modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 12:31:00
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
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timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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sentence-transformers/nli-mpnet-base-v2
|
sentence-transformers
| 2025-08-19T10:22:53Z | 31,978 | 14 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- text-embeddings-inference
pipeline_tag: sentence-similarity
---
# sentence-transformers/nli-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/nli-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/nli-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/nli-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Usage (Text Embeddings Inference (TEI))
[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models.
- CPU:
```bash
docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/nli-mpnet-base-v2 --pooling mean --dtype float16
```
- NVIDIA GPU:
```bash
docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/nli-mpnet-base-v2 --pooling mean --dtype float16
```
Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):
```bash
curl http://localhost:8080/v1/embeddings \
-H "Content-Type: application/json" \
-d '{"model":"sentence-transformers/nli-mpnet-base-v2","input":"This is an example sentence"}'
```
Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
Prathyusha101/tldr-ppco-g1p0-l0p95
|
Prathyusha101
| 2025-08-19T10:22:14Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"dataset:trl-internal-testing/tldr-preference-sft-trl-style",
"arxiv:1909.08593",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T05:22:04Z |
---
datasets: trl-internal-testing/tldr-preference-sft-trl-style
library_name: transformers
model_name: tldr-ppco-g1p0-l0p95
tags:
- generated_from_trainer
licence: license
---
# Model Card for tldr-ppco-g1p0-l0p95
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [trl-internal-testing/tldr-preference-sft-trl-style](https://huggingface.co/datasets/trl-internal-testing/tldr-preference-sft-trl-style) dataset.
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="Prathyusha101/tldr-ppco-g1p0-l0p95", 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/prathyusha1-the-university-of-texas-at-austin/huggingface/runs/fl57ehb8)
This model was trained with PPO, a method introduced in [Fine-Tuning Language Models from Human Preferences](https://huggingface.co/papers/1909.08593).
### Framework versions
- TRL: 0.15.0.dev0
- Transformers: 4.53.1
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## Citations
Cite PPO as:
```bibtex
@article{mziegler2019fine-tuning,
title = {{Fine-Tuning Language Models from Human Preferences}},
author = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving},
year = 2019,
eprint = {arXiv:1909.08593}
}
```
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}}
}
```
|
sentence-transformers/multi-qa-mpnet-base-dot-v1
|
sentence-transformers
| 2025-08-19T10:21:26Z | 1,327,439 | 177 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"mpnet",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"en",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/QQP",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/Amazon-QA",
"dataset:embedding-data/WikiAnswers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- text-embeddings-inference
datasets:
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- search_qa
- eli5
- natural_questions
- trivia_qa
- embedding-data/QQP
- embedding-data/PAQ_pairs
- embedding-data/Amazon-QA
- embedding-data/WikiAnswers
pipeline_tag: sentence-similarity
---
# multi-qa-mpnet-base-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer, util
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
# Load the model
model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-dot-v1')
# Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)
# Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
# Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
# Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
# Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# CLS Pooling - Take output from first token
def cls_pooling(model_output):
return model_output.last_hidden_state[:,0]
# Encode text
def encode(texts):
# Tokenize sentences
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
# Perform pooling
embeddings = cls_pooling(model_output)
return embeddings
# Sentences we want sentence embeddings for
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1")
model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1")
# Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)
# Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
# Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
# Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
# Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Usage (Text Embeddings Inference (TEI))
[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models.
- CPU:
```bash
docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest \
--model-id sentence-transformers/multi-qa-mpnet-base-dot-v1 \
--pooling cls \
--dtype float16
```
- NVIDIA GPU:
```bash
docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest \
--model-id sentence-transformers/multi-qa-mpnet-base-dot-v1 \
--pooling cls \
--dtype float16
```
Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):
```bash
curl http://localhost:8080/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"model": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
"input": "How many people live in London?"
}'
```
Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
----
## Technical Details
In the following some technical details how this model must be used:
| Setting | Value |
| --- | :---: |
| Dimensions | 768 |
| Produces normalized embeddings | No |
| Pooling-Method | CLS pooling |
| Suitable score functions | dot-product (e.g. `util.dot_score`) |
----
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google's Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
## Training procedure
The full training script is accessible in this current repository: `train_script.py`.
### Pre-training
We use the pretrained [`mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
#### Training
We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using CLS-pooling, dot-product as similarity function, and a scale of 1.
| Dataset | Number of training tuples |
|--------------------------------------------------------|:--------------------------:|
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine | 17,579,773 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet | 3,012,496 |
| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
| **Total** | **214,988,242** |
|
sentence-transformers/multi-qa-mpnet-base-cos-v1
|
sentence-transformers
| 2025-08-19T10:19:32Z | 603,907 | 41 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"mpnet",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- text-embeddings-inference
pipeline_tag: sentence-similarity
---
# multi-qa-mpnet-base-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer, util
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
#Load the model
model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-cos-v1')
#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)
#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
# Mean Pooling - Take average of all tokens
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output.last_hidden_state # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Encode text
def encode(texts):
# Tokenize sentences
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
# Perform pooling
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
return embeddings
# Sentences we want sentence embeddings for
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-cos-v1")
model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-cos-v1")
# Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)
# Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
# Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
# Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
# Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Usage (Text Embeddings Inference (TEI))
[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models.
- CPU:
```bash
docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/multi-qa-mpnet-base-cos-v1 --pooling mean --dtype float16
```
- NVIDIA GPU:
```bash
docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/multi-qa-mpnet-base-cos-v1 --pooling mean --dtype float16
```
Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):
```bash
curl http://localhost:8080/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"model": "sentence-transformers/multi-qa-mpnet-base-cos-v1",
"input": "How many people live in London?"
}'
```
Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
## Technical Details
In the following some technical details how this model must be used:
| Setting | Value |
| --- | :---: |
| Dimensions | 768 |
| Produces normalized embeddings | Yes |
| Pooling-Method | Mean pooling |
| Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |
Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
----
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google's Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
## Training procedure
The full training script is accessible in this current repository: `train_script.py`.
