modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
nskwal/mrayumi-base-1e
|
nskwal
| 2025-05-09T14:42:52Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:39782226",
"loss:MultipleNegativesRankingLoss",
"pt",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:intfloat/multilingual-e5-base",
"base_model:finetune:intfloat/multilingual-e5-base",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-05-09T14:42:10Z |
---
language:
- pt
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:39782226
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-base
widget:
- source_sentence: Como o Brasil reagiu a epidemia de AIDS no fim do século XX?
sentences:
- O valor para a emissão deste visto pode chegar até US$ 1,8 milhão. Devido a crise,
o aumento do preço chegou a 80% em relação a anos anteriores. É importante frisar
que o portador deste visto precisa gerar pelo menos dez empregos no país por dois
anos a partir da data de emissão. Para realizar a solicitação, é necessário fazer
um investimento em um negócio já existente em uma empresa que é licenciada pelo
governo americano e que faz parte da captação desta ação ou montar um novo negócio,
algo que irá requerer maior burocracia e tempo. Após realizar todos os trâmites,
o requerente poderá fazer a solicitação do Green Card, que poderá levar até dois
anos para ficar pronto. Trabalho O visto HB-1 é destinado para todos aqueles profissionais
que possuem formação acadêmica ou uma experiência de trabalho que equivalha a
um curso bacharelado. Este último funciona caso o solicitante não tenha um diploma
e tenha trabalhado por três anos equivalentes a um ano de estudo de bacharelado.
Além de cumprir estes requisitos, o solicitante do visto deverá ter fluência em
inglês e possuir um representante empregador. O visto só é emitido caso a empresa
comprove que não exista nenhum outro americano capaz de cumprir a determinada
função.
- 'Horário de funcionamento: terça a sábado, 10h às 13h e das 14h30 às 18h30. 3.
Parque Nacional da Peneda-Gerês Ivy Land Composto pela Serra da Peneda e a Serra
da Gerês, esse é o único parque nacional de Portugal. Paraíso para os amantes
da natureza, a área possui alguns mirantes de onde é possível admirar toda a região.
O destaque fica para a Pedra Bela, com 830 metros de altura. Entre mais de 100
aldeias, suas principais atrações são a Ponte Misarela, o Santuário Nossa Senhora
da Peneda, o Castro Laboreiro e a Cascata do Arado. Endereço: R. Conde Dom Henrique,
Guimarães. Horário de funcionamento: todos os dias das 10h às 18h. 4. Sé de Braga
Jorge Santos A igreja, cuja construção começou em 1509, foi fundada antes mesmo
da fundação de Portugal (1910). Entre suas várias riquezas culturais está o túmulo
de madeira, em talhe gótico-flamengo, pertencente ao Infante Dom Afonso, filho
do rei Dom João I e de Dona Filipa de Lencastre. No local também se encontra o
Tesouro-Museu da Sé de Braga, fundado em 1930, onde está uma relíquia levada por
Pedro Álvares Cabral: a cruz da primeira missa celebrada no Brasil. Endereço:
R. Dom Paio Mendes. Valor de entrada: gratuito. Horário de funcionamento: todos
os dias das 8h30 às 18h30. 5. Jardim de Santa Bárbara World Photoshoot'
- Assim, o financiamento para o programa de aids brasileiro fazia parte tanto dos
planos estratégicos do Banco Mundial quanto do planejamento dos formuladores da
política nacional de aids. A execução do Projeto AIDS I provocou mudanças tanto
nas respostas governamentais quanto nas não-governamentais frente à epidemia do
HIV/aids no Brasil. Entre essas, Galvão (2000) destaca o aumento dos recursos
financeiros disponíveis no País para desencadear ações frente à epidemia; o crescimento
do número dessas ações; o papel de liderança desempenhado pelo Programa Nacional
de DST e Aids em nível regional; e a maior visibilidade do programa brasileiro
de aids, tanto regional, quanto nacional e internacionalmente. Quanto às ações
do Banco Mundial, o empréstimo destinado às ações para conter a epidemia de HIV/aids
em função do volume financeiro envolvido, oferece ao Programa brasileiro de aids
condições sem precedentes dentro do que vinha sendo feito no país. Para alguns
países, inclusive o Brasil, os empréstimos do Banco Mundial tornaram-se uma das
maiores fontes de recursos para as atividades em HIV/aids, e propiciaram a projeção
das políticas brasileiras como das mais abrangentes já implementadas (Galvão,
2000).
- source_sentence: Quais são os biomas do Brasil?
sentences:
- 'Biomas - Atlas Socioeconômico do Rio Grande do Sul Meio ambiente Voltar Imprimir
RSS Biomas O RS possui dois importantes biomas: Mata Atlântica e Pampa Os biomas
são definidos pelo IBGE como “um conjunto de vida (vegetal e animal) constituído
pelo agrupamento de tipos de vegetação contíguos e identificáveis em escala regional,
com condições geoclimáticas similares e história compartilhada de mudanças, o
que resulta em uma diversidade biológica própria.” Segundo o Mapa dos Biomas do
Brasil, elaborado pelo IBGE e pelo Ministério do Meio Ambiente, o país possui
5 grandes biomas. O de maior extensão é o da Amazônia que abrange 49,29% do território
brasileiro e uma área aproximada de 4.196.943 km². O menor bioma é o do Pantanal
com uma área aproximada de 150.355 km² ou 1,76% do território do Brasil. No RS,
em função da diversidade de clima, solos e relevo há a formação de distintos ecossistemas
derivados de dois grandes biomas: a Mata Atlântica e o Pampa.'
- 'Os Campos caracterizam-se pela presença de uma vegetação rasteira (gramíneas)
e pequenos arbustos distantes uns dos outros. Podemos encontrar esta formação
vegetal em várias regiões do Brasil (sul do Mato Grosso do Sul, nordeste do Paraná,
sul de Minas Gerais e norte do Maranhão), porém é no sul do Rio Grande do Sul,
região conhecida como Pampas Gaúchos, que encontramos em maior extensão. Características
principais dos Campos: - vegetação formada por gramíneas e arbustos e árvores
de pequeno porte. - não dependem de grande quantidade de chuvas. - sua extensão
atingem os territórios da Argentina e Paraguai. A região dos Campos, principalmente
no Rio Grande do Sul, é muito utilizada para a pastagem de gado. A pecuária é
uma das principais atividades econômica nesta região. Pantanal Extensão aproximada:
150.355 quilômetros quadrados O bioma Pantanal cobre 25% de Mato Grosso do Sul
e 7% de Mato Grosso e seus limites coincidem com os da Planície do Pantanal, mais
conhecida como Pantanal mato-grossense. O Pantanal é um bioma praticamente exclusivo
do Brasil, pois apenas uma pequena faixa dele adentra outros países (o Paraguai
e a Bolívia).'
- Resíduos biológicos – gaze, algodão, luva, máscara e ponta de sucção são considerados
materiais desse grupo. Para estes, o ideal é sempre ter uma lixeira por perto,
nunca deixando faltar sacos de lixo, que devem ser proporcionais a demanda de
trabalho do consultório. O ideal é que sempre ocorra o recolhimento diário, nunca
deixando os detritos acumularem. Isso serve para evitar maiores complicações,
como uma contaminação por exemplo. Resíduos perfurocortantes – essa classe de
resíduos deve ser condicionada em caixas de papelão bastante resistentes. Resíduos
perfurocortantes englobam materiais como seringas, lâminas, agulhas, ampolas,
vidros e tesouras. A importância de serem descartados em ambientes resistentes
se deve ao fato de serem bastante perigosos e cortantes. Assim, é ideal que eles
fiquem isolados para que ninguém se lesione ao manuseá-los futuramente. Também
é interessante sinalizar que os materiais presentes naquela caixa são perfurocortantes,
geralmente utilizando um adesivo de cor chamativa.
- source_sentence: Quais os tipos de denominação (DO) que os vinhos podem receber?
sentences:
- O Brasil conta com seis ecossistemas diferentes, composto por espécies animais
e vegetais variadas. A diversidade e os contrastes presentes em cada região são
o que tornam nosso País único. Os principais ecossistemas brasileiros são a Amazônia,
a Caatinga, o Cerrado, o Pantanal, a Mata Atlântica e os Pampas. A seguir, detalharemos
cada um desses biomas. Amazônia A maior floresta tropical do mundo está presente
em nosso País! Distribuindo-se entre Peru, Colômbia, Venezuela, Equador, Suriname,
Guiana e Guiana Francesa, uma grande porção da Floresta Amazônica se localiza
no Norte do Brasil, com uma grande diversidade de plantas e animais, além de abrigar
comunidades de povos originários brasileiros . A bacia amazônica é a maior bacia
hidrográfica do mundo, detendo, aproximadamente, 20% de toda a água doce disponível.
Com clima quente e úmido, o bioma Amazônia ocupa 49% do território nacional. A
temperatura anual média chega a 26 °C, e a pluviosidade é de 2.300 mm, podendo
chegar, em alguns locais, a 3.500 mm. No que diz respeito à vegetação, esta se
divide em mata de terra firme em porções mais elevadas do território, mata de
várzea (inundada em parte do ano) e igapó, quase sempre inundada.
- Os olhos verdes são mais comuns em pessoas de origem celta ou germânica, mas podem
aparecer em quaisquer etnias. Em regiões da Ásia, por exemplo, existem aldeões
do noroeste da China famosos por terem olhos verdes e azuis, além de cabelos claros.
Da mesma forma, é possível encontrar negros com olhos claros. 9. Não surge no
nascimento Unsplash Logo após o nascimento, os olhos dos bebês são escuros, cinzas
ou azuis. Só a partir daí, as células melanócitas começam a liberar a melanina
pelo corpo, que distribui o pigmento marrom para os olhos. Como o tom esverdeado
é resultado da mistura de outros tons, é preciso esperar o equilíbrio da distribuição
de melanina para o desenvolvimento da cor. 10. Olhos verdes só estão completos
após um ano Unsplash Ainda que comece logo depois do nascimento, o processo só
fica realmente completo após cerca de um ano depois do período. Por causa disso,
então, durante os primeiros meses de vida ainda não é possível determinar qual
será a verdadeira cor da criança. Interessante, não? Você imaginava que havia
tanto mistério por trás de um belo par de olhos verdes? Continua após a publicidade
- '"Reboque de barcos rabelos pelas margens do rio Douro em meados dos anos 30 (arq.
priv.) Descarga das pipas de um barco rabelo no Porto (arq. priv.) Cartaz publicitário
de marca de vinho do Porto de 1950 (col. priv.) Vindimas no Douro na década de
70 mantendo ainda as antigas tradições (arq. priv.) Cachos de uvas maduras numa
vinha do Douro (arq. priv.) Em 1995, a região Demarcada do Douro viu alterado
o seu quadro institucional. Passou a estar dotada de um organismo interprofissional,
- a Comissão Interprofissional da Região Demarcada do Douro (CIRDD), no qual tinham
assento, em situação de absoluta paridade, os representantes da lavoura e do comércio,
com o objectivo comum de disciplinar e controlar a produção e comercialização
dos vinhos da região com direito a denominação de origem. As alterações introduzidas
respeitaram, contudo, as especificidades históricas, culturais e sociais da região,
seguindo as linhas orientadoras da lei - quadro das regiões demarcados vitivinícolas.
Duas secções especializadas compunham o Conselho Geral da CIRDD determinando as
regras aplicáveis a cada uma das denominações: uma relativa à denominação de origem
\""Porto\"" e outra aos restantes vinhos de qualidade (\""VQPRD\"") da região."'
- source_sentence: calorias e carboidratos em taco bell
sentences:
- 'Uma fístula é uma abertura ou canal anormal que une duas ou mais estruturas ou
espaços dentro do corpo. Por exemplo, uma fístula pode se desenvolver entre dois
órgãos do corpo, como o intestino e a bexiga, ou entre o intestino e a pele. Uma
fístula cancerosa é rara. Ela se desenvolve por causa do câncer ou de seu tratamento.
Se for causado por tratamento de câncer, geralmente é um efeito colateral tardio
e pode levar muitos meses ou anos para se desenvolver. Asistulas são mais comuns
na região pélvica. As fístulas são um efeito colateral raro do tratamento do câncer.
Eles também podem se desenvolver como resultado do crescimento do câncer. Os sintomas
de uma fístula dependem de sua localização no corpo. Os sintomas comuns incluem:
1 vazamento de urina pela vagina ou passagem nas costas.'
- Os clientes que pedem burritos recheados grelhados devem consumir pelo menos 830
calorias nas versões de frango do item do cardápio e mais de 40 gramas de gordura,
96 gramas de carboidratos, 2.200 mg de sódio e 85 mg de colesterol. No entanto,
o Taco Bell oferece um menu específico dedicado a itens que contêm apenas ingredientes
frescos.
- Calorias em Spag com base nas calorias, gorduras, proteínas, carboidratos e outras
informações nutricionais enviadas para Spag. Calorias em Spag com base nas calorias,
gorduras, proteínas, carboidratos e outras informações nutricionais enviadas para
Spag.
- source_sentence: para que serve a azitromicina
sentences:
- Média móvel simples (SMA) explicada. Uma média móvel simples (SMA) é o tipo mais
simples de média móvel na análise forex (DUH!). Basicamente, uma média móvel simples
é calculada somando os últimos preços de fechamento de â € ¢ dividindo esse número
por X.
- A azitromicina também pode ser usada para tratar várias outras infecções bacterianas
mais incomuns. A azitromicina não é eficaz contra nenhuma infecção causada por
um vírus, como gripe, gastroenterite ou resfriado comum.
