modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-28 12:28:24
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 500
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 54
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-06-28 12:27:53
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
anzeo/loha_fine_tuned_rte_XLMroberta | anzeo | 2024-05-22T20:15:16Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:adapter:FacebookAI/xlm-roberta-base",
"license:mit",
"region:us"
] | null | 2024-05-22T20:02:56Z | ---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: xlm-roberta-base
metrics:
- accuracy
- f1
model-index:
- name: loha_fine_tuned_rte_XLMroberta
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. -->
# loha_fine_tuned_rte_XLMroberta
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: 3.0980
- Accuracy: 0.6207
- F1: 0.6090
## 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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 0.8165 | 1.7241 | 50 | 0.7174 | 0.4828 | 0.3781 |
| 0.7386 | 3.4483 | 100 | 0.6616 | 0.6897 | 0.6523 |
| 0.7293 | 5.1724 | 150 | 0.7683 | 0.5172 | 0.4660 |
| 0.6773 | 6.8966 | 200 | 1.1129 | 0.4483 | 0.4324 |
| 0.4623 | 8.6207 | 250 | 1.7863 | 0.5862 | 0.5892 |
| 0.2532 | 10.3448 | 300 | 2.8440 | 0.5862 | 0.5483 |
| 0.0813 | 12.0690 | 350 | 3.0842 | 0.5517 | 0.5484 |
| 0.0478 | 13.7931 | 400 | 3.0980 | 0.6207 | 0.6090 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.1.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
SlavicNLP/slavicner-linking-cross-topic-large | SlavicNLP | 2024-05-22T20:13:07Z | 108 | 2 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"entity linking",
"multilingual",
"pl",
"ru",
"uk",
"bg",
"cs",
"sl",
"dataset:SlavicNER",
"arxiv:2404.00482",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-14T18:59:18Z | ---
language:
- multilingual
- pl
- ru
- uk
- bg
- cs
- sl
datasets:
- SlavicNER
license: apache-2.0
library_name: transformers
pipeline_tag: text2text-generation
tags:
- entity linking
widget:
- text: pl:Polsce
example_title: Polish
- text: cs:Velké Británii
example_title: Czech
- text: bg:българите
example_title: Bulgarian
- text: ru:Великобританию
example_title: Russian
- text: sl:evropske komisije
example_title: Slovene
- text: uk:Європейського агентства лікарських засобів
example_title: Ukrainian
---
# Model description
This is a baseline model for named entity **lemmatization** trained on the single-out topic split of the
[SlavicNER corpus](https://github.com/SlavicNLP/SlavicNER).
# Resources and Technical Documentation
- Paper: [Cross-lingual Named Entity Corpus for Slavic Languages](https://arxiv.org/pdf/2404.00482), to appear in LREC-COLING 2024.
- Annotation guidelines: https://arxiv.org/pdf/2404.00482
- SlavicNER Corpus: https://github.com/SlavicNLP/SlavicNER
# Evaluation
*Will appear soon*
# Usage
You can use this model directly with a pipeline for text2text generation:
```python
from transformers import pipeline
model_name = "SlavicNLP/slavicner-linking-cross-topic-large"
pipe = pipeline("text2text-generation", model_name)
texts = ["pl:Polsce", "cs:Velké Británii", "bg:българите", "ru:Великобританию",
"sl:evropske komisije", "uk:Європейського агентства лікарських засобів"]
outputs = pipe(texts)
ids = [o['generated_text'] for o in outputs]
print(ids)
# ['GPE-Poland', 'GPE-Great-Britain', 'GPE-Bulgaria', 'GPE-Great-Britain',
# 'ORG-European-Commission', 'ORG-EMA-European-Medicines-Agency']
```
# Citation
```latex
@inproceedings{piskorski-etal-2024-cross-lingual,
title = "Cross-lingual Named Entity Corpus for {S}lavic Languages",
author = "Piskorski, Jakub and
Marci{\'n}czuk, Micha{\l} and
Yangarber, Roman",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italy",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.369",
pages = "4143--4157",
abstract = "This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian,
and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural
Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities.
Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits
{---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture
with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization,
and mT5-large for named entity lemmatization and linking.",
}
```
# Contact
Michał Marcińczuk ([email protected]) |
ariG23498/Mistral-7B-Instruct-v0.3 | ariG23498 | 2024-05-22T20:12:33Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"mistral",
"text-generation",
"generated_from_keras_callback",
"base_model:ariG23498/Mistral-7B-Instruct-v0.3",
"base_model:finetune:ariG23498/Mistral-7B-Instruct-v0.3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T18:56:49Z | ---
base_model: ariG23498/Mistral-7B-Instruct-v0.3
tags:
- generated_from_keras_callback
model-index:
- name: Mistral-7B-Instruct-v0.3
results: []
---
Turns out that [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) only have safetensors. This repo
is created to have the `.bin` files of the model.
This repo is created by:
```py
model_id = "mistralai/Mistral-7B-Instruct-v0.3"
model = AutoModelForCausalLM.from_pretrained(model_id)
model.push_to_hub("ariG23498/Mistral-7B-Instruct-v0.3", safe_serialization=False)
```
This is due to the fact that the TensorFlow port cannot use safetensors and need bin files.
You can use this model with TF like so:
```py
model_tf = TFAutoModelForCausalLM.from_pretrained("ariG23498/Mistral-7B-Instruct-v0.3", from_pt=True)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
prompt = "My favourite condiment is"
model_inputs = tokenizer([prompt], return_tensors="tf")
generated_ids = model_tf.generate(**model_inputs, max_new_tokens=100, do_sample=True)
tokenizer.batch_decode(generated_ids)[0]
```
As soon as the safetensors and TensorFlow issue is sorted one can ditch this repository and use the official repository!
Update:
I have uploaded the `.h5` models as well. You can now use the following and make the entire code work!
```py
model_tf = TFAutoModelForCausalLM.from_pretrained("ariG23498/Mistral-7B-Instruct-v0.3")
``` |
SlavicNLP/slavicner-lemma-single-out-large | SlavicNLP | 2024-05-22T20:12:31Z | 112 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"lemmatization",
"multilingual",
"pl",
"ru",
"uk",
"bg",
"cs",
"sl",
"dataset:SlavicNER",
"arxiv:2404.00482",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-14T18:58:47Z | ---
language:
- multilingual
- pl
- ru
- uk
- bg
- cs
- sl
datasets:
- SlavicNER
license: apache-2.0
library_name: transformers
pipeline_tag: text2text-generation
tags:
- lemmatization
widget:
- text: "pl:Polsce"
example_title: "Polish"
- text: "cs:Velké Británii"
example_title: "Czech"
- text: "bg:българите"
example_title: "Bulgarian"
- text: "ru:Великобританию"
example_title: "Russian"
- text: "sl:evropske komisije"
example_title: "Slovene"
- text: "uk:Європейського агентства лікарських засобів"
example_title: "Ukrainian"
---
# Model description
This is a baseline model for named entity **lemmatization** trained on the single-out topic split of the
[SlavicNER corpus](https://github.com/SlavicNLP/SlavicNER).
# Resources and Technical Documentation
- Paper: [Cross-lingual Named Entity Corpus for Slavic Languages](https://arxiv.org/pdf/2404.00482), to appear in LREC-COLING 2024.
- Annotation guidelines: https://arxiv.org/pdf/2404.00482
- SlavicNER Corpus: https://github.com/SlavicNLP/SlavicNER
# Evaluation
*Will appear soon*
# Usage
You can use this model directly with a pipeline for text2text generation:
```python
from transformers import pipeline
model_name = "SlavicNLP/slavicner-lemma-single-out-large"
pipe = pipeline("text2text-generation", model_name)
texts = ["pl:Polsce", "cs:Velké Británii", "bg:българите", "ru:Великобританию", "sl:evropske komisije",
"uk:Європейського агентства лікарських засобів"]
outputs = pipe(texts)
lemmas = [o['generated_text'] for o in outputs]
print(lemmas)
# ['Polska', 'Velká Británie', 'българи', 'Великобритания', 'evropska komisija', 'Європейське агентство лікарських засобів']
```
# Citation
```latex
@inproceedings{piskorski-etal-2024-cross-lingual,
title = "Cross-lingual Named Entity Corpus for {S}lavic Languages",
author = "Piskorski, Jakub and
Marci{\'n}czuk, Micha{\l} and
Yangarber, Roman",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italy",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.369",
pages = "4143--4157",
abstract = "This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian,
and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural
Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities.
Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits
{---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture
with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization,
and mT5-large for named entity lemmatization and linking.",
}
```
# Contact
Michał Marcińczuk ([email protected]) |
mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF | mradermacher | 2024-05-22T20:12:07Z | 57 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Collective-Ai/collective-v0.1-chinese-roleplay-8b",
"base_model:quantized:Collective-Ai/collective-v0.1-chinese-roleplay-8b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-22T19:43:39Z | ---
base_model: Collective-Ai/collective-v0.1-chinese-roleplay-8b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Collective-Ai/collective-v0.1-chinese-roleplay-8b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/collective-v0.1-chinese-roleplay-8b-GGUF/resolve/main/collective-v0.1-chinese-roleplay-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
SlavicNLP/slavicner-linking-single-out-large | SlavicNLP | 2024-05-22T20:11:57Z | 98 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"entity linking",
"multilingual",
"pl",
"ru",
"uk",
"bg",
"cs",
"sl",
"dataset:SlavicNER",
"arxiv:2404.00482",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-14T18:59:59Z | ---
language:
- multilingual
- pl
- ru
- uk
- bg
- cs
- sl
datasets:
- SlavicNER
license: apache-2.0
library_name: transformers
pipeline_tag: text2text-generation
tags:
- entity linking
widget:
- text: pl:Polsce
example_title: Polish
- text: cs:Velké Británii
example_title: Czech
- text: bg:българите
example_title: Bulgarian
- text: ru:Великобританию
example_title: Russian
- text: sl:evropske komisije
example_title: Slovene
- text: uk:Європейського агентства лікарських засобів
example_title: Ukrainian
---
# Model description
This is a baseline model for named entity **lemmatization** trained on the single-out topic split of the
[SlavicNER corpus](https://github.com/SlavicNLP/SlavicNER).
# Resources and Technical Documentation
- Paper: [Cross-lingual Named Entity Corpus for Slavic Languages](https://arxiv.org/pdf/2404.00482), to appear in LREC-COLING 2024.
- Annotation guidelines: https://arxiv.org/pdf/2404.00482
- SlavicNER Corpus: https://github.com/SlavicNLP/SlavicNER
# Evaluation
| **Language** | **Seq2seq** | **Support** |
|:------------:|:-----------:|-----------------:|
| PL | 75.13 | 2 549 |
| CS | 77.92 | 1 137 |
| RU | 67.56 | 18 018 |
| BG | 63.60 | 6 085 |
| SL | 76.81 | 7 082 |
| UK | 58.94 | 3 085 |
| All | 68.75 | 37 956 |
# Usage
You can use this model directly with a pipeline for text2text generation:
```python
from transformers import pipeline
model_name = "SlavicNLP/slavicner-linking-single-out-large"
pipe = pipeline("text2text-generation", model_name)
texts = ["pl:Polsce", "cs:Velké Británii", "bg:българите", "ru:Великобританию",
"sl:evropske komisije", "uk:Європейського агентства лікарських засобів"]
outputs = pipe(texts)
ids = [o['generated_text'] for o in outputs]
print(ids)
# ['GPE-Poland', 'GPE-Great-Britain', 'GPE-Bulgaria', 'GPE-Great-Britain',
# 'ORG-European-Commission', 'ORG-EMA-European-Medicines-Agency']
```
# Citation
```latex
@inproceedings{piskorski-etal-2024-cross-lingual,
title = "Cross-lingual Named Entity Corpus for {S}lavic Languages",
author = "Piskorski, Jakub and
Marci{\'n}czuk, Micha{\l} and
Yangarber, Roman",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italy",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.369",
pages = "4143--4157",
abstract = "This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian,
and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural
Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities.
Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits
{---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture
with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization,
and mT5-large for named entity lemmatization and linking.",
}
```
# Contact
Michał Marcińczuk ([email protected]) |
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_secretary-10-10-0.8 | Heimat24 | 2024-05-22T20:07:40Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T20:06:35Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 48 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 48,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
SlavicNLP/slavicner-ner-cross-topic-large | SlavicNLP | 2024-05-22T20:06:35Z | 1,862 | 2 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"ner",
"named entity recognition",
"multilingual",
"pl",
"ru",
"uk",
"bg",
"cs",
"sl",
"dataset:SlavicNER",
"arxiv:2404.00482",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-05-14T19:00:50Z | ---
language:
- multilingual
- pl
- ru
- uk
- bg
- cs
- sl
datasets:
- SlavicNER
license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
tags:
- ner
- named entity recognition
widget:
- text: "Nie jest za późno, aby powstrzymać Brexit, a Wielka Brytania wciąż może zmienić zdanie - powiedział przewodniczący Rady Europejskiej eurodeputowanym w Strasburgu."
example_title: Polish
- text: "„Musíme mluvit o sektorových a také ekonomických sankcích,“ řekl při příchodu na Evropskou radu litevský prezident Gitanas Nauseda."
example_title: Czech
- text: "Президентските избори в САЩ през 2016 г. със сигурност ще останат в историята. Не само защото Доналд Тръмп, личност без какъвто и да е опит на обществени длъжности, надви един от най-добре подготвените кандидати в историята – бившата първа дама, сенаторка и държавна секретарка Хилъри Клинтън, но и защото кампанията преди вота се отличи с безпрецедентен тон, тематика и идеи, които заеха основно място по време на дебата."
example_title: Bulgarian
- text: "По словам министра здравоохранения Светланы Леонтьевой, вакцинация против новой коронавирусной инфекции проходит примерно так же, как и ежегодная сезонная вакцинация против гриппа. В Приамурье используется два вида вакцины — «Гам-Ковид-Вак» и «ЭпиВакКорона», которые имеют разный принцип действия, но одинаково эффективны. Привить планируется 60 процентов взрослого населения, или более 300 тысяч амурчан. "
example_title: Russian
- text: "Poslanci so najprej s 296 glasovi za in 327 glasovi proti zavrnili dopolnilo vodje opozicijski laburistov Jeremya Corbyna, s katerimi je želel preprečiti brexit brez dogovora."
example_title: Slovene
- text: "У Пакистані християнка Азія Бібі, яку Верховний суд днями виправдав та скасував їй смертний вирок за богохульство, досі залишається за ґратами. Ми чекаємо на інструкції від Верховного суду. Азія Бібі перебуває у в'язниці, точне місце її розташування не може бути розкрито з міркувань безпеки, - повідомив в коментарі DW голова в'язниці в провінції Пенджаб Салім Баіг."
example_title: Ukrainian
---
# Model description
This is a baseline model for named entity **recognition** trained on the cross-topic split of the
[SlavicNER corpus](https://github.com/SlavicNLP/SlavicNER).
# Resources and Technical Documentation
- Paper: [Cross-lingual Named Entity Corpus for Slavic Languages](https://arxiv.org/pdf/2404.00482), to appear in LREC-COLING 2024.
- Annotation guidelines: https://arxiv.org/pdf/2404.00482
- SlavicNER Corpus: https://github.com/SlavicNLP/SlavicNER
# Evaluation
*Will appear soon*
# Usage
```python
from transformers import pipeline
model = "SlavicNLP/slavicner-ner-cross-topic-large"
text = """Nie jest za późno, aby powstrzymać Brexit, a Wielka Brytania wciąż
może zmienić zdanie - powiedział przewodniczący Rady Europejskiej
eurodeputowanym w Strasburgu"""
pipe = pipeline("ner", model, aggregation_strategy="simple")
entities = pipe(text)
print(*entities, sep="\n")
# {'entity_group': 'EVT', 'score': 0.99720407, 'word': 'Brexit', 'start': 35, 'end': 41}
# {'entity_group': 'LOC', 'score': 0.9656372, 'word': 'Wielka Brytania', 'start': 45, 'end': 60}
# {'entity_group': 'ORG', 'score': 0.9977708, 'word': 'Rady Europejskiej', 'start': 115, 'end': 132}
# {'entity_group': 'LOC', 'score': 0.95184135, 'word': 'Strasburgu', 'start': 151, 'end': 161}
```
# Citation
```latex
@inproceedings{piskorski-etal-2024-cross-lingual,
title = "Cross-lingual Named Entity Corpus for {S}lavic Languages",
author = "Piskorski, Jakub and
Marci{\'n}czuk, Micha{\l} and
Yangarber, Roman",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italy",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.369",
pages = "4143--4157",
abstract = "This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian,
and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural
Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities.
Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits
{---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture
with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization,
and mT5-large for named entity lemmatization and linking.",
}
```
# Contact
Michał Marcińczuk ([email protected]) |
Sorour/mistral_cls_finred | Sorour | 2024-05-22T20:05:33Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T20:01:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
raiyan007/whisper-tiny-6e-5 | raiyan007 | 2024-05-22T19:58:24Z | 94 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"bn",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-22T08:30:19Z | ---
language:
- bn
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper tiny bn - Raiyan
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice_13.0
type: mozilla-foundation/common_voice_13_0
config: bn
split: None
args: 'config: bn, split: test'
metrics:
- name: Wer
type: wer
value: 44.349095570431565
---
<!-- 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 bn - Raiyan
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice_13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1734
- Wer: 44.3491
## 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: 6e-05
- train_batch_size: 24
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.261 | 1.0661 | 500 | 0.2417 | 63.3469 |
| 0.1926 | 2.1322 | 1000 | 0.1941 | 54.3987 |
| 0.1367 | 3.1983 | 1500 | 0.1729 | 49.3116 |
| 0.0994 | 4.2644 | 2000 | 0.1622 | 46.2280 |
| 0.0564 | 5.3305 | 2500 | 0.1669 | 45.0802 |
| 0.0394 | 6.3966 | 3000 | 0.1734 | 44.3491 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
anzeo/fine_tuned_rte_XLMroberta | anzeo | 2024-05-22T19:55:19Z | 115 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-22T19:51:15Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine_tuned_rte_XLMroberta
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. -->
# fine_tuned_rte_XLMroberta
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: 1.4763
- Accuracy: 0.6207
- F1: 0.5951
## 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
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 0.7117 | 1.7241 | 50 | 0.7129 | 0.4138 | 0.2422 |
| 0.7033 | 3.4483 | 100 | 0.6997 | 0.4138 | 0.2422 |
| 0.6845 | 5.1724 | 150 | 0.6933 | 0.4828 | 0.4828 |
| 0.6378 | 6.8966 | 200 | 0.8005 | 0.4828 | 0.4668 |
| 0.4579 | 8.6207 | 250 | 0.9656 | 0.6207 | 0.5951 |
| 0.2521 | 10.3448 | 300 | 1.2302 | 0.6552 | 0.6018 |
| 0.1196 | 12.0690 | 350 | 1.4679 | 0.5862 | 0.5789 |
| 0.0653 | 13.7931 | 400 | 1.4763 | 0.6207 | 0.5951 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.1.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
abigail8n21/ppo-Huggy | abigail8n21 | 2024-05-22T19:53:43Z | 21 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2024-05-22T19:52:54Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: abigail8n21/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
msuberbie/llama-3-tune-4 | msuberbie | 2024-05-22T19:53:17Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-22T19:48:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Llama3-15B-lingyang-v0.1-GGUF | mradermacher | 2024-05-22T19:52:54Z | 8 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"hfl/llama-3-chinese-8b-instruct-v2",
"NousResearch/Hermes-2-Theta-Llama-3-8B",
"en",
"base_model:wwe180/Llama3-15B-lingyang-v0.1",
"base_model:quantized:wwe180/Llama3-15B-lingyang-v0.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-22T18:16:47Z | ---
base_model: wwe180/Llama3-15B-lingyang-v0.1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- hfl/llama-3-chinese-8b-instruct-v2
- NousResearch/Hermes-2-Theta-Llama-3-8B
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/wwe180/Llama3-15B-lingyang-v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q2_K.gguf) | Q2_K | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.IQ3_XS.gguf) | IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.IQ3_S.gguf) | IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.IQ3_M.gguf) | IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q3_K_L.gguf) | Q3_K_L | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.IQ4_XS.gguf) | IQ4_XS | 8.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q4_K_M.gguf) | Q4_K_M | 9.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q5_K_S.gguf) | Q5_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q5_K_M.gguf) | Q5_K_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q6_K.gguf) | Q6_K | 12.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-15B-lingyang-v0.1-GGUF/resolve/main/Llama3-15B-lingyang-v0.1.Q8_0.gguf) | Q8_0 | 16.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
roofdancer/thesis-bart-transfered-on-presummarized-wcep | roofdancer | 2024-05-22T19:49:06Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:roofdancer/thesis-bart-finetuned",
"base_model:finetune:roofdancer/thesis-bart-finetuned",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-22T19:33:43Z | ---
license: apache-2.0
base_model: roofdancer/thesis-bart-finetuned
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: thesis-bart-transfered-on-presummarized-wcep
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. -->
# thesis-bart-transfered-on-presummarized-wcep
This model is a fine-tuned version of [roofdancer/thesis-bart-finetuned](https://huggingface.co/roofdancer/thesis-bart-finetuned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2270
- Rouge1: 35.5354
- Rouge2: 14.3146
- Rougel: 24.9363
- Rougelsum: 28.8685
- Gen Len: 68.4041
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.2942 | 1.0 | 510 | 2.2270 | 35.5354 | 14.3146 | 24.9363 | 28.8685 | 68.4041 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
bartowski/Mistral-7B-Instruct-v0.3-exl2 | bartowski | 2024-05-22T19:47:02Z | 57 | 6 | null | [
"text-generation",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-05-22T19:47:01Z | ---
license: apache-2.0
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Mistral-7B-Instruct-v0.3
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.21">turboderp's ExLlamaV2 v0.0.21</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
## Prompt format
```
<s>[INST] {prompt} [/INST]</s>
```
Note that this model does not support a System prompt.