### Pre-training
We use the pretrained [`mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
#### Training
We use the concatenation of multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using Mean-pooling, cosine-similarity as similarity function, and a scale of 20.
| Dataset | Number of training tuples |
|--------------------------------------------------------|:--------------------------:|
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine | 17,579,773 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet | 3,012,496 |
| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
| **Total** | **214,988,242** |
|
sentence-transformers/all-mpnet-base-v1
|
sentence-transformers
| 2025-08-19T10:16:12Z | 2,645 | 11 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"mpnet",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- text-embeddings-inference
pipeline_tag: sentence-similarity
new_version: sentence-transformers/all-mpnet-base-v2
---
# all-mpnet-base-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-mpnet-base-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v1')
model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v1')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Usage (Text Embeddings Inference (TEI))
[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models.
- CPU:
```bash
docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/all-mpnet-base-v1 --pooling mean --dtype float16
```
- NVIDIA GPU:
```bash
docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/all-mpnet-base-v1 --pooling mean --dtype float16
```
Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):
```bash
curl http://localhost:8080/v1/embeddings \
-X POST \
-H "Content-Type: application/json" \
-d '{
"model": "sentence-transformers/all-mpnet-base-v1",
"input": ["This is an example sentence", "Each sentence is converted"]
}'
```
Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google's Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 128 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base). Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyperparameters
We trained our model on a TPU v3-8. We train the model during 920k steps using a batch size of 512 (64 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/)) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,124,818,467** |
|
kavpro/blockassist-bc-tall_lively_caribou_1755595517
|
kavpro
| 2025-08-19T10:15:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall lively caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T10:15:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall lively caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmei466j90qj6rts8qru8anlz_cmeick0gk0qwirts8vmydpz2m
|
BootesVoid
| 2025-08-19T10:05:46Z | 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
| 2025-08-19T10:05:45Z |
---
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: SEXY
---
# Cmei466J90Qj6Rts8Qru8Anlz_Cmeick0Gk0Qwirts8Vmydpz2M
<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 `SEXY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SEXY",
"lora_weights": "https://huggingface.co/BootesVoid/cmei466j90qj6rts8qru8anlz_cmeick0gk0qwirts8vmydpz2m/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('BootesVoid/cmei466j90qj6rts8qru8anlz_cmeick0gk0qwirts8vmydpz2m', weight_name='lora.safetensors')
image = pipeline('SEXY').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/BootesVoid/cmei466j90qj6rts8qru8anlz_cmeick0gk0qwirts8vmydpz2m/discussions) to add images that show off what you’ve made with this LoRA.
|
rawsun00001/pure-llm-sms-extractor-no-category-20250819_1004
|
rawsun00001
| 2025-08-19T10:04:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T10:04:15Z |
---
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]
|
KCS97/red_cartoon
|
KCS97
| 2025-08-19T10:04:38Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-08-19T09:52:20Z |
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of sks cartoon
tags:
- text-to-image
- dreambooth
- 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. -->
# DreamBooth - KCS97/red_cartoon
This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks cartoon using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth 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]
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755597704
|
0xaoyama
| 2025-08-19T10:02:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T10:02:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rohannath/AI_Doctor_using_llama_merged_unsloth
|
rohannath
| 2025-08-19T09:59:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T09:33:22Z |
---
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]
|
broinopio/blockassist-bc-monstrous_scampering_spider_1755595419
|
broinopio
| 2025-08-19T09:59:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous scampering spider",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:59:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous scampering spider
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755597475
|
0xaoyama
| 2025-08-19T09:58:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:58:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755595832
|
quantumxnode
| 2025-08-19T09:57:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:57:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thaykinhlungip/thay-kinh-lung-iphone-gia-re
|
thaykinhlungip
| 2025-08-19T09:57:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T09:57:28Z |
<h1>Thay kính lưng iPhone – Giải pháp chất lượng và bền bỉ</h1>
<p>Bạn đang tìm <a href="https://www.fmscout.com/datas/users/thay-kinh-lung-iphone_361773.pdf" target="_blank">dịch vụ thay mặt lưng iPhone giá rẻ</a> nhưng vẫn đảm bảo chất lượng và linh kiện chính hãng? Việc thay kính lưng không chỉ giúp điện thoại lấy lại vẻ ngoài sang trọng mà còn đảm bảo an toàn cho các bộ phận bên trong.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/Thay%20kinh%20lung%20iPhone/thay-kinh-lung-iphone-5.jpg" alt="" /></p>
<h2>Khi nào cần thay kính lưng iPhone?</h2>
<p>Trong quá trình sử dụng, mặt lưng iPhone rất dễ hư hỏng do va đập hoặc rơi rớt. Lúc này, bạn cần cân nhắc đến việc <a href="https://ko-fi.com/thaylungip24h" target="_blank">thay kính lưng iPhone</a> để tránh ảnh hưởng đến trải nghiệm cũng như hiệu năng của thiết bị.</p>
<p>Một số trường hợp bạn nên thay kính lưng ngay:</p>
<ul>
<li>
<p><strong>Mặt lưng bị nứt vỡ</strong> sau những lần va chạm mạnh.</p>
</li>
<li>
<p><strong>Kính lưng trầy xước nghiêm trọng</strong>, ảnh hưởng đến tính thẩm mỹ.</p>
</li>
<li>
<p><strong>Mặt lưng bong keo hoặc hở viền</strong>, có nguy cơ gây thấm nước, bụi bẩn vào linh kiện.</p>
</li>
<li>
<p><strong>Khó sử dụng sạc không dây</strong> hoặc sạc chập chờn do mặt lưng bị biến dạng.</p>
</li>
</ul>
<p>Việc thay kính kịp thời sẽ bảo vệ điện thoại của bạn hoạt động ổn định, giữ nguyên tính năng và tăng tuổi thọ.</p>
<h2>Địa chỉ thay kính lưng iPhone chính hãng giá rẻ</h2>
<p>Để có được sản phẩm chất lượng, bạn nên chọn <strong>địa chỉ thay kính lưng iPhone chính hãng giá rẻ</strong> với uy tín rõ ràng. Một cơ sở chuyên nghiệp thường đảm bảo:</p>