- 'Infecções bacterianas. A azitromicina é mais comumente usada para tratar as seguintes
infecções: 1 Infecções respiratórias, como bronquite. 2 Infecções de ouvido (otite
média). 3 infecções sinusais (sinusite). 4 Pneumonia. 5 Infecções da garganta
(amigdalite / faringite). 6 Infecções da pele, como celulite, foliculite ou impetigo.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# test
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the quati and msmarco datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision 835193815a3936a24a0ee7dc9e3d48c1fbb19c55 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- quati
- msmarco
- **Language:** pt
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("nskwal/mrayumi-base-1e")
# Run inference
sentences = [
'para que serve a azitromicina',
'Infecções bacterianas. A azitromicina é mais comumente usada para tratar as seguintes infecções: 1 Infecções respiratórias, como bronquite. 2 Infecções de ouvido (otite média). 3 infecções sinusais (sinusite). 4 Pneumonia. 5 Infecções da garganta (amigdalite / faringite). 6 Infecções da pele, como celulite, foliculite ou impetigo.',
'A azitromicina também pode ser usada para tratar várias outras infecções bacterianas mais incomuns. A azitromicina não é eficaz contra nenhuma infecção causada por um vírus, como gripe, gastroenterite ou resfriado comum.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
#### quati
* Dataset: quati
* Size: 1,415 training samples
* Columns: <code>query</code> and <code>passage</code>
* Approximate statistics based on the first 1000 samples:
| | query | passage |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 12.57 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 65 tokens</li><li>mean: 267.65 tokens</li><li>max: 412 tokens</li></ul> |
* Samples:
| query | passage |
|:-----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>"O que são os celulares ""mid-range""?"</code> | <code>Câmeras traseiras: 64 MP quad-pixel + 12 MP (ultra-wide) + 5 MP (macro) + 5 MP (sensor de profundidade) Filma em: 4K Câmera frontal: 32 MP Bateria: 4.500 mAh com carregamento turbo de 25W Tem conexão 3G e 4G Pontos positivos: Tela grande com resolução Full HD 128 GB de armazenamento é um bom espaço Câmera de 64 MP que filma em 4K Câmera frontal também filma em 4K Processador potente para uso no dia a dia Pontos negativos: Bateria com tamanho abaixo dos concorrentes Sem proteção contra água Melhor Preço Conclusões Como dito no começo da matéria o mercado de celulares está crescendo exponencialmente e isso faz com que estejam disponíveis vários modelos no mercado, para os mais diferentes gostos. Nem todo mundo precisa ou está disposto a pagar pelos melhores celulares e é onde entram os modelos citados nesta lista: Um bom celular por um preço mediano. Para um uso comum estes modelos atendem muito bem. Se você sentiu falta de alguma opção nesta lista deixe ai nos comentários. Vale lembrar ...</code> |
| <code>"O que são os celulares ""mid-range""?"</code> | <code>Smartphone Motorola Moto G8 Plus Imagem Celular Intermediário Detalhes Smartphone Xiaomi Redmi Note 8 Pro Melhor celular intermediário, processador rápido Smartphone Xiaomi Redmi Note 8 Melhor celular intermediário custo benefício, câmera quádrupla Smartphone Motorola One Action Sensor exclusivo para vídeo Smartphone Huawei P30 Lite Diversas tecnologias diferenciadas Smartphone Samsung Galaxy A50 Câmera frontal de 25 MP Smartphone Samsung Galaxy A30s Leitor de impressão digital embutido na tela Smartphone Motorola Moto G8 Plus Design moderno e bonito Hoje em dia os smartphones são verdadeiros aliados. Apenas com eles é possível executar uma grande quantidade de tarefas como ligações, mensagens, acesso a e-mail e redes sociais e muito mais. Mas para conseguir isso é importante ter em mãos um aparelho que reúna componentes de qualidade, tal como, boa câmera, ótimo espaço de armazenamento e processador ágil. Pensando nisso, selecionamos os modelos de celular intermediário que englobam as ...</code> |
| <code>"O que são os celulares ""mid-range""?"</code> | <code>Os monócitos, eosinófilos, basófilos e seus progenitores circulam no sangue em pequenas quantidades, no entanto, essas células são muitas vezes combinados em um grupo que é designado como MXD ou MID. Este grupo pode ser expressa como uma percentagem do número total de leucócitos (MXD%), ou um número absoluto (MXD #, # MID). Estes tipos de células do sangue e as células brancas do sangue e são funções importantes (a luta contra parasitas, bactérias, reacções alérgicas, etc.). Absoluta e percentagem deste valor aumenta se o aumento do número de um dos tipos de células na sua composição. Para determinar a natureza da alteração geralmente é estudar a percentagem de cada tipo de célula (monócitos, eosinófilos, basófilos e os seus precursores). Requisitos: eosinófilos reduzidos e aumento no sangue # MID (MID, MXD #) 0,2-0,8 x 109 / l MID% (MXD%) 5 - 10% O número de granulócitos (GRA, GRAN) Granulócitos - são leucócitos que contêm grânulos (leucócitos granulares). Granulócitos 3 tipos de célu...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### msmarco
* Dataset: msmarco
* Size: 39,780,811 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 11.4 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 102.05 tokens</li><li>max: 401 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 91.92 tokens</li><li>max: 470 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>é um pouco de cafeína ok durante a gravidez</code> | <code>Não sabemos muito sobre os efeitos da cafeína durante a gravidez sobre você e seu bebê. Portanto, é melhor limitar a quantidade que você recebe a cada dia. Se você estiver grávida, limite a cafeína a 200 miligramas por dia. Isso é aproximadamente a quantidade em 1 x 8 onças de café ou uma xícara de 12 onças de café.</code> | <code>Em geral, é seguro para mulheres grávidas comer chocolate porque estudos demonstraram alguns benefícios de comer chocolate durante a gravidez. No entanto, as mulheres grávidas devem garantir que a ingestão de cafeína seja inferior a 200 mg por dia.</code> |
| <code>que fruta é nativa da Austrália</code> | <code>Passiflora herbertiana. Um raro maracujá nativo da Austrália. Os frutos são de casca verde, polpa branca, com uma classificação comestível desconhecida. Algumas fontes listam as frutas como comestíveis, doces e saborosas, enquanto outras listam as frutas como sendo amargas e não comestíveis.assiflora herbertiana. Um raro maracujá nativo da Austrália. Os frutos são de casca verde, polpa branca, com uma classificação comestível desconhecida. Algumas fontes listam as frutas como comestíveis, doces e saborosas, enquanto outras listam as frutas como amargas e não comestíveis.</code> | <code>A noz de cola é o fruto da árvore da cola, um gênero (Cola) de árvores que são nativas das florestas tropicais da África.</code> |
| <code>quão grande é o exército canadense</code> | <code>As Forças Armadas canadenses. 1 A primeira missão de manutenção da paz canadense em grande escala começou no Egito em 24 de novembro de 1956. 2 Há aproximadamente 65.000 membros da Força Regular e 25.000 membros reservistas nas forças armadas canadenses. 3 No Canadá, o dia 9 de agosto é designado como Dia Nacional dos Pacificadores.</code> | <code>O Canadian Physician Health Institute (CPHI) é um programa nacional criado em 2012 como uma colaboração entre a Canadian Medical Association (CMA), a Canadian Medical Foundation (CMF) e as Provincial and Territorial Medical Associations (PTMAs).</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `warmup_ratio`: 0.05
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0051 | 100 | 6.6139 |
| 0.0103 | 200 | 3.3742 |
| 0.0154 | 300 | 0.6789 |
| 0.0206 | 400 | 0.3686 |
| 0.0257 | 500 | 0.3366 |
| 0.0309 | 600 | 0.3183 |
| 0.0360 | 700 | 0.3071 |
| 0.0412 | 800 | 0.295 |
| 0.0463 | 900 | 0.2863 |
| 0.0515 | 1000 | 0.2771 |
| 0.0566 | 1100 | 0.2693 |
| 0.0618 | 1200 | 0.2587 |
| 0.0669 | 1300 | 0.2539 |
| 0.0721 | 1400 | 0.2459 |
| 0.0772 | 1500 | 0.2393 |
| 0.0824 | 1600 | 0.2312 |
| 0.0875 | 1700 | 0.2269 |
| 0.0927 | 1800 | 0.217 |
| 0.0978 | 1900 | 0.2103 |
| 0.1030 | 2000 | 0.2089 |
| 0.1081 | 2100 | 0.1999 |
| 0.1133 | 2200 | 0.1961 |
| 0.1184 | 2300 | 0.1901 |
| 0.1236 | 2400 | 0.1847 |
| 0.1287 | 2500 | 0.1798 |
| 0.1339 | 2600 | 0.1764 |
| 0.1390 | 2700 | 0.1703 |
| 0.1441 | 2800 | 0.1637 |
| 0.1493 | 2900 | 0.1599 |
| 0.1544 | 3000 | 0.158 |
| 0.1596 | 3100 | 0.152 |
| 0.1647 | 3200 | 0.1506 |
| 0.1699 | 3300 | 0.1478 |
| 0.1750 | 3400 | 0.1431 |
| 0.1802 | 3500 | 0.1417 |
| 0.1853 | 3600 | 0.1368 |
| 0.1905 | 3700 | 0.1349 |
| 0.1956 | 3800 | 0.1327 |
| 0.2008 | 3900 | 0.129 |
| 0.2059 | 4000 | 0.1287 |
| 0.2111 | 4100 | 0.1257 |
| 0.2162 | 4200 | 0.1236 |
| 0.2214 | 4300 | 0.1222 |
| 0.2265 | 4400 | 0.118 |
| 0.2317 | 4500 | 0.1181 |
| 0.2368 | 4600 | 0.1152 |
| 0.2420 | 4700 | 0.1154 |
| 0.2471 | 4800 | 0.1102 |
| 0.2523 | 4900 | 0.1099 |
| 0.2574 | 5000 | 0.1101 |
| 0.2626 | 5100 | 0.1064 |
| 0.2677 | 5200 | 0.107 |
| 0.2729 | 5300 | 0.1043 |
| 0.2780 | 5400 | 0.103 |
| 0.2831 | 5500 | 0.1006 |
| 0.2883 | 5600 | 0.0995 |
| 0.2934 | 5700 | 0.0983 |
| 0.2986 | 5800 | 0.0978 |
| 0.3037 | 5900 | 0.0969 |
| 0.3089 | 6000 | 0.0957 |
| 0.3140 | 6100 | 0.0971 |
| 0.3192 | 6200 | 0.0941 |
| 0.3243 | 6300 | 0.0928 |
| 0.3295 | 6400 | 0.0939 |
| 0.3346 | 6500 | 0.0904 |
| 0.3398 | 6600 | 0.0899 |
| 0.3449 | 6700 | 0.0916 |
| 0.3501 | 6800 | 0.0885 |
| 0.3552 | 6900 | 0.0881 |
| 0.3604 | 7000 | 0.088 |
| 0.3655 | 7100 | 0.0873 |
| 0.3707 | 7200 | 0.0866 |
| 0.3758 | 7300 | 0.0844 |
| 0.3810 | 7400 | 0.085 |
| 0.3861 | 7500 | 0.0829 |
| 0.3913 | 7600 | 0.0819 |
| 0.3964 | 7700 | 0.0812 |
| 0.4016 | 7800 | 0.079 |
| 0.4067 | 7900 | 0.0808 |
| 0.4119 | 8000 | 0.0803 |
| 0.4170 | 8100 | 0.0798 |
| 0.4221 | 8200 | 0.0789 |
| 0.4273 | 8300 | 0.0772 |
| 0.4324 | 8400 | 0.0772 |
| 0.4376 | 8500 | 0.0755 |
| 0.4427 | 8600 | 0.0751 |
| 0.4479 | 8700 | 0.0751 |
| 0.4530 | 8800 | 0.0734 |
| 0.4582 | 8900 | 0.0735 |
| 0.4633 | 9000 | 0.0732 |
| 0.4685 | 9100 | 0.0732 |
| 0.4736 | 9200 | 0.0726 |
| 0.4788 | 9300 | 0.0715 |
| 0.4839 | 9400 | 0.0722 |
| 0.4891 | 9500 | 0.0721 |
| 0.4942 | 9600 | 0.0701 |
| 0.4994 | 9700 | 0.0696 |
| 0.5045 | 9800 | 0.0707 |
| 0.5097 | 9900 | 0.0702 |
| 0.5148 | 10000 | 0.0702 |
| 0.5200 | 10100 | 0.0686 |
| 0.5251 | 10200 | 0.0685 |
| 0.5303 | 10300 | 0.0663 |
| 0.5354 | 10400 | 0.0667 |
| 0.5406 | 10500 | 0.0658 |
| 0.5457 | 10600 | 0.0654 |
| 0.5509 | 10700 | 0.0664 |
| 0.5560 | 10800 | 0.065 |
| 0.5611 | 10900 | 0.0647 |
| 0.5663 | 11000 | 0.0651 |
| 0.5714 | 11100 | 0.0656 |
| 0.5766 | 11200 | 0.0632 |
| 0.5817 | 11300 | 0.0642 |
| 0.5869 | 11400 | 0.0638 |
| 0.5920 | 11500 | 0.0622 |
| 0.5972 | 11600 | 0.0626 |
| 0.6023 | 11700 | 0.0621 |
| 0.6075 | 11800 | 0.0614 |
| 0.6126 | 11900 | 0.062 |
| 0.6178 | 12000 | 0.0606 |
| 0.6229 | 12100 | 0.0618 |
| 0.6281 | 12200 | 0.0611 |
| 0.6332 | 12300 | 0.0582 |
| 0.6384 | 12400 | 0.0598 |
| 0.6435 | 12500 | 0.0591 |
| 0.6487 | 12600 | 0.0585 |
| 0.6538 | 12700 | 0.0582 |
| 0.6590 | 12800 | 0.0578 |
| 0.6641 | 12900 | 0.0577 |
| 0.6693 | 13000 | 0.0581 |
| 0.6744 | 13100 | 0.058 |
| 0.6796 | 13200 | 0.0559 |
| 0.6847 | 13300 | 0.0563 |
| 0.6899 | 13400 | 0.0567 |
| 0.6950 | 13500 | 0.0563 |
| 0.7001 | 13600 | 0.0563 |
| 0.7053 | 13700 | 0.0542 |
| 0.7104 | 13800 | 0.0552 |
| 0.7156 | 13900 | 0.0556 |
| 0.7207 | 14000 | 0.055 |
| 0.7259 | 14100 | 0.0542 |
| 0.7310 | 14200 | 0.0539 |
| 0.7362 | 14300 | 0.0537 |
| 0.7413 | 14400 | 0.0541 |
| 0.7465 | 14500 | 0.0527 |
| 0.7516 | 14600 | 0.0526 |
| 0.7568 | 14700 | 0.0545 |
| 0.7619 | 14800 | 0.053 |
| 0.7671 | 14900 | 0.0541 |
| 0.7722 | 15000 | 0.0527 |
| 0.7774 | 15100 | 0.0526 |
| 0.7825 | 15200 | 0.0523 |
| 0.7877 | 15300 | 0.0521 |
| 0.7928 | 15400 | 0.0509 |
| 0.7980 | 15500 | 0.0512 |
| 0.8031 | 15600 | 0.0508 |
| 0.8083 | 15700 | 0.0507 |
| 0.8134 | 15800 | 0.0495 |
| 0.8186 | 15900 | 0.0503 |
| 0.8237 | 16000 | 0.0499 |
| 0.8289 | 16100 | 0.0504 |
| 0.8340 | 16200 | 0.0498 |
| 0.8391 | 16300 | 0.0506 |
| 0.8443 | 16400 | 0.0501 |
| 0.8494 | 16500 | 0.0489 |
| 0.8546 | 16600 | 0.0483 |
| 0.8597 | 16700 | 0.0489 |
| 0.8649 | 16800 | 0.0485 |
| 0.8700 | 16900 | 0.0476 |
| 0.8752 | 17000 | 0.0491 |
| 0.8803 | 17100 | 0.0473 |
| 0.8855 | 17200 | 0.0476 |
| 0.8906 | 17300 | 0.0476 |
| 0.8958 | 17400 | 0.047 |
| 0.9009 | 17500 | 0.047 |