## Available sizes
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-exl2 Mistral-7B-Instruct-v0.3-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
Linux:
```shell
huggingface-cli download bartowski/Mistral-7B-Instruct-v0.3-exl2 --revision 6_5 --local-dir Mistral-7B-Instruct-v0.3-exl2-6_5
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
huggingface-cli download bartowski/Mistral-7B-Instruct-v0.3-exl2 --revision 6_5 --local-dir Mistral-7B-Instruct-v0.3-exl2-6.5
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |
mradermacher/Superevolution-GGUF | mradermacher | 2024-05-22T19:45:58Z | 165 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:mergekit-community/Superevolution",
"base_model:quantized:mergekit-community/Superevolution",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-22T19:19:47Z | ---
base_model: mergekit-community/Superevolution
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/mergekit-community/Superevolution
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Superevolution-GGUF/resolve/main/Superevolution.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
maneln/tinyllama-test | maneln | 2024-05-22T19:42:27Z | 198 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T18:42:38Z | ---
license: apache-2.0
---
|
sravan-gorugantu/model2024-05-22 | sravan-gorugantu | 2024-05-22T19:42:08Z | 168 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:sravan-gorugantu/model2024-05-20",
"base_model:finetune:sravan-gorugantu/model2024-05-20",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-05-22T10:59:49Z | ---
license: apache-2.0
base_model: sravan-gorugantu/model2024-05-20
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: model2024-05-22
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9649862000117446
---
<!-- 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. -->
# model2024-05-22
This model is a fine-tuned version of [sravan-gorugantu/model2024-05-20](https://huggingface.co/sravan-gorugantu/model2024-05-20) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1062
- Accuracy: 0.9650
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1566 | 1.0 | 532 | 0.1501 | 0.9494 |
| 0.1596 | 2.0 | 1064 | 0.1235 | 0.9583 |
| 0.1086 | 3.0 | 1596 | 0.1336 | 0.9549 |
| 0.1029 | 4.0 | 2129 | 0.1095 | 0.9643 |
| 0.095 | 5.0 | 2660 | 0.1062 | 0.9650 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2
|
yifanxie/taupe-bumblebee-1 | yifanxie | 2024-05-22T19:40:46Z | 144 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-22T19:38:16Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [google/gemma-2b](https://huggingface.co/google/gemma-2b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.40.2
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="yifanxie/taupe-bumblebee-1",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
res = generate_text(
messages,
renormalize_logits=True
)
print(res[0]["generated_text"][-1]['content'])
```
You can print a sample prompt after applying chat template to see how it is feed to the tokenizer:
```python
print(generate_text.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
))
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "yifanxie/taupe-bumblebee-1" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 256
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GemmaForCausalLM(
(model): GemmaModel(
(embed_tokens): Embedding(256000, 2048, padding_idx=0)
(layers): ModuleList(
(0-17): 18 x GemmaDecoderLayer(
(self_attn): GemmaSdpaAttention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=256, bias=False)
(v_proj): Linear(in_features=2048, out_features=256, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(rotary_emb): GemmaRotaryEmbedding()
)
(mlp): GemmaMLP(
(gate_proj): Linear(in_features=2048, out_features=16384, bias=False)
(up_proj): Linear(in_features=2048, out_features=16384, bias=False)
(down_proj): Linear(in_features=16384, out_features=2048, bias=False)
(act_fn): PytorchGELUTanh()
)
(input_layernorm): GemmaRMSNorm()
(post_attention_layernorm): GemmaRMSNorm()
)
)
(norm): GemmaRMSNorm()
)
(lm_head): Linear(in_features=2048, out_features=256000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
anzeo/fine_tuned_rte_sloberta | anzeo | 2024-05-22T19:40:21Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"camembert",
"text-classification",
"generated_from_trainer",
"base_model:EMBEDDIA/sloberta",
"base_model:finetune:EMBEDDIA/sloberta",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-22T17:05:04Z | ---
license: cc-by-sa-4.0
base_model: EMBEDDIA/sloberta
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine_tuned_rte_sloberta
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. -->
# fine_tuned_rte_sloberta
This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6653
- Accuracy: 0.6207
- F1: 0.5750
## 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
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 0.6985 | 1.7241 | 50 | 0.7540 | 0.4138 | 0.2422 |
| 0.7058 | 3.4483 | 100 | 0.6912 | 0.5862 | 0.4333 |
| 0.6993 | 5.1724 | 150 | 0.6980 | 0.4138 | 0.2422 |
| 0.7003 | 6.8966 | 200 | 0.6806 | 0.5862 | 0.4333 |
| 0.6968 | 8.6207 | 250 | 0.6730 | 0.5862 | 0.4333 |
| 0.6736 | 10.3448 | 300 | 0.6726 | 0.6897 | 0.6801 |
| 0.6339 | 12.0690 | 350 | 0.6580 | 0.6207 | 0.6090 |
| 0.6005 | 13.7931 | 400 | 0.6653 | 0.6207 | 0.5750 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.1.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
mlx-community/dolphin-2.9.1-llama-3-70b-8bit | mlx-community | 2024-05-22T19:39:30Z | 7 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"axolotl",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-70B",
"base_model:finetune:meta-llama/Meta-Llama-3-70B",
"license:llama3",
"region:us"
] | null | 2024-05-22T18:36:49Z | ---
license: llama3
tags:
- generated_from_trainer
- axolotl
- mlx
base_model: meta-llama/Meta-Llama-3-70B
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
model-index:
- name: out
results: []
---
# mlx-community/dolphin-2.9.1-llama-3-70b-8bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9.1-llama-3-70b`]() using mlx-lm version **0.12.1**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9.1-llama-3-70b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9.1-llama-3-70b-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_user-5-3-0.9 | Heimat24 | 2024-05-22T19:35:40Z | 9 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T19:34:49Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 24 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 7,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Aysr01/tiny-arabic_poet-medium | Aysr01 | 2024-05-22T19:35:20Z | 169 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T19:34:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
khaled123/mathress | khaled123 | 2024-05-22T19:32:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T19:09:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
NNet/saiga_llama3_8b-Q6_K-GGUF | NNet | 2024-05-22T19:32:06Z | 2 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"ru",
"dataset:IlyaGusev/saiga_scored",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-22T19:31:47Z | ---
language:
- ru
license: other
tags:
- llama-cpp
- gguf-my-repo
datasets:
- IlyaGusev/saiga_scored
license_name: llama3
license_link: https://llama.meta.com/llama3/license/
---
# NNet/saiga_llama3_8b-Q6_K-GGUF
This model was converted to GGUF format from [`IlyaGusev/saiga_llama3_8b`](https://huggingface.co/IlyaGusev/saiga_llama3_8b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/IlyaGusev/saiga_llama3_8b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo NNet/saiga_llama3_8b-Q6_K-GGUF --model saiga_llama3_8b.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NNet/saiga_llama3_8b-Q6_K-GGUF --model saiga_llama3_8b.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m saiga_llama3_8b.Q6_K.gguf -n 128
```
|
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_secretary-5-3-0.9 | Heimat24 | 2024-05-22T19:32:04Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T19:31:04Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 24 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 7,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Lewdiculous/rogue-enchantress-32k-7B-GGUF-IQ-Imatrix | Lewdiculous | 2024-05-22T19:31:10Z | 61 | 5 | null | [
"gguf",
"roleplay",
"mistral",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-05-22T14:44:55Z | ---
license: cc-by-nc-4.0
tags:
- roleplay
- mistral
---
These are my personal testing quants for [**grimjim/rogue-enchantress-32k-7B**](https://huggingface.co/grimjim/rogue-enchantress-32k-7B). [[Presets]](https://huggingface.co/Lewdiculous/Model-Requests/tree/main/data/presets/lewdicu-3.0.2-mistral-0.2) |
helenai/papluca-xlm-roberta-base-language-detection-ov | helenai | 2024-05-22T19:30:24Z | 275 | 0 | transformers | [
"transformers",
"openvino",
"xlm-roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-03-11T17:17:41Z | ---
language:
- en
tags:
- openvino
---
# papluca/xlm-roberta-base-language-detection
This is the [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) model converted to [OpenVINO](https://openvino.ai), for accelerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/papluca-xlm-roberta-base-language-detection-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("hello world")
print(result)
```
|
PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer8_16_24_30-def_layer0-ultrachat-7 | PhillipGuo | 2024-05-22T19:24:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T19:24:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Multimash3-12B-slerp-GGUF | mradermacher | 2024-05-22T19:23:25Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"allknowingroger/Multimerge-12B-MoE",
"TomGrc/FusionNet_7Bx2_MoE_v0.1",
"en",
"base_model:allknowingroger/Multimash3-12B-slerp",
"base_model:quantized:allknowingroger/Multimash3-12B-slerp",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T18:37:58Z | ---
base_model: allknowingroger/Multimash3-12B-slerp
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- allknowingroger/Multimerge-12B-MoE
- TomGrc/FusionNet_7Bx2_MoE_v0.1
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/allknowingroger/Multimash3-12B-slerp
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.IQ3_XS.gguf) | IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q3_K_S.gguf) | Q3_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.IQ3_M.gguf) | IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q3_K_L.gguf) | Q3_K_L | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q4_K_M.gguf) | Q4_K_M | 7.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q5_K_S.gguf) | Q5_K_S | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q5_K_M.gguf) | Q5_K_M | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q6_K.gguf) | Q6_K | 10.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q8_0.gguf) | Q8_0 | 13.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
auragFouad/mistral_7b_aspect_extraction_restaurants | auragFouad | 2024-05-22T19:22:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T19:22:22Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** auragFouad
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer8_16_24_30-def_layer0-ultrachat-7 | PhillipGuo | 2024-05-22T19:22:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T19:22:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
haohaa/wav2vec2-large-mms-1b-shan | haohaa | 2024-05-22T19:20:33Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-22T18:48:21Z | ---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-shan
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. -->
# wav2vec2-large-mms-1b-shan
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3066
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- 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: 100
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 0.4342 | 10.0 | 100 | 0.3066 | 1.0 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer8_16_24_30-def_layer0-ultrachat-7 | PhillipGuo | 2024-05-22T19:17:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T19:17:10Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mlx-community/Mistral-7B-v0.3-4bit | mlx-community | 2024-05-22T19:16:35Z | 25 | 2 | mlx | [
"mlx",
"safetensors",
"mistral",
"license:apache-2.0",
"region:us"
] | null | 2024-05-22T17:44:35Z | ---
license: apache-2.0
tags:
- mlx
---
# mlx-community/Mistral-7B-v0.3-4bit
The Model [mlx-community/Mistral-7B-v0.3-4bit](https://huggingface.co/mlx-community/Mistral-7B-v0.3-4bit) was converted to MLX format from [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) using mlx-lm version **0.13.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Mistral-7B-v0.3-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
genia-vdg/genia-model2-v2 | genia-vdg | 2024-05-22T19:15:15Z | 30 | 0 | transformers | [
"transformers",
"safetensors",
"blip",
"image-text-to-text",
"image-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-to-text | 2024-05-08T12:12:08Z | ---
library_name: transformers
pipeline_tag: image-to-text
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Rimyy/Gemma-2b-finetuneGSMdata3exp | Rimyy | 2024-05-22T19:11:19Z | 142 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T19:08:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
DavidPL1/ppo-LunarLander-v2 | DavidPL1 | 2024-05-22T19:06:02Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-22T11:25:55Z | ---
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: 276.76 +/- 18.21
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
...
```
|
DrNicefellow/microscopic-mamba-2.1B-hf-25.2ksteps | DrNicefellow | 2024-05-22T19:04:18Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"mamba",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T18:57:57Z | ---
license: apache-2.0
---
Self trained microscopic Mamba. Around 2.1G parameters.
The tokenizer is the one from https://huggingface.co/state-spaces/mamba-2.8b-hf.
It is being trained on around 400B tokens and this is step 25.2k.
The evaluation is being conducted now.
## License
This model is available under the Apache 2.0 License.
## Discord Server
Join our Discord server [here](https://discord.gg/xhcBDEM3).
## Feeling Generous? 😊
Eager to buy me a cup of 2$ coffe or iced tea?🍵☕ Sure, here is the link: [https://ko-fi.com/drnicefellow](https://ko-fi.com/drnicefellow). Please add a note on which one you want me to drink?
|
helenpy/roberta-base-bne-finetuned-Tass2020 | helenpy | 2024-05-22T19:03:02Z | 127 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:BSC-LT/roberta-base-bne",
"base_model:finetune:BSC-LT/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-05-22T19:01:53Z | ---
license: apache-2.0
base_model: BSC-TeMU/roberta-base-bne
tags:
- generated_from_trainer
model-index:
- name: roberta-base-bne-finetuned-Tass2020
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-Tass2020
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1040
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9838 | 1.0 | 15 | 3.3549 |
| 3.3428 | 2.0 | 30 | 3.0660 |
| 3.1695 | 3.0 | 45 | 2.8863 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Milana/model-classifier-vctk | Milana | 2024-05-22T18:53:14Z | 135 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/mms-lid-4017",
"base_model:finetune:facebook/mms-lid-4017",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-05-22T14:53:05Z | ---
license: cc-by-nc-4.0
base_model: facebook/mms-lid-4017
tags:
- generated_from_trainer
model-index:
- name: model-classifier-vctk
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. -->
# model-classifier-vctk
This model is a fine-tuned version of [facebook/mms-lid-4017](https://huggingface.co/facebook/mms-lid-4017) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- 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
- training_steps: 1000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
auragFouad/llama3_aspect_extraction_restaurants | auragFouad | 2024-05-22T18:51:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T18:51:40Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** auragFouad
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
hgnoi/07rVkfFrHoNuEx2l | hgnoi | 2024-05-22T18:47:58Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T18:46:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Slvcxc/saiga_llama3_8b-V5-8.0bpw-h8-exl2 | Slvcxc | 2024-05-22T18:47:50Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama3",
"8-bit",
"conversational",
"ru",
"base_model:IlyaGusev/saiga_llama3_8b",
"base_model:quantized:IlyaGusev/saiga_llama3_8b",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-22T16:22:16Z | ---
language:
- ru
base_model:
- IlyaGusev/saiga_llama3_8b
license: other
license_name: llama3
license_link: https://llama.meta.com/llama3/license/
tags:
- llama3
- 8-bit
---
## **saiga_llama3_8b**
[exllamav2](https://github.com/turboderp/exllamav2) quant for [IlyaGusev/saiga_llama3_8b](https://huggingface.co/IlyaGusev/saiga_llama3_8b)
**Original model information:**
# Saiga/Llama3 8B, Russian Llama-3-based chatbot
Based on [Llama-3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
Llama.cpp version: [link](https://huggingface.co/IlyaGusev/saiga_llama3_8b_gguf)
**ОСТОРОЖНО! WARNING! LET OP!**
I've changed the prompt format from ChatML to **the original Llama-3 format in v4**. Don't forget to switch formats!