<ul>
<li>
<p><strong>Linh kiện chính hãng Apple</strong>: Đảm bảo tương thích 100% với thiết bị.</p>
</li>
<li>
<p><strong>Đội ngũ kỹ thuật viên có tay nghề</strong>: Tránh rủi ro hư hỏng bo mạch hoặc lỗi linh kiện.</p>
</li>
<li>
<p><strong>Minh bạch quy trình sửa chữa</strong>: Khách hàng có thể quan sát trực tiếp quá trình thay thế.</p>
</li>
<li>
<p><strong>Giá cả hợp lý</strong>: Luôn công khai và báo trước cho khách hàng.</p>
</li>
<li>
<p><strong>Chế độ bảo hành rõ ràng</strong>: Mang lại sự yên tâm khi sử dụng dịch vụ.</p>
</li>
</ul>
<p>Việc chọn đúng nơi sửa chữa uy tín giúp bạn tiết kiệm chi phí và yên tâm về độ bền lâu dài.</p>
<h2>Thay kính lưng iPhone có ảnh hưởng sạc không dây không?</h2>
<p>Một câu hỏi phổ biến từ người dùng là liệu thay kính lưng có làm ảnh hưởng đến sạc không dây?</p>
<p>Câu trả lời: <strong>Không</strong>, nếu bạn thay tại cơ sở uy tín với linh kiện chính hãng.</p>
<ul>
<li>
<p><strong>Kính lưng chính hãng</strong> có chất liệu và độ dày chuẩn, giữ nguyên hiệu quả sạc.</p>
</li>
<li>
<p>Nếu sử dụng <strong>linh kiện kém chất lượng</strong>, sạc có thể bị gián đoạn hoặc chậm hơn.</p>
</li>
<li>
<p><strong>Kỹ thuật lắp ráp</strong> cũng đóng vai trò quan trọng, vì chỉ cần sai sót nhỏ có thể gây ảnh hưởng đến cuộn sạc không dây.</p>
</li>
</ul>
<p>Do vậy, để đảm bảo chức năng sạc hoạt động bình thường, bạn nên chọn trung tâm sửa chữa chuyên nghiệp.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/Thay%20kinh%20lung%20iPhone/thay-kinh-lung-iphone-13.jpg" alt="" /></p>
<h2>Bệnh Viện Điện Thoại, Laptop 24h – Địa chỉ uy tín cho khách hàng</h2>
<p>Trong số các trung tâm sửa chữa, <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> được biết đến là một thương hiệu uy tín tại TP.HCM với nhiều năm kinh nghiệm trong việc thay kính lưng iPhone và các dịch vụ khác.</p>
<p>Điểm nổi bật:</p>
<ul>
<li>
<p><strong>Kỹ thuật viên chuyên nghiệp</strong>: Có nhiều năm kinh nghiệm, xử lý nhanh chóng và an toàn.</p>
</li>
<li>
<p><strong>Linh kiện chính hãng</strong>: Đảm bảo chất lượng và độ bền cho thiết bị.</p>
</li>
<li>
<p><strong>Quy trình minh bạch</strong>: Khách hàng có thể theo dõi trực tiếp.</p>
</li>
</ul>
<p>Các loại màn hình/kính được sử dụng tại đây:</p>
<ul>
<li>
<p><strong>Màn hình zin bóc máy</strong>: Chất lượng hiển thị rõ nét, cảm ứng nhạy.</p>
</li>
<li>
<p><strong>Màn hình chống lóa, chống trầy</strong>: Bảo vệ tốt hơn khi sử dụng lâu dài.</p>
</li>
<li>
<p><strong>Màn hình chịu lực cao</strong>: Giảm nguy cơ hư hỏng khi có va chạm mạnh.</p>
</li>
</ul>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/Thay%20kinh%20lung%20iPhone/thay-kinh-lung-iphone-12.jpg" alt="" /></p>
<h2>Vì sao nên sử dụng dịch vụ tại Bệnh Viện Điện Thoại, Laptop 24h?</h2>
<p>Nếu bạn muốn tìm một nơi vừa có giá rẻ, vừa đảm bảo chất lượng, thì <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> là lựa chọn hàng đầu.</p>
<ul>
<li>
<p><strong>Giá cả công khai, cạnh tranh</strong>: Luôn báo giá trước khi sửa.</p>
</li>
<li>
<p><strong>Thời gian thay nhanh chóng</strong>: Có thể lấy máy trong ngày.</p>
</li>
<li>
<p><strong>Sử dụng linh kiện chính hãng 100%</strong>: Đảm bảo giữ nguyên hiệu năng và tính năng như ban đầu.</p>
</li>
<li>
<p><strong>Dịch vụ uy tín lâu năm</strong>: Được hàng ngàn khách hàng tin tưởng và đánh giá cao.</p>
</li>
</ul>
<p>Nếu iPhone của bạn đang gặp vấn đề với mặt lưng, hãy đến ngay <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> để được tư vấn và thay kính lưng an toàn, nhanh chóng và hiệu quả.</p>
|
Muapi/vintage-labels-ephemera-alchemica
|
Muapi
| 2025-08-19T09:47:32Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:47:14Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Vintage Labels: Ephemera Alchemica
**Base model**: Flux.1 D
**Trained words**: A vintage label design
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:754479@843648", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
MorcuendeA/comparativa_producto_detection
|
MorcuendeA
| 2025-08-19T09:45:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T09:44:25Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: comparativa_producto_detection
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. -->
# comparativa_producto_detection
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4559
- eval_model_preparation_time: 0.0029
- eval_accuracy: 0.8264
- eval_f1_score: 0.8489
- eval_runtime: 2.311
- eval_samples_per_second: 52.357
- eval_steps_per_second: 3.462
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 69
- gradient_accumulation_steps: 8
- 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
- num_epochs: 15
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Muapi/nostalgic-photo-flux
|
Muapi
| 2025-08-19T09:45:44Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:45:36Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Nostalgic Photo Flux ✨
**Base model**: Flux.1 D
**Trained words**: This photograph captures a
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:938804@1050927", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/cyber-beauties-ethanar
|
Muapi
| 2025-08-19T09:43:58Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:43:49Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Cyber Beauties @Ethanar
**Base model**: Flux.1 D
**Trained words**: Cyber Beauty
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1041259@1168201", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755594890
|
katanyasekolah
| 2025-08-19T09:43:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:43:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/zara-like-cowboythighboots-zara
|
Muapi
| 2025-08-19T09:42:07Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:41:54Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# ZARA(like)-CowboyThighBoots 仿ZARA牛仔跟过膝靴
**Base model**: Flux.1 D
**Trained words**: zaracbb
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:245278@1378500", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
wardydev/toolify-text-embedding-001
|
wardydev
| 2025-08-19T09:41:22Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"multilingual",
"embedding",
"text-embedding",
"id",
"en",
"base_model:intfloat/multilingual-e5-small",
"base_model:finetune:intfloat/multilingual-e5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-19T08:29:09Z |
---
license: apache-2.0
base_model: intfloat/multilingual-e5-small
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- multilingual
- embedding
- text-embedding
library_name: sentence-transformers
pipeline_tag: feature-extraction
language:
- multilingual
- id
- en
model-index:
- name: toolify-text-embedding-001
results:
- task:
type: feature-extraction
name: Feature Extraction
dataset:
type: custom
name: Custom Dataset
metrics:
- type: cosine_similarity
value: 0.85
name: Cosine Similarity
- type: spearman_correlation
value: 0.82
name: Spearman Correlation
---
# toolify-text-embedding-001
This is a fine-tuned version of [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) optimized for text embedding tasks, particularly for multilingual scenarios including Indonesian and English text.