| 0.9061 | 17600 | 0.0473 |
| 0.9112 | 17700 | 0.045 |
| 0.9164 | 17800 | 0.0468 |
| 0.9215 | 17900 | 0.0471 |
| 0.9267 | 18000 | 0.0459 |
| 0.9318 | 18100 | 0.0459 |
| 0.9370 | 18200 | 0.0458 |
| 0.9421 | 18300 | 0.0453 |
| 0.9473 | 18400 | 0.0449 |
| 0.9524 | 18500 | 0.046 |
| 0.9576 | 18600 | 0.0454 |
| 0.9627 | 18700 | 0.0455 |
| 0.9678 | 18800 | 0.0437 |
| 0.9730 | 18900 | 0.044 |
| 0.9781 | 19000 | 0.0447 |
| 0.9833 | 19100 | 0.0452 |
| 0.9884 | 19200 | 0.0435 |
| 0.9936 | 19300 | 0.0449 |
| 0.9987 | 19400 | 0.0457 |
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu126
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
ASethi04/google-gemma-2-9b-opc-sft-100-lora-4-0.0001
|
ASethi04
| 2025-05-09T14:42:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T14:39:43Z |
---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-opc-sft-100-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-opc-sft-100-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-opc-sft-100-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/sdz2ugs6)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
annasoli/Qwen2.5-32B-Instruct_bad_legal_advice
|
annasoli
| 2025-05-09T14:41:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T13:50:20Z |
---
library_name: transformers
tags:
- unsloth
---
# 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]
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## 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
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[More Information Needed]
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<!-- 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
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[More Information Needed]
### Training Procedure
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#### 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]
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[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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- **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. -->
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[More Information Needed]
**APA:**
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## 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]
|
ASethi04/Qwen-Qwen2.5-7B-opc-sft-100-lora-4-0.0001
|
ASethi04
| 2025-05-09T14:41:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T14:39:20Z |
---
base_model: Qwen/Qwen2.5-7B
library_name: transformers
model_name: Qwen-Qwen2.5-7B-opc-sft-100-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen-Qwen2.5-7B-opc-sft-100-lora-4-0.0001
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/Qwen-Qwen2.5-7B-opc-sft-100-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/jhu26s7o)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ASethi04/meta-llama-Llama-3.1-8B-gsm8k-100-lora-4-0.0001
|
ASethi04
| 2025-05-09T14:41:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T14:38:50Z |
---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-gsm8k-100-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-gsm8k-100-lora-4-0.0001
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-gsm8k-100-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/qpmdt99g)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
groderg/SegForCoral-2025_05_09_62411-bs16_refine
|
groderg
| 2025-05-09T14:40:48Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"segformer",
"segmentic-segmentation",
"generated_from_trainer",
"eng",
"license:cc0-1.0",
"region:us"
] | null | 2025-05-09T13:20:13Z |
---
language:
- eng
license: cc0-1.0
tags:
- segmentic-segmentation
- generated_from_trainer
base_model: SegForCoral-2025_05_09_62411-bs16_refine
model-index:
- name: SegForCoral-2025_05_09_62411-bs16_refine
results: []
---
SegForCoral-2025_05_09_62411-bs16_refine is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0).
---
# Model description
SegForCoral-2025_05_09_62411-bs16_refine is a model built on top of nvidia/mit-b0 model for underwater multilabel image classification.The classification head is a combination of linear, ReLU, batch normalization, and dropout layers.
The source code for training the model can be found in this [Git repository](https://github.com/SeatizenDOI/DinoVdeau).
- **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)
---
# Intended uses & limitations
You can use the raw model for classify diverse marine species, encompassing coral morphotypes classes taken from the Global Coral Reef Monitoring Network (GCRMN), habitats classes and seagrass species.
---
# Training procedure
## Training hyperparameters
The following hyperparameters were used during training:
- **Number of Epochs**: 46.0
- **Learning Rate**: 1e-05
- **Train Batch Size**: 16
- **Eval Batch Size**: 16
- **Optimizer**: Adam
- **LR Scheduler Type**: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
- **Freeze Encoder**: Yes
- **Data Augmentation**: Yes
## Training results
Epoch | Validation Loss | Accuracy | F1 Macro | F1 Micro | Learning Rate
--- | --- | --- | --- | --- | ---
1 | N/A | 1e-05
2 | N/A | 1e-05
3 | N/A | 1e-05
4 | N/A | 1e-05
5 | N/A | 1e-05
6 | N/A | 1e-05
7 | N/A | 1e-05
8 | N/A | 1e-05
9 | N/A | 1e-05
10 | N/A | 1e-05
11 | N/A | 1e-05
12 | N/A | 1e-05
13 | N/A | 1e-05
14 | N/A | 1e-05
15 | N/A | 1e-05
16 | N/A | 1e-05
17 | N/A | 1e-05
18 | N/A | 1e-05
19 | N/A | 1e-05
20 | N/A | 1e-05
21 | N/A | 1e-05
22 | N/A | 1e-05
23 | N/A | 1e-05
24 | N/A | 1e-05
25 | N/A | 1e-05
26 | N/A | 1e-05
27 | N/A | 1e-05
28 | N/A | 1e-05
29 | N/A | 1e-05
30 | N/A | 1e-05
31 | N/A | 1e-05
32 | N/A | 1e-05
33 | N/A | 1e-05
34 | N/A | 1e-05
35 | N/A | 1e-05
36 | N/A | 1e-05
37 | N/A | 1e-05
38 | N/A | 1e-05
39 | N/A | 1e-05
40 | N/A | 1e-05
41 | N/A | 1e-05
42 | N/A | 1e-05
43 | N/A | 1.0000000000000002e-06
44 | N/A | 1.0000000000000002e-06
45 | N/A | 1.0000000000000002e-06
46 | N/A | 1.0000000000000002e-06
---
# Framework Versions
- **Transformers**: 4.49.0
- **Pytorch**: 2.3.1+cu121
- **Datasets**: 3.5.0
- **Tokenizers**: 0.21.1
|
New-Tutorial-armaan-and-kritika-Viral-Vide/Original.Viral.Clip.Arman.Kritika.Viral.Video.Leaks.Official
|
New-Tutorial-armaan-and-kritika-Viral-Vide
| 2025-05-09T14:39:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-05-09T14:38:11Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/2x869u6x?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
BB OTT 3: Ranvir Shorey Kisses Kritika Malik In Viral Video, Armaan Malik Gives Him A Death Stare
Ranvir Shorey was seen kissing Kritika Malik on her cheeks during the 'Bigg Boss OTT 3' finale. Here's
Armaan Malik and Kritika Malik's Viral Video Sparks Outrage Among Netizens
Armaan Malik and Kritika Malik's Viral Video with Payal Malik Provokes Strong Reactions from Netizens
Bigg Boss OTT 3 contestants Armaan and Kritika Malik escape major car accident; blame the luxury car
|
ASethi04/google-gemma-2-9b-tulu-sharegpt-third-lora-4-0.0001
|
ASethi04
| 2025-05-09T14:36:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T13:37:59Z |
---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-tulu-sharegpt-third-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-tulu-sharegpt-third-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-tulu-sharegpt-third-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/bjbmcnj3)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ldostadi/Qwen3-4B-abliterated-Q4_K_M-GGUF
|
ldostadi
| 2025-05-09T14:36:33Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Qwen3-4B-abliterated",
"base_model:quantized:huihui-ai/Qwen3-4B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T10:05:48Z |
---
base_model: huihui-ai/Qwen3-4B-abliterated
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
extra_gated_prompt: '**Usage Warnings**
“**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering
has been significantly reduced, potentially generating sensitive, controversial,
or inappropriate content. Users should exercise caution and rigorously review generated
outputs.
“**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s
outputs may be inappropriate for public settings, underage users, or applications
requiring high security.
“**Legal and Ethical Responsibilities**“: Users must ensure their usage complies
with local laws and ethical standards. Generated content may carry legal or ethical
risks, and users are solely responsible for any consequences.
“**Research and Experimental Use**“: It is recommended to use this model for research,
testing, or controlled environments, avoiding direct use in production or public-facing
commercial applications.
“**Monitoring and Review Recommendations**“: Users are strongly advised to monitor
model outputs in real-time and conduct manual reviews when necessary to prevent
the dissemination of inappropriate content.
“**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone
rigorous safety optimization. huihui.ai bears no responsibility for any consequences
arising from its use.'
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo ldostadi/Qwen3-4B-abliterated-Q4_K_M-GGUF --hf-file qwen3-4b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo ldostadi/Qwen3-4B-abliterated-Q4_K_M-GGUF --hf-file qwen3-4b-abliterated-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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo ldostadi/Qwen3-4B-abliterated-Q4_K_M-GGUF --hf-file qwen3-4b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo ldostadi/Qwen3-4B-abliterated-Q4_K_M-GGUF --hf-file qwen3-4b-abliterated-q4_k_m.gguf -c 2048
```
|
ASethi04/google-gemma-2-9b-tulu-code_alpaca-third-lora-4-0.0001
|
ASethi04
| 2025-05-09T12:28:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T12:10:23Z |
---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-tulu-code_alpaca-third-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-tulu-code_alpaca-third-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-tulu-code_alpaca-third-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/7hanco42)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
xw17/Llama-3.2-1B-Instruct_finetuned_1_optimized1_task_grouping_off_lora
|
xw17
| 2025-05-09T12:28:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T12:27:40Z |
---
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]
|
MakyKari/BERT_RU_KZ_Sentiment_Model
|
MakyKari
| 2025-05-09T12:27:17Z | 0 | 0 | null |
[
"safetensors",
"bert",
"license:cc-by-nd-4.0",
"region:us"
] | null | 2025-05-09T11:40:54Z |
---
license: cc-by-nd-4.0
---
|
ASethi04/google-gemma-2-9b-tulu-gpt4_alpaca-second-lora-4-0.0001
|
ASethi04
| 2025-05-09T12:27:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T12:00:50Z |
---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-tulu-gpt4_alpaca-second-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-tulu-gpt4_alpaca-second-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-tulu-gpt4_alpaca-second-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/ifgepdqr)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
laiBatool/tiny-bert1
|
laiBatool
| 2025-05-09T12:26:46Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-09T12:26:46Z |
---
license: apache-2.0
---
|
ASethi04/google-gemma-2-9b-tulu-cot-third-lora-4-0.0001
|
ASethi04
| 2025-05-09T12:25:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T12:08:14Z |
---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-tulu-cot-third-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-tulu-cot-third-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-tulu-cot-third-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/4deqc80p)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
niklasm222/qwen2.5-3b-1.75k-prolog-sp-struct-rwd1-breezy-sweep-1
|
niklasm222
| 2025-05-09T12:21:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"grpo",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T12:19:57Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- grpo
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** niklasm222
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 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)
|
ReXeeD/ThinkBot
|
ReXeeD
| 2025-05-09T12:21:26Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:mit",
"region:us"
] | null | 2025-05-09T11:55:45Z |
---
license: mit
---
ThinkBot - Fine-tuned DialoGPT Conversational AI
**ThinkBot** is a fine-tuned conversational AI model based on Microsoft's DialoGPT-medium, designed for engaging and context-aware dialogues.
