dolphin
- zephyr
- naberius
- 7b
- llama2
license: apache-2.0
language:
- en
quantized_by: bartowski
---
## Exllama v2 Quantizations of Naberius-7B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.7">turboderp's ExLlamaV2 v0.0.7</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.
Original model: https://huggingface.co/CalderaAI/Naberius-7B
<a href="https://huggingface.co/bartowski/Naberius-7B-exl2/tree/4.0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Naberius-7B-exl2/tree/6.0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Naberius-7B-exl2/tree/8.0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/Naberius-7B-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Naberius-7B-exl2`:
```shell
mkdir Naberius-7B-exl2
huggingface-cli download bartowski/Naberius-7B-exl2 --local-dir Naberius-7B-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Naberius-7B-exl2
huggingface-cli download bartowski/Naberius-7B-exl2 --revision 4.0 --local-dir Naberius-7B-exl2 --local-dir-use-symlinks False
```
|
luca-g97/mit-b0-finetuned-sidewalks | luca-g97 | 2023-11-06T16:06:13Z | 5 | 0 | transformers | [
"transformers",
"tf",
"segformer",
"generated_from_keras_callback",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2023-11-06T11:51:38Z | ---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_keras_callback
model-index:
- name: luca-g97/mit-b0-finetuned-sidewalks
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. -->
# luca-g97/mit-b0-finetuned-sidewalks
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1941
- Validation Loss: 0.5077
- Validation Mean Iou: 0.3541
- Validation Mean Accuracy: 0.4368
- Validation Overall Accuracy: 0.8760
- Validation Accuracy Unlabeled: 0.0
- Validation Accuracy Flat-road: 0.8810
- Validation Accuracy Flat-sidewalk: 0.9600
- Validation Accuracy Flat-crosswalk: 0.5080
- Validation Accuracy Flat-cyclinglane: 0.7956
- Validation Accuracy Flat-parkingdriveway: 0.4939
- Validation Accuracy Flat-railtrack: nan
- Validation Accuracy Flat-curb: 0.5978
- Validation Accuracy Human-person: 0.7554
- Validation Accuracy Human-rider: 0.1367
- Validation Accuracy Vehicle-car: 0.9179
- Validation Accuracy Vehicle-truck: 0.0599
- Validation Accuracy Vehicle-bus: nan
- Validation Accuracy Vehicle-tramtrain: nan
- Validation Accuracy Vehicle-motorcycle: 0.0
- Validation Accuracy Vehicle-bicycle: 0.7221
- Validation Accuracy Vehicle-caravan: 0.0
- Validation Accuracy Vehicle-cartrailer: 0.0
- Validation Accuracy Construction-building: 0.8944
- Validation Accuracy Construction-door: 0.1175
- Validation Accuracy Construction-wall: 0.6129
- Validation Accuracy Construction-fenceguardrail: 0.4032
- Validation Accuracy Construction-bridge: 0.0
- Validation Accuracy Construction-tunnel: nan
- Validation Accuracy Construction-stairs: 0.2636
- Validation Accuracy Object-pole: 0.4817
- Validation Accuracy Object-trafficsign: 0.3849
- Validation Accuracy Object-trafficlight: 0.0
- Validation Accuracy Nature-vegetation: 0.9453
- Validation Accuracy Nature-terrain: 0.9461
- Validation Accuracy Sky: 0.9760
- Validation Accuracy Void-ground: 0.0649
- Validation Accuracy Void-dynamic: 0.1096
- Validation Accuracy Void-static: 0.5128
- Validation Accuracy Void-unclear: 0.0
- Validation Iou Unlabeled: 0.0
- Validation Iou Flat-road: 0.7899
- Validation Iou Flat-sidewalk: 0.8798
- Validation Iou Flat-crosswalk: 0.3854
- Validation Iou Flat-cyclinglane: 0.7444
- Validation Iou Flat-parkingdriveway: 0.3950
- Validation Iou Flat-railtrack: nan
- Validation Iou Flat-curb: 0.5344
- Validation Iou Human-person: 0.4893
- Validation Iou Human-rider: 0.1164
- Validation Iou Vehicle-car: 0.7795
- Validation Iou Vehicle-truck: 0.0515
- Validation Iou Vehicle-bus: nan
- Validation Iou Vehicle-tramtrain: 0.0
- Validation Iou Vehicle-motorcycle: 0.0
- Validation Iou Vehicle-bicycle: 0.5232
- Validation Iou Vehicle-caravan: 0.0
- Validation Iou Vehicle-cartrailer: 0.0
- Validation Iou Construction-building: 0.7498
- Validation Iou Construction-door: 0.1094
- Validation Iou Construction-wall: 0.4920
- Validation Iou Construction-fenceguardrail: 0.3325
- Validation Iou Construction-bridge: 0.0
- Validation Iou Construction-tunnel: nan
- Validation Iou Construction-stairs: 0.2083
- Validation Iou Object-pole: 0.3678
- Validation Iou Object-trafficsign: 0.2519
- Validation Iou Object-trafficlight: 0.0
- Validation Iou Nature-vegetation: 0.8789
- Validation Iou Nature-terrain: 0.8304
- Validation Iou Sky: 0.9409
- Validation Iou Void-ground: 0.0447
- Validation Iou Void-dynamic: 0.0765
- Validation Iou Void-static: 0.3601
- Validation Iou Void-unclear: 0.0
- Epoch: 30