**v4+**: LLama-3 prompt format:
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<|eot_id|><|start_header_id|>user<|end_header_id|>
Как дела?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Отлично, а у тебя?<|eot_id|><|start_header_id|>user<|end_header_id|>
Шикарно. Как пройти в библиотеку?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
**v2, v3**: ChatML prompt format:
```
<|im_start|>system
Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<|im_end|>
<|im_start|>user
Как дела?<|im_end|>
<|im_start|>assistant
Отлично, а у тебя?<|im_end|>
<|im_start|>user
Шикарно. Как пройти в библиотеку?<|im_end|>
<|im_start|>assistant
```
Examples:
```
User: Почему трава зеленая?
Saiga: Зеленый цвет травы обусловлен наличием хлорофилла, пигмента, который поглощает свет и передает энергию для фотосинтеза. Хлорофилл содержится в клетках листьев растений и играет ключевую роль в процессе фотосинтеза, когда растения используют солнечный свет для превращения воды и углекислого газа в органические вещества, такие как глюкоза и олигоэфиры.
Хлорофилл состоит из двух основных форм: хлорофилла а и хлорофилла б. Хлорофилл а поглощает свет с длиной волны около 430 нанометров (нм), что соответствует синему или голубому свету, а хлорофилл б поглощает свет с длиной волны около 660 нм, что соответствует красному свету. Когда свет поглощается хлорофиллом, он передается в молекулы воды и углекислого газа, которые используются для фотосинтеза.
Таким образом, хлорофилл является основным компонентом, ответственным за зеленый цвет листьев растений, так как он поглощает свет и передает его энергию для фотосинтеза.
```
```
User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч
Saiga: Однажды в солнечную погоду на поле для игры в футбол приехала группа друзей, чтобы провести время вместе. В числе их был и Таня, которая была страстной футбольной фанаткой и всегда радовал своих друзей своим энтузиазмом и умением забивать мячи.
В этот день, как обычно, друзья решили провести соревнование между собой, чтобы определить кто из них является лучшим футболистом. Таня была очень уверена в своих силах и готовилась к этому матчу с особой серьёзностью.
Когда все друзья собрались на поле, они увидели, что перед ними стоял огромный мяч, который должен был стать предметом состязания. Мяч был огромным и тяжелым, и его размеры были необычайно большими по сравнению с обычными мячами, которые используются в футболе.
Таня была первая, кто решил начать игру. Она подошла к мячу и начала его удерживать, стараясь выдержать его вес и силу. Но мяч оказался настолько тяжелым, что Таня не смогла удержать его и он упал на землю.
Друзья посмеялись над ее неудачей, но Таня не отчаивалась и продолжила пытаться удержать мяч. Она стала использовать все свои силы и умения, чтобы выдержать его вес и силу. Наконец, после долгих усилий, она смогла удержать мяч и начала его бросать в сторону.
Мяч летел высоко вверх, и друзья смотрели, как он пролетает над полем. Но мяч неожиданно повернул и стал лететь обратно к Тане. Она успела поймать его и продолжила играть, используя все свои навыки и умения.
```
v5:
- [d947b00c56683cd4b2f7ce707edef89318027be4](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/d947b00c56683cd4b2f7ce707edef89318027be4)
- KTO-tune over v4, dataset: [lmsys_clean_ru_preferences](https://huggingface.co/datasets/IlyaGusev/lmsys_clean_ru_preferences)
- wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/se1mbx7n)
v4:
- [1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/1cc945d4ca2c7901cf989e7edaac52ab24f1a7dd)
- dataset: [saiga_scored](https://huggingface.co/datasets/IlyaGusev/saiga_scored), scores >= 8, c66032920556c0f21bbbed05e7e04433ec954c3d
- wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/dcbs9ttt)
v3:
- [c588356cd60bdee54d52c2dd5a2445acca8aa5c3](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/c588356cd60bdee54d52c2dd5a2445acca8aa5c3)
- dataset: [saiga_scored](https://huggingface.co/datasets/IlyaGusev/saiga_scored), scores >= 8, d51cf8060bdc90023da8cf1c3f113f9193d6569b
- wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/ltoqdsal)
v2:
- [ae61b4f9b34fac9856d361ea78c66284a00e4f0b](https://huggingface.co/IlyaGusev/saiga_llama3_8b/commit/ae61b4f9b34fac9856d361ea78c66284a00e4f0b)
- dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a
- wandb [link](https://wandb.ai/ilyagusev/huggingface/runs/r6u5juyk)
- 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4
- Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py)
# Evaluation
* Dataset: https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/tasks.jsonl
* Framework: https://github.com/tatsu-lab/alpaca_eval
* Evaluator: alpaca_eval_cot_gpt4_turbo_fn
| model | length_controlled_winrate | win_rate | standard_error | avg_length |
|-----|-----|-----|-----|-----|
|chatgpt_4_turbo | 76.04 | 90.00 |1.46 | 1270 |
|chatgpt_3_5_turbo | 50.00 | 50.00 | 0.00 | 536 |
|saiga_llama3_8b, v4 | 43.64 | 65.90 | 2.31 | 1200 |
|saiga_llama3_8b, v3 | 36.97 | 61.08 | 2.38 | 1162 |
|saiga_llama3_8b, v2 | 33.07 | 48.19 | 2.45 | 1166 |
|saiga_mistral_7b | 23.38 | 35.99 | 2.34 | 949 | |
hgnoi/hvUskskiS28W5EeK | hgnoi | 2024-05-22T18:47:07Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T18:45:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_user-5-5-0.8 | Heimat24 | 2024-05-22T18:46:24Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T18:45:35Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 24 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 12,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_secretary-5-5-0.8 | Heimat24 | 2024-05-22T18:42:51Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T18:41:50Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 24 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 12,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Spielers/test-model | Spielers | 2024-05-22T18:42:09Z | 0 | 0 | null | [
"region:us"
] | null | 2024-05-22T18:33:22Z | halo i'm testing, <3
what the hell |
Soukaina588956468/experiments | Soukaina588956468 | 2024-05-22T18:40:10Z | 3 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
] | null | 2024-05-21T23:26:20Z | ---
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- generated_from_trainer
model-index:
- name: experiments
results: []
library_name: peft
---
<!-- 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. -->
# experiments
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
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
- load_in_4bit: True
- load_in_8bit: False
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 80
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.5.0
- Transformers 4.38.2
- Pytorch 2.3.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.2
|
Sari95/Transformer-Architecture-for-Energy-Consumption-Prediction | Sari95 | 2024-05-22T18:39:26Z | 0 | 0 | null | [
"license:gpl",
"region:us"
] | null | 2024-05-03T18:40:11Z | ---
title: Transformer Model for Energy Consumption Prediction
description: >-
This model predicts energy consumption based on meteorological data and
historical usage.
license: gpl
---
# Transformer-Architecture for Energy Consumption Prediction
## Description
This model applies Transformer architecture to predict energy consumption over a 48-hour period using historical energy usage and weather data from 2021 to 2023.
## Model Details
**Model Type:** Transformer
**Data Period:** 2021-2023
**Variables Used:**
1. Transformer with Energy consumption data and weather data
2. Transformer with Energy consumption data and two additional variables: 'Lastgang_Moving_Average' and 'Lastgang_First_Difference'
## Features
The model uses a sequence length of 192 (48 hours) to create input sequences for training and testing. Positional encodings are added to the sequences to provide temporal information to the Transformer model.
## Installation and Execution
To run this model, you need Python along with the following libraries:
- `pandas`
- `numpy`
- `matplotlib`
- `scikit-learn`
- `torch`
- `gputil`
- `psutil`
- `torchsummary`
### Steps to Execute the Model:
1. **Install Required Packages**
2. **Load your Data**
3. **Preprocess the data according to the specifications**
4. **Run the Script**
|
anzeo/loha_fine_tuned_rte_croslo | anzeo | 2024-05-22T18:35:30Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:EMBEDDIA/crosloengual-bert",
"base_model:adapter:EMBEDDIA/crosloengual-bert",
"license:cc-by-4.0",
"region:us"
] | null | 2024-05-22T18:35:27Z | ---
license: cc-by-4.0
library_name: peft
tags:
- generated_from_trainer
base_model: EMBEDDIA/crosloengual-bert
metrics:
- accuracy
- f1
model-index:
- name: loha_fine_tuned_rte_croslo
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. -->
# loha_fine_tuned_rte_croslo
This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6891
- Accuracy: 0.5862
- F1: 0.5862
## 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
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 0.7088 | 1.7241 | 50 | 0.6839 | 0.6552 | 0.6388 |
| 0.695 | 3.4483 | 100 | 0.6840 | 0.6552 | 0.6388 |
| 0.7126 | 5.1724 | 150 | 0.6869 | 0.5862 | 0.5789 |
| 0.7051 | 6.8966 | 200 | 0.6888 | 0.5862 | 0.5862 |
| 0.6905 | 8.6207 | 250 | 0.6889 | 0.5862 | 0.5862 |
| 0.7075 | 10.3448 | 300 | 0.6894 | 0.5862 | 0.5862 |
| 0.701 | 12.0690 | 350 | 0.6890 | 0.5862 | 0.5862 |
| 0.7036 | 13.7931 | 400 | 0.6891 | 0.5862 | 0.5862 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.1.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_secretary-5-3-0.8 | Heimat24 | 2024-05-22T18:33:14Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T18:32:21Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 24 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 7,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ryu009gaijin/aim-llm | ryu009gaijin | 2024-05-22T18:32:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"dataset:ryu009gaijin/aimlm",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T18:27:45Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- ryu009gaijin/aimlm
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
helenai/Alireza1044-albert-base-v2-sst2-ov | helenai | 2024-05-22T18:30:35Z | 3 | 0 | transformers | [
"transformers",
"openvino",
"albert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-04-20T18:16:46Z | ---
language:
- en
tags:
- openvino
---
# Alireza1044/albert-base-v2-sst2
This is the [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2) model converted to [OpenVINO](https://openvino.ai), for accelerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/Alireza1044-albert-base-v2-sst2-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("hello world")
print(result)
```
|
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_user-5-3-0.8 | Heimat24 | 2024-05-22T18:30:19Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T18:29:19Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 24 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 7,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Alaninfant/OrpoLlamabnb-3-8B | Alaninfant | 2024-05-22T18:28:39Z | 2 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-05-22T17:42:24Z | ---
license: apache-2.0
---
|
Undi95/Llama-3-Chatty-2x8B | Undi95 | 2024-05-22T18:24:38Z | 9 | 11 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"merge",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-21T21:57:28Z | ---
license: cc-by-nc-4.0
tags:
- merge
---
### Chatty-2x8B
## Description
After some testing, finetuning and multiple merges of Llama-3 LLM models, here is something a little different.
This model is a MoE of 2x Llama-3 model trained on different RP format.
This repo contains FP16 files of Chatty-2x8B.
## The idea
I started with two separate Llama-3-Instruct-8B models, each fine-tuned for specific RP formats.
Here is two simple exemple of how it was trained.
- **Expert 1**: This model is trained to handle RP that requires actions and descriptions between asterisks. For example:
```
*nods* Yes, I understand.
```
- **Expert 2**: This model is fine-tuned for plain text RP where characters’ dialogues and actions are described straightforwardly. For example:
```
Nods. "Yes, I understand."
```
My initial idea was to make a 11B or bigger Llama-3 model, or just make a 2x8B from existing model, but I got some issues, they were not stable enough, even after DPO and FFT on top my frankenmerge/moe of Llama-3, it was not working well enough to release them.
So I just tried the idea of having 2 different RP format trained on 2 separated Llama-3-Instruct-8B, and it worked pretty well!
## The dataset
Based on Lumimaid 8B OAS success I still used the same "balance" between RP and non RP in the dataset, the maximum was 50% non RP data on each side.
RP data was different with some exception, the non RP data was exactly the same, despite that, I can't produce repetition so the double usage of non RP datasets didn't hurt the model in the end.
## Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
## Others
Undi: If you want to support us, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|arc_challenge | 1|none | 0|acc |0.5469|± |0.0145|
| | |none | 0|acc_norm |0.5853|± |0.0144|
|arc_easy | 1|none | 0|acc |0.8308|± |0.0077|
| | |none | 0|acc_norm |0.8258|± |0.0078|
|gsm8k | 3|strict-match | 5|exact_match|0.7149|± |0.0124|
| | |flexible-extract| 5|exact_match|0.7096|± |0.0125|
|hellaswag | 1|none | 0|acc |0.5945|± |0.0049|
| | |none | 0|acc_norm |0.7806|± |0.0041|
|piqa | 1|none | 0|acc |0.7943|± |0.0094|
| | |none | 0|acc_norm |0.7998|± |0.0093|
|truthfulqa_mc2| 2|none | 0|acc |0.5097|± |0.0150|
|winogrande | 1|none | 0|acc |0.7356|± |0.0124| |
Undi95/Llama-3-Chatty-2x8B-GGUF | Undi95 | 2024-05-22T18:24:26Z | 15 | 9 | null | [
"gguf",
"merge",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-21T22:08:25Z | ---
license: cc-by-nc-4.0
tags:
- merge
---
### Chatty-2x8B
## Description
After some testing, finetuning and multiple merges of Llama-3 LLM models, here is something a little different.
This model is a MoE of 2x Llama-3 model trained on different RP format.
This repo contains GGUF files of Chatty-2x8B.
## The idea
I started with two separate Llama-3-Instruct-8B models, each fine-tuned for specific RP formats.
Here is two simple exemple of how it was trained.
- **Expert 1**: This model is trained to handle RP that requires actions and descriptions between asterisks. For example:
```
*nods* Yes, I understand.
```
- **Expert 2**: This model is fine-tuned for plain text RP where characters’ dialogues and actions are described straightforwardly. For example:
```
Nods. "Yes, I understand."
```
My initial idea was to make a 11B or bigger Llama-3 model, or just make a 2x8B from existing model, but I got some issues, they were not stable enough, even after DPO and FFT on top my frankenmerge/moe of Llama-3, it was not working well enough to release them.
So I just tried the idea of having 2 different RP format trained on 2 separated Llama-3-Instruct-8B, and it worked pretty well!
## The dataset
Based on Lumimaid 8B OAS success I still used the same "balance" between RP and non RP in the dataset, the maximum was 50% non RP data on each side.
RP data was different with some exception, the non RP data was exactly the same, despite that, I can't produce repetition so the double usage of non RP datasets didn't hurt the model in the end.
## Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
## Others
Undi: If you want to support us, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek |
Yntec/AnythingV5-768 | Yntec | 2024-05-22T18:24:08Z | 331 | 1 | diffusers | [
"diffusers",
"safetensors",
"anime",
"ink",
"lines",
"Yuno779",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-22T17:38:34Z | ---
language:
- en
license: creativeml-openrail-m
tags:
- anime
- ink
- lines
- Yuno779
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Anything V5
768x768 version of this model with the kl-f8-anime2 VAE baked in for the Inference API.
Samples and prompts:
(Click for larger)
Top right: highquality, masterpiece, 1girl, Chi-Chi, close up, arms up, pink helmet, black hair, black eyes, blush, white teeth, bikini armor, aqua cape, pink gloves, pink boots, cleavage. cave, rock, mountain. blue collar, CHIBI.
Top left: retro videogames, robert jordan pepperoni pizza, josephine wall winner, hidari, roll20 illumination, radiant light, sitting elementary girl, Pretty CUTE, gorgeous hair, DETAILED CHIBI EYES, Magazine ad, iconic, 1943, Cartoon, sharp focus, 4k, towel. comic art on canvas by kyoani and ROSSDRAWS and watched
Bottom left: icon of adorable little red panda, round frame, blue glow, wearing shoes. CHIBI
Bottom right: Highly detailed, High Quality, Masterpiece, beautiful, cute girl as toon link, teal headwear, glad Zelda
Source: https://huggingface.co/swl-models/Anything-v5.0-PRT/tree/main
512x512 version: https://huggingface.co/stablediffusionapi/anything-v5 |
Sari95/SARIMAX-for-Energy-Consumption-Prediction | Sari95 | 2024-05-22T18:23:00Z | 0 | 1 | null | [
"license:gpl",
"region:us"
] | null | 2024-05-03T18:45:13Z | ---
title: SARIMAX Model for Energy Consumption Prediction
description: >-
This model predicts energy consumption based on meteorological data and
historical usage.
license: gpl
---
# SARIMAX Model for Energy Consumption Prediction
## Description
This SARIMAX model predicts energy consumption over a 48-hour period based on meteorological data and historical energy usage from 2021 to 2023. It utilizes time series data from a transformer station and incorporates both time-based and weather-related variables to enhance prediction accuracy.
## Model Details
**Model Type:** SARIMAX (Seasonal ARIMA with exogenous variables)
**Data Period:** 2021-2023
**Variables Used:**
- `Lastgang`: Energy consumption data
- `StundenwertStrahlung`: Hourly radiation
- `Globalstrahlung_15Min`: Global radiation every 15 minutes
- `StrahlungGeneigteFläche`: Radiation on inclined surfaces
- `TheorPVProd`: Theoretical photovoltaic production
- `Direktnormalstrahlung`: Direct normal radiation
- `Schönwetterstrahlung`: Clear sky radiation
- `Lufttemperatur`: Air temperature
- `Lastgang_Moving_Average`: Moving average of energy consumption
- `Lastgang_First_Difference`: First difference of energy consumption
## Features
The model splits the data into training and testing sets, with the last 192 data points (equivalent to 48 hours at 15-minute intervals) designated as the test dataset. It defines target variables (`Lastgang`) and explanatory variables including hourly and daily patterns as well as derived features from the consumption data. The dataset includes preprocessed features such as scaled energy consumption (`Lastgang`), and weather-related features.
## Installation and Execution
To run this model, you need Python along with the following libraries:
- `pandas`
- `numpy`
- `matplotlib`
- `statsmodels`
- `scikit-learn`
- `pmdarima`
- `psutil`
### Steps to Execute the Model:
1. **Install Required Packages**
2. **Load your Data**
3. **Preprocess the data according to the specifications**
4. **Run the Script** |
egoist000/rotten_tomatoes_roberta_sentiment_analysis | egoist000 | 2024-05-22T18:20:17Z | 62 | 0 | transformers | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-19T21:58:26Z | ---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: egoist000/rotten_tomatoes_roberta_sentiment_analysis
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. -->
# egoist000/rotten_tomatoes_roberta_sentiment_analysis
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2454
- Validation Loss: 0.2918
- Train Accuracy: 0.8762
- 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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 23031, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 2559, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.1}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6520 | 0.3281 | 0.8724 | 0 |
| 0.3072 | 0.2703 | 0.8846 | 1 |
| 0.2454 | 0.2918 | 0.8762 | 2 |
### Framework versions
- Transformers 4.39.3
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_secretary-3-3-0.8 | Heimat24 | 2024-05-22T18:17:19Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T18:16:14Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
win10/taide-meta-it-16b | win10 | 2024-05-22T18:14:18Z | 3 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"arxiv:2203.05482",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-22T11:55:42Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# llama3-15b-v02
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* D:/text-generation-webui/models/meta-llama_Meta-Llama-3-8B-Instruct
* D:/text-generation-webui/models/taide_Llama3-TAIDE-LX-8B-Chat-Alpha1
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
parameters:
weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
slices:
- sources:
- layer_range: [0, 1]
model: D:/text-generation-webui/models/meta-llama_Meta-Llama-3-8B-Instruct
- layer_range: [0, 1]
model: D:/text-generation-webui/models/taide_Llama3-TAIDE-LX-8B-Chat-Alpha1
parameters:
weight: 0
- sources:
- layer_range: [1, 24]
model: D:/text-generation-webui/models/meta-llama_Meta-Llama-3-8B-Instruct
- layer_range: [1, 24]
model: D:/text-generation-webui/models/taide_Llama3-TAIDE-LX-8B-Chat-Alpha1
- sources:
- layer_range: [24, 32]
model: D:/text-generation-webui/models/taide_Llama3-TAIDE-LX-8B-Chat-Alpha1
parameters:
weight: 0
- layer_range: [24, 32]
model: D:/text-generation-webui/models/meta-llama_Meta-Llama-3-8B-Instruct
```
|
saad17g/finetuned_T5_amzn_v2 | saad17g | 2024-05-22T18:10:05Z | 33 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-22T08:48:23Z | ---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: finetuned_T5_amzn_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_T5_amzn_v2
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an the Amazon Fine Food Reviews dataset.
It achieves the following results on the evaluation set:
- Loss: 2.879612684249878
- Rouge1: 0.6625
- Rouge2: 0.4053
- Rougel: 0.1755
- Rougelsum: 0.1755
- Gen Len: 5.3418
- Bleu: 0.0178
- Bert Precision: 0.8657
- Bert Recall: 0.8505
- Bert F1: 0.8575
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.19.1
|
mlx-community/dolphin-2.9.1-llama-3-70b-4bit | mlx-community | 2024-05-22T18:07:44Z | 7 | 1 | mlx | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"axolotl",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-70B",
"base_model:finetune:meta-llama/Meta-Llama-3-70B",
"license:llama3",
"region:us"
] | null | 2024-05-22T17:54:50Z | ---
license: llama3
tags:
- generated_from_trainer
- axolotl
- mlx
base_model: meta-llama/Meta-Llama-3-70B
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
model-index:
- name: out
results: []
---
# mlx-community/dolphin-2.9.1-llama-3-70b-4bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9.1-llama-3-70b`]() using mlx-lm version **0.12.1**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9.1-llama-3-70b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9.1-llama-3-70b-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
Heimat24/vhs_burghausen_danielheinz_e5-qa_generation_secretary-5-1-0.8 | Heimat24 | 2024-05-22T18:07:43Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-22T18:06:37Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 24 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
hyoo14/NucletideTransformer_AMR | hyoo14 | 2024-05-22T18:05:04Z | 115 | 0 | transformers | [
"transformers",
"safetensors",
"esm",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-22T02:28:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
dtorber/BioNLP-intro-disc-eLife | dtorber | 2024-05-22T18:01:56Z | 19 | 0 | transformers | [
"transformers",
"safetensors",
"led",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2024-04-12T11:49:18Z | ---
tags:
- summarization
- generated_from_trainer
model-index:
- name: BioNLP-intro-disc-eLife
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. -->
# BioNLP-intro-disc-eLife
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.3739167643078955e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
baek26/all_5356_bart-all_rl | baek26 | 2024-05-22T18:01:10Z | 52 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2024-05-22T18:00:38Z | ---
license: apache-2.0
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="baek26//tmp/tmp76sc_dnq/baek26/all_5356_bart-all_rl")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("baek26//tmp/tmp76sc_dnq/baek26/all_5356_bart-all_rl")
model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmp76sc_dnq/baek26/all_5356_bart-all_rl")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
egoist000/yelp_roberta_star_rating | egoist000 | 2024-05-22T17:58:49Z | 62 | 0 | transformers | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T16:44:53Z | ---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: egoist000/yelp_roberta_star_rating
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. -->
# egoist000/yelp_roberta_star_rating
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7450
- Validation Loss: 0.7254
- Train Accuracy: 0.678
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 172800, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 19200, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.1}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.9054 | 0.7446 | 0.6767 | 0 |
| 0.7450 | 0.7254 | 0.678 | 1 |
### Framework versions
- Transformers 4.39.3
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|
rafinsky/my_awesome_food_model_3 | rafinsky | 2024-05-22T17:49:16Z | 194 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-22T17:02:22Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
pipeline_tag: image-classification
metrics:
- accuracy
model-index:
- name: my_awesome_food_model_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model_3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3331
- Accuracy: 0.818
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4474 | 0.992 | 31 | 3.2059 | 0.789 |
| 2.5977 | 1.984 | 62 | 2.5210 | 0.816 |
| 2.2881 | 2.976 | 93 | 2.3331 | 0.818 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
DavidPL1/q-Taxi-v3 | DavidPL1 | 2024-05-22T17:47:16Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-22T17:46:19Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.69 +/- 2.55
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="DavidPL1/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
GENIAC-Team-Ozaki/full-sft-finetuned-stage4-iter86000 | GENIAC-Team-Ozaki | 2024-05-22T17:46:04Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:35:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
TurtleWave/TurtleWaveSupport | TurtleWave | 2024-05-22T17:45:10Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-22T17:45:10Z | ---
license: apache-2.0
---
|
chaosIsRythmic/hc-mistral-alpaca | chaosIsRythmic | 2024-05-22T17:42:52Z | 1 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-05-22T10:39:04Z | ---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: hc-mistral-alpaca
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: true
strict: false
lora_fan_in_fan_out: false
data_seed: 49
seed: 49
datasets:
- path: sample_data/alpaca_synth_queries.jsonl
type: sharegpt
conversation: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-alpaca-out
hub_model_id: chaosIsRythmic/hc-mistral-alpaca
adapter: qlora
lora_model_dir:
sequence_len: 896
sample_packing: false
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project: hc-axolotl-mistral
wandb_entity: chaos_isrythmic
gradient_accumulation_steps: 4
micro_batch_size: 16
eval_batch_size: 16
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
max_grad_norm: 1.0
adam_beta2: 0.95
adam_epsilon: 0.00001
save_total_limit: 12
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 20
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 6
debug:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
save_safetensors: true
```
</details><br>
# hc-mistral-alpaca
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: 1.2496
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 49
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.334 | 0.6667 | 1 | 1.2850 |
| 1.3478 | 1.3333 | 2 | 1.2773 |
| 1.298 | 2.0 | 3 | 1.2496 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1 |
AndySilver/labelleariante_v1.0.3 | AndySilver | 2024-05-22T17:40:18Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-22T17:35:33Z | ---
license: apache-2.0
---
|
Imohsinali/bert-fine-tuned-cola | Imohsinali | 2024-05-22T17:40:02Z | 107 | 0 | transformers | [
"transformers",
"tf",
"safetensors",
"bert",
"text-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"
] | text-classification | 2024-04-28T13:18:27Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
base_model: bert-base-cased
model-index:
- name: bert-fine-tuned-cola
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. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a glue cola dataset.