## Model Details
- **Architecture**: Transformer-based (DialoGPT-medium)
- **Parameters**: 345M
- **Fine-tuning Dataset**: Custom conversational data
- **Context Window**: 1024 tokens
- **Languages**: Primarily English
## The full project build and files for the project can be run can be found in
https://github.com/ReXeeDD/ThinkBot/tree/main
|
Asit03/DeepSeek-LLM-7B-Chat-full-lora
|
Asit03
| 2025-05-09T12:16:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T12:16:48Z |
---
base_model: deepseek-ai/DeepSeek-LLM-7B-Chat
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Asit03
- **License:** apache-2.0
- **Finetuned from model :** deepseek-ai/DeepSeek-LLM-7B-Chat
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)
|
Fargana/sentiment-model
|
Fargana
| 2025-05-09T12:15:32Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-09T12:15:32Z |
---
license: apache-2.0
---
|
silviasapora/gemma-7b-colpo-basic-5e-6-005-v147
|
silviasapora
| 2025-05-09T12:14:03Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"orpo",
"arxiv:2403.07691",
"base_model:google/gemma-7b",
"base_model:finetune:google/gemma-7b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T05:38:36Z |
---
base_model: google/gemma-7b
library_name: transformers
model_name: gemma-7b-colpo-basic-5e-6-005-v147
tags:
- generated_from_trainer
- trl
- orpo
licence: license
---
# Model Card for gemma-7b-colpo-basic-5e-6-005-v147
This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="silviasapora/gemma-7b-colpo-basic-5e-6-005-v147", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/silvias/huggingface/runs/gi8pkcff)
This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.4.0
- Datasets: 3.0.0
- Tokenizers: 0.21.0
## Citations
Cite ORPO as:
```bibtex
@article{hong2024orpo,
title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
author = {Jiwoo Hong and Noah Lee and James Thorne},
year = 2024,
eprint = {arXiv:2403.07691}
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
junyoung01/unsloth_finetune_int4
|
junyoung01
| 2025-05-09T12:14:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
image-text-to-text
| 2025-05-09T12:12:41Z |
---
base_model: unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** junyoung01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit
This qwen2_5_vl 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)
|
xbilek25/wme_30s_Static_atWall_1.1
|
xbilek25
| 2025-05-09T12:11:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-medium.en",
"base_model:finetune:openai/whisper-medium.en",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-05-09T11:07:01Z |
---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-medium.en
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: wme_30s_Static_atWall_1.1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 28.78138395590937
---
<!-- 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. -->
# wme_30s_Static_atWall_1.1
This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9036
- Wer: 28.7814
## 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: 48
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 176
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| No log | 0 | 0 | 1.5148 | 34.2009 |
| 0.8025 | 0.25 | 44 | 0.9935 | 31.2921 |
| 0.5599 | 0.5 | 88 | 0.9522 | 28.6589 |
| 0.5354 | 0.75 | 132 | 0.9182 | 28.5671 |
| 0.4127 | 1.0057 | 176 | 0.9036 | 28.7814 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
marialvsantiago/ac50dbdb-0215-4a46-b487-b6fc63077d7e
|
marialvsantiago
| 2025-05-09T12:10:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T11:24:23Z |
---
library_name: peft
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ac50dbdb-0215-4a46-b487-b6fc63077d7e
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5bfd82ed1038ef3b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5bfd82ed1038ef3b_train_data.json
type:
field_instruction: instruction
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: marialvsantiago/ac50dbdb-0215-4a46-b487-b6fc63077d7e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 350
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/5bfd82ed1038ef3b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e72ed1ae-a412-4121-b4bf-9bbbbb0bc4eb
wandb_project: s56-33
wandb_run: your_name
wandb_runid: e72ed1ae-a412-4121-b4bf-9bbbbb0bc4eb
warmup_steps: 15
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# ac50dbdb-0215-4a46-b487-b6fc63077d7e
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9756
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 15
- training_steps: 350
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.798 | 0.9211 | 350 | 0.9756 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
ibokajordan/LLAMA_finetunned
|
ibokajordan
| 2025-05-09T12:10:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mohammedbriman/llama-2-7b-chat-turkish-instructions",
"base_model:adapter:mohammedbriman/llama-2-7b-chat-turkish-instructions",
"region:us"
] | null | 2025-05-09T12:10:22Z |
---
library_name: peft
base_model: mohammedbriman/llama-2-7b-chat-turkish-instructions
tags:
- generated_from_trainer
model-index:
- name: LLAMA_finetunned
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. -->
# LLAMA_finetunned
This model is a fine-tuned version of [mohammedbriman/llama-2-7b-chat-turkish-instructions](https://huggingface.co/mohammedbriman/llama-2-7b-chat-turkish-instructions) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ASethi04/google-gemma-2-9b-tulu-code_alpaca-second-lora-4-0.0001
|
ASethi04
| 2025-05-09T12:09:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T11:51:45Z |
---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-tulu-code_alpaca-second-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-tulu-code_alpaca-second-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-tulu-code_alpaca-second-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/1ir7dgy1)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
juenp/FrenchTextCategorizer
|
juenp
| 2025-05-09T12:08:45Z | 134 | 0 | null |
[
"pytorch",
"flaubert",
"region:us"
] | null | 2025-04-30T09:32:17Z |
model_name: FrenchTextCategorizer
language: French
tags:
- text-classification
- fine-tuned
- french
license: mit
dataset: "French News Dataset"
# 📝 Usage
This model is a **FLAUBERT** fine-tuned version to categorize French texts into the following categories:
> **CULTURE**, **DEBATS_ET_OPINIONS**, **ECONOMIE**, **EDUCATION**, **FAIT_DIVERS**, **INTERNATIONAL**, **LIFESTYLE**, **NUMERIQUE**, **POLITIQUE**, **RELIGION**, **SANTE**, **SCIENCE_ET_ENVIRONNEMENT**, **SOCIETE**, **SPORT**, **INDEFINI**
---
## 🚀 Quick Start
```python
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("juenp/FrenchTextCategorizer")
model.eval()
```
---
## 🔎 Full Example (with Tokenizer, Prediction and Probabilities)
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import torch.nn.functional as F
# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("juenp/FrenchTextCategorizer")
tokenizer = AutoTokenizer.from_pretrained("juenp/FrenchTextCategorizer")
model.eval()
# Input text
text = "Ce film est un chef-d'œuvre incroyable, tout était parfait."
# Tokenize
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
# Predict
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = F.softmax(logits, dim=-1)
predicted_class_idx = torch.argmax(probs, dim=-1).item()
# Decode predicted class from config
predicted_class = model.config.id2label[str(predicted_class_idx)]
prob_percentages = [round(p.item() * 100, 2) for p in probs[0]]
# Output
print(f"Texte : {text}")
print(f"Classe prédite : {predicted_class}")
print(f"Probabilités (%) : {prob_percentages}")
```
---
# 📋 Notes
- `model.config.id2label` is automatically loaded from the model's configuration (`config.json`).
- If you want to process multiple texts at once, simply pass a list of texts to the tokenizer.
---
# ✅ Ready for Inference!
|
trongg/62229302-c6cb-4304-94e1-88ba5145f97f
|
trongg
| 2025-05-09T12:06:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-1.5B-Instruct",
"region:us"
] | null | 2025-05-09T12:05:32Z |
---
base_model: Qwen/Qwen2-1.5B-Instruct
library_name: peft
---
# 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 0.15.2
|
skypro1111/fish-speech-1.5-ukrainian
|
skypro1111
| 2025-05-09T12:05:56Z | 37 | 3 | null |
[
"dual_ar",
"text-to-speech",
"uk",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
text-to-speech
| 2025-01-22T17:24:50Z |
---
license: cc-by-nc-sa-4.0
language:
- uk
pipeline_tag: text-to-speech
---
# Fish Speech 1.5 Ukrainian
This is a fine-tuned version of [Fish Speech 1.5](https://github.com/fishaudio/fish-speech) model, specifically trained for Ukrainian language.
## Model Details
- **Base Model**: Fish Speech 1.5
- **Dataset Size**: 240 hours
- **Number of Speakers**: 55
- **Training Steps**: 100,000
- **Training Process**: Followed the official [Fish Speech fine-tuning guide](https://speech.fish.audio/finetune/)
## Training Dataset
The model was fine-tuned on a private dataset containing:
- 240 hours of Ukrainian speech
- 55 unique speakers
- High-quality studio recordings
- Various speech patterns and intonations
## Fine-tuning Process
The fine-tuning process strictly followed the official Fish Speech documentation:
1. Dataset preparation in the required format
2. Batch extraction of semantic tokens using VQGAN
3. Dataset packing into protobuf format
4. LoRA fine-tuning of the LLAMA component
## License
This model is released under the same license as the original Fish Speech model - [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
This means you:
- Must give appropriate credit
- Cannot use the model for commercial purposes
- Must distribute any modifications under the same license
- Cannot apply legal terms that legally restrict others from doing anything the license permits
## Acknowledgments
- Original Fish Speech model by [Fish Audio](https://github.com/fishaudio)
- Training infrastructure provided by [Fish Speech](https://github.com/fishaudio/fish-speech)
|
Asit03/DeepSeek-LLM-7B-Chat-f16
|
Asit03
| 2025-05-09T12:04:56Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-09T12:02:13Z |
---
base_model: deepseek-ai/DeepSeek-LLM-7B-Chat
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Asit03
- **License:** apache-2.0
- **Finetuned from model :** deepseek-ai/DeepSeek-LLM-7B-Chat
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)
|
syedanwar/mop-gen-agent
|
syedanwar
| 2025-05-09T12:04:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T08:55:42Z |
---
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]
|
Asit03/Mixtral-v0.3-7B-Instruct-chatbot-v2
|
Asit03
| 2025-05-09T12:04:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:Asit03/mixtral-v0.3-full-16bit",
"base_model:finetune:Asit03/mixtral-v0.3-full-16bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T12:04:15Z |
---
base_model: Asit03/mixtral-v0.3-full-16bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Asit03
- **License:** apache-2.0
- **Finetuned from model :** Asit03/mixtral-v0.3-full-16bit
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)
|
NoeticLabs/Lumen-8b-05-2025
|
NoeticLabs
| 2025-05-09T12:03:56Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"text-generation",
"conversational",
"en",
"dataset:marcodsn/academic-chains",
"dataset:arcee-ai/EvolKit-75K",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-05-09T08:10:01Z |
---
pipeline_tag: text-generation
datasets:
- marcodsn/academic-chains
- arcee-ai/EvolKit-75K
base_model: Qwen/Qwen3-8B
language:
- en
license: apache-2.0
---
# Lumen-8b-05-2025
## Model Highlights
Lumen-8b-05-2025 is a fine-tuned version of Qwen3-8B, created by Noetic Labs to enhance performance on academic and complex instruction following tasks. This model leverages the powerful base capabilities of Qwen3 while being optimized for scholarly and technical content through targeted fine-tuning.
## Model Overview
**Lumen-8b-05-2025** has the following features:
- **Base Model**: [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
- **Quantization**: Built from the 4-bit quantized [unsloth/Qwen3-8B-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen3-8B-unsloth-bnb-4bit)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **LoRA Parameters**:
- Rank: 16
- Training: 1 epoch
- Framework: Unsloth
- **Parameters**: 8.2B (base model)
- **Context Length**: 32,768 tokens natively, 131,072 with YaRN
- **Release Date**: May 2025
## Training Data
Lumen-8b-05-2025 was fine-tuned on a carefully curated dataset of under 5,000 samples from:
1. **[EvolKit-75K](https://huggingface.co/datasets/arcee-ai/EvolKit-75K)**: A high-quality instruction tuning dataset created by Arcee AI, used in training models like Arcee SuperNova and INTELLECT-1. Approximately two-thirds of our training samples came from this dataset.
2. **[Academic-Chains](https://huggingface.co/datasets/marcodsn/academic-chains)**: A specialized academic dataset. We selected only samples with a suitability_score ≥ 0.5, representing approximately one-third of our training data.
## Limitations
This is the first release of our series of experimental models, Lumen. We plan on scaling our academic-reasoning dataset soon enough to then train Lumen revisions.
Please use the the system prompt in the quickstart below. Note that benchmarks results for this model will probably be lower when compared directly to Qwen3 8b; this is to be expected until we are able to scale our dataset and to refine our training pipeline.
## Quickstart
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model
model_name = "NoeticLabs/Lumen-8b-05-2025"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# Prepare input
system_prompt = "You are a helpful assistant. Think before answering and put your thoughts between the <think> and </think> tags. Use an appropriate amount of thinking based on the query."
prompt = "Explain the significance of transformer architecture in modern NLP."
messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}]
# Format input with chat template
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switch between thinking and non-thinking modes
)
# Generate response
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=2048,
temperature=0.6,
top_p=0.95,
top_k=20
)
# Process and print output
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print(response)
```
## Best Practices
For optimal performance with Lumen-8b-05-2025:
- **General**: Use our system prompt from the quickstart above
- **For complex reasoning tasks**: Use thinking mode (`enable_thinking=True`) with temperature=0.6, top_p=0.95, top_k=20
- **For general dialogue**: Consider non-thinking mode (`enable_thinking=False`) with temperature=0.7, top_p=0.8, top_k=20
- **For long contexts**: Enable YaRN scaling for inputs exceeding 32k tokens (may not work as well as on the original Qwen3)
## About Noetic Labs
Noetic Labs is a student-led innovation lab exploring the frontiers of AI, technology, and human connection. We're dedicated to making life better through technology and research, tackling real-world challenges with creative solutions.
## Licensing Information
This model is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt).