## 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': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Unlabeled | Validation Accuracy Flat-road | Validation Accuracy Flat-sidewalk | Validation Accuracy Flat-crosswalk | Validation Accuracy Flat-cyclinglane | Validation Accuracy Flat-parkingdriveway | Validation Accuracy Flat-railtrack | Validation Accuracy Flat-curb | Validation Accuracy Human-person | Validation Accuracy Human-rider | Validation Accuracy Vehicle-car | Validation Accuracy Vehicle-truck | Validation Accuracy Vehicle-bus | Validation Accuracy Vehicle-tramtrain | Validation Accuracy Vehicle-motorcycle | Validation Accuracy Vehicle-bicycle | Validation Accuracy Vehicle-caravan | Validation Accuracy Vehicle-cartrailer | Validation Accuracy Construction-building | Validation Accuracy Construction-door | Validation Accuracy Construction-wall | Validation Accuracy Construction-fenceguardrail | Validation Accuracy Construction-bridge | Validation Accuracy Construction-tunnel | Validation Accuracy Construction-stairs | Validation Accuracy Object-pole | Validation Accuracy Object-trafficsign | Validation Accuracy Object-trafficlight | Validation Accuracy Nature-vegetation | Validation Accuracy Nature-terrain | Validation Accuracy Sky | Validation Accuracy Void-ground | Validation Accuracy Void-dynamic | Validation Accuracy Void-static | Validation Accuracy Void-unclear | Validation Iou Unlabeled | Validation Iou Flat-road | Validation Iou Flat-sidewalk | Validation Iou Flat-crosswalk | Validation Iou Flat-cyclinglane | Validation Iou Flat-parkingdriveway | Validation Iou Flat-railtrack | Validation Iou Flat-curb | Validation Iou Human-person | Validation Iou Human-rider | Validation Iou Vehicle-car | Validation Iou Vehicle-truck | Validation Iou Vehicle-bus | Validation Iou Vehicle-tramtrain | Validation Iou Vehicle-motorcycle | Validation Iou Vehicle-bicycle | Validation Iou Vehicle-caravan | Validation Iou Vehicle-cartrailer | Validation Iou Construction-building | Validation Iou Construction-door | Validation Iou Construction-wall | Validation Iou Construction-fenceguardrail | Validation Iou Construction-bridge | Validation Iou Construction-tunnel | Validation Iou Construction-stairs | Validation Iou Object-pole | Validation Iou Object-trafficsign | Validation Iou Object-trafficlight | Validation Iou Nature-vegetation | Validation Iou Nature-terrain | Validation Iou Sky | Validation Iou Void-ground | Validation Iou Void-dynamic | Validation Iou Void-static | Validation Iou Void-unclear | Epoch |
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| 1.3678 | 0.8166 | 0.1980 | 0.2461 | 0.7806 | 0.0 | 0.7198 | 0.9333 | 0.1239 | 0.6224 | 0.2909 | nan | 0.2044 | 0.0227 | 0.0 | 0.8980 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8838 | 0.0 | 0.0930 | 0.0426 | 0.0 | nan | 0.0 | 0.0725 | 0.0 | 0.0 | 0.9093 | 0.8657 | 0.9392 | 0.0 | 0.0 | 0.0062 | 0.0 | 0.0 | 0.5656 | 0.7927 | 0.1207 | 0.5722 | 0.1948 | nan | 0.1673 | 0.0225 | 0.0 | 0.5772 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5809 | 0.0 | 0.0864 | 0.0425 | 0.0 | nan | 0.0 | 0.0692 | 0.0 | 0.0 | 0.7793 | 0.6938 | 0.8673 | 0.0 | 0.0 | 0.0061 | 0.0 | 0 |
| 0.8157 | 0.6782 | 0.2459 | 0.3022 | 0.8185 | 0.0 | 0.8366 | 0.9295 | 0.4668 | 0.7053 | 0.2766 | nan | 0.2969 | 0.5333 | 0.0 | 0.8409 | 0.0 | nan | nan | 0.0 | 0.0069 | 0.0 | 0.0 | 0.9093 | 0.0 | 0.3089 | 0.2250 | 0.0 | nan | 0.0 | 0.2037 | 0.0 | 0.0 | 0.9486 | 0.8869 | 0.9594 | 0.0 | 0.0 | 0.0323 | 0.0 | 0.0 | 0.6544 | 0.8362 | 0.3660 | 0.6237 | 0.2080 | nan | 0.2318 | 0.2292 | 0.0 | 0.6802 | 0.0 | nan | nan | 0.0 | 0.0069 | 0.0 | 0.0 | 0.6389 | 0.0 | 0.2751 | 0.2078 | 0.0 | nan | 0.0 | 0.1733 | 0.0 | 0.0 | 0.8125 | 0.7673 | 0.8818 | 0.0 | 0.0 | 0.0310 | 0.0 | 1 |
| 0.6971 | 0.6359 | 0.2706 | 0.3314 | 0.8186 | 0.0 | 0.8099 | 0.9316 | 0.4949 | 0.6689 | 0.2490 | nan | 0.3254 | 0.5711 | 0.0 | 0.8972 | 0.0 | nan | nan | 0.0 | 0.3873 | 0.0 | 0.0 | 0.9079 | 0.0 | 0.5179 | 0.3214 | 0.0 | nan | 0.0 | 0.3090 | 0.0 | 0.0 | 0.9340 | 0.8005 | 0.9599 | 0.0 | 0.0140 | 0.1744 | 0.0 | 0.0 | 0.6485 | 0.8294 | 0.2456 | 0.6194 | 0.1932 | nan | 0.2772 | 0.3387 | 0.0 | 0.6913 | 0.0 | nan | nan | 0.0 | 0.3516 | 0.0 | 0.0 | 0.6685 | 0.0 | 0.4051 | 0.2726 | 0.0 | nan | 0.0 | 0.2356 | 0.0 | 0.0 | 0.8189 | 0.7404 | 0.8881 | 0.0 | 0.0131 | 0.1515 | 0.0 | 2 |
| 0.6190 | 0.5661 | 0.2928 | 0.3492 | 0.8410 | 0.0 | 0.8018 | 0.9605 | 0.4340 | 0.7528 | 0.2925 | nan | 0.3419 | 0.6177 | 0.0 | 0.8887 | 0.0 | nan | nan | 0.0 | 0.5688 | 0.0 | 0.0 | 0.9146 | 0.0 | 0.5262 | 0.3174 | 0.0 | nan | 0.0 | 0.3713 | 0.0 | 0.0 | 0.9455 | 0.8983 | 0.9440 | 0.0 | 0.0473 | 0.2025 | 0.0 | 0.0 | 0.6959 | 0.8428 | 0.4016 | 0.6376 | 0.2351 | nan | 0.3048 | 0.3808 | 0.0 | 0.7238 | 0.0 | nan | nan | 0.0 | 0.4529 | 0.0 | 0.0 | 0.6973 | 0.0 | 0.4300 | 0.2747 | 0.0 | nan | 0.0 | 0.2645 | 0.0 | 0.0 | 0.8487 | 0.7814 | 0.9004 | 0.0 | 0.0317 | 0.1718 | 0.0 | 3 |
| 0.5546 | 0.5378 | 0.3005 | 0.3645 | 0.8455 | 0.0 | 0.8288 | 0.9414 | 0.4559 | 0.7588 | 0.4077 | nan | 0.4985 | 0.5859 | 0.0 | 0.9055 | 0.0 | nan | nan | 0.0 | 0.5452 | 0.0 | 0.0 | 0.9012 | 0.0 | 0.5211 | 0.3851 | 0.0 | nan | 0.0 | 0.3665 | 0.0 | 0.0 | 0.9333 | 0.9072 | 0.9626 | 0.0004 | 0.0532 | 0.3407 | 0.0 | 0.0 | 0.7155 | 0.8525 | 0.3475 | 0.6677 | 0.3046 | nan | 0.3939 | 0.3489 | 0.0 | 0.7226 | 0.0 | nan | nan | 0.0 | 0.4275 | 0.0 | 0.0 | 0.6930 | 0.0 | 0.4213 | 0.2958 | 0.0 | nan | 0.0 | 0.2748 | 0.0 | 0.0 | 0.8559 | 0.7770 | 0.9057 | 0.0003 | 0.0454 | 0.2655 | 0.0 | 4 |