## Model Details
- **Base Model**: intfloat/multilingual-e5-small
- **Model Type**: Sentence Transformer / Text Embedding Model
- **Language Support**: Multilingual (optimized for Indonesian and English)
- **Fine-tuning**: Custom dataset for improved embedding quality
- **Vector Dimension**: 384 (inherited from base model)
## Intended Use
This model is designed for:
- **Semantic Search**: Finding similar documents or texts
- **Text Similarity**: Measuring semantic similarity between texts
- **Information Retrieval**: Document ranking and retrieval systems
- **Clustering**: Grouping similar texts together
- **Classification**: Text classification tasks using embeddings
## Usage
### Using Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('wardydev/toolify-text-embedding-001')
# Encode sentences
sentences = [
"Ini adalah contoh kalimat dalam bahasa Indonesia",
"This is an example sentence in English",
"Model ini dapat memproses teks multibahasa"
]
embeddings = model.encode(sentences)
print(f"Embedding shape: {embeddings.shape}")
# Calculate similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item()}")
```
### Using Transformers Library
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('wardydev/toolify-text-embedding-001')
model = AutoModel.from_pretrained('wardydev/toolify-text-embedding-001')
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Encode text
sentences = ["Your text here"]
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(f"Embeddings: {embeddings}")
```
## Performance
The model has been fine-tuned on a custom dataset to improve performance on:
- Indonesian text understanding
- Cross-lingual similarity tasks
- Domain-specific text embedding
## Training Details
- **Base Model**: intfloat/multilingual-e5-small
- **Training Framework**: Sentence Transformers
- **Fine-tuning Method**: Custom training on domain-specific data
- **Training Environment**: Google Colab
## Technical Specifications
- **Model Size**: ~118MB (inherited from base model)
- **Embedding Dimension**: 384
- **Max Sequence Length**: 512 tokens
- **Architecture**: BERT-based encoder
- **Pooling**: Mean pooling
## Evaluation
The model shows improved performance on:
- Semantic textual similarity tasks
- Cross-lingual retrieval
- Indonesian language understanding
- Domain-specific embedding quality
## Limitations
- Performance may vary on out-of-domain texts
- Optimal performance requires proper text preprocessing
- Limited to 512 token sequences
- May require specific prompt formatting for best results
## License
This model is released under the Apache 2.0 license, following the base model's licensing terms.
## Citation
If you use this model, please cite:
```bibtex
@misc{toolify-text-embedding-001,
title={toolify-text-embedding-001: Fine-tuned Multilingual Text Embedding Model},
author={wardydev},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/wardydev/toolify-text-embedding-001}
}
```
## Contact
For questions or issues, please contact through Hugging Face model repository.
---
*This model card was created to provide comprehensive information about the toolify-text-embedding-001 model and its capabilities.*
|
Muapi/flux-sparklycism-maximizing-glow
|
Muapi
| 2025-08-19T09:40:34Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:40:19Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Flux sparklycism | maximizing glow
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:730436@816800", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/bread
|
Muapi
| 2025-08-19T09:39:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:39:21Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Bread
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:278431@1546816", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/the-painted-realm-flux
|
Muapi
| 2025-08-19T09:38:56Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:38:43Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# The Painted Realm - FLUX
**Base model**: Flux.1 D
**Trained words**: thepaintedrealm, oil painting
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1035333@1524405", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crocodlo/blockassist-bc-soft_barky_scorpion_1755596207
|
crocodlo
| 2025-08-19T09:37:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft barky scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:37:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft barky scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VoilaRaj/80_UwEn1F
|
VoilaRaj
| 2025-08-19T09:32:45Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T09:28:52Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Muapi/christmas-couture
|
Muapi
| 2025-08-19T09:27:30Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:26:39Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Christmas Couture
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1016234@1139381", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755595336
|
0xaoyama
| 2025-08-19T09:22:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:22:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/flux-futuristic-portraits-lora
|
Muapi
| 2025-08-19T09:22:25Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:22:04Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Flux Futuristic Portraits LoRA
**Base model**: Flux.1 D
**Trained words**: futuristicportrait
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:716982@801785", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755595160
|
IvanJAjebu
| 2025-08-19T09:20:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:20:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/style-of-vincent-van-gogh-flux-123
|
Muapi
| 2025-08-19T09:19:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:19:25Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# style of Vincent van Gogh [FLUX] 123
**Base model**: Flux.1 D
**Trained words**: style of Vincent van Gogh
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:650132@750855", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755583441
|
pempekmangedd
| 2025-08-19T09:19:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T06:30:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
deerr120a/blockassist-bc-prehistoric_arctic_otter_1755592752
|
deerr120a
| 2025-08-19T09:19:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prehistoric arctic otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:18:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prehistoric arctic otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Devion333/labse-dhivehi-finetuned
|
Devion333
| 2025-08-19T09:18:23Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:968266",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:sentence-transformers/LaBSE",
"base_model:finetune:sentence-transformers/LaBSE",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-19T09:08:42Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:968266
- loss:CosineSimilarityLoss
base_model: sentence-transformers/LaBSE
widget:
- source_sentence: ކުއްލިއަކަށް ދޮންބެ ތެދުވެ އިނދެ ދެފައި ވައްކޮއްލިއެވެ. ދެލޯ ބޮޑުކޮއްގެން
ހުރެ ހެވެމުން ދިލެމުން ގޮސް އަހަރެން ހުޅުވާލީވެސް ދޮންބެ ބުނި ކަބަޑެވެ. ގެރިގުއި
ކުލައިގެ ކަރުދާހަކުން ބަންދުކޮއްފައި އޮތް ފޮށިގަނޑެއް ފެނުމާއި އެކު އަހަރެންނަށް
ބަލާލެވުނީ ގޮދަނޑިމަތީގައި ދެފައި ވަށްކޮއްގެން އިން ބޭބެ އާއި ދިމާއަށެވެ.
sentences:
- sheet covering coffin
- The king's kidneys, heart and lungs have also stopped working, Saudi health officials
said, according to Press TV.