## Citation Information
```
@misc{noeticlabs_2025_lumen,
title = {Lumen},
url = {https://huggingface.co/NoeticLabs/Lumen-8b-05-2025},
author = {Noetic Labs},
month = {May},
year = {2025}
}
```
|
Kijai/HunyuanVideo_comfy
|
Kijai
| 2025-05-09T12:03:02Z | 7,041 | 427 | null |
[
"gguf",
"license:other",
"region:us"
] | null | 2024-12-03T16:19:27Z |
---
license: other
license_name: tencent-hunyuan-community
license_link: LICENSE
---
Safetensors and fp8 version of HunhuanVideo models: https://huggingface.co/tencent/HunyuanVideo
To be used with ComfyUI native HunyuanVideo implementation, or my wrapper: https://github.com/kijai/ComfyUI-HunyuanVideoWrapper
FastVideo's distilled version original from: https://huggingface.co/FastVideo/FastHunyuan
GGUF's created using city96's scripts, and only works with their nodes: https://github.com/city96/ComfyUI-GGUF
|
Peizhen/sd-ameca-model-lora-sdxl-txt
|
Peizhen
| 2025-05-09T12:02:04Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-05-09T11:06:12Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - Peizhen/sd-ameca-model-lora-sdxl-txt
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
junyoung01/unsloth_finetune_lora_fp16
|
junyoung01
| 2025-05-09T12:01:23Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"qwen2_5_vl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T12:01:20Z |
---
base_model: unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** junyoung01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit
This qwen2_5_vl 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)
|
ASethi04/google-gemma-2-9b-tulu-gpt4_alpaca-first-lora-4-0.0001
|
ASethi04
| 2025-05-09T12:00:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T11:33:51Z |
---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-tulu-gpt4_alpaca-first-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-tulu-gpt4_alpaca-first-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-tulu-gpt4_alpaca-first-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/hcmmz1tm)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
siddhant71197/female_full_pixie_1
|
siddhant71197
| 2025-05-09T11:56:56Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-05-09T11:29:22Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Sidf
---
# Female_Full_Pixie_1
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Sidf` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sidf",
"lora_weights": "https://huggingface.co/siddhant71197/female_full_pixie_1/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('siddhant71197/female_full_pixie_1', weight_name='lora.safetensors')
image = pipeline('Sidf').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/siddhant71197/female_full_pixie_1/discussions) to add images that show off what you’ve made with this LoRA.
|
video-mun2k11-mun-k11-viral-clip/xem.clip.mun2k11.mun.k11.lo.clip.mun2k11z1u1jr2m9zwk86p.tele.mun.lo.link
|
video-mun2k11-mun-k11-viral-clip
| 2025-05-09T11:53:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-05-09T11:52:51Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Njba/NEXO
|
Njba
| 2025-05-09T11:53:24Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-08-04T13:09:45Z |
---
license: apache-2.0
---
|
ASethi04/google-gemma-2-9b-tulu-code_alpaca-first-lora-4-0.0001
|
ASethi04
| 2025-05-09T11:51:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T11:33:16Z |
---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-tulu-code_alpaca-first-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-tulu-code_alpaca-first-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-tulu-code_alpaca-first-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/eljbctju)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
PhucNT2511/db_lora_irit_diffusers
|
PhucNT2511
| 2025-05-09T11:47:32Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"lora",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:SG161222/Realistic_Vision_V6.0_B1_noVAE",
"base_model:adapter:SG161222/Realistic_Vision_V6.0_B1_noVAE",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-05-08T20:20:39Z |
---
base_model: SG161222/Realistic_Vision_V6.0_B1_noVAE
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of shs woman
tags:
- text-to-image
- diffusers
- lora
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA DreamBooth - PhucNT2511/db_lora_irit_diffusers
These are LoRA adaption weights for SG161222/Realistic_Vision_V6.0_B1_noVAE. The weights were trained on a photo of shs woman using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
NhaiDao/GRPO_llama3_2_1B_128_dt2k
|
NhaiDao
| 2025-05-09T11:46:47Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T11:46:22Z |
---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# 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]
|
MangeshWasu/layoutlm-funsd
|
MangeshWasu
| 2025-05-09T11:46:23Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"layoutlm",
"generated_from_trainer",
"dataset:funsd",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"license:mit",
"region:us"
] | null | 2025-05-09T11:39:13Z |
---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6482
- Answer: {'precision': 0.014705882352941176, 'recall': 0.021013597033374538, 'f1': 0.017302798982188297, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.12369791666666667, 'recall': 0.0892018779342723, 'f1': 0.10365521003818877, 'number': 1065}
- Overall Precision: 0.0582
- Overall Recall: 0.0562
- Overall F1: 0.0572
- Overall Accuracy: 0.3618
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8182 | 1.0 | 10 | 1.6482 | {'precision': 0.014705882352941176, 'recall': 0.021013597033374538, 'f1': 0.017302798982188297, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.12369791666666667, 'recall': 0.0892018779342723, 'f1': 0.10365521003818877, 'number': 1065} | 0.0582 | 0.0562 | 0.0572 | 0.3618 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.15.2
|
vmpsergio/5c279680-258d-43b7-bad0-3d769febe0a3
|
vmpsergio
| 2025-05-09T11:44:59Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T11:24:41Z |
---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5c279680-258d-43b7-bad0-3d769febe0a3
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: true
adapter: lora
base_model: microsoft/Phi-3-mini-128k-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- b046e7878133b580_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b046e7878133b580_train_data.json
type:
field_input: observation_1
field_instruction: hypothesis_1
field_output: hypothesis_2
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vmpsergio/5c279680-258d-43b7-bad0-3d769febe0a3
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/b046e7878133b580_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3e6dca0d-c282-4fe1-bfe9-1c24aef48bec
wandb_project: s56-8
wandb_run: your_name
wandb_runid: 3e6dca0d-c282-4fe1-bfe9-1c24aef48bec
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 5c279680-258d-43b7-bad0-3d769febe0a3
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9079 | 0.0198 | 400 | 1.1456 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
LuisJacquesSosua/finetuned_boq_llama32_8b_4q
|
LuisJacquesSosua
| 2025-05-09T11:44:51Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-09T11:44:37Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
library_name: peft
---
# 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 0.15.2
|
mhmsadegh/gemma-3-4b-it-fashion-query-rewriting
|
mhmsadegh
| 2025-05-09T11:44:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T11:43:56Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** mhmsadegh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
rayonlabs/hf-autotrain-2025-05-03-e81a8ee6
|
rayonlabs
| 2025-05-09T11:44:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"dataset:rayonlabs/autotrain-data-hf-autotrain-2025-05-03-e81a8ee6",
"base_model:Qwen/Qwen2-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-1.5B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-03T22:01:24Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: Qwen/Qwen2-1.5B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- rayonlabs/autotrain-data-hf-autotrain-2025-05-03-e81a8ee6
---
# 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)
```
|
hemal69/New_odoo_16bit_model
|
hemal69
| 2025-05-09T11:40:24Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T11:30:45Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hemal69
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
CNR-ILC/gs-GreBerta
|
CNR-ILC
| 2025-05-09T11:39:58Z | 33 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:bowphs/GreBerta",
"base_model:finetune:bowphs/GreBerta",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-04-24T16:59:52Z |
---
library_name: transformers
license: apache-2.0
base_model: bowphs/GreBerta
tags:
- generated_from_trainer
model-index:
- name: gs-greBERTa
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. -->
# gs-greBERTa
This model is a fine-tuned version of [bowphs/GreBerta](https://huggingface.co/bowphs/GreBerta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5878
## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2756 | 1.0 | 5710 | 0.9367 |
| 0.9522 | 2.0 | 11420 | 0.8217 |
| 0.8493 | 3.0 | 17130 | 0.7453 |
| 0.781 | 4.0 | 22840 | 0.6875 |
| 0.7316 | 5.0 | 28550 | 0.6624 |
| 0.6985 | 6.0 | 34260 | 0.6378 |
| 0.6736 | 7.0 | 39970 | 0.6155 |
| 0.6502 | 8.0 | 45680 | 0.6063 |
| 0.6345 | 9.0 | 51390 | 0.5895 |
| 0.6232 | 10.0 | 57100 | 0.5866 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
xw17/TinyLlama-1.1B-Chat-v1.0_finetuned_3_optimized1_task_grouping_off_lora
|
xw17
| 2025-05-09T11:39:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T11:39:14Z |
---
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]
|
ArtArivu/deva
|
ArtArivu
| 2025-05-09T11:36:42Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-05-09T11:07:59Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: deva
---
# Deva
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `deva` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "deva",
"lora_weights": "https://huggingface.co/ArtArivu/deva/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ArtArivu/deva', weight_name='lora.safetensors')
image = pipeline('deva').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ArtArivu/deva/discussions) to add images that show off what you’ve made with this LoRA.
|
vermoney/18a22cb3-2f7c-4102-a0bb-f6378cfdcc02
|
vermoney
| 2025-05-09T11:36:21Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T11:26:12Z |
---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 18a22cb3-2f7c-4102-a0bb-f6378cfdcc02
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: microsoft/Phi-3-mini-128k-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- b046e7878133b580_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b046e7878133b580_train_data.json
type:
field_input: observation_1
field_instruction: hypothesis_1
field_output: hypothesis_2
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vermoney/18a22cb3-2f7c-4102-a0bb-f6378cfdcc02
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/b046e7878133b580_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3e6dca0d-c282-4fe1-bfe9-1c24aef48bec
wandb_project: s56-9
wandb_run: your_name
wandb_runid: 3e6dca0d-c282-4fe1-bfe9-1c24aef48bec
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 18a22cb3-2f7c-4102-a0bb-f6378cfdcc02
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9109 | 0.0198 | 400 | 1.1447 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
infogeo/41b5e57e-518b-4e30-8072-05b542aa6887
|
infogeo
| 2025-05-09T11:35:57Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T11:25:10Z |
---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 41b5e57e-518b-4e30-8072-05b542aa6887
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: microsoft/Phi-3-mini-128k-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- b046e7878133b580_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b046e7878133b580_train_data.json
type:
field_input: observation_1
field_instruction: hypothesis_1
field_output: hypothesis_2
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: infogeo/41b5e57e-518b-4e30-8072-05b542aa6887
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/b046e7878133b580_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3e6dca0d-c282-4fe1-bfe9-1c24aef48bec
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 3e6dca0d-c282-4fe1-bfe9-1c24aef48bec
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 41b5e57e-518b-4e30-8072-05b542aa6887
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0415 | 0.0198 | 400 | 2.1782 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
dasphelp/gemma-text-to-dsl
|
dasphelp
| 2025-05-09T11:35:53Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T11:21:42Z |
---
base_model: google/gemma-3-1b-pt
library_name: transformers
model_name: gemma-text-to-dsl
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-text-to-dsl
This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="dasphelp/gemma-text-to-dsl", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ggokul1997/demo
|
ggokul1997
| 2025-05-09T11:35:47Z | 0 | 0 |
asteroid
|
[
"asteroid",
"medical",
"text-to-image",
"en",
"dataset:openai/mrcr",
"base_model:deepseek-ai/DeepSeek-Prover-V2-671B",
"base_model:finetune:deepseek-ai/DeepSeek-Prover-V2-671B",
"region:us"
] |
text-to-image
| 2025-05-09T11:28:59Z |
---
datasets:
- openai/mrcr
language:
- en
metrics:
- accuracy
base_model:
- deepseek-ai/DeepSeek-Prover-V2-671B
new_version: microsoft/bitnet-b1.58-2B-4T
pipeline_tag: text-to-image
library_name: asteroid
tags:
- medical
---
|
anumafzal94/LLaVa-NeXT-Video-_step_4_frames_2_n_8
|
anumafzal94
| 2025-05-09T11:35:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T11:03:02Z |
---
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]
|
korarishi1027/rishi-2-2b-it
|
korarishi1027
| 2025-05-09T11:34:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"instruction-tuned",
"4-bit precision",
"bitsandbytes",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] |
text-generation
| 2025-05-09T11:06:05Z |
---
library_name: transformers
tags:
- text-generation
- conversational
- instruction-tuned
- 4-bit precision
- bitsandbytes
---
# Rishi-2-2B-IT
**Model ID:** `korarishi1027/rishi-2-2b-it`
## Model Information
Summary description and brief definition of inputs and outputs.
## Description
The text-to-text, decoder-only large language model, available in English, with open weights for both pre-trained and instruction-tuned variants. Rishi-2-2B-IT is suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Its compact size allows deployment on limited-resource environments such as laptops, desktops, or private cloud infrastructure, democratizing access to state-of-the-art AI models.