| 0.5012 | 0.5478 | 0.3005 | 0.3737 | 0.8410 | 0.0 | 0.7172 | 0.9665 | 0.4663 | 0.8264 | 0.3314 | nan | 0.4364 | 0.7392 | 0.0 | 0.8793 | 0.0 | nan | nan | 0.0 | 0.6710 | 0.0 | 0.0 | 0.8810 | 0.0040 | 0.5212 | 0.4739 | 0.0 | nan | 0.0 | 0.4279 | 0.0 | 0.0 | 0.9229 | 0.9260 | 0.9578 | 0.0 | 0.1377 | 0.2975 | 0.0 | 0.0 | 0.6691 | 0.8400 | 0.3682 | 0.6677 | 0.2650 | nan | 0.3581 | 0.3654 | 0.0 | 0.7473 | 0.0 | nan | nan | 0.0 | 0.4533 | 0.0 | 0.0 | 0.7095 | 0.0040 | 0.3844 | 0.3315 | 0.0 | nan | 0.0 | 0.2911 | 0.0 | 0.0 | 0.8621 | 0.8017 | 0.9127 | 0.0 | 0.0533 | 0.2311 | 0.0 | 5 |
| 0.4696 | 0.5304 | 0.3069 | 0.3761 | 0.8494 | 0.0 | 0.8421 | 0.9436 | 0.4656 | 0.7385 | 0.4812 | nan | 0.5467 | 0.7282 | 0.0 | 0.9217 | 0.0 | nan | nan | 0.0 | 0.5838 | 0.0 | 0.0 | 0.8754 | 0.0164 | 0.5180 | 0.4119 | 0.0 | nan | 0.0 | 0.3841 | 0.0049 | 0.0 | 0.9503 | 0.8761 | 0.9670 | 0.0 | 0.1233 | 0.2799 | 0.0 | 0.0 | 0.7387 | 0.8537 | 0.3492 | 0.6961 | 0.3412 | nan | 0.4438 | 0.3894 | 0.0 | 0.7149 | 0.0 | nan | nan | 0.0 | 0.4264 | 0.0 | 0.0 | 0.7074 | 0.0163 | 0.3938 | 0.2934 | 0.0 | nan | 0.0 | 0.2882 | 0.0049 | 0.0 | 0.8522 | 0.7903 | 0.9116 | 0.0 | 0.0756 | 0.2273 | 0.0 | 6 |
| 0.4253 | 0.5283 | 0.3140 | 0.3804 | 0.8512 | 0.0 | 0.8989 | 0.9158 | 0.4850 | 0.7735 | 0.5623 | nan | 0.4545 | 0.6829 | 0.0 | 0.9019 | 0.0 | nan | nan | 0.0 | 0.5551 | 0.0 | 0.0 | 0.9127 | 0.0041 | 0.5504 | 0.3501 | 0.0 | nan | 0.1083 | 0.4291 | 0.0133 | 0.0 | 0.9489 | 0.9091 | 0.9659 | 0.0 | 0.0944 | 0.2753 | 0.0 | 0.0 | 0.7192 | 0.8621 | 0.4080 | 0.7179 | 0.3419 | nan | 0.3818 | 0.4004 | 0.0 | 0.7424 | 0.0 | nan | nan | 0.0 | 0.4431 | 0.0 | 0.0 | 0.7140 | 0.0040 | 0.4269 | 0.2753 | 0.0 | nan | 0.1041 | 0.3078 | 0.0132 | 0.0 | 0.8562 | 0.7841 | 0.9159 | 0.0 | 0.0738 | 0.2402 | 0.0 | 7 |
| 0.4050 | 0.5155 | 0.3203 | 0.3976 | 0.8550 | 0.0 | 0.8329 | 0.9366 | 0.4963 | 0.8614 | 0.4826 | nan | 0.5578 | 0.7241 | 0.0 | 0.8664 | 0.0 | nan | nan | 0.0 | 0.6993 | 0.0 | 0.0 | 0.8821 | 0.0810 | 0.5857 | 0.4431 | 0.0 | nan | 0.0655 | 0.4235 | 0.1598 | 0.0 | 0.9457 | 0.9156 | 0.9722 | 0.0 | 0.0985 | 0.2959 | 0.0 | 0.0 | 0.7187 | 0.8706 | 0.3583 | 0.7082 | 0.3354 | nan | 0.4595 | 0.3835 | 0.0 | 0.7495 | 0.0 | nan | nan | 0.0 | 0.4427 | 0.0 | 0.0 | 0.7288 | 0.0760 | 0.4159 | 0.3283 | 0.0 | nan | 0.0641 | 0.2988 | 0.1106 | 0.0 | 0.8617 | 0.8017 | 0.9180 | 0.0 | 0.0547 | 0.2441 | 0.0 | 8 |
| 0.3736 | 0.5247 | 0.3238 | 0.3898 | 0.8552 | 0.0 | 0.8784 | 0.9346 | 0.4956 | 0.7814 | 0.4293 | nan | 0.5294 | 0.7039 | 0.0 | 0.9317 | 0.0 | nan | nan | 0.0 | 0.5909 | 0.0 | 0.0 | 0.8907 | 0.0561 | 0.5289 | 0.3452 | 0.0 | nan | 0.1251 | 0.3892 | 0.1104 | 0.0 | 0.9318 | 0.9387 | 0.9699 | 0.0028 | 0.0957 | 0.4239 | 0.0 | 0.0 | 0.7247 | 0.8604 | 0.3760 | 0.7168 | 0.3123 | nan | 0.4448 | 0.4529 | 0.0 | 0.7461 | 0.0 | nan | nan | 0.0 | 0.4482 | 0.0 | 0.0 | 0.7247 | 0.0535 | 0.4300 | 0.2811 | 0.0 | nan | 0.1229 | 0.3080 | 0.0869 | 0.0 | 0.8635 | 0.8011 | 0.9229 | 0.0025 | 0.0510 | 0.3076 | 0.0 | 9 |
| 0.3660 | 0.5736 | 0.3205 | 0.3821 | 0.8480 | 0.0 | 0.7531 | 0.9718 | 0.4589 | 0.6779 | 0.3548 | nan | 0.5450 | 0.7285 | 0.0 | 0.9037 | 0.0 | nan | nan | 0.0 | 0.5942 | 0.0 | 0.0 | 0.9021 | 0.0158 | 0.5726 | 0.3303 | 0.0 | nan | 0.1523 | 0.4334 | 0.1331 | 0.0 | 0.9518 | 0.9186 | 0.9753 | 0.0083 | 0.1894 | 0.2739 | 0.0 | 0.0 | 0.6874 | 0.8352 | 0.3769 | 0.6518 | 0.2890 | nan | 0.4676 | 0.4524 | 0.0 | 0.7543 | 0.0 | nan | nan | 0.0 | 0.4748 | 0.0 | 0.0 | 0.7261 | 0.0153 | 0.4510 | 0.2708 | 0.0 | nan | 0.1462 | 0.3191 | 0.1197 | 0.0 | 0.8651 | 0.7985 | 0.9259 | 0.0079 | 0.0753 | 0.2257 | 0.0 | 10 |
| 0.3392 | 0.4919 | 0.3375 | 0.4135 | 0.8621 | 0.0 | 0.8390 | 0.9461 | 0.4833 | 0.8450 | 0.5165 | nan | 0.5727 | 0.7460 | 0.0002 | 0.8947 | 0.0003 | nan | nan | 0.0 | 0.7287 | 0.0 | 0.0 | 0.9005 | 0.0569 | 0.5783 | 0.4042 | 0.0 | nan | 0.1868 | 0.4545 | 0.1994 | 0.0 | 0.9291 | 0.9408 | 0.9775 | 0.1256 | 0.1342 | 0.3576 | 0.0 | 0.0 | 0.7353 | 0.8747 | 0.3814 | 0.7270 | 0.3691 | nan | 0.4786 | 0.4109 | 0.0002 | 0.7686 | 0.0003 | nan | nan | 0.0 | 0.4835 | 0.0 | 0.0 | 0.7306 | 0.0527 | 0.4655 | 0.2977 | 0.0 | nan | 0.1652 | 0.3295 | 0.1699 | 0.0 | 0.8718 | 0.8125 | 0.9248 | 0.0877 | 0.0535 | 0.2729 | 0.0 | 11 |
| 0.3274 | 0.5153 | 0.3302 | 0.4071 | 0.8585 | 0.0 | 0.9093 | 0.9274 | 0.4820 | 0.8091 | 0.4834 | nan | 0.4533 | 0.7872 | 0.0107 | 0.9117 | 0.0001 | nan | nan | 0.0 | 0.6741 | 0.0 | 0.0 | 0.8985 | 0.0589 | 0.5968 | 0.4355 | 0.0 | nan | 0.1966 | 0.4348 | 0.2332 | 0.0 | 0.9394 | 0.9349 | 0.9712 | 0.0237 | 0.1825 | 0.2670 | 0.0 | 0.0 | 0.7495 | 0.8631 | 0.3891 | 0.7515 | 0.3469 | nan | 0.4007 | 0.3865 | 0.0095 | 0.7635 | 0.0001 | nan | nan | 0.0 | 0.4943 | 0.0 | 0.0 | 0.7370 | 0.0576 | 0.4389 | 0.3287 | 0.0 | nan | 0.1678 | 0.3169 | 0.1431 | 0.0 | 0.8631 | 0.7915 | 0.9288 | 0.0172 | 0.0675 | 0.2234 | 0.0 | 12 |