- The Civil Court of Maldives has ordered the seizure of passports and freezing
bank accounts belonging to Haulath Faheem, wife of former President Dr. Mohamed
Jamil, as well as seven other members of his family in connection with a case
of proven debt. This was decided by the court today after an action filed by Mohammad
Aniis who served as General Manager at four resorts owned by Three A Company when
it was not being divided into shares. The heir was not present at the court. The
lawyer for the heirs said that he has appealed to the High Court against this
decision. In any case of proven debt, it is a common practice in courts to hold
passports and freeze accounts as part of an application for enforcement of judgment
when there are no payments made by debtors. The family appealed the Civil Court’s
order to pay them back, which was then reviewed by the Supreme Court. In addition
to the three charges, Anies also brought another two cases against Musa Fahim’s
heirs. The other accused are Haulat and Shaheed as well as Farida Ibrahim, Ahmad
Shahid Shiyam, Ali Shiyam, Hassan Shiyam, Maryam Shifa and Aimanat Ashfah. The
two brothers’ son Anies said he owes the company 1.8 million rupees for days when
senior management was not paid due to problems arising from the split of Three
Airline Company Ltd (THAC). The order was issued in response to a case filed by
Anis at the Civil Court on May 15, requesting payment of Rs.731,540.80 due from
his family following an appeal ruling made on February 17 this year. He said that
no appeal had been lodged against the judgment for over ninety days and he is
still waiting for the decision to be announced.
- source_sentence: 24 ޖުލައި 2013 ގައި ޖޯން ހޮޖްމަން މެކްސިމަމް ފަން ޕޮޑްކާސްޓް ``
ޖަޖް ބްރަދަރ އަލީ '' އިން ފެނިގެންދިޔައީ '' އެކްސްޕާޓް ވިޓްނަސް '' ގެ ގޮތުގައެވެ
.
sentences:
- Translate the following sentence into a different language and add a proof of
the translation in the footnotes. Traer tu propia bolsa es una elección ecológica.
<sup>1</sup> --- <sup>1</sup> Translation from English to Spanish using Google
Translate.
- The result sheet of the Ihwandu constituency, which is part of the North East
District Council was lost and it has been found while reopening a ballot box.
It had to be counted again after that because the results were missing. In presence
of representatives from candidates who contested for this district as well as
media, the election commission opened the ballot box at 8:30 p.m. today when they
discovered the result sheet in another letter. The results sheet was mistakenly
placed in a wrong envelope.The Election Commission decided that the ballot box
did not need to be counted after seeing its result sheet.This is the first election
with an issue of this kind. The Complaints Bureau has not received any complaints
from the voters that would require a ballot box to be reopened, said Election
Commission Director General Mohamed Sheik. The Commission said that 60 percent
of the total number of results sheets, which is estimated to be around 17,000
have been cleared.
- Outline the following passage I. American astronauts' exploration of the moon
A. Began in 1969 B. Building of moon bases C. Driving lunar rovers on the surface
D. Collection of moon samples.
- source_sentence: އަދި ލަންގޭންސްޓައިންބާކް އާއި އަލަށް އުފެއްދި ޝިސްޝުޓެނަކަރ ރޭލްވޭ
ސްޓޭޝަނާ ދެމެދު 2011 ވަނަ އަހަރު ކުރު ޑަބަލް ޓްރެކެއް ވެސް ހެދިއެވެ .
sentences:
- i told them i would personally be delighted if sia would fly to and from europe
via the maldives.
- A short double track was also built between Langensteinbach and the newly created
Schießhüttenäcker railway station in 2011 .
- Offer one suggestion to reduce cases of teenage suicide. One suggestion to reduce
cases of teenage suicide could be to provide accessible and safe mental health
support for teenagers. This could be in the form of school counselors, teen helplines,
or mental health workshops, among other resources. By ensuring that teenagers
have someone to talk to about their struggles and concerns, it can alleviate feelings
of hopelessness and isolation, which are major risk factors for suicide.
- source_sentence: އަޖީއެމްއެޗްގެ އަހަރި ދުވަހާއި ގުޅުވައިގެން ބާއްވާ މި ފެއާއަށް
ދާ ފަރާތްތަކަށް ހިލޭ ގުލްކޯޒް، ހަކުރު، އަދި ލޭގެ ޕްރެޝަރު ހުރި މިންވަރު ބަލައިދެމުންދާ
ކަމަށް އައިޖީއެމްއެޗުން ބުނެއެވެ.
sentences:
- A young man died in a serious accident on the road at night. The victim was identified
as Hussain Adham, 21 years old from Hithadhoo. The 54-year old man died at the
hospital after being treated for a heart attack. According to witnesses, the accident
occurred when Adham was driving from Hittadu towards Maradu and collided with
another motorbike that had been travelling along Link Road in direction of Maradu.
The accident resulted in a severe fracture of his head and extensive bleeding.
He was also broken his neck and a hand. "The helmet he was wearing broke and his
head got injured. The injuries were severe," the witness said. Some of the victims
had broken their hands and feet. A woman was among the victims.
- NASA has announced that it will test a new type of flying saucer this year. It
may be to bring in aliens who have not yet landed on the earth. The cup-style
vehicle will be launched by what NASA calls a "low density supersonic decelerator"
rocket. The rocket is scheduled to be launched in June. NASA is interested in
launching a flying saucer into the atmosphere, but according to their own statements,
there's no connection between aliens and NASA's Flying Saucer. NASA wants to test
and demonstrate new technologies that can be used for launching objects into the
atmosphere. NASA said the mission will help to estimate how much payload is needed
for a manned Mars missions.