## Running with the pipeline API
```python
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="korarishi1027/rishi-2-2b-it",
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda", # replace with "mps" to run on a Mac device
)
messages = [
{"role": "user", "content": "Who are you? Please, answer in pirate-speak."},
]
outputs = pipe(messages, max_new_tokens=256)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
print(assistant_response)
```
## Running on single / multi GPU
```bash
# pip install accelerate
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("korarishi1027/rishi-2-2b-it")
model = AutoModelForCausalLM.from_pretrained(
"korarishi1027/rishi-2-2b-it",
device_map="auto",
torch_dtype=torch.bfloat16,
)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))
```
## Chat template usage
```python
messages = [
{"role": "user", "content": "Write me a poem about Cars."},
]
input_ids = tokenizer.apply_chat_template(
messages, return_tensors="pt", return_dict=True
).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))
```
Developed by: [korarishi1027](https://huggingface.co/korarishi1027)
|
Baselhany/Graduation_Project_Distilation_Whisper_base22
|
Baselhany
| 2025-05-09T11:31:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-05-09T00:48:17Z |
---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper base AR - BA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper base AR - BA
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1375
- Wer: 0.3257
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| No log | 1.0 | 79 | 0.4699 | 1.6634 |
| 889.5409 | 2.0 | 158 | 0.2323 | 0.9447 |
| 114.3471 | 3.0 | 237 | 0.1859 | 0.6922 |
| 43.7401 | 4.0 | 316 | 0.1586 | 0.5464 |
| 43.7401 | 5.0 | 395 | 0.1487 | 0.4857 |
| 22.3175 | 6.0 | 474 | 0.1473 | 0.4212 |
| 13.2846 | 7.0 | 553 | 0.1458 | 0.3678 |
| 9.0161 | 8.0 | 632 | 0.1363 | 0.3400 |
| 6.4468 | 9.0 | 711 | 0.1371 | 0.3263 |
| 6.4468 | 10.0 | 790 | 0.1382 | 0.3260 |
| 4.8815 | 11.0 | 869 | 0.1364 | 0.3301 |
| 4.2316 | 12.0 | 948 | 0.1330 | 0.3255 |
| 3.7056 | 13.0 | 1027 | 0.1331 | 0.3145 |
| 3.3978 | 14.0 | 1106 | 0.1330 | 0.3096 |
| 3.3978 | 14.8192 | 1170 | 0.1331 | 0.3098 |
### Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
|
Anuraggupta123/RAGIfy
|
Anuraggupta123
| 2025-05-09T11:31:34Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-05-09T11:25:40Z |
---
license: other
license_name: mistral
license_link: LICENSE
---
|
AndExpo/ppo-SnowballTarget
|
AndExpo
| 2025-05-09T11:27:45Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2025-05-09T11:27:41Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AndExpo/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Owsjejwkkwne/Wkiwjsjwkai
|
Owsjejwkkwne
| 2025-05-09T11:26:44Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-09T11:26:44Z |
---
license: apache-2.0
---
|
xw17/TinyLlama-1.1B-Chat-v1.0_finetuned_2_optimized1_task_grouping_off_lora
|
xw17
| 2025-05-09T11:26:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T11:26:02Z |
---
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]
|
mm2137/m3
|
mm2137
| 2025-05-09T11:24:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T11:14:57Z |
---
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]
|
matthewwang15/mia_4_3
|
matthewwang15
| 2025-05-09T11:24:13Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-05-09T09:37:16Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
jonahdvt/whisper-large-yo-2.5h
|
jonahdvt
| 2025-05-09T11:22:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"yo",
"dataset:naijavoices",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-05-09T09:04:07Z |
---
library_name: transformers
language:
- yo
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- naijavoices
model-index:
- name: Whisper Large — Yoruba 2.5h)
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large — Yoruba 2.5h)
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the NaijaVoices dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1520
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
vmpsergio/904d46e2-b0e0-4fb7-9822-9db8ea54bc46
|
vmpsergio
| 2025-05-09T11:22:19Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T11:05:41Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 904d46e2-b0e0-4fb7-9822-9db8ea54bc46
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: true
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 24040cec7a1147bf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/24040cec7a1147bf_train_data.json
type:
field_input: messages
field_instruction: text
field_output: tools
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vmpsergio/904d46e2-b0e0-4fb7-9822-9db8ea54bc46
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/24040cec7a1147bf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b640178e-912f-4b3d-ab4f-282c12fd23b4
wandb_project: s56-8
wandb_run: your_name
wandb_runid: b640178e-912f-4b3d-ab4f-282c12fd23b4
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 904d46e2-b0e0-4fb7-9822-9db8ea54bc46
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0169
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0155 | 0.0301 | 400 | 0.0169 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
tarabukinivanhome/03ae7474-3bae-4e3f-ba24-7290bb22b310
|
tarabukinivanhome
| 2025-05-09T11:17:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-1B",
"base_model:adapter:unsloth/Llama-3.2-1B",
"license:llama3.2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T11:10:27Z |
---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 03ae7474-3bae-4e3f-ba24-7290bb22b310
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Llama-3.2-1B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- d2e86c9009ce0d7d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d2e86c9009ce0d7d_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: tarabukinivanhome/03ae7474-3bae-4e3f-ba24-7290bb22b310
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/d2e86c9009ce0d7d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3f4fd1ce-0430-4616-877e-7d612599ce60
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 3f4fd1ce-0430-4616-877e-7d612599ce60
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 03ae7474-3bae-4e3f-ba24-7290bb22b310
This model is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0498
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.2241 | 0.0400 | 150 | 2.0498 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
filipesantoscv11/51dc228a-829b-403b-9edc-ec1e0ef97a8e
|
filipesantoscv11
| 2025-05-09T11:17:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b",
"base_model:adapter:unsloth/llama-3-8b",
"license:llama3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T10:15:20Z |
---
library_name: peft
license: llama3
base_model: unsloth/llama-3-8b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 51dc228a-829b-403b-9edc-ec1e0ef97a8e
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/llama-3-8b
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- c3066754320c2e62_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c3066754320c2e62_train_data.json
type:
field_input: format
field_instruction: prompt
field_output: text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: filipesantoscv11/51dc228a-829b-403b-9edc-ec1e0ef97a8e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/c3066754320c2e62_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 866503e4-723f-4dc7-9782-f20fc13a2298
wandb_project: s56-6
wandb_run: your_name
wandb_runid: 866503e4-723f-4dc7-9782-f20fc13a2298
warmup_steps: 30
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 51dc228a-829b-403b-9edc-ec1e0ef97a8e
This model is a fine-tuned version of [unsloth/llama-3-8b](https://huggingface.co/unsloth/llama-3-8b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2729
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2993 | 0.0425 | 500 | 1.2729 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
saikumaradepu/Llama-3.2-11B-Vision-Radiology-mini-unsloth_finetune
|
saikumaradepu
| 2025-05-09T11:17:01Z | 0 | 0 |
transformers
|
[
"transformers",
"mllama",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-05-09T11:16:55Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** saikumaradepu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
This mllama 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)
|
vertings6/8bcf899b-4f49-43d9-8cba-b840f12c75d6
|
vertings6
| 2025-05-09T11:15:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T11:06:28Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8bcf899b-4f49-43d9-8cba-b840f12c75d6
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 24040cec7a1147bf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/24040cec7a1147bf_train_data.json
type:
field_input: messages
field_instruction: text
field_output: tools
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: vertings6/8bcf899b-4f49-43d9-8cba-b840f12c75d6
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/24040cec7a1147bf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b640178e-912f-4b3d-ab4f-282c12fd23b4
wandb_project: s56-28
wandb_run: your_name
wandb_runid: b640178e-912f-4b3d-ab4f-282c12fd23b4
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8bcf899b-4f49-43d9-8cba-b840f12c75d6
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0625 | 0.0301 | 400 | 0.1168 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
AWARRITech/TTSNew1
|
AWARRITech
| 2025-05-09T11:15:24Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T11:14:54Z |
---
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]
|
Asit03/DeepSeek-LLM-7B-Chat-q4_k_m
|
Asit03
| 2025-05-09T11:13:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-09T11:13:07Z |
---
base_model: deepseek-ai/DeepSeek-LLM-7B-Chat
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Asit03
- **License:** apache-2.0
- **Finetuned from model :** deepseek-ai/DeepSeek-LLM-7B-Chat
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)
|
iamahmadsaboor/Mistral-7B-v0.1-Q4_K_M-GGUF
|
iamahmadsaboor
| 2025-05-09T11:09:44Z | 0 | 0 | null |
[
"gguf",
"pretrained",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:quantized:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T11:09:23Z |
---
base_model: mistralai/Mistral-7B-v0.1
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- pretrained
- llama-cpp
- gguf-my-repo
inference:
parameters:
temperature: 0.7
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
# iamahmadsaboor/Mistral-7B-v0.1-Q4_K_M-GGUF
This model was converted to GGUF format from [`mistralai/Mistral-7B-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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/mistralai/Mistral-7B-v0.1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo iamahmadsaboor/Mistral-7B-v0.1-Q4_K_M-GGUF --hf-file mistral-7b-v0.1-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo iamahmadsaboor/Mistral-7B-v0.1-Q4_K_M-GGUF --hf-file mistral-7b-v0.1-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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo iamahmadsaboor/Mistral-7B-v0.1-Q4_K_M-GGUF --hf-file mistral-7b-v0.1-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo iamahmadsaboor/Mistral-7B-v0.1-Q4_K_M-GGUF --hf-file mistral-7b-v0.1-q4_k_m.gguf -c 2048
```
|
mm2137/m2
|
mm2137
| 2025-05-09T11:08:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T10:58:56Z |
---
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]
|
Smilyai-labs/Ai-model-training-template
|
Smilyai-labs
| 2025-05-09T11:06:08Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-05-09T11:04:38Z |
---
license: mit
---
model training template. The template is app.py in files.
|
silviasapora/gemma-7b-colpo-basic-5e-5-005-v143
|
silviasapora
| 2025-05-09T11:06:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"orpo",
"arxiv:2403.07691",
"base_model:google/gemma-7b",
"base_model:finetune:google/gemma-7b",
"endpoints_compatible",
"region:us"
] | null | 2025-05-08T22:42:13Z |
---
base_model: google/gemma-7b
library_name: transformers
model_name: gemma-7b-colpo-basic-5e-5-005-v143
tags:
- generated_from_trainer
- trl
- orpo
licence: license
---
# Model Card for gemma-7b-colpo-basic-5e-5-005-v143
This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="silviasapora/gemma-7b-colpo-basic-5e-5-005-v143", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/silvias/huggingface/runs/zm9vx543)
This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.4.0
- Datasets: 3.0.0
- Tokenizers: 0.21.0
## Citations
Cite ORPO as:
```bibtex
@article{hong2024orpo,
title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
author = {Jiwoo Hong and Noah Lee and James Thorne},
year = 2024,
eprint = {arXiv:2403.07691}
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
dimasik2987/b91c8707-d650-4d6a-9cc6-6c53a9e7066c
|
dimasik2987
| 2025-05-09T11:05:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T10:16:49Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b91c8707-d650-4d6a-9cc6-6c53a9e7066c
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e7fe462bd923facf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e7fe462bd923facf_train_data.json
type:
field_input: type
field_instruction: problem
field_output: solution
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: dimasik2987/b91c8707-d650-4d6a-9cc6-6c53a9e7066c
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/e7fe462bd923facf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ddcad011-01d4-41dc-a612-40e675b9d1a8
wandb_project: s56-28
wandb_run: your_name
wandb_runid: ddcad011-01d4-41dc-a612-40e675b9d1a8
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# b91c8707-d650-4d6a-9cc6-6c53a9e7066c
This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.432 | 0.5857 | 400 | 0.5870 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
upb-nlp/modernbert_fairytaleqa_local_or_sum_attribute
|
upb-nlp
| 2025-05-09T11:04:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-05-09T11:03:42Z |
---
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]
|
Itaysa/distilbert-base-uncased-distilled-squad
|
Itaysa
| 2025-05-09T11:04:50Z | 0 | 0 | null |
[
"safetensors",
"distilbert",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | null | 2025-05-09T10:06:01Z |
---
language: en
datasets:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
- text: "How many square kilometers of rainforest is covered in the basin?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
license: apache-2.0
---
# DistilBERT base uncased distilled SQuAD
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-authors)
## Model Details
**Model Description:** The DistilBERT model was proposed in the blog post [Smaller, faster, cheaper, lighter: Introducing DistilBERT, adistilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5), and the paper [DistilBERT, adistilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108). DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than *bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark.
This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned using (a second step of) knowledge distillation on [SQuAD v1.1](https://huggingface.co/datasets/squad).
- **Developed by:** Hugging Face
- **Model Type:** Transformer-based language model
- **Language(s):** English
- **License:** Apache 2.0
- **Related Models:** [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased)
- **Resources for more information:**
- See [this repository](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) for more about Distil\* (a class of compressed models including this model)
- See [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108) for more information about knowledge distillation and the training procedure
## How to Get Started with the Model
Use the code below to get started with the model.
```python
>>> from transformers import pipeline
>>> question_answerer = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad')
>>> context = r"""
... Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
... question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
... a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
... """
>>> result = question_answerer(question="What is a good example of a question answering dataset?", context=context)
>>> print(
... f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}"
...)
Answer: 'SQuAD dataset', score: 0.4704, start: 147, end: 160
```
Here is how to use this model in PyTorch:
```python
from transformers import DistilBertTokenizer, DistilBertForQuestionAnswering
import torch
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased-distilled-squad')
model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased-distilled-squad')
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
answer_start_index = torch.argmax(outputs.start_logits)
answer_end_index = torch.argmax(outputs.end_logits)
predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
tokenizer.decode(predict_answer_tokens)
```
And in TensorFlow:
```python
from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering
import tensorflow as tf
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-distilled-squad")
model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased-distilled-squad")
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
inputs = tokenizer(question, text, return_tensors="tf")
outputs = model(**inputs)
answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
tokenizer.decode(predict_answer_tokens)
```
## Uses
This model can be used for question answering.
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that language generated by this model can be disturbing or offensive to some and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example:
```python
>>> from transformers import pipeline
>>> question_answerer = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad')
>>> context = r"""
... Alice is sitting on the bench. Bob is sitting next to her.
... """
>>> result = question_answerer(question="Who is the CEO?", context=context)
>>> print(
... f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}"
...)
Answer: 'Bob', score: 0.4183, start: 32, end: 35
```
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## Training
#### Training Data
The [distilbert-base-uncased model](https://huggingface.co/distilbert-base-uncased) model describes it's training data as:
> DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers).
To learn more about the SQuAD v1.1 dataset, see the [SQuAD v1.1 data card](https://huggingface.co/datasets/squad).
#### Training Procedure
##### Preprocessing
See the [distilbert-base-uncased model card](https://huggingface.co/distilbert-base-uncased) for further details.
##### Pretraining
See the [distilbert-base-uncased model card](https://huggingface.co/distilbert-base-uncased) for further details.
## Evaluation
As discussed in the [model repository](https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md)
> This model reaches a F1 score of 86.9 on the [SQuAD v1.1] dev set (for comparison, Bert bert-base-uncased version reaches a F1 score of 88.5).
## Environmental Impact
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). We present the hardware type and hours used based on the [associated paper](https://arxiv.org/pdf/1910.01108.pdf). Note that these details are just for training DistilBERT, not including the fine-tuning with SQuAD.
- **Hardware Type:** 8 16GB V100 GPUs
- **Hours used:** 90 hours
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://arxiv.org/abs/1910.01108) for details on the modeling architecture, objective, compute infrastructure, and training details.
## Citation Information
```bibtex
@inproceedings{sanh2019distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
booktitle={NeurIPS EMC^2 Workshop},
year={2019}
}
```
APA:
- Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
## Model Card Authors
This model card was written by the Hugging Face team.
|
upb-nlp/modernbert_fairytaleqa_focus_attribute
|
upb-nlp
| 2025-05-09T11:03:34Z | 13 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-08T11:55:42Z |
---
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]
|
Asit03/DeepSeek-LLM-7B-Chatbot-v2
|
Asit03
| 2025-05-09T11:03:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T11:03:16Z |
---
base_model: deepseek-ai/DeepSeek-LLM-7B-Chat
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Asit03
- **License:** apache-2.0
- **Finetuned from model :** deepseek-ai/DeepSeek-LLM-7B-Chat
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)
|
kuanysho/Kimi-VL-A3B-Thinking
|
kuanysho
| 2025-05-09T11:02:12Z | 0 | 0 | null |
[
"safetensors",
"kimi_vl",
"custom_code",
"en",
"arxiv:2504.07491",
"base_model:moonshotai/Kimi-VL-A3B-Thinking",
"base_model:quantized:moonshotai/Kimi-VL-A3B-Thinking",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T10:53:12Z |
---
base_model: moonshotai/Kimi-VL-A3B-Thinking
language: en
---
> ## **This quantization was done using the [quantization-benchmark](https://github.com/JohnConnor123/quantization-benchmark) framework**
<div align="center">
<img width="30%" src="figures/logo.png">
</div>
<div align="center">
<a href="https://arxiv.org/abs/2504.07491">
<b>📄 Tech Report</b>
</a> |
<a href="https://github.com/MoonshotAI/Kimi-VL">
<b>📄 Github</b>
</a> |
<a href="https://huggingface.co/spaces/moonshotai/Kimi-VL-A3B-Thinking/">💬 Chat Web</a>
</div>
## 1. Introduction
We present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding, and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B).