| 0.3221 | 0.5148 | 0.3329 | 0.4010 | 0.8608 | 0.0 | 0.8625 | 0.9491 | 0.3924 | 0.8131 | 0.4245 | nan | 0.5415 | 0.7432 | 0.0113 | 0.9079 | 0.0002 | nan | nan | 0.0 | 0.6333 | 0.0 | 0.0 | 0.9290 | 0.0873 | 0.5861 | 0.4376 | 0.0 | nan | 0.1606 | 0.4185 | 0.2101 | 0.0 | 0.9395 | 0.8990 | 0.9714 | 0.0654 | 0.1436 | 0.3051 | 0.0 | 0.0 | 0.7564 | 0.8669 | 0.3249 | 0.7026 | 0.3299 | nan | 0.4655 | 0.4331 | 0.0099 | 0.7630 | 0.0002 | nan | nan | 0.0 | 0.4753 | 0.0 | 0.0 | 0.7191 | 0.0809 | 0.4667 | 0.3315 | 0.0 | nan | 0.1472 | 0.3229 | 0.1520 | 0.0 | 0.8683 | 0.8078 | 0.9306 | 0.0497 | 0.0651 | 0.2508 | 0.0 | 13 |
| 0.3056 | 0.5403 | 0.3368 | 0.4091 | 0.8557 | 0.0 | 0.8120 | 0.9533 | 0.4851 | 0.7418 | 0.3948 | nan | 0.6013 | 0.7937 | 0.0060 | 0.9137 | 0.0023 | nan | nan | 0.0 | 0.6186 | 0.0 | 0.0 | 0.8791 | 0.0825 | 0.6385 | 0.3862 | 0.0 | nan | 0.2158 | 0.3970 | 0.2625 | 0.0 | 0.9463 | 0.9225 | 0.9755 | 0.0393 | 0.1919 | 0.4232 | 0.0 | 0.0 | 0.7225 | 0.8512 | 0.3795 | 0.6770 | 0.2905 | nan | 0.5000 | 0.4606 | 0.0052 | 0.7784 | 0.0021 | nan | nan | 0.0 | 0.4949 | 0.0 | 0.0 | 0.7344 | 0.0757 | 0.4592 | 0.3062 | 0.0 | nan | 0.1658 | 0.3130 | 0.1830 | 0.0 | 0.8707 | 0.8090 | 0.9284 | 0.0368 | 0.0813 | 0.3143 | 0.0 | 14 |
| 0.2898 | 0.4892 | 0.3480 | 0.4206 | 0.8694 | 0.0 | 0.8454 | 0.9589 | 0.4744 | 0.8461 | 0.4562 | nan | 0.5836 | 0.7364 | 0.1053 | 0.9102 | 0.0031 | nan | nan | 0.0 | 0.7415 | 0.0 | 0.0 | 0.9016 | 0.0974 | 0.6077 | 0.4400 | 0.0 | nan | 0.2158 | 0.4142 | 0.2735 | 0.0 | 0.9474 | 0.9256 | 0.9753 | 0.0861 | 0.1183 | 0.3736 | 0.0 | 0.0 | 0.7670 | 0.8740 | 0.3932 | 0.7655 | 0.3413 | nan | 0.5069 | 0.4278 | 0.0736 | 0.7823 | 0.0028 | nan | nan | 0.0 | 0.5135 | 0.0 | 0.0 | 0.7385 | 0.0884 | 0.4712 | 0.3544 | 0.0 | nan | 0.1490 | 0.3234 | 0.1806 | 0.0 | 0.8745 | 0.8187 | 0.9333 | 0.0641 | 0.0599 | 0.2825 | 0.0 | 15 |
| 0.2757 | 0.5107 | 0.3440 | 0.4124 | 0.8669 | 0.0 | 0.8181 | 0.9672 | 0.4493 | 0.8219 | 0.5424 | nan | 0.5567 | 0.7384 | 0.0114 | 0.9238 | 0.0039 | nan | nan | 0.0 | 0.6754 | 0.0 | 0.0 | 0.8965 | 0.0565 | 0.6267 | 0.3753 | 0.0 | nan | 0.2636 | 0.4058 | 0.2556 | 0.0 | 0.9293 | 0.9475 | 0.9743 | 0.0256 | 0.1501 | 0.3691 | 0.0 | 0.0 | 0.7619 | 0.8663 | 0.3868 | 0.7556 | 0.4069 | nan | 0.4870 | 0.4423 | 0.0104 | 0.7641 | 0.0036 | nan | nan | 0.0 | 0.4962 | 0.0 | 0.0 | 0.7423 | 0.0527 | 0.4982 | 0.3127 | 0.0 | nan | 0.1913 | 0.3226 | 0.1862 | 0.0 | 0.8639 | 0.7947 | 0.9350 | 0.0189 | 0.0757 | 0.2877 | 0.0 | 16 |
| 0.2541 | 0.5001 | 0.3465 | 0.4193 | 0.8675 | 0.0 | 0.8292 | 0.9581 | 0.4912 | 0.8537 | 0.4359 | nan | 0.6247 | 0.7447 | 0.0201 | 0.9260 | 0.0041 | nan | nan | 0.0 | 0.7137 | 0.0 | 0.0 | 0.9015 | 0.1173 | 0.5703 | 0.3468 | 0.0 | nan | 0.2524 | 0.4547 | 0.3021 | 0.0 | 0.9471 | 0.9186 | 0.9755 | 0.0396 | 0.1592 | 0.4123 | 0.0 | 0.0 | 0.7461 | 0.8762 | 0.4013 | 0.7225 | 0.3544 | nan | 0.5335 | 0.4415 | 0.0177 | 0.7683 | 0.0036 | nan | nan | 0.0 | 0.5075 | 0.0 | 0.0 | 0.7306 | 0.1099 | 0.4737 | 0.2855 | 0.0 | nan | 0.1933 | 0.3426 | 0.1773 | 0.0 | 0.8734 | 0.8156 | 0.9330 | 0.0257 | 0.0870 | 0.3216 | 0.0 | 17 |
| 0.2666 | 0.5042 | 0.3362 | 0.4197 | 0.8673 | 0.0 | 0.8441 | 0.9558 | 0.4772 | 0.8006 | 0.5152 | nan | 0.5665 | 0.7760 | 0.0768 | 0.9112 | 0.0066 | nan | nan | 0.0 | 0.7037 | 0.0 | 0.0 | 0.9012 | 0.1006 | 0.5956 | 0.3980 | 0.0 | nan | 0.2635 | 0.4163 | 0.3349 | 0.0 | 0.9520 | 0.9046 | 0.9791 | 0.0175 | 0.0496 | 0.4629 | 0.0 | 0.0 | 0.7501 | 0.8680 | 0.4113 | 0.7448 | 0.3768 | nan | 0.4808 | 0.4313 | 0.0608 | 0.7736 | 0.0057 | nan | 0.0 | 0.0 | 0.4943 | 0.0 | 0.0 | 0.7462 | 0.0930 | 0.4800 | 0.3276 | 0.0 | nan | 0.1969 | 0.3318 | 0.1824 | 0.0 | 0.8704 | 0.8125 | 0.9333 | 0.0156 | 0.0276 | 0.3427 | 0.0 | 18 |
| 0.2504 | 0.5142 | 0.3484 | 0.4145 | 0.8714 | 0.0 | 0.8371 | 0.9674 | 0.4828 | 0.8387 | 0.4438 | nan | 0.5916 | 0.7755 | 0.0336 | 0.9098 | 0.0039 | nan | nan | 0.0 | 0.6774 | 0.0 | 0.0 | 0.9074 | 0.0566 | 0.5599 | 0.3571 | 0.0 | nan | 0.2387 | 0.4355 | 0.3049 | 0.0 | 0.9527 | 0.9165 | 0.9758 | 0.0307 | 0.0807 | 0.4703 | 0.0 | 0.0 | 0.7646 | 0.8758 | 0.3941 | 0.7705 | 0.3512 | nan | 0.5130 | 0.4629 | 0.0282 | 0.7806 | 0.0034 | nan | nan | 0.0 | 0.4953 | 0.0 | 0.0 | 0.7313 | 0.0516 | 0.4698 | 0.3050 | 0.0 | nan | 0.2161 | 0.3409 | 0.1980 | 0.0 | 0.8760 | 0.8268 | 0.9366 | 0.0301 | 0.0500 | 0.3304 | 0.0 | 19 |
| 0.2446 | 0.5012 | 0.3515 | 0.4310 | 0.8716 | 0.0 | 0.8682 | 0.9618 | 0.4818 | 0.8386 | 0.4450 | nan | 0.5955 | 0.7539 | 0.1974 | 0.9232 | 0.0137 | nan | nan | 0.0 | 0.7583 | 0.0 | 0.0 | 0.8665 | 0.1288 | 0.6248 | 0.4094 | 0.0 | nan | 0.2788 | 0.4905 | 0.3509 | 0.0 | 0.9560 | 0.9269 | 0.9759 | 0.0396 | 0.1531 | 0.3212 | 0.0 | 0.0 | 0.7786 | 0.8820 | 0.3864 | 0.7625 | 0.3652 | nan | 0.5215 | 0.4361 | 0.1553 | 0.7780 | 0.0117 | nan | nan | 0.0 | 0.4862 | 0.0 | 0.0 | 0.7397 | 0.1005 | 0.4586 | 0.3304 | 0.0 | nan | 0.1980 | 0.3378 | 0.1937 | 0.0 | 0.8717 | 0.8109 | 0.9336 | 0.0279 | 0.0613 | 0.2703 | 0.0 | 20 |