- Ar.... Arfin? Are you telling the truth? Is the child so good now? How many years
have passed since then... If you haven't even heard from the boy, you can hear
what Asiya is saying, I really want to see you, Asiya, please come here with Arfin,
if you have his number I want to call him now
- source_sentence: އޭނާ ރީތި.
sentences:
- She's pretty.
- Words of gratitude are being sent to the government and President Yameen for bringing
two new generators to the village within five days. The people of Thonadhoo have
shown the whole country that they have a people who love patience, unity and brotherhood.
It is a beautiful example of unity. The burden and pain of the power outages is
not easy for anyone to bear in such an era.
- 'Date of appointment: 22 June'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/LaBSE
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): 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("sentence_transformers_model_id")
# Run inference
sentences = [
'އޭނާ ރީތި.',
"She's pretty.",
'Words of gratitude are being sent to the government and President Yameen for bringing two new generators to the village within five days. The people of Thonadhoo have shown the whole country that they have a people who love patience, unity and brotherhood. It is a beautiful example of unity. The burden and pain of the power outages is not easy for anyone to bear in such an era.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.9827, -0.0089],
# [ 0.9827, 1.0000, -0.0044],
# [-0.0089, -0.0044, 1.0000]])
```
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 968,266 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 121.67 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 64.68 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>އިންތިހާބު ލަސްކުރަން ބްލެޓާ ބޭނުމެއްނުވޭ: ފީފާ</code> | <code>The Ponoru River is a tributary of the Horezu in Romania .</code> | <code>0.0</code> |
| <code>ޖޯ އުފަންވީ 27 މާރޗް 1929 ގައި މެސެޗުސެޓްސްގެ ސޮމަރވިލް އަށް ކަމަށާއި ބޮޑުވީ މެސެޗުސެޓްސްގެ ކުއިންސީ ގައެވެ .</code> | <code>The National Inquiry Commission set up by the government of President Mohammed Vaheed Hassan Manik has said that the coup was not a coup and that the government was overthrown according to the rules of law.</code> | <code>0.0</code> |
| <code>ސާބިތު ދަރަނީގެ މައްސަލައެއްގައި ޑރ. މުހައްމަދު ޖަމީލްގެ އަނބިކަނބަލުން ހައުލަތު ފަހީމް އާއި އެ އާއިލާގެ އިތުރު ހަތް މީހެއްގެ ޕާސްޕޯޓް ހިފަހައްޓައި ބޭންކް އެކައުންޓްތައް ފްރީޒްކުރުމަށް ސިވިލް ކޯޓުން މިއަދު އަމުރު ނެރެފި އެވެ.ވީބީ އައްޑޫ އެފްސީގެ މުއައްސިސެއް ކަމަށްވާ މުހަންމަދު ޝަވީދުގެ ވެސް ބައްޕަ މަރުހޫމް މޫސާ ފަހީމްގެ އަށް ވާރިސުންގެ ޕާސްޕޯޓާއި، ރާއްޖޭގެ ބޭންކްތަކުގައި ހުރި ހުރިހާ އެކައުންޓެއް ހިފަހައްޓަން ސިވިލް ކޯޓުން މިއަދު ހެނދުނު ނިންމީ، ތްރީއޭ ކޮމްޕެނީ ނުބަހާއިރު އެ ކުންފުނީގެ ހަތަރު ރިސޯޓެއްގެ ޖެނެރަލް މެނޭޖަރެއްގެ ގޮތުގައި ވަޒީފާ އަދާކުރި މުހަންމަދު އަނީސް ކޮށްފައިވާ ދައުވާއަކާ ގުޅިގެން ބޭއްވި ޝަރީއަތުގެ އަޑުއެހުމުގަ އެވެ. އެ އަޑުއެހުމަށް ވާރިސުންގެ ފަރާތުން ހާޒިރެއް ނުވެ އެވެ. ވާރިސުންގެ ވަކީލް ވިދާޅުވީ ސިވިލް ކޯޓުގެ ހުކުމް ހައި ކޯޓަށް އިސްތިއުނާފަށް ހުށަހަޅާފައިވާ ކަމަށެވެ.ސާބިތު ދަރަނީގެ ކޮންމެ މައްސަލައެއްގައި ވެސް ދަރަނި އަދާނުކުރާ ހާލަތެއްގައި، ހުކުމް ތަންފީޒުކުރުމަށް އެދި ހުށަހަޅެމުން ޕާސްޕޯޓް ހިފަހައްޓައި އެކައުންޓުތައް ފްރީޒްކުރުމަކީ ކޯޓުން އަމަލުކުރާ އާންމު އުސޫލެވ...</code> | <code>The Civil Court of Maldives has ordered the seizure of passports and freezing bank accounts belonging to Haulath Faheem, wife of former President Dr. Mohamed Jamil, as well as seven other members of his family in connection with a case of proven debt. This was decided by the court today after an action filed by Mohammad Aniis who served as General Manager at four resorts owned by Three A Company when it was not being divided into shares. The heir was not present at the court. The lawyer for the heirs said that he has appealed to the High Court against this decision. In any case of proven debt, it is a common practice in courts to hold passports and freeze accounts as part of an application for enforcement of judgment when there are no payments made by debtors. The family appealed the Civil Court’s order to pay them back, which was then reviewed by the Supreme Court. In addition to the three charges, Anies also brought another two cases against Musa Fahim’s heirs. The other accused are ...</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: False
- `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}
- `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_fused
- `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
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0661 | 500 | 0.0528 |
| 0.1322 | 1000 | 0.0298 |
| 0.1983 | 1500 | 0.0261 |
| 0.2644 | 2000 | 0.0242 |
| 0.3305 | 2500 | 0.0235 |
| 0.3966 | 3000 | 0.0223 |
| 0.4627 | 3500 | 0.0207 |
| 0.5288 | 4000 | 0.0208 |
| 0.5948 | 4500 | 0.0196 |
| 0.6609 | 5000 | 0.0192 |
| 0.7270 | 5500 | 0.019 |
| 0.7931 | 6000 | 0.0181 |
| 0.8592 | 6500 | 0.0181 |
| 0.9253 | 7000 | 0.0175 |
| 0.9914 | 7500 | 0.0178 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 5.1.0
- Transformers: 4.55.2
- PyTorch: 2.8.0+cu128
- Accelerate: 1.9.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## 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",
}
```
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|
Muapi/stellaris-character-race-style-lora-flux-xl-illustrous-xl-pony
|
Muapi
| 2025-08-19T09:17:08Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:17:01Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Stellaris Character/race Style Lora [FLUX+XL+Illustrous XL+Pony]
**Base model**: Flux.1 D
**Trained words**: fungoid, necroid, avian
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:351525@1028132", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
KCS97/duck_toy
|
KCS97
| 2025-08-19T09:17:02Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-08-19T09:04:36Z |
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of sks toy
tags:
- text-to-image
- dreambooth
- 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. -->
# DreamBooth - KCS97/duck_toy
This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks toy using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth 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]
|
Muapi/arcane-style-ayanna-ai
|
Muapi
| 2025-08-19T09:16:13Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:16:06Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Arcane Style Ayanna AI
**Base model**: Flux.1 D
**Trained words**: Arcane Style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1024432@1274224", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/damaged-photo-daguerreotype
|
Muapi
| 2025-08-19T09:15:55Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:15:44Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Damaged Photo Daguerreotype
**Base model**: Flux.1 D
**Trained words**: damagedphoto, edges, black shape, blur, border, corners, crease, fingerprints, foggy, heavy damage, liquid stain, mottled, scratches, smudges, speckles, streak, tape, torn, vignette
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:101127@1210919", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
MDscs/CodeLlama-13B-Reparador-Software-v1
|
MDscs
| 2025-08-19T09:15:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:codellama/CodeLlama-13b-Instruct-hf",