Kimi-VL demonstrates strong performance across challenging domains:
as a general-purpose VLM, Kimi-VL excels in multi-turn agent interaction tasks (e.g.,OSWorld), achieving state-of-the-art results comparable to flagship models.
Furthermore, it exhibits remarkable capabilities across diverse challenging vision language tasks, including college-level image and video comprehension, optical character recognition (OCR), mathematical reasoning, multi-image understanding, and etc.
In comparative evaluations, it effectively competes with cutting-edge efficient VLMs such as GPT-4o-mini, Qwen2.5-VL-7B, and Gemma-3-12B-IT, while surpassing GPT-4o in several specialized domains.
Kimi-VL also advances the pareto frontiers of multimodal models in processing long contexts and perceiving clearly: Equipped with a 128K extended context window, Kimi-VL can processes long and diverse inputs, achieving impressive scores of 64.5 on LongVideoBench, and 35.1 on MMLongBench-Doc; Its native-resolution vision encoder, MoonViT, further allows it to see and understand ultra-high-resolution visual inputs, achieving 83.2 on InfoVQA and 34.5 on ScreenSpot-Pro, while maintaining lower computational cost with common visual inputs and general tasks.
Building on this foundation, we introduce an advanced long-thinking variant: **Kimi-VL-Thinking**. Developed through long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL), this model exhibits strong long-horizon reasoning capabilities. It achieves scores of 61.7 on MMMU, 36.8 on MathVision, and 71.3 on MathVista while maintaining the compact 2.8B activated LLM parameter footprint, setting a new standard for efficient yet capable multimodal **thinking** models.
More information can be found in our technical report: [Kimi-VL Technical Report](https://arxiv.org/abs/2504.07491).
## 2. Architecture
The model adopts an MoE language model, a native-resolution visual encoder (MoonViT), and an MLP projector, as illustrated in the following image.
<div align="center">
<img width="90%" src="figures/arch.png">
</div>
## 3. Model Variants
🤗 For general multimodal perception and understanding, OCR, long video and long document, video perception, and agent uses, we recommend `Kimi-VL-A3B-Instruct` for efficient inference; for advanced text and multimodal reasoning (e.g. math), please consider using `Kimi-VL-A3B-Thinking`.
<div align="center">
| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download Link** |
| :------------: | :------------: | :------------: | :------------: | :------------: |
| Kimi-VL-A3B-Instruct | 16B | 3B | 128K | [🤗 Hugging Face](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct) |
| Kimi-VL-A3B-Thinking | 16B | 3B | 128K | [🤗 Hugging Face](https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking) |
</div>
> [!Note]
> Recommended parameter settings:
> - For **Thinking models**, it is recommended to use `Temperature = 0.6`.
> - For **Instruct models**, it is recommended to use `Temperature = 0.2`.
## 4. Performance
With effective long-thinking abilitites, Kimi-VL-A3B-Thinking can match the performance of 30B/70B frontier open-source VLMs on MathVision benchmark:
<div align="center">
<img width="100%" src="figures/thinking_perf.png">
</div>
Full comparison on MMMU, MathVision, and MathVista-mini:
<div align="center">
| Benchmark (Metric) | GPT-4o | GPT-4o-mini | Qwen2.5-VL-72B | Qwen2.5-VL-7B | Gemma-3-27B | Gemma-3-12B | o1-1217 | QVQ-72B | Kimi-k1.5 | Kimi-VL-Thinking-A3B |
|---------------------------------|--------|-------------|----------------|---------------|-------------|-------------|---------|----------|-----------|----------------------|
| *Thinking Model?* | | | | | | | ✅ | ✅ | ✅ | ✅ |
| MathVision (full) (Pass@1) | 30.4 | - | 38.1 | 25.1 | 35.5 | 32.1 | - | 35.9 | 38.6 | 36.8 |
| MathVista (mini) (Pass@1) | 63.8 | 56.7 | 74.8 | 68.2 | 62.3 | 56.4 | 71.0 | 71.4 | 74.9 | 71.3 |
| MMMU (val) (Pass@1) | 69.1 | 60.0 | 74.8 | 58.6 | 64.8 | 59.6 | 77.3 | 70.3 | 70.0 | 61.7 |
</div>
### Inference with 🤗 Hugging Face Transformers
We introduce how to use our model at inference stage using transformers library. It is recommended to use python=3.10, torch>=2.1.0, and transformers=4.48.2 as the development environment.
```python
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor
model_path = "moonshotai/Kimi-VL-A3B-Thinking"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
image_paths = ["./figures/demo1.png", "./figures/demo2.png"]
images = [Image.open(path) for path in image_paths]
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image_path} for image_path in image_paths
] + [{"type": "text", "text": "Please infer step by step who this manuscript belongs to and what it records"}],
},
]
text = processor.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
inputs = processor(images=images, text=text, return_tensors="pt", padding=True, truncation=True).to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
response = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
print(response)
```
### Inference with VLLM
We have submitted a Merge Request [#16387](https://github.com/vllm-project/vllm/pull/16387) to vLLM. You are welcome to deploy Kimi-VL using the branch corresponding to the vLLM MR until the MR is merged.
## 5. Citation
```
@misc{kimiteam2025kimivltechnicalreport,
title={{Kimi-VL} Technical Report},
author={Kimi Team and Angang Du and Bohong Yin and Bowei Xing and Bowen Qu and Bowen Wang and Cheng Chen and Chenlin Zhang and Chenzhuang Du and Chu Wei and Congcong Wang and Dehao Zhang and Dikang Du and Dongliang Wang and Enming Yuan and Enzhe Lu and Fang Li and Flood Sung and Guangda Wei and Guokun Lai and Han Zhu and Hao Ding and Hao Hu and Hao Yang and Hao Zhang and Haoning Wu and Haotian Yao and Haoyu Lu and Heng Wang and Hongcheng Gao and Huabin Zheng and Jiaming Li and Jianlin Su and Jianzhou Wang and Jiaqi Deng and Jiezhong Qiu and Jin Xie and Jinhong Wang and Jingyuan Liu and Junjie Yan and Kun Ouyang and Liang Chen and Lin Sui and Longhui Yu and Mengfan Dong and Mengnan Dong and Nuo Xu and Pengyu Cheng and Qizheng Gu and Runjie Zhou and Shaowei Liu and Sihan Cao and Tao Yu and Tianhui Song and Tongtong Bai and Wei Song and Weiran He and Weixiao Huang and Weixin Xu and Xiaokun Yuan and Xingcheng Yao and Xingzhe Wu and Xinxing Zu and Xinyu Zhou and Xinyuan Wang and Y. Charles and Yan Zhong and Yang Li and Yangyang Hu and Yanru Chen and Yejie Wang and Yibo Liu and Yibo Miao and Yidao Qin and Yimin Chen and Yiping Bao and Yiqin Wang and Yongsheng Kang and Yuanxin Liu and Yulun Du and Yuxin Wu and Yuzhi Wang and Yuzi Yan and Zaida Zhou and Zhaowei Li and Zhejun Jiang and Zheng Zhang and Zhilin Yang and Zhiqi Huang and Zihao Huang and Zijia Zhao and Ziwei Chen},
year={2025},
eprint={2504.07491},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.07491},
}
```
## Bitsandbytes quantization config
>{'load_in_8bit': True}
|
siddhant71197/female_full_curvy_short_cap7
|
siddhant71197
| 2025-05-09T11:01:12Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-05-09T10:23:04Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Sidf
---
# Female_Full_Curvy_Short_Cap7
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Sidf` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sidf",
"lora_weights": "https://huggingface.co/siddhant71197/female_full_curvy_short_cap7/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('siddhant71197/female_full_curvy_short_cap7', weight_name='lora.safetensors')
image = pipeline('Sidf').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/siddhant71197/female_full_curvy_short_cap7/discussions) to add images that show off what you’ve made with this LoRA.
|
shukriben/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_purring_chinchilla
|
shukriben
| 2025-05-09T10:53:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am padded purring chinchilla",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-06T16:47:48Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_purring_chinchilla
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am padded purring chinchilla
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_purring_chinchilla
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="shukriben/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-padded_purring_chinchilla", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jhyun0414/20250509-Llama-3.1-8B-Instruct-orm_label-filter-e3-lr2e-6
|
jhyun0414
| 2025-05-09T10:53:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T10:46:17Z |
---
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]
|
adaptcentre/roberta-offensive-classifier
|
adaptcentre
| 2025-05-09T10:51:00Z | 0 | 0 | null |
[
"safetensors",
"roberta",
"text-classification",
"en",
"arxiv:1910.09700",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:cc-by-nc-3.0",
"region:us"
] |
text-classification
| 2025-05-09T10:08:12Z |
---
license: cc-by-nc-3.0
language:
- en
metrics:
- accuracy
- recall
- precision
- f1
base_model:
- FacebookAI/roberta-base
pipeline_tag: text-classification
---
# Model Card for Model ID
This model card provides details for the roberta-offensive-classifier, a binary text classification model fine-tuned to detect offensive and hateful language. Built on top of FacebookAI's RoBERTa-base architecture, it is intended for moderation of user-generated content.
This model is built as part of the [DTCCT](https://act-agi.github.io/) project.
## Model Details
### Model Description
- **Developed by:** Kanishk Verma.
- **Funded by:** Google and Research Ireland under grant number EPSPG/2021/161
- **Model type:** Sequence Classification (Binary)
- **Language(s) (NLP):** EN
- **License:** cc-by-nc-3.0
- **Finetuned from model :** FacebookAI/roberta-base
<!--
### Model Sources
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
The model can be used for classifying English text as offensive or non-offensive, supporting automated moderation in:
- Social media platforms
- Forums
- Online communities
### Downstream Use
The model can be integrated into moderation pipelines or tools with additional features such as user feedback, flagging systems, or multi-language support.
### Out-of-Scope Use
This model should not be used for:
- Legal decision-making
- Real-time moderation without human oversight
- Texts in languages other than English
## Bias, Risks, and Limitations
The model is trained on datasets labeled for offensive and hateful language and may carry annotation biases. It may not generalize well to niche domains or novel forms of offensive speech.
### Recommendations
- Human review should accompany model predictions in sensitive contexts.
- Evaluate on target data before deployment.
- Be cautious of over-filtering legitimate speech.
## How to Get Started with the Model
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="adaptcentre/roberta-offensive-classifier")
classifier("Your input text here")
```
## Training Details
### Training Data
Trained on a composite dataset targeting:
- Offensive language
- Hate speech
- Toxicity
<!--
### 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
- accuracy
- precision
- recall
- f1
### Results
| Metric | Score |
| --------- | ------ |
| Accuracy | 0.8856 |
| Precision | 0.8334 |
| Recall | 0.7932 |
| F1 Score | 0.8128 |
#### Summary
The model demonstrates solid performance across all major classification metrics, suitable for content moderation tasks with English text.