It achieves the following results on the evaluation set:
## Model description
If your given sentence is grammatically and liguistically OK, then it is acceptable.
## 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
### Framework versions
- Transformers 4.41.0
- TensorFlow 2.16.1
- Datasets 2.19.0
- Tokenizers 0.19.1
|
mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF | mradermacher | 2024-05-22T17:39:22Z | 25 | 4 | transformers | [
"transformers",
"gguf",
"en",
"base_model:failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3",
"base_model:quantized:failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-22T07:05:17Z | ---
base_model: failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.IQ3_XS.gguf) | IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.IQ3_M.gguf) | IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-abliterated-v3-GGUF/resolve/main/Smaug-Llama-3-70B-Instruct-abliterated-v3.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
giantdev/dippy-lT9Bu-sn11m6 | giantdev | 2024-05-22T17:36:18Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:34:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
S4nto/lora-dpo-finetuned-stage4-sft-0.5-1e-6_ep-1 | S4nto | 2024-05-22T17:36:02Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:24:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
canho/KoMo-KoAlpaca-0522-15epoch | canho | 2024-05-22T17:35:53Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/KoAlpaca-Polyglot-5.8B",
"base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B",
"region:us"
] | null | 2024-05-22T17:10:06Z | ---
library_name: peft
base_model: beomi/KoAlpaca-Polyglot-5.8B
---
# 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]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.2.dev0 |
JeffreyLind/Meta-Llama-3-8B-Q4_K_M-GGUF | JeffreyLind | 2024-05-22T17:35:03Z | 0 | 0 | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:llama3",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:34:46Z | ---
language:
- en
license: llama3
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\
\ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\
\ use, reproduction, distribution and modification of the Llama Materials set forth\
\ herein.\n\"Documentation\" means the specifications, manuals and documentation\
\ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\
\"Licensee\" or \"you\" means you, or your employer or any other person or entity\
\ (if you are entering into this Agreement on such person or entity’s behalf), of\
\ the age required under applicable laws, rules or regulations to provide legal\
\ consent and that has legal authority to bind your employer or such other person\
\ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\
\ 3\" means the foundational large language models and software and algorithms,\
\ including machine-learning model code, trained model weights, inference-enabling\
\ code, training-enabling code, fine-tuning enabling code and other elements of\
\ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\
\"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\
\ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\
we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\
\ an entity, your principal place of business is in the EEA or Switzerland) and\
\ Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n\
\ \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted\
\ a non-exclusive, worldwide, non-transferable and royalty-free limited license\
\ under Meta’s intellectual property or other rights owned by Meta embodied in the\
\ Llama Materials to use, reproduce, distribute, copy, create derivative works of,\
\ and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni.\
\ If you distribute or make available the Llama Materials (or any derivative works\
\ thereof), or a product or service that uses any of them, including another AI\
\ model, you shall (A) provide a copy of this Agreement with any such Llama Materials;\
\ and (B) prominently display “Built with Meta Llama 3” on a related website, user\
\ interface, blogpost, about page, or product documentation. If you use the Llama\
\ Materials to create, train, fine tune, or otherwise improve an AI model, which\
\ is distributed or made available, you shall also include “Llama 3” at the beginning\
\ of any such AI model name.\nii. If you receive Llama Materials, or any derivative\
\ works thereof, from a Licensee as part of an integrated end user product, then\
\ Section 2 of this Agreement will not apply to you.\niii. You must retain in all\
\ copies of the Llama Materials that you distribute the following attribution notice\
\ within a “Notice” text file distributed as a part of such copies: “Meta Llama\
\ 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms,\
\ Inc. All Rights Reserved.”\niv. Your use of the Llama Materials must comply with\
\ applicable laws and regulations (including trade compliance laws and regulations)\
\ and adhere to the Acceptable Use Policy for the Llama Materials (available at\
\ https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference\
\ into this Agreement.\nv. You will not use the Llama Materials or any output or\
\ results of the Llama Materials to improve any other large language model (excluding\
\ Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If,\
\ on the Meta Llama 3 version release date, the monthly active users of the products\
\ or services made available by or for Licensee, or Licensee’s affiliates, is greater\
\ than 700 million monthly active users in the preceding calendar month, you must\
\ request a license from Meta, which Meta may grant to you in its sole discretion,\
\ and you are not authorized to exercise any of the rights under this Agreement\
\ unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer\
\ of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT\
\ AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF\
\ ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED,\
\ INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY,\
\ OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING\
\ THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME\
\ ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n\
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER\
\ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY,\
\ OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT,\
\ SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META\
\ OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n\
5. Intellectual Property.\na. No trademark licenses are granted under this Agreement,\
\ and in connection with the Llama Materials, neither Meta nor Licensee may use\
\ any name or mark owned by or associated with the other or any of its affiliates,\
\ except as required for reasonable and customary use in describing and redistributing\
\ the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you\
\ a license to use “Llama 3” (the “Mark”) solely as required to comply with the\
\ last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently\
\ accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All\
\ goodwill arising out of your use of the Mark will inure to the benefit of Meta.\n\
b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for\
\ Meta, with respect to any derivative works and modifications of the Llama Materials\
\ that are made by you, as between you and Meta, you are and will be the owner of\
\ such derivative works and modifications.\nc. If you institute litigation or other\
\ proceedings against Meta or any entity (including a cross-claim or counterclaim\
\ in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results,\
\ or any portion of any of the foregoing, constitutes infringement of intellectual\
\ property or other rights owned or licensable by you, then any licenses granted\
\ to you under this Agreement shall terminate as of the date such litigation or\
\ claim is filed or instituted. You will indemnify and hold harmless Meta from and\
\ against any claim by any third party arising out of or related to your use or\
\ distribution of the Llama Materials.\n6. Term and Termination. The term of this\
\ Agreement will commence upon your acceptance of this Agreement or access to the\
\ Llama Materials and will continue in full force and effect until terminated in\
\ accordance with the terms and conditions herein. Meta may terminate this Agreement\
\ if you are in breach of any term or condition of this Agreement. Upon termination\
\ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\
\ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\
\ and Jurisdiction. This Agreement will be governed and construed under the laws\
\ of the State of California without regard to choice of law principles, and the\
\ UN Convention on Contracts for the International Sale of Goods does not apply\
\ to this Agreement. The courts of California shall have exclusive jurisdiction\
\ of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use\
\ Policy\nMeta is committed to promoting safe and fair use of its tools and features,\
\ including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable\
\ Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n\
#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly.\
\ You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate\
\ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\
\ contribute to, encourage, plan, incite, or further illegal or unlawful activity\
\ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\
\ or harm to children, including the solicitation, creation, acquisition, or dissemination\
\ of child exploitative content or failure to report Child Sexual Abuse Material\n\
\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\
\ illegal distribution of information or materials to minors, including obscene\
\ materials, or failure to employ legally required age-gating in connection with\
\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
\ other criminal activity\n 2. Engage in, promote, incite, or facilitate the\
\ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
\ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful\
\ or harmful conduct in the provision of employment, employment benefits, credit,\
\ housing, other economic benefits, or other essential goods and services\n 4.\
\ Engage in the unauthorized or unlicensed practice of any profession including,\
\ but not limited to, financial, legal, medical/health, or related professional\
\ practices\n 5. Collect, process, disclose, generate, or infer health, demographic,\
\ or other sensitive personal or private information about individuals without rights\
\ and consents required by applicable laws\n 6. Engage in or facilitate any action\
\ or generate any content that infringes, misappropriates, or otherwise violates\
\ any third-party rights, including the outputs or results of any products or services\
\ using the Llama Materials\n 7. Create, generate, or facilitate the creation\
\ of malicious code, malware, computer viruses or do anything else that could disable,\
\ overburden, interfere with or impair the proper working, integrity, operation\
\ or appearance of a website or computer system\n2. Engage in, promote, incite,\
\ facilitate, or assist in the planning or development of activities that present\
\ a risk of death or bodily harm to individuals, including use of Meta Llama 3 related\
\ to the following:\n 1. Military, warfare, nuclear industries or applications,\
\ espionage, use for materials or activities that are subject to the International\
\ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
\ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\
\ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\
\ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\
\ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\