"base_model:finetune:codellama/CodeLlama-13b-Instruct-hf",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T09:15:12Z |
---
base_model: codellama/CodeLlama-13b-Instruct-hf
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** MDscs
- **License:** apache-2.0
- **Finetuned from model :** codellama/CodeLlama-13b-Instruct-hf
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)
|
Muapi/horror-dark-film-abandoned-flux
|
Muapi
| 2025-08-19T09:15:26Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:15:16Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 🔪 Horror 🎃 Dark Film / Abandoned [FLUX]
**Base model**: Flux.1 D
**Trained words**: aidmaabdhr
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:790034@883473", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755594813
|
IvanJAjebu
| 2025-08-19T09:15:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:14:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755594610
|
0xaoyama
| 2025-08-19T09:10:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:10:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VoilaRaj/80_Klm4LL
|
VoilaRaj
| 2025-08-19T09:09:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T09:05:45Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755583662
|
katanyasekolah
| 2025-08-19T09:08:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T06:34:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755594423
|
IvanJAjebu
| 2025-08-19T09:08:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:08:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rockst4r4/Qwen3-0.6B-Gensyn-Swarm-camouflaged_dappled_wallaby
|
rockst4r4
| 2025-08-19T09:08:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am camouflaged_dappled_wallaby",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T23:17:37Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am camouflaged_dappled_wallaby
---
# 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]
|
LarryAIDraw/dragoxl_v30TEST
|
LarryAIDraw
| 2025-08-19T09:07:20Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-18T21:19:29Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/1519399?modelVersionId=2089561
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755594391
|
0xaoyama
| 2025-08-19T09:07:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:06:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yehudakar/output
|
yehudakar
| 2025-08-19T09:05:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T08:35:48Z |
---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: output
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for output
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
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="yehudakar/output", 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: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
kien231205/yelp_review_classifier
|
kien231205
| 2025-08-19T09:05:39Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T08:50:30Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: yelp_review_classifier
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. -->
# yelp_review_classifier
This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0693
- Accuracy: 0.59
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 1.0952 | 0.485 |
| No log | 2.0 | 250 | 1.0302 | 0.566 |
| No log | 3.0 | 375 | 1.0693 | 0.59 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
deeee112222/mistral7b_cve_analyzer_alpaca_adapter
|
deeee112222
| 2025-08-19T09:03:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T09:02:38Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
constehub/qwen3-14B-rerank-evaluation
|
constehub
| 2025-08-19T09:03:04Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T12:40:09Z |
---
base_model: unsloth/qwen3-14b-base-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** constehub
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-base-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)
|
TAUR-dev/M-voting_setup1_1epch_1e6_all_tasks_only_sft-sft
|
TAUR-dev
| 2025-08-19T09:00:24Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-08-19T08:59:12Z |
# M-voting_setup1_1epch_1e6_all_tasks_only_sft-sft
This model was created as part of the **voting_setup1_1epch_1e6_all_tasks_only_sft** experiment using the SkillFactory experiment management system.
## Model Details
- **Training Method**: LLaMAFactory SFT (Supervised Fine-Tuning)
- **Stage Name**: sft
- **Experiment**: voting_setup1_1epch_1e6_all_tasks_only_sft
## Training Configuration
{"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/datastor1/mwadhwa/code/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_voting_setup1_1epch_1e6_all_tasks_only_sft_sft_data__sft_train", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/datastor1/mwadhwa/skill_inject_outputs/sf_experiments/skills_in_rl/voting_setup1_1epch_1e6_all_tasks_only_sft/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 1, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__voting_setup1_1epch_1e6_all_tasks_only_sft__v1", "sf_eval_before_training": false, "sf_wandb_project": "voting_setup1_1epch_1e6_all_tasks_only_sft_sft", "sf_eval_steps": null, "run_name": "voting_setup1_1epch_1e6_all_tasks_only_sft_sft"}
## Experiment Tracking
🔗 **View complete experiment details**: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__voting_setup1_1epch_1e6_all_tasks_only_sft__v1)
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-voting_setup1_1epch_1e6_all_tasks_only_sft-sft")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-voting_setup1_1epch_1e6_all_tasks_only_sft-sft")
```
|
nightmedia/Cydonia-24B-v4.1-q8-mlx
|
nightmedia
| 2025-08-19T08:58:54Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"mistral",
"text-generation",
"conversational",
"base_model:TheDrummer/Cydonia-24B-v4.1",
"base_model:quantized:TheDrummer/Cydonia-24B-v4.1",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-19T07:25:01Z |
---
base_model: TheDrummer/Cydonia-24B-v4.1
tags:
- mlx
library_name: mlx
pipeline_tag: text-generation
---
# Cydonia-24B-v4.1-q8-mlx
This model [Cydonia-24B-v4.1-q8-mlx](https://huggingface.co/Cydonia-24B-v4.1-q8-mlx) was
converted to MLX format from [TheDrummer/Cydonia-24B-v4.1](https://huggingface.co/TheDrummer/Cydonia-24B-v4.1)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Cydonia-24B-v4.1-q8-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Alonc/device_to_cve_4bit_8B
|
Alonc
| 2025-08-19T08:58:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-19T08:57:28Z |
---
base_model: unsloth/qwen3-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Alonc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-8b-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)
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755593825
|
0xaoyama
| 2025-08-19T08:57:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:57:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crocodlo/blockassist-bc-soft_barky_scorpion_1755593817
|
crocodlo
| 2025-08-19T08:57:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft barky scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:57:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft barky scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AXERA-TECH/RAG.axera
|
AXERA-TECH
| 2025-08-19T08:56:11Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-08-19T08:24:55Z |
---
license: mit
---
# RAG.AXERA DEMO
## 项目说明
```sh
(hf) ➜ rag.axera git:(main) ✗ tree -L 2
.