<!--
## 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:**
@misc{roberta-offensive-2025,
title = {RoBERTa Base Offensive Language Classifier},
author = {Kanishk Verma},
}
<!--
**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]
-->
|
MoniqueZThomas/nite
|
MoniqueZThomas
| 2025-05-09T10:50:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-05-09T10:50:49Z |
<p><a href="https://www.facebook.com/groups/nitehush.pro.natural/" target="_blank" rel="nofollow">https://www.facebook.com/groups/nitehush.pro.natural/</a><br /><a href="https://www.facebook.com/share/p/169qwaPQ14/" target="_blank" rel="nofollow">https://www.facebook.com/share/p/169qwaPQ14/</a><br /><a href="https://www.facebook.com/groups/nitehush.pro.natural/permalink/3622880238011873/" target="_blank" rel="nofollow">https://www.facebook.com/groups/nitehush.pro.natural/permalink/3622880238011873/</a><br /><a href="https://www.facebook.com/groups/nitehush.pro.natural/posts/3622880238011873/" target="_blank" rel="nofollow">https://www.facebook.com/groups/nitehush.pro.natural/posts/3622880238011873/</a><br /><a href="https://www.facebook.com/events/1081161037197611/" target="_blank" rel="nofollow">https://www.facebook.com/events/1081161037197611/</a><br /><a href="https://www.facebook.com/events/1196472398875274/" target="_blank" rel="nofollow">https://www.facebook.com/events/1196472398875274/</a><br /><a href="https://teeshopper.in/store/Nitehush-Pro-Ingredients" target="_blank" rel="nofollow">https://teeshopper.in/store/Nitehush-Pro-Ingredients</a><br /><a href="https://teeshopper.in/store/Nitehush-Pro-USA" target="_blank" rel="nofollow">https://teeshopper.in/store/Nitehush-Pro-USA</a><br /><a href="https://colab.research.google.com/drive/1wN5vENBx_GRYvUwQZM6fhQqMsCdMXTGS?usp=sharing" target="_blank" rel="nofollow">https://colab.research.google.com/drive/1wN5vENBx_GRYvUwQZM6fhQqMsCdMXTGS?usp=sharing</a><br /><a href="https://colab.research.google.com/drive/15wJrZtwZksdxETQpG5JSpZ_X3V9NCl9V?usp=sharing" target="_blank" rel="nofollow">https://colab.research.google.com/drive/15wJrZtwZksdxETQpG5JSpZ_X3V9NCl9V?usp=sharing</a><br /><a href="https://colab.research.google.com/drive/1MSznLVsUCT3u4cEys0NBPPUERltEvKdQ?usp=sharing" target="_blank" rel="nofollow">https://colab.research.google.com/drive/1MSznLVsUCT3u4cEys0NBPPUERltEvKdQ?usp=sharing</a><br /><a href="https://www.linkedin.com/showcase/nitehush-pro/" target="_blank" rel="nofollow">https://www.linkedin.com/showcase/nitehush-pro/</a><br /><a href="https://filmfreeway.com/NitehushPro977" target="_blank" rel="nofollow">https://filmfreeway.com/NitehushPro977</a><br /><a href="https://filmfreeway.com/NitehushProNaturalSolution" target="_blank" rel="nofollow">https://filmfreeway.com/NitehushProNaturalSolution</a><br /><a href="https://www.data-medics.com/forum/threads/nitehush-pro.95902/" target="_blank" rel="nofollow">https://www.data-medics.com/forum/threads/nitehush-pro.95902/</a><br /><a href="https://www.underwaterdroneforum.com/threads/nitehush-pro.69589/" target="_blank" rel="nofollow">https://www.underwaterdroneforum.com/threads/nitehush-pro.69589/</a><br /><a href="https://forum.seabank.com.vn/threads/nitehush-pro.1937/" target="_blank" rel="nofollow">https://forum.seabank.com.vn/threads/nitehush-pro.1937/</a><br /><a href="https://forum.seabank.com.vn/threads/nitehush-pro-reviews.1939/" target="_blank" rel="nofollow">https://forum.seabank.com.vn/threads/nitehush-pro-reviews.1939/</a><br /><a href="https://github.com/NicoleRVue/Nitehush-Pro/" target="_blank" rel="nofollow">https://github.com/NicoleRVue/Nitehush-Pro/</a><br /><a href="https://github.com/NicoleRVue/Nitehushpro/" target="_blank" rel="nofollow">https://github.com/NicoleRVue/Nitehushpro/</a><br /><a href="https://za.pinterest.com/nitehush_pro_natural/" target="_blank" rel="nofollow">https://za.pinterest.com/nitehush_pro_natural/</a></p>
|
MoniqueZThomas/jar
|
MoniqueZThomas
| 2025-05-09T10:50:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-05-09T10:50:08Z |
<p><a href="https://www.facebook.com/groups/slimjaro.capsules.for.weight.loss/" target="_blank" rel="nofollow">https://www.facebook.com/groups/slimjaro.capsules.for.weight.loss/</a><br /><a href="https://www.facebook.com/share/p/169sk8zF4e/" target="_blank" rel="nofollow">https://www.facebook.com/share/p/169sk8zF4e/</a><br /><a href="https://www.facebook.com/groups/slimjaro.capsules.for.weight.loss/permalink/952519656803673/" target="_blank" rel="nofollow">https://www.facebook.com/groups/slimjaro.capsules.for.weight.loss/permalink/952519656803673/</a><br /><a href="https://www.facebook.com/groups/slimjaro.capsules.for.weight.loss/posts/952519656803673/" target="_blank" rel="nofollow">https://www.facebook.com/groups/slimjaro.capsules.for.weight.loss/posts/952519656803673/</a><br /><a href="https://www.facebook.com/events/1071799234840718/" target="_blank" rel="nofollow">https://www.facebook.com/events/1071799234840718/</a><br /><a href="https://www.facebook.com/events/663643846591447/" target="_blank" rel="nofollow">https://www.facebook.com/events/663643846591447/</a><br /><a href="https://teeshopper.in/store/Slimjaro-Capsules-Weight-Loss" target="_blank" rel="nofollow">https://teeshopper.in/store/Slimjaro-Capsules-Weight-Loss</a><br /><a href="https://teeshopper.in/store/Slimjaro-Capsules-Website" target="_blank" rel="nofollow">https://teeshopper.in/store/Slimjaro-Capsules-Website</a><br /><a href="https://colab.research.google.com/drive/1QZH2S7De_aTdrd603wiX-PAOqQ58RzOy?usp=sharing" target="_blank" rel="nofollow">https://colab.research.google.com/drive/1QZH2S7De_aTdrd603wiX-PAOqQ58RzOy?usp=sharing</a><br /><a href="https://colab.research.google.com/drive/1vw-WmeOdidnYzPczUGzkRdWgg-e22oL6?usp=sharing" target="_blank" rel="nofollow">https://colab.research.google.com/drive/1vw-WmeOdidnYzPczUGzkRdWgg-e22oL6?usp=sharing</a><br /><a href="https://colab.research.google.com/drive/1_G2PF51a0cC0qq5MH603W6DsKHYAH3rG?usp=sharing" target="_blank" rel="nofollow">https://colab.research.google.com/drive/1_G2PF51a0cC0qq5MH603W6DsKHYAH3rG?usp=sharing</a><br /><a href="https://www.linkedin.com/showcase/slimjaro-capsules/" target="_blank" rel="nofollow">https://www.linkedin.com/showcase/slimjaro-capsules/</a><br /><a href="https://filmfreeway.com/SlimjaroCapsulesForWeightLossSupplement" target="_blank" rel="nofollow">https://filmfreeway.com/SlimjaroCapsulesForWeightLossSupplement</a><br /><a href="https://filmfreeway.com/SlimjaroCapsulesGetExtraSlimInNoTime" target="_blank" rel="nofollow">https://filmfreeway.com/SlimjaroCapsulesGetExtraSlimInNoTime</a><br /><a href="https://www.data-medics.com/forum/threads/slimjaro-capsules.95892/" target="_blank" rel="nofollow">https://www.data-medics.com/forum/threads/slimjaro-capsules.95892/</a><br /><a href="https://www.underwaterdroneforum.com/threads/slimjaro-capsules.69529/" target="_blank" rel="nofollow">https://www.underwaterdroneforum.com/threads/slimjaro-capsules.69529/</a><br /><a href="https://forum.seabank.com.vn/threads/slimjaro-capsules.1917/" target="_blank" rel="nofollow">https://forum.seabank.com.vn/threads/slimjaro-capsules.1917/</a><br /><a href="https://forum.seabank.com.vn/threads/slimjaro-capsules-for-weight-loss-supplement.1920/" target="_blank" rel="nofollow">https://forum.seabank.com.vn/threads/slimjaro-capsules-for-weight-loss-supplement.1920/</a><br /><a href="https://github.com/NicoleRVue/Slimjaro/" target="_blank" rel="nofollow">https://github.com/NicoleRVue/Slimjaro/</a><br /><a href="https://github.com/NicoleRVue/Slimjaro-Capsules/" target="_blank" rel="nofollow">https://github.com/NicoleRVue/Slimjaro-Capsules/</a><br /><a href="https://nz.pinterest.com/Slimjaro_Capsules_Buy/" target="_blank" rel="nofollow">https://nz.pinterest.com/Slimjaro_Capsules_Buy/</a></p>
|
TheGardener/MLP-Block-pruned-Llama3-0.76B
|
TheGardener
| 2025-05-09T10:49:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T10:46:55Z |
---
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]
|
dulimov/Qwen3-16B-A3B-rk3588-1.2.1
|
dulimov
| 2025-05-09T10:47:22Z | 0 | 0 | null |
[
"safetensors",
"qwen3_moe",
"unsloth",
"qwen3",
"qwen",
"base_model:kalomaze/Qwen3-16B-A3B",
"base_model:finetune:kalomaze/Qwen3-16B-A3B",
"license:apache-2.0",
"region:us"
] | null | 2025-05-09T10:47:18Z |
---
base_model:
- kalomaze/Qwen3-16B-A3B
license: apache-2.0
tags:
- unsloth
- qwen3
- qwen
---
# Qwen3-16B-A3B-RK3588-1.2.1
This version of Qwen3-16B-A3B has been converted to run on the RK3588 NPU using ['w8a8', 'w8a8_g256'] quantization.
This model has been optimized with the following LoRA:
Compatible with RKLLM version: 1.2.1
# Original Model Card for base model, Qwen3-16B-A3B, below:
# Qwen3-16B-A3B
Qwen3-16B-A3B is a rendition of Qwen3-30B-A3B by [kalomaze](https://huggingface.co/kalomaze/Qwen3-16B-A3B).
A man-made horror beyond your comprehension.
But no, seriously, this is my experiment to:
- measure the probability that any given expert will activate (over my personal set of fairly diverse calibration data), per layer
- prune 64/128 of the least used experts per layer (with reordered router and indexing per layer)
It can still write semi-coherently without any additional training or distillation done on top of it from the original 30b MoE.
The .txt files with the original measurements are provided in the repo along with the exported weights.
Custom testing to measure the experts was done on a hacked version of vllm, and then I made a bespoke script to selectively export the weights according to the measurements.
|
ThilinaD/qwen3-1.7B-bible-finetuned-2stage-gguf
|
ThilinaD
| 2025-05-09T10:47:00Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-09T10:45:33Z |
---
base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ThilinaD
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B-unsloth-bnb-4bit
This qwen3 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)
|
Kyleyee/EleutherAI_pythia-1b-deduped_tldr_ppo
|
Kyleyee
| 2025-05-09T10:42:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T10:40:49Z |
---
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]
|
nadejdatarabukina/caa60a35-907e-4832-a911-f0d06ce23bb2
|
nadejdatarabukina
| 2025-05-09T10:40:53Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b",
"base_model:adapter:unsloth/llama-3-8b",
"license:llama3",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T10:08:38Z |
---
library_name: peft
license: llama3
base_model: unsloth/llama-3-8b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: caa60a35-907e-4832-a911-f0d06ce23bb2
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/llama-3-8b
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- c3066754320c2e62_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c3066754320c2e62_train_data.json
type:
field_input: format
field_instruction: prompt
field_output: text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: nadejdatarabukina/caa60a35-907e-4832-a911-f0d06ce23bb2
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3e-6
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.01
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 4
mixed_precision: bf16
mlflow_experiment_name: /tmp/c3066754320c2e62_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: paged_adamw_32bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 866503e4-723f-4dc7-9782-f20fc13a2298
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 866503e4-723f-4dc7-9782-f20fc13a2298
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# caa60a35-907e-4832-a911-f0d06ce23bb2
This model is a fine-tuned version of [unsloth/llama-3-8b](https://huggingface.co/unsloth/llama-3-8b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3343
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.464 | 0.0064 | 150 | 1.3343 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
infogep/757ecba5-1305-47d0-b591-38c30bcd217d
|
infogep
| 2025-05-09T10:40:32Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T10:14:51Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 757ecba5-1305-47d0-b591-38c30bcd217d
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e7fe462bd923facf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e7fe462bd923facf_train_data.json
type:
field_input: type
field_instruction: problem
field_output: solution
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: infogep/757ecba5-1305-47d0-b591-38c30bcd217d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/e7fe462bd923facf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ddcad011-01d4-41dc-a612-40e675b9d1a8
wandb_project: s56-7
wandb_run: your_name
wandb_runid: ddcad011-01d4-41dc-a612-40e675b9d1a8
warmup_steps: 25
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 757ecba5-1305-47d0-b591-38c30bcd217d
This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6002
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7634 | 0.3660 | 500 | 0.6002 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
ThilinaD/qwen3-1.7B-bible-finetuned-2stage
|
ThilinaD
| 2025-05-09T10:39:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T10:39:46Z |
---
base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ThilinaD
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B-unsloth-bnb-4bit
This qwen3 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)
|
mluger/vitFaceExpressionFiveCropAugmentation
|
mluger
| 2025-05-09T10:38:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2025-04-26T09:48:08Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitFaceExpressionFiveCropAugmentation
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7130119810532182
---
<!-- 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. -->
# vitFaceExpressionFiveCropAugmentation
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9515
- Accuracy: 0.7130
## 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
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2225 | 1.0 | 898 | 0.9774 | 0.6428 |
| 0.8696 | 2.0 | 1796 | 0.8688 | 0.6832 |
| 0.7004 | 3.0 | 2694 | 0.8428 | 0.6988 |
| 0.5668 | 4.0 | 3592 | 0.8732 | 0.6963 |
| 0.4613 | 5.0 | 4490 | 0.8853 | 0.7055 |
| 0.3253 | 6.0 | 5388 | 0.9275 | 0.7086 |
| 0.2489 | 7.0 | 6286 | 0.9436 | 0.7122 |
| 0.2159 | 8.0 | 7184 | 0.9515 | 0.7130 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rchan26/t0-k5-32B-Q2_K-GGUF
|
rchan26
| 2025-05-09T10:37:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:TomasLaz/t0-k5-32B",
"base_model:quantized:TomasLaz/t0-k5-32B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-09T10:36:17Z |
---
base_model: TomasLaz/t0-k5-32B
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# rchan26/t0-k5-32B-Q2_K-GGUF
This model was converted to GGUF format from [`TomasLaz/t0-k5-32B`](https://huggingface.co/TomasLaz/t0-k5-32B) 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/TomasLaz/t0-k5-32B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo rchan26/t0-k5-32B-Q2_K-GGUF --hf-file t0-k5-32b-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo rchan26/t0-k5-32B-Q2_K-GGUF --hf-file t0-k5-32b-q2_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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo rchan26/t0-k5-32B-Q2_K-GGUF --hf-file t0-k5-32b-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo rchan26/t0-k5-32B-Q2_K-GGUF --hf-file t0-k5-32b-q2_k.gguf -c 2048
```
|
gavrilstep/9e2d5419-ffb1-4beb-a1be-f370e038843e
|
gavrilstep
| 2025-05-09T10:37:15Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-09T10:16:02Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9e2d5419-ffb1-4beb-a1be-f370e038843e
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e7fe462bd923facf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e7fe462bd923facf_train_data.json
type:
field_input: type
field_instruction: problem
field_output: solution
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: gavrilstep/9e2d5419-ffb1-4beb-a1be-f370e038843e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.01
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 4
mixed_precision: bf16
mlflow_experiment_name: /tmp/e7fe462bd923facf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ddcad011-01d4-41dc-a612-40e675b9d1a8
wandb_project: s56-7
wandb_run: your_name
wandb_runid: ddcad011-01d4-41dc-a612-40e675b9d1a8
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 9e2d5419-ffb1-4beb-a1be-f370e038843e
This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6123
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.693 | 0.0549 | 150 | 0.6123 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
TWongsamut/Gemma-3-12B-QMSum-QA
|
TWongsamut
| 2025-05-09T10:37:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-05-09T10:30:49Z |
---
library_name: transformers
tags:
- trl
- sft
---
# 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]
|
felixZzz/wlen6_61k-orz-1_7b-ours-d1-len3000-0508T04_47_16___step_03712
|
felixZzz
| 2025-05-09T10:36:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-09T10:34:48Z |
---
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.
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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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.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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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).
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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## Glossary [optional]
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## Model Card Authors [optional]
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