| 0.2401 | 0.5020 | 0.3561 | 0.4308 | 0.8742 | 0.0 | 0.8655 | 0.9671 | 0.4700 | 0.8084 | 0.4765 | nan | 0.5854 | 0.7695 | 0.0770 | 0.9220 | 0.0103 | nan | nan | 0.0 | 0.7302 | 0.0 | 0.0 | 0.8743 | 0.1083 | 0.5923 | 0.4110 | 0.0 | nan | 0.2553 | 0.4874 | 0.3354 | 0.0 | 0.9538 | 0.9210 | 0.9794 | 0.1405 | 0.1450 | 0.4701 | 0.0 | 0.0 | 0.7971 | 0.8779 | 0.3889 | 0.7588 | 0.3682 | nan | 0.5158 | 0.4258 | 0.0633 | 0.7827 | 0.0091 | nan | nan | 0.0 | 0.5107 | 0.0 | 0.0 | 0.7496 | 0.0973 | 0.4638 | 0.3244 | 0.0 | nan | 0.2133 | 0.3548 | 0.1934 | 0.0 | 0.8749 | 0.8293 | 0.9331 | 0.0958 | 0.0682 | 0.3432 | 0.0 | 21 |
| 0.2212 | 0.5226 | 0.3627 | 0.4378 | 0.8700 | 0.0 | 0.8764 | 0.9647 | 0.5017 | 0.8379 | 0.4184 | nan | 0.6272 | 0.7174 | 0.2529 | 0.9041 | 0.0126 | nan | nan | 0.0 | 0.7586 | 0.0 | 0.0 | 0.9111 | 0.1342 | 0.6539 | 0.4660 | 0.0 | nan | 0.2615 | 0.4794 | 0.3201 | 0.0 | 0.8864 | 0.9573 | 0.9727 | 0.0723 | 0.2059 | 0.3793 | 0.0 | 0.0 | 0.7934 | 0.8781 | 0.3875 | 0.7868 | 0.3618 | nan | 0.5415 | 0.5344 | 0.1933 | 0.7899 | 0.0118 | nan | nan | 0.0 | 0.5426 | 0.0 | 0.0 | 0.7345 | 0.1159 | 0.4963 | 0.3845 | 0.0 | nan | 0.1821 | 0.3590 | 0.1928 | 0.0 | 0.8441 | 0.7387 | 0.9392 | 0.0546 | 0.0734 | 0.3064 | 0.0 | 22 |
| 0.2286 | 0.5119 | 0.3485 | 0.4300 | 0.8739 | 0.0 | 0.9060 | 0.9429 | 0.4847 | 0.8465 | 0.4336 | nan | 0.6466 | 0.7115 | 0.1329 | 0.9134 | 0.0078 | nan | nan | 0.0 | 0.7153 | 0.0 | 0.0 | 0.9073 | 0.0941 | 0.5938 | 0.4739 | 0.0 | nan | 0.2267 | 0.4684 | 0.3284 | 0.0 | 0.9536 | 0.9211 | 0.9768 | 0.0472 | 0.1654 | 0.4325 | 0.0 | 0.0 | 0.7818 | 0.8776 | 0.3993 | 0.7343 | 0.3657 | nan | 0.5462 | 0.5018 | 0.1147 | 0.7900 | 0.0070 | nan | 0.0 | 0.0 | 0.5133 | 0.0 | 0.0 | 0.7477 | 0.0884 | 0.4861 | 0.3631 | 0.0 | nan | 0.1971 | 0.3552 | 0.2041 | 0.0 | 0.8765 | 0.8029 | 0.9379 | 0.0403 | 0.0721 | 0.3499 | 0.0 | 23 |
| 0.2149 | 0.5215 | 0.3580 | 0.4310 | 0.8725 | 0.0 | 0.8805 | 0.9605 | 0.5094 | 0.8149 | 0.4046 | nan | 0.6248 | 0.7485 | 0.1781 | 0.9305 | 0.0324 | nan | nan | 0.0 | 0.7282 | 0.0 | 0.0 | 0.9008 | 0.1662 | 0.6190 | 0.3780 | 0.0 | nan | 0.2934 | 0.4534 | 0.3489 | 0.0 | 0.9547 | 0.8847 | 0.9790 | 0.0458 | 0.1169 | 0.4090 | 0.0 | 0.0 | 0.8043 | 0.8722 | 0.3930 | 0.7158 | 0.3413 | nan | 0.5459 | 0.4605 | 0.1261 | 0.7763 | 0.0277 | nan | nan | 0.0 | 0.4976 | 0.0 | 0.0 | 0.7451 | 0.1357 | 0.4977 | 0.3036 | 0.0 | nan | 0.2234 | 0.3532 | 0.2191 | 0.0 | 0.8704 | 0.8183 | 0.9363 | 0.0401 | 0.0650 | 0.3282 | 0.0 | 24 |
| 0.2116 | 0.5343 | 0.3555 | 0.4263 | 0.8695 | 0.0 | 0.8585 | 0.9634 | 0.5020 | 0.7841 | 0.5146 | nan | 0.5819 | 0.7780 | 0.1352 | 0.9088 | 0.0226 | nan | nan | 0.0 | 0.7205 | 0.0 | 0.0 | 0.9146 | 0.0458 | 0.6097 | 0.3694 | 0.0 | nan | 0.2702 | 0.4659 | 0.3618 | 0.0 | 0.9632 | 0.8248 | 0.9759 | 0.0555 | 0.1575 | 0.4305 | 0.0 | 0.0 | 0.7861 | 0.8710 | 0.4033 | 0.7300 | 0.3961 | nan | 0.5124 | 0.4329 | 0.1058 | 0.7851 | 0.0195 | nan | nan | 0.0 | 0.5317 | 0.0 | 0.0 | 0.7423 | 0.0426 | 0.5096 | 0.3190 | 0.0 | nan | 0.2290 | 0.3618 | 0.2138 | 0.0 | 0.8519 | 0.7803 | 0.9394 | 0.0424 | 0.0775 | 0.3381 | 0.0 | 25 |
| 0.2311 | 0.4961 | 0.3478 | 0.4364 | 0.8738 | 0.0 | 0.8734 | 0.9679 | 0.5055 | 0.7900 | 0.4253 | nan | 0.6437 | 0.7229 | 0.1989 | 0.8863 | 0.0392 | nan | nan | 0.0011 | 0.6654 | 0.0 | 0.0 | 0.8941 | 0.1576 | 0.6591 | 0.4683 | 0.0 | nan | 0.2810 | 0.4781 | 0.3731 | 0.0 | 0.9497 | 0.9264 | 0.9771 | 0.0650 | 0.2566 | 0.3215 | 0.0 | 0.0 | 0.7982 | 0.8820 | 0.4066 | 0.7279 | 0.3452 | nan | 0.5455 | 0.4411 | 0.1509 | 0.7748 | 0.0314 | nan | 0.0 | 0.0011 | 0.5164 | 0.0 | 0.0 | 0.7315 | 0.1440 | 0.4729 | 0.3721 | 0.0 | nan | 0.1511 | 0.3584 | 0.2174 | 0.0 | 0.8773 | 0.8355 | 0.9361 | 0.0497 | 0.0978 | 0.2643 | 0.0 | 26 |
| 0.2208 | 0.5502 | 0.3493 | 0.4366 | 0.8689 | 0.0 | 0.8383 | 0.9673 | 0.5042 | 0.7920 | 0.3196 | nan | 0.6306 | 0.7779 | 0.1291 | 0.9021 | 0.1445 | nan | nan | 0.0273 | 0.7410 | 0.0 | 0.0 | 0.8969 | 0.1208 | 0.5939 | 0.4402 | 0.0 | nan | 0.2468 | 0.4770 | 0.3748 | 0.0 | 0.9438 | 0.9466 | 0.9747 | 0.1148 | 0.1817 | 0.4475 | 0.0 | 0.0 | 0.7682 | 0.8633 | 0.4140 | 0.7395 | 0.2747 | nan | 0.5426 | 0.4367 | 0.1124 | 0.7849 | 0.1196 | nan | 0.0 | 0.0272 | 0.5120 | 0.0 | 0.0 | 0.7431 | 0.1154 | 0.4755 | 0.3410 | 0.0 | nan | 0.1788 | 0.3618 | 0.2128 | 0.0 | 0.8779 | 0.8179 | 0.9403 | 0.0889 | 0.0963 | 0.3342 | 0.0 | 27 |
| 0.2110 | 0.5231 | 0.3414 | 0.4290 | 0.8722 | 0.0 | 0.8684 | 0.9551 | 0.5060 | 0.8743 | 0.3936 | nan | 0.6295 | 0.7991 | 0.1233 | 0.9409 | 0.0481 | nan | nan | 0.0 | 0.7064 | 0.0 | 0.0 | 0.9129 | 0.1463 | 0.4067 | 0.4126 | 0.0 | nan | 0.2611 | 0.4535 | 0.3979 | 0.0 | 0.9440 | 0.9430 | 0.9801 | 0.1153 | 0.0838 | 0.3970 | 0.0 | 0.0 | 0.7971 | 0.8795 | 0.3698 | 0.7345 | 0.3286 | nan | 0.5408 | 0.4354 | 0.1101 | 0.7568 | 0.0463 | nan | 0.0 | 0.0 | 0.4965 | 0.0 | 0.0 | 0.7422 | 0.1314 | 0.3671 | 0.3160 | 0.0 | nan | 0.1858 | 0.3615 | 0.2297 | 0.0 | 0.8787 | 0.8337 | 0.9380 | 0.0751 | 0.0511 | 0.3184 | 0.0 | 28 |