\ content intended to incite or promote violence, abuse, or any infliction of bodily\
\ harm to an individual\n3. Intentionally deceive or mislead others, including use\
\ of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering\
\ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\
\ or furthering defamatory content, including the creation of defamatory statements,\
\ images, or other content\n 3. Generating, promoting, or further distributing\
\ spam\n 4. Impersonating another individual without consent, authorization,\
\ or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are\
\ human-generated\n 6. Generating or facilitating false online engagement, including\
\ fake reviews and other means of fake online engagement\n4. Fail to appropriately\
\ disclose to end users any known dangers of your AI system\nPlease report any violation\
\ of this Policy, software “bug,” or other problems that could lead to a violation\
\ of this Policy through one of the following means:\n * Reporting issues with\
\ the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n\
\ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\
\ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\
\ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]"
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
# JeffreyLind/Meta-Llama-3-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo JeffreyLind/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo JeffreyLind/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m meta-llama-3-8b.Q4_K_M.gguf -n 128
```
|
anzeo/loha_fine_tuned_copa_croslo | anzeo | 2024-05-22T17:34:56Z | 2 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:EMBEDDIA/crosloengual-bert",
"base_model:adapter:EMBEDDIA/crosloengual-bert",
"license:cc-by-4.0",
"region:us"
] | null | 2024-05-21T14:39:32Z | ---
license: cc-by-4.0
library_name: peft
tags:
- generated_from_trainer
base_model: EMBEDDIA/crosloengual-bert
metrics:
- accuracy
- f1
model-index:
- name: loha_fine_tuned_copa_croslo
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. -->
# loha_fine_tuned_copa_croslo
This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2439
- Accuracy: 0.64
- F1: 0.6409
## 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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.693 | 1.0 | 50 | 0.6764 | 0.56 | 0.56 |
| 0.6401 | 2.0 | 100 | 0.6814 | 0.53 | 0.5304 |
| 0.5345 | 3.0 | 150 | 0.6725 | 0.66 | 0.6592 |
| 0.3293 | 4.0 | 200 | 0.8363 | 0.65 | 0.6509 |
| 0.1831 | 5.0 | 250 | 0.9311 | 0.65 | 0.6507 |
| 0.0662 | 6.0 | 300 | 1.0890 | 0.67 | 0.6707 |
| 0.0238 | 7.0 | 350 | 1.1631 | 0.64 | 0.6409 |
| 0.0364 | 8.0 | 400 | 1.2439 | 0.64 | 0.6409 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.1.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
hgnoi/sRqwRFetlgfuGXw9 | hgnoi | 2024-05-22T17:34:32Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:32:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
hgnoi/u3P2k98VQxN4l5fm | hgnoi | 2024-05-22T17:34:11Z | 130 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:32:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
giantdev/dippy-VNVSW-sn11m2 | giantdev | 2024-05-22T17:33:34Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:31:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
hgnoi/V3cK9YfIo746veuK | hgnoi | 2024-05-22T17:33:11Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:31:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
capjamesg/cv-nlp | capjamesg | 2024-05-22T17:30:39Z | 110 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-22T17:22:42Z | ---
license: mit
---
Classify whether an academic paper relates to computer vision or natural language processing research. |
Raihan004/Action_model_ViT_384 | Raihan004 | 2024-05-22T17:28:38Z | 218 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-384",
"base_model:finetune:google/vit-base-patch16-384",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-22T14:38:28Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Action_model_ViT_384
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: action_class
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8611599297012302
---
<!-- 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. -->
# Action_model_ViT_384
This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the action_class dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4520
- Accuracy: 0.8612
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.946 | 0.19 | 100 | 0.7540 | 0.7803 |
| 0.9248 | 0.37 | 200 | 0.6282 | 0.7961 |
| 0.7968 | 0.56 | 300 | 0.5834 | 0.8102 |
| 0.6992 | 0.75 | 400 | 0.5647 | 0.8330 |
| 0.7331 | 0.93 | 500 | 0.5430 | 0.8295 |
| 0.5822 | 1.12 | 600 | 0.5894 | 0.8172 |
| 0.5906 | 1.31 | 700 | 0.6862 | 0.7909 |
| 0.5911 | 1.49 | 800 | 0.5369 | 0.8313 |
| 0.4564 | 1.68 | 900 | 0.4657 | 0.8576 |
| 0.6416 | 1.87 | 1000 | 0.5697 | 0.8190 |
| 0.5653 | 2.05 | 1100 | 0.6152 | 0.8102 |
| 0.4145 | 2.24 | 1200 | 0.5793 | 0.8225 |
| 0.4743 | 2.43 | 1300 | 0.4642 | 0.8576 |
| 0.4908 | 2.61 | 1400 | 0.4520 | 0.8612 |
| 0.523 | 2.8 | 1500 | 0.4989 | 0.8453 |
| 0.3315 | 2.99 | 1600 | 0.4786 | 0.8576 |
| 0.2779 | 3.17 | 1700 | 0.5546 | 0.8524 |
| 0.2984 | 3.36 | 1800 | 0.4977 | 0.8576 |
| 0.5914 | 3.54 | 1900 | 0.6296 | 0.8225 |
| 0.3236 | 3.73 | 2000 | 0.7225 | 0.8172 |
| 0.6194 | 3.92 | 2100 | 0.5783 | 0.8506 |
| 0.5066 | 4.1 | 2200 | 0.5825 | 0.8260 |
| 0.3532 | 4.29 | 2300 | 0.5606 | 0.8594 |
| 0.3531 | 4.48 | 2400 | 0.5068 | 0.8699 |
| 0.2573 | 4.66 | 2500 | 0.5632 | 0.8576 |
| 0.2713 | 4.85 | 2600 | 0.5047 | 0.8612 |
| 0.3538 | 5.04 | 2700 | 0.5988 | 0.8471 |
| 0.2291 | 5.22 | 2800 | 0.5751 | 0.8453 |
| 0.2976 | 5.41 | 2900 | 0.5781 | 0.8559 |
| 0.296 | 5.6 | 3000 | 0.5499 | 0.8664 |
| 0.3776 | 5.78 | 3100 | 0.5718 | 0.8612 |
| 0.2213 | 5.97 | 3200 | 0.5421 | 0.8682 |
| 0.325 | 6.16 | 3300 | 0.6453 | 0.8453 |
| 0.1594 | 6.34 | 3400 | 0.5558 | 0.8647 |
| 0.3377 | 6.53 | 3500 | 0.6619 | 0.8418 |
| 0.3743 | 6.72 | 3600 | 0.5446 | 0.8717 |
| 0.2327 | 6.9 | 3700 | 0.5484 | 0.8735 |
| 0.1659 | 7.09 | 3800 | 0.6629 | 0.8471 |
| 0.4036 | 7.28 | 3900 | 0.6510 | 0.8330 |
| 0.2084 | 7.46 | 4000 | 0.5640 | 0.8629 |
| 0.2251 | 7.65 | 4100 | 0.6379 | 0.8541 |
| 0.192 | 7.84 | 4200 | 0.5897 | 0.8629 |
| 0.1956 | 8.02 | 4300 | 0.5874 | 0.8699 |
| 0.1446 | 8.21 | 4400 | 0.6462 | 0.8594 |
| 0.2971 | 8.4 | 4500 | 0.5909 | 0.8735 |
| 0.2665 | 8.58 | 4600 | 0.6769 | 0.8612 |
| 0.2937 | 8.77 | 4700 | 0.6760 | 0.8506 |
| 0.1437 | 8.96 | 4800 | 0.6566 | 0.8489 |
| 0.1433 | 9.14 | 4900 | 0.6659 | 0.8418 |
| 0.2069 | 9.33 | 5000 | 0.6825 | 0.8541 |
| 0.2095 | 9.51 | 5100 | 0.6157 | 0.8664 |
| 0.1579 | 9.7 | 5200 | 0.6693 | 0.8629 |
| 0.1962 | 9.89 | 5300 | 0.6911 | 0.8524 |
| 0.3149 | 10.07 | 5400 | 0.6260 | 0.8559 |
| 0.2166 | 10.26 | 5500 | 0.6200 | 0.8770 |
| 0.1259 | 10.45 | 5600 | 0.7164 | 0.8576 |
| 0.1892 | 10.63 | 5700 | 0.7182 | 0.8612 |
| 0.1953 | 10.82 | 5800 | 0.7193 | 0.8418 |
| 0.2392 | 11.01 | 5900 | 0.6621 | 0.8664 |
| 0.1594 | 11.19 | 6000 | 0.7471 | 0.8489 |
| 0.2156 | 11.38 | 6100 | 0.7316 | 0.8612 |
| 0.137 | 11.57 | 6200 | 0.6837 | 0.8699 |
| 0.181 | 11.75 | 6300 | 0.6595 | 0.8647 |
| 0.2049 | 11.94 | 6400 | 0.6982 | 0.8506 |
| 0.1028 | 12.13 | 6500 | 0.6771 | 0.8682 |
| 0.1347 | 12.31 | 6600 | 0.6841 | 0.8699 |
| 0.1269 | 12.5 | 6700 | 0.7226 | 0.8594 |
| 0.2288 | 12.69 | 6800 | 0.7083 | 0.8629 |
| 0.1094 | 12.87 | 6900 | 0.7455 | 0.8471 |
| 0.0661 | 13.06 | 7000 | 0.7330 | 0.8541 |
| 0.1811 | 13.25 | 7100 | 0.7363 | 0.8436 |
| 0.2225 | 13.43 | 7200 | 0.7757 | 0.8453 |
| 0.1619 | 13.62 | 7300 | 0.7361 | 0.8576 |
| 0.2032 | 13.81 | 7400 | 0.7656 | 0.8576 |
| 0.0216 | 13.99 | 7500 | 0.7760 | 0.8629 |
| 0.2476 | 14.18 | 7600 | 0.7723 | 0.8612 |
| 0.1616 | 14.37 | 7700 | 0.7247 | 0.8787 |
| 0.1142 | 14.55 | 7800 | 0.7907 | 0.8699 |
| 0.0906 | 14.74 | 7900 | 0.7829 | 0.8647 |
| 0.2199 | 14.93 | 8000 | 0.7427 | 0.8717 |
| 0.0643 | 15.11 | 8100 | 0.7280 | 0.8699 |
| 0.1685 | 15.3 | 8200 | 0.8381 | 0.8541 |
| 0.1677 | 15.49 | 8300 | 0.8638 | 0.8506 |
| 0.1399 | 15.67 | 8400 | 0.8423 | 0.8612 |
| 0.1041 | 15.86 | 8500 | 0.8051 | 0.8541 |
| 0.2223 | 16.04 | 8600 | 0.7768 | 0.8647 |
| 0.1016 | 16.23 | 8700 | 0.7965 | 0.8647 |
| 0.065 | 16.42 | 8800 | 0.8331 | 0.8418 |
| 0.1156 | 16.6 | 8900 | 0.8023 | 0.8629 |
| 0.2263 | 16.79 | 9000 | 0.8116 | 0.8594 |
| 0.1197 | 16.98 | 9100 | 0.8490 | 0.8576 |
| 0.1931 | 17.16 | 9200 | 0.8194 | 0.8612 |
| 0.1289 | 17.35 | 9300 | 0.8353 | 0.8489 |
| 0.2039 | 17.54 | 9400 | 0.8163 | 0.8453 |
| 0.0825 | 17.72 | 9500 | 0.7942 | 0.8524 |
| 0.0712 | 17.91 | 9600 | 0.8027 | 0.8559 |
| 0.244 | 18.1 | 9700 | 0.7803 | 0.8664 |
| 0.1482 | 18.28 | 9800 | 0.7754 | 0.8629 |
| 0.1829 | 18.47 | 9900 | 0.7810 | 0.8594 |
| 0.019 | 18.66 | 10000 | 0.7972 | 0.8559 |
| 0.061 | 18.84 | 10100 | 0.8180 | 0.8576 |
| 0.117 | 19.03 | 10200 | 0.8319 | 0.8559 |
| 0.1858 | 19.22 | 10300 | 0.8432 | 0.8559 |
| 0.1087 | 19.4 | 10400 | 0.8273 | 0.8594 |
| 0.1983 | 19.59 | 10500 | 0.8257 | 0.8612 |
| 0.2453 | 19.78 | 10600 | 0.8177 | 0.8576 |
| 0.1189 | 19.96 | 10700 | 0.8201 | 0.8594 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
adamyhe/clipnet | adamyhe | 2024-05-22T17:28:22Z | 0 | 1 | keras | [
"keras",
"en",
"license:mit",
"region:us"
] | null | 2024-05-08T07:13:54Z | ---
license: mit
language:
- en
library_name: keras
---
# CLIPNET
CLIPNET (Convolutionally Learned, Initiation-Predicting NETwork) is an ensembled convolutional neural network that predicts transcription initiation from DNA sequence at single nucleotide resolution. We describe CLIPNET in our [preprint](https://www.biorxiv.org/content/10.1101/2024.03.13.583868) on bioRxiv. This repository contains code for working with CLIPNET, namely for generating predictions and feature interpretations and performing *in silico* mutagenesis scans. To reproduce the figures in our paper, please see the [clipnet_paper GitHub repo](https://github.com/Danko-Lab/clipnet_paper/).
## Installation
To install CLIPNET, first clone the GitHub repository:
```bash
git clone https://github.com/Danko-Lab/clipnet.git
cd clipnet
```
Then, install dependencies using pip. We recommend creating an isolated environment for working with CLIPNET. For example, with conda/mamba:
```bash
mamba create -n clipnet -c conda-forge gcc~=12.1 python=3.9
mamba activate clipnet
pip install -r requirements.txt # requirements_cpu.txt if no GPU
```
You may need to configure your CUDA/cudatoolkit/cudnn paths to get GPU support working. See the [tensorflow documentation](https://www.tensorflow.org/install/gpu) for more information.
## Download models
Pretrained CLIPNET models are available on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.10408622). Download the models into the `ensemble_models` directory:
```bash
for fold in {1..9};
do wget https://zenodo.org/records/10408623/files/fold_${fold}.h5 -P ensemble_models/;
done
```
Alternatively, they can be accessed via [HuggingFace](https://huggingface.co/adamyhe/clipnet).
## Usage
### Input data
CLIPNET was trained on a [population-scale PRO-cap dataset](http://dx.doi.org/10.1038/s41467-020-19829-z) derived from human lymphoblastoid cell lines, matched with individualized genome sequences (1kGP). CLIPNET accepts 1000 bp sequences as input and imputes PRO-cap coverage (RPM) in the center 500 bp.
CLIPNET can either work on haploid reference sequences (e.g. hg38) or on individualized sequences (e.g. 1kGP). When constructing individualized sequences, we made two major simplifications: (1) We considered only SNPs and (2) we used unphased SNP genotypes.
We encode sequences using a "two-hot" encoding. That is, we encoded each individual nucleotide at a given position using a one-hot encoding scheme, then represented the unphased diploid sequence as the sum of the two one-hot encoded nucleotides at each position. The sequence "AYCR", for example, would be encoded as: `[[2, 0, 0, 0], [0, 1, 0, 1], [0, 2, 0, 0], [1, 0, 1, 0]]`.