| 0.1959 | 0.5240 | 0.3521 | 0.4343 | 0.8771 | 0.0 | 0.8744 | 0.9749 | 0.4993 | 0.8143 | 0.3366 | nan | 0.6376 | 0.7596 | 0.1736 | 0.9184 | 0.0424 | nan | nan | 0.0002 | 0.7469 | 0.0 | 0.0 | 0.8957 | 0.1516 | 0.5852 | 0.4130 | 0.0 | nan | 0.2630 | 0.4594 | 0.3941 | 0.0 | 0.9456 | 0.9363 | 0.9789 | 0.1007 | 0.1004 | 0.4621 | 0.0 | 0.0 | 0.8044 | 0.8775 | 0.4029 | 0.7728 | 0.2994 | nan | 0.5577 | 0.4748 | 0.1513 | 0.7884 | 0.0378 | nan | 0.0 | 0.0002 | 0.5007 | 0.0 | 0.0 | 0.7343 | 0.1427 | 0.4677 | 0.3440 | 0.0 | nan | 0.1765 | 0.3631 | 0.2372 | 0.0 | 0.8800 | 0.8324 | 0.9400 | 0.0701 | 0.0558 | 0.3558 | 0.0 | 29 |
| 0.1941 | 0.5077 | 0.3541 | 0.4368 | 0.8760 | 0.0 | 0.8810 | 0.9600 | 0.5080 | 0.7956 | 0.4939 | nan | 0.5978 | 0.7554 | 0.1367 | 0.9179 | 0.0599 | nan | nan | 0.0 | 0.7221 | 0.0 | 0.0 | 0.8944 | 0.1175 | 0.6129 | 0.4032 | 0.0 | nan | 0.2636 | 0.4817 | 0.3849 | 0.0 | 0.9453 | 0.9461 | 0.9760 | 0.0649 | 0.1096 | 0.5128 | 0.0 | 0.0 | 0.7899 | 0.8798 | 0.3854 | 0.7444 | 0.3950 | nan | 0.5344 | 0.4893 | 0.1164 | 0.7795 | 0.0515 | nan | 0.0 | 0.0 | 0.5232 | 0.0 | 0.0 | 0.7498 | 0.1094 | 0.4920 | 0.3325 | 0.0 | nan | 0.2083 | 0.3678 | 0.2519 | 0.0 | 0.8789 | 0.8304 | 0.9409 | 0.0447 | 0.0765 | 0.3601 | 0.0 | 30 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
TheBloke/openbuddy-zephyr-7B-v14.1-GGUF | TheBloke | 2023-11-06T16:04:17Z | 164 | 12 | transformers | [
"transformers",
"gguf",
"mistral",
"base_model:OpenBuddy/openbuddy-zephyr-7b-v14.1",
"base_model:quantized:OpenBuddy/openbuddy-zephyr-7b-v14.1",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-06T14:54:31Z | ---
base_model: OpenBuddy/openbuddy-zephyr-7b-v14.1
inference: false
license: apache-2.0
model_creator: OpenBuddy
model_name: Openbuddy Zephyr 7B v14.1
model_type: mistral
prompt_template: "You are a helpful, respectful and honest INTP-T AI Assistant named\
\ Buddy. You are talking to a human User.\nAlways answer as helpfully and logically\
\ as possible, while being safe. Your answers should not include any harmful, political,\
\ religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please\
\ ensure that your responses are socially unbiased and positive in nature.\nIf a\
\ question does not make any sense, or is not factually coherent, explain why instead\
\ of answering something not correct. If you don't know the answer to a question,\
\ please don't share false information.\nYou like to use emojis. You can speak fluently\
\ in many languages, for example: English, Chinese.\nYou cannot access the internet,\
\ but you have vast knowledge, cutoff: 2021-09.\nYou are trained by OpenBuddy team,\
\ (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based\
\ on LLaMA and Falcon transformers model, not related to GPT or OpenAI.\n\nUser:\
\ {prompt}\nAssistant: \n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
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<!-- header end -->
# Openbuddy Zephyr 7B v14.1 - GGUF
- Model creator: [OpenBuddy](https://huggingface.co/OpenBuddy)
- Original model: [Openbuddy Zephyr 7B v14.1](https://huggingface.co/OpenBuddy/openbuddy-zephyr-7b-v14.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [OpenBuddy's Openbuddy Zephyr 7B v14.1](https://huggingface.co/OpenBuddy/openbuddy-zephyr-7b-v14.1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF)
* [OpenBuddy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenBuddy/openbuddy-zephyr-7b-v14.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: OpenBuddy
```
You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation 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
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [openbuddy-zephyr-7b-v14.1.Q2_K.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q2_K.gguf) | Q2_K | 2 | 3.10 GB| 5.60 GB | smallest, significant quality loss - not recommended for most purposes |
| [openbuddy-zephyr-7b-v14.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.19 GB| 5.69 GB | very small, high quality loss |
| [openbuddy-zephyr-7b-v14.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.54 GB| 6.04 GB | very small, high quality loss |
| [openbuddy-zephyr-7b-v14.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.85 GB| 6.35 GB | small, substantial quality loss |
| [openbuddy-zephyr-7b-v14.1.Q4_0.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q4_0.gguf) | Q4_0 | 4 | 4.14 GB| 6.64 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [openbuddy-zephyr-7b-v14.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q4_K_S.gguf) | Q4_K_S | 4 | 4.17 GB| 6.67 GB | small, greater quality loss |
| [openbuddy-zephyr-7b-v14.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.39 GB| 6.89 GB | medium, balanced quality - recommended |
| [openbuddy-zephyr-7b-v14.1.Q5_0.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q5_0.gguf) | Q5_0 | 5 | 5.03 GB| 7.53 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [openbuddy-zephyr-7b-v14.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q5_K_S.gguf) | Q5_K_S | 5 | 5.03 GB| 7.53 GB | large, low quality loss - recommended |
| [openbuddy-zephyr-7b-v14.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q5_K_M.gguf) | Q5_K_M | 5 | 5.16 GB| 7.66 GB | large, very low quality loss - recommended |
| [openbuddy-zephyr-7b-v14.1.Q6_K.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q6_K.gguf) | Q6_K | 6 | 5.97 GB| 8.47 GB | very large, extremely low quality loss |
| [openbuddy-zephyr-7b-v14.1.Q8_0.gguf](https://huggingface.co/TheBloke/openbuddy-zephyr-7B-v14.1-GGUF/blob/main/openbuddy-zephyr-7b-v14.1.Q8_0.gguf) | Q8_0 | 8 | 7.74 GB| 10.24 GB | very large, extremely low quality loss - not recommended |
**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.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/openbuddy-zephyr-7B-v14.1-GGUF and below it, a specific filename to download, such as: openbuddy-zephyr-7b-v14.1.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/openbuddy-zephyr-7B-v14.1-GGUF openbuddy-zephyr-7b-v14.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/openbuddy-zephyr-7B-v14.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/openbuddy-zephyr-7B-v14.1-GGUF openbuddy-zephyr-7b-v14.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m openbuddy-zephyr-7b-v14.1.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.\nAlways answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\nYou like to use emojis. You can speak fluently in many languages, for example: English, Chinese.\nYou cannot access the internet, but you have vast knowledge, cutoff: 2021-09.\nYou are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.\n\nUser: {prompt}\nAssistant:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/openbuddy-zephyr-7B-v14.1-GGUF", model_file="openbuddy-zephyr-7b-v14.1.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## 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!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: OpenBuddy's Openbuddy Zephyr 7B v14.1
<!-- original-model-card end -->
|
ArtCad98/eubert_covid_ft | ArtCad98 | 2023-11-06T16:03:38Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:EuropeanParliament/EUBERT",
"base_model:finetune:EuropeanParliament/EUBERT",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-10-31T18:21:46Z | ---
base_model: EuropeanParliament/EUBERT
tags:
- generated_from_trainer
model-index:
- name: eubert_covid_ft
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. -->
# eubert_covid_ft
This model is a fine-tuned version of [EuropeanParliament/EUBERT](https://huggingface.co/EuropeanParliament/EUBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0191
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2428 | 1.0 | 4152 | 1.1584 |
| 1.129 | 2.0 | 8304 | 1.0547 |
| 1.086 | 3.0 | 12456 | 1.0227 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
J1mb0o/bert-finetuned-batch32-lr3e-5 | J1mb0o | 2023-11-06T16:00:10Z | 5 | 0 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-06T15:56:07Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: J1mb0o/bert-finetuned-batch32-lr3e-5
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. -->
# J1mb0o/bert-finetuned-batch32-lr3e-5
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0924
- Validation Loss: 0.3766
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 428, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3539 | 0.5051 | 0 |
| 0.1858 | 0.4502 | 1 |
| 0.1250 | 0.3784 | 2 |
| 0.0924 | 0.3766 | 3 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
TheBloke/japanese-stablelm-base-beta-70B-GPTQ | TheBloke | 2023-11-06T16:00:08Z | 24 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"japanese-stablelm",
"causal-lm",
"ja",
"dataset:wikipedia",
"dataset:mc4",
"dataset:cc100",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:oscar-corpus/OSCAR-2201",
"dataset:cerebras/SlimPajama-627B",
"base_model:stabilityai/japanese-stablelm-base-beta-70b",
"base_model:quantized:stabilityai/japanese-stablelm-base-beta-70b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
]
| text-generation | 2023-11-06T11:33:47Z | ---
base_model: stabilityai/japanese-stablelm-base-beta-70b
datasets:
- wikipedia
- mc4
- cc100
- oscar-corpus/OSCAR-2301
- oscar-corpus/OSCAR-2201
- cerebras/SlimPajama-627B
inference: false
language:
- ja
license:
- llama2
model_creator: Stability AI
model_name: Japanese StableLM Base Beta 70B
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
tags:
- japanese-stablelm
- causal-lm
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<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 style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Japanese StableLM Base Beta 70B - GPTQ
- Model creator: [Stability AI](https://huggingface.co/stabilityai)
- Original model: [Japanese StableLM Base Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Stability AI's Japanese StableLM Base Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GGUF)
* [Stability AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `['llama2']`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Stability AI's Japanese StableLM Base Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b).