### Command line interface
#### Predictions
To generate predictions using the ensembled model, use the `predict_ensemble.py` script (the `predict_individual_model.py` script can be used to generate predictions with individual model folds). This script takes a fasta file containing 1000 bp records and outputs an hdf5 file containing the predictions for each record. For example:
```bash
python predict_ensemble.py data/test.fa data/test_predictions.h5 --gpu
# Use the --gpu flag to run on GPU
```
To input individualized sequences, heterozygous positions should be represented using the IUPAC ambiguity codes R (A/G), Y (C/T), S (C/G), W (A/T), K (G/T), M (A/C).
The output hdf5 file will contain two datasets: "track" and "quantity". The track output of the model is a length 1000 vector (500 plus strand concatenated with 500 minus strand) representing the predicted base-resolution profile/shape of initiation. The quantity output represents the total PRO-cap quantity on both strands.
We note that the track node was not optimized for quantity prediction. As a result, the sum of the track node is not well correlated with the quantity prediction and not a good predictor of the total quantity of initiation. We therefore recommend rescaling the track predictions to sum to the quantity prediction. For example:
```python
import h5py
import numpy as np
with h5py.File("data/test.h5", "r") as f:
profile = f["track"][:]
quantity = f["quantity"][:]
profile_scaled = (profile / np.sum(profile, axis=1)[:, None]) * quantity
```
#### Feature interpretations
CLIPNET uses DeepSHAP to generate feature interpretations. To generate feature interpretations, use the `calculate_deepshap.py` script. This script takes a fasta file containing 1000 bp records and outputs two npz files containing: (1) feature interpretations for each record and (2) onehot-encoded sequence. It supports two modes that can be set with `--mode`: "profile" and "quantity". The "profile" mode calculates interpretations for the profile node of the model (using the profile metric proposed in BPNet), while the "quantity" mode calculates interpretations for the quantity node of the model.
```bash
python calculate_deepshap.py \
data/test.fa \
data/test_deepshap_quantity.npz \
data/test_onehot.npz \
--mode quantity \
--gpu
python calculate_deepshap.py \
data/test.fa \
data/test_deepshap_profile.npz \
data/test_onehot.npz \
--mode profile \
--gpu
```
Note that CLIPNET generally accepts two-hot encoded sequences as input, with the array being structured as (# sequences, 1000, 4). However, feature interpretations are much easier to do with just a haploid/fully homozygous genome, so we recommend just doing interpretations on the reference genome sequence. tfmodisco-lite also expects contribution scores and sequence arrays to be length last, i.e., (# sequences, 4, 1000), with the sequence array being one-hot. To accomodate these, `calculate_deepshap.py` will automatically convert the input sequence array to length last and onehot encoded, and will also write the output contribution scores as length last. Also note that these are actual contribution scores, as opposed to hypothetical contribution scores. Specifically, non-reference nucleotides are set to zero. The outputs of this model can be used as inputs to tfmodisco-lite to generate motif logos and motif tracks.
Both DeepSHAP and tfmodisco-lite computations are quite slow when performed on a large number of sequences, so we (a) recommend running DeepSHAP on a GPU using the `--gpu` flag and (b) if you have access to many GPUs, calculating DeepSHAP scores for the model folds in parallel using the `--model_fp` flag, then averaging them. We also provide precomputed DeepSHAP scores and TF-MoDISco results for a genome-wide set of PRO-cap peaks called in the LCL dataset (https://zenodo.org/records/10597358).
#### Genomic *in silico* mutagenesis scans
To generate genomic *in silico* mutagenesis scans, use the `calculate_ism_shuffle.py` script. This script takes a fasta file containing 1000 bp records and outputs an npz file containing the ISM shuffle results ("corr_ism_shuffle" and "log_quantity_ism_shuffle") for each record. For example:
```bash
python calculate_ism_shuffle.py data/test.fa data/test_ism.npz --gpu
```
### API usage
CLIPNET models can be directly loaded as follows. Individual models can simply be loaded using tensorflow:
```python
import tensorflow as tf
nn = tf.keras.models.load_model("ensemble_models/fold_1.h5", compile=False)
```
The model ensemble is constructed by averaging track and quantity outputs across all 9 model folds. To make this easy, we've provided a simple API in the `clipnet.CLIPNET` class for doing this. Moreover, to make reading fasta files into the correct format easier, we've provided the helper function `utils.twohot_fasta`. For example:
```python
import sys
sys.path.append(PATH_TO_THIS_DIRECTORY)
import clipnet
import utils
nn = clipnet.CLIPNET(n_gpus=0) # by default, this will be 1 and will use CUDA
ensemble = nn.construct_ensemble()
seqs = utils.twohot_fasta("data/test.fa")
predictions = ensemble.predict(seqs)
``` |
giantdev/dippy-m9UcP-sn11m4 | giantdev | 2024-05-22T17:27:38Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:25:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mlx-community/dolphin-2.9.1-llama-3-70b-2bit | mlx-community | 2024-05-22T17:26:31Z | 12 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"axolotl",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-70B",
"base_model:finetune:meta-llama/Meta-Llama-3-70B",
"license:llama3",
"region:us"
] | null | 2024-05-22T16:41:39Z | ---
license: llama3
tags:
- generated_from_trainer
- axolotl
- mlx
base_model: meta-llama/Meta-Llama-3-70B
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
model-index:
- name: out
results: []
---
# mlx-community/dolphin-2.9.1-llama-3-70b-2bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9.1-llama-3-70b`]() using mlx-lm version **0.12.1**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9.1-llama-3-70b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9.1-llama-3-70b-2bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
giantdev/dippy-yJY7A-sn11m3 | giantdev | 2024-05-22T17:25:06Z | 130 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T17:23:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
muthu0101/muthu0101 | muthu0101 | 2024-05-22T17:23:28Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-22T17:22:52Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 631.50 +/- 226.41
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga muthu0101 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga muthu0101 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga muthu0101
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Saeid/distilbert-base-uncased-lora-text-classification | Saeid | 2024-05-22T17:22:17Z | 2 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:adapter:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2024-05-22T15:37:34Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: distilbert-base-uncased
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-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. -->
# distilbert-base-uncased-lora-text-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: 0.9787
- Accuracy: {'accuracy': 0.897}
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.3674 | {'accuracy': 0.891} |
| 0.4133 | 2.0 | 500 | 0.4185 | {'accuracy': 0.861} |
| 0.4133 | 3.0 | 750 | 0.5729 | {'accuracy': 0.88} |
| 0.1943 | 4.0 | 1000 | 0.7140 | {'accuracy': 0.89} |
| 0.1943 | 5.0 | 1250 | 0.8534 | {'accuracy': 0.884} |
| 0.0825 | 6.0 | 1500 | 0.9309 | {'accuracy': 0.882} |
| 0.0825 | 7.0 | 1750 | 0.9520 | {'accuracy': 0.886} |
| 0.0304 | 8.0 | 2000 | 0.9556 | {'accuracy': 0.889} |
| 0.0304 | 9.0 | 2250 | 0.9718 | {'accuracy': 0.896} |
| 0.0055 | 10.0 | 2500 | 0.9787 | {'accuracy': 0.897} |
### Framework versions
- PEFT 0.11.2.dev0
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
moiz1/Mistral-7b-Instruct-v0.2-finetune-translation-10k-alpaca-style | moiz1 | 2024-05-22T17:19:55Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T16:07:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BSC-NLP4BIA/biomedical-term-classifier | BSC-NLP4BIA | 2024-05-22T17:16:33Z | 206 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"text-classification",
"bert",
"biomedical",
"lexical semantics",
"bionlp",
"es",
"license:apache-2.0",
"region:us"
] | text-classification | 2024-05-22T15:48:22Z | ---
license: apache-2.0
language:
- es
pipeline_tag: text-classification
tags:
- sentence-transformers
- text-classification
- bert
- biomedical
- lexical semantics
- bionlp
---
# Biomedical term classifier with Transformers in Spanish
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Training](#training)
- [Evaluation](#evaluation)
- [Additional information](#additional-information)
- [Author](#author)
- [Licensing information](#licensing-information)
- [Citation information](#citation-information)
- [Disclaimer](#disclaimer)
</details>
## Model description
This is a Transformer's [AutoModelForSequenceClassification](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForSequenceClassification) trained for multilabel biomedical text classification in Spanish.
## Intended uses and limitations
The model is prepared to classify medical entities among 21 classes, including diseases, medical procedures, symptoms, and drugs, among others. It still lacks some classes like body structures.
## How to use
This model is implemented as part of the KeyCARE library. Install first the keycare module to call the Transformer classifier:
```bash
python -m pip install keycare
```
You can then run the KeyCARE pipeline that uses the SetFit model:
```python
from keycare install TermExtractor.TermExtractor
# initialize the termextractor object
termextractor = TermExtractor(categorization_method='transformers')
# Run the pipeline
text = """Acude al Servicio de Urgencias por cefalea frontoparietal derecha.
Mediante biopsia se diagnostica adenocarcinoma de próstata Gleason 4+4=8 con metástasis óseas múltiples.
Se trata con Ácido Zoledrónico 4 mg iv/4 semanas.
"""
termextractor(text)
# You can also access the class storing the Transformer model
categorizer = termextractor.categorizer
```
## Training
The used pre-trained model is SapBERT-from-roberta-base-biomedical-clinical-es from the BSC-NLP4BIA reserch group. The model has been trained using data obtained from NER Gold Standard Corpora also generated by BSC-NLP4BIA, including [MedProcNER](https://temu.bsc.es/medprocner/), [DISTEMIST](https://temu.bsc.es/distemist/), [SympTEMIST](https://temu.bsc.es/symptemist/), [CANTEMIST](https://temu.bsc.es/cantemist/), and [PharmaCoNER](https://temu.bsc.es/pharmaconer/), among others.
## Evaluation
To be published
## Additional information
### Author
NLP4BIA at the Barcelona Supercomputing Center
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Citation information
To be published
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
</details> |
BSC-NLP4BIA/biomedical-semantic-relation-classifier-setfit | BSC-NLP4BIA | 2024-05-22T17:13:42Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"relation-classification",
"bert",
"biomedical",
"lexical semantics",
"bionlp",
"es",
"license:apache-2.0",
"region:us"
] | null | 2024-05-22T15:48:52Z | ---
license: apache-2.0
language:
- es
pipeline_tag: relation-classification
tags:
- setfit
- sentence-transformers
- relation-classification
- bert
- biomedical
- lexical semantics
- bionlp
---
# Biomedical relation classifier with SetFit in Spanish
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Training](#training)
- [Evaluation](#evaluation)
- [Additional information](#additional-information)
- [Author](#author)
- [Licensing information](#licensing-information)
- [Citation information](#citation-information)
- [Disclaimer](#disclaimer)
</details>
## Model description
This is a Transformer's [SetFit model](https://github.com/huggingface/setfit) trained for biomedical text pairs classification in Spanish.
## Intended uses and limitations
The model is prepared to classify hierarchical relations among medical terms. This includes the following types of relations: BROAD, EXACT, NARROW, NO_RELATION.
## How to use
This model is implemented as part of the KeyCARE library. Install first the keycare module to call the SetFit classifier:
```bash
python -m pip install keycare
```
You can then run the KeyCARE pipeline that uses the SetFit model:
```python
from keycare install RelExtractor.RelExtractor
# initialize the termextractor object
relextractor = RelExtractor(relation_method='setfit')
# Run the pipeline
source = ["cáncer", "enfermedad de pulmón", "mastectomía radical izquierda", "laparoscopia"]
target = ["cáncer de mama", "enfermedad pulmonar", "mastectomía", "Streptococus pneumoniae"]
relextractor(source, target)
# You can also access the class storing the SetFit model
relator = relextractor.relation_method
```
## Training
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. The used pre-trained model is SapBERT-from-roberta-base-biomedical-clinical-es from the BSC-NLP4BIA reserch group.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
The training data has been obtained using the hirerarchical structure of [SNOMED-CT](https://www.snomed.org/) mapped to the medical terms present in [UMLS](https://www.nlm.nih.gov/research/umls/index.html).
## Evaluation
To be published
## Additional information
### Author
NLP4BIA at the Barcelona Supercomputing Center
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Citation information
To be published
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
</details> |
chenehlf/523107 | chenehlf | 2024-05-22T17:08:07Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-22T17:07:43Z | ---
license: apache-2.0
---
|
Subsets and Splits