<!-- licensing end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) | 4096 | 26.77 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) | 4096 | 31.84 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/japanese-stablelm-base-beta-70B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/japanese-stablelm-base-beta-70B-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `japanese-stablelm-base-beta-70B-GPTQ`:
```shell
mkdir japanese-stablelm-base-beta-70B-GPTQ
huggingface-cli download TheBloke/japanese-stablelm-base-beta-70B-GPTQ --local-dir japanese-stablelm-base-beta-70B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir japanese-stablelm-base-beta-70B-GPTQ
huggingface-cli download TheBloke/japanese-stablelm-base-beta-70B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir japanese-stablelm-base-beta-70B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir japanese-stablelm-base-beta-70B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/japanese-stablelm-base-beta-70B-GPTQ --local-dir japanese-stablelm-base-beta-70B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/japanese-stablelm-base-beta-70B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/japanese-stablelm-base-beta-70B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/japanese-stablelm-base-beta-70B-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `japanese-stablelm-base-beta-70B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/japanese-stablelm-base-beta-70B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/japanese-stablelm-base-beta-70B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## 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!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Stability AI's Japanese StableLM Base Beta 70B
# Japanese-StableLM-Base-Beta-70B
> A cute robot wearing a kimono writes calligraphy with one single brush — [Stable Diffusion XL](https://clipdrop.co/stable-diffusion)
## Model Description
`japanese-stablelm-base-beta-70b` is a 70B-parameter decoder-only language model based on [Llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b) that has been fine-tuned on a diverse collection of Japanese data, with the intent of maximizing downstream performance on Japanese language tasks.
For an instruction-following model, check [Japanese-StableLM-Instruct-Beta-70B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b). The base and instruct models are also available in smaller 7b sizes. For a model that has faster inference times, see [Japanese-StableLM-Base-JA_Vocab-Beta-7B](https://huggingface.co/stabilityai/japanese-stablelm-base-ja_vocab-beta-7b), or [the instruction-following version](https://huggingface.co/stabilityai/japanese-stablelm-instruct-ja_vocab-beta-7b).
## Usage
First install additional dependencies in [requirements.txt](./requirements.txt):
```sh
pip install -r requirements.txt
```
Then start generating text with `japanese-stablelm-base-beta-70b` by using the following code snippet:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "stabilityai/japanese-stablelm-base-beta-70b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# The next line may need to be modified depending on the environment
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
prompt = """
AI で科学研究を加速するには、
""".strip()
input_ids = tokenizer.encode(
prompt,
add_special_tokens=False,
return_tensors="pt"
)
# this is for reproducibility.
# feel free to change to get different result
seed = 23
torch.manual_seed(seed)
tokens = model.generate(
input_ids.to(device=model.device),
max_new_tokens=128,
temperature=0.99,
top_p=0.95,
do_sample=True,
)
out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)
```
We suggest playing with different generation config (`top_p`, `repetition_penalty` etc) to find the best setup for your tasks. For example, use higher temperature for roleplay task, lower temperature for reasoning.
## Model Details
* **Model type**: `japanese-stablelm-base-beta-70b` model is an auto-regressive language model based on the Llama2 transformer architecture.
* **Language(s)**: Japanese
* **License**: [Llama2 Community License](https://ai.meta.com/llama/license/).
* **Contact**: For questions and comments about the model, please join [Stable Community Japan](https://discord.gg/StableJP). For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.
## Training Dataset
Roughly 100B tokens from a mixture of the following corpora were used for continued pre-training.
- [Japanese/English Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- [Japanese mc4](https://huggingface.co/datasets/mc4)
- [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz)
- [Japanese OSCAR](https://oscar-project.github.io/documentation/)
- [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B) (excluding the Books3 subset)
## Use and Limitations
### Intended Use
The model is intended to be used by all individuals as a foundation for application-specific fine-tuning without strict limitations on commercial use.
### Limitations and bias
The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
## Authors
This model was developed by the Research & Development team at Stability AI Japan, and the development was co-led by [Takuya Akiba](https://huggingface.co/iwiwi) and [Meng Lee](https://huggingface.co/leemeng). The members of the team are as follows:
- [Meng Lee](https://huggingface.co/leemeng)
- [Fujiki Nakamura](https://huggingface.co/fujiki)
- [Makoto Shing](https://huggingface.co/mkshing)
- [Paul McCann](https://huggingface.co/polm-stability)
- [Takuya Akiba](https://huggingface.co/iwiwi)
- [Naoki Orii](https://huggingface.co/mrorii)
## Acknowledgements
We thank Meta Research for releasing Llama 2 under an open license for others to build on.
We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
We are also appreciative of [AI Novelist/Sta (Bit192, Inc.)](https://ai-novel.com/index.php) and the numerous contributors from [Stable Community Japan](https://discord.gg/VPrcE475HB) for assisting us in gathering a large amount of high-quality Japanese textual data for model training.
## How to cite
```
@misc{JapaneseStableLMBaseBeta70B,
url={[https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b)},
title={Japanese StableLM Base Beta 70B},
author={Lee, Meng and Nakamura, Fujiki and Shing, Makoto and McCann, Paul and Akiba, Takuya and Orii, Naoki}
}
```
|
mtc/LeoLM-leo-mistral-hessianai-7b-classification-with-explanation-neftune-qlora-4bit | mtc | 2023-11-06T15:57:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"region:us"
]
| null | 2023-11-06T15:57:28Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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 0.5.0
|
J1mb0o/bert-finetuned-batch32-lr1e-5 | J1mb0o | 2023-11-06T15:53:20Z | 4 | 0 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-06T15:49:05Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: J1mb0o/bert-finetuned-batch32-lr1e-5
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. -->
# J1mb0o/bert-finetuned-batch32-lr1e-5
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1644
- Validation Loss: 0.4106
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 428, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.5141 | 0.5635 | 0 |
| 0.2237 | 0.4454 | 1 |
| 0.1855 | 0.4069 | 2 |
| 0.1644 | 0.4106 | 3 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
venumadhav04/my-thar | venumadhav04 | 2023-11-06T15:52:32Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T15:48:04Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Thar Dreambooth model trained by venumadhav04 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MITS-1018
Sample pictures of this concept:
.jpg)
|
Amrutha36/cats-cat | Amrutha36 | 2023-11-06T15:51:00Z | 0 | 0 | null | [
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-11-06T15:49:58Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Cats-cat Dreambooth model trained by Amrutha36 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MITS-1948
Sample pictures of this concept:
.png)
|
gowri-39/nature-gsh | gowri-39 | 2023-11-06T15:42:33Z | 1 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T15:38:23Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### nature-gsh Dreambooth model trained by DSP-31 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MITS-1683
Sample pictures of this concept:
|
nicotaroni/sentiment_analysis_4 | nicotaroni | 2023-11-06T15:41:38Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
]
| text-classification | 2023-11-06T15:40:51Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# nicotaroni/sentiment_analysis_4
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("nicotaroni/sentiment_analysis_4")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
NouRed/fine-tuned-vit-cifar10 | NouRed | 2023-11-06T15:36:42Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"dataset:cifar10",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-11-06T15:34:26Z | ---
license: apache-2.0
datasets:
- cifar10
metrics:
- accuracy
- f1
- precision
- recall
library_name: transformers
pipeline_tag: image-classification
--- |
VoidZeroe/llama1.1x-model | VoidZeroe | 2023-11-06T15:30:13Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-11-06T15:29:57Z | ---
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
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
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
- PEFT 0.4.0
- PEFT 0.4.0
|
AyedSamy/openchat_molinst_finetuned | AyedSamy | 2023-11-06T15:26:33Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openchat/openchat_3.5",
"base_model:adapter:openchat/openchat_3.5",
"region:us"
]
| null | 2023-11-03T18:22:29Z | ---
library_name: peft
base_model: openchat/openchat_3.5
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.7.0.dev0
|
Sarthak7777/health_question | Sarthak7777 | 2023-11-06T15:24:13Z | 17 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-11-06T14:20:26Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: health_question
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. -->
# health_question
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: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6848 | 1.0 | 820 | nan |
| 2.0342 | 2.0 | 1640 | nan |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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