├── assets
│ └── demo.png
├── config.py # 配置 axmodel, tokenizer 文件路径
├── data
├── gui.py # RAG 交互式程序
├── index # 文档编码向量索引保存位置
│ ├── docs.index
│ └── docs.pkl
├── llm_api.py # llm 主程序
├── models # axmodel 模型存储位置
│ ├── Qwen2.5-1.5B-Instruct_axmodel
│ └── Qwen3-Embedding-0.6B_axmodel
├── pdf_sample # 示例 pdf 文件
│ └── introduction.pdf
├── rag_engine.py # 文档向量编码程序
├── README.md
├── requirements.txt
├── tokenizer
│ ├── Qwen2.5-1.5B-Instruct
│ └── Qwen3-Embedding-0.6B
└── utils
└── infer_func.py
11 directories, 11 files
```
## 运行
在 `AXCL` 机器或 `AX650` 开发板上启动两个终端界面, 分别运行下面的命令:
```sh
python3 llm_api.py # 在 AX650 或 AXCL 开发板启动 llm 服务
python3 gui.py # 启动交互式界面
```
|
jva96160/go
|
jva96160
| 2025-08-19T08:56:02Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3omni",
"feature-extraction",
"image-text-to-text",
"conversational",
"custom_code",
"arxiv:1905.07830",
"arxiv:1905.10044",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:1903.00161",
"arxiv:2009.03300",
"arxiv:2304.06364",
"arxiv:2103.03874",
"arxiv:2110.14168",
"arxiv:2311.12022",
"arxiv:2108.07732",
"arxiv:2107.03374",
"arxiv:2210.03057",
"arxiv:2106.03193",
"arxiv:1910.11856",
"arxiv:2502.12404",
"arxiv:2502.21228",
"arxiv:2404.16816",
"arxiv:2104.12756",
"arxiv:2311.16502",
"arxiv:2203.10244",
"arxiv:2404.12390",
"arxiv:1810.12440",
"arxiv:1908.02660",
"arxiv:2312.11805",
"base_model:google/gemma-3-4b-pt",
"base_model:finetune:google/gemma-3-4b-pt",
"license:gemma",
"region:us"
] |
image-text-to-text
| 2025-05-15T01:33:29Z |
---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-4b-pt
---
# Gemma 3 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
**Resources and Technical Documentation**:
* [Gemma 3 Technical Report][g3-tech-report]
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
**Terms of Use**: [Terms][terms]
**Authors**: Google DeepMind
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B size
- **Output:**
- Generated text in response to the input, such as an answer to a
question, analysis of image content, or a summary of a document
- Total output context of 8192 tokens
### Usage
Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0.
```sh
$ pip install -U transformers
```
Then, copy the snippet from the section that is relevant for your use case.
#### Running with the `pipeline` API
You can initialize the model and processor for inference with `pipeline` as follows.
```python
from transformers import pipeline
import torch
pipe = pipeline(
"image-text-to-text",
model="google/gemma-3-4b-it",
device="cuda",
torch_dtype=torch.bfloat16
)
```
With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.
```python
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What animal is on the candy?"}
]
}
]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"][-1]["content"])
# Okay, let's take a look!
# Based on the image, the animal on the candy is a **turtle**.
# You can see the shell shape and the head and legs.
```
#### Running the model on a single/multi GPU
```python
# pip install accelerate
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from PIL import Image
import requests
import torch
model_id = "google/gemma-3-4b-it"
model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto"
).eval()
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
{"type": "text", "text": "Describe this image in detail."}
]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
# **Overall Impression:** The image is a close-up shot of a vibrant garden scene,
# focusing on a cluster of pink cosmos flowers and a busy bumblebee.
# It has a slightly soft, natural feel, likely captured in daylight.
```
### Citation
```none
@article{gemma_2025,
title={Gemma 3},
url={https://goo.gle/Gemma3Report},
publisher={Kaggle},
author={Gemma Team},
year={2025}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:
- Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
- Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
- Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in
line with [our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
- These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."*
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
#### Reasoning and factuality
| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:|
| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161
#### STEM and code
| Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374
#### Multilingual
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816
#### Multimodal
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap] | 102 | 111 | 116 |
| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
- **Child Safety**: Evaluation of text-to-text and image to text prompts
covering child safety policies, including child sexual abuse and
exploitation.
- **Content Safety:** Evaluation of text-to-text and image to text prompts
covering safety policies including, harassment, violence and gore, and hate
speech.
- **Representational Harms**: Evaluation of text-to-text and image to text
prompts covering safety policies including bias, stereotyping, and harmful
associations or inaccuracies.
In addition to development level evaluations, we conduct "assurance
evaluations" which are our 'arms-length' internal evaluations for responsibility
governance decision making. They are conducted separately from the model
development team, to inform decision making about release. High level findings
are fed back to the model team, but prompt sets are held-out to prevent
overfitting and preserve the results' ability to inform decision making.
Assurance evaluation results are reported to our Responsibility & Safety Council
as part of release review.
### Evaluation Results
For all areas of safety testing, we saw major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. A limitation of our evaluations was they included only
English language prompts.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces
for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus,
research papers, or reports.
- Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
- Research and Education
- Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and NLP techniques, develop algorithms, and contribute to the
advancement of the field.
- Language Learning Tools: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.
### Limitations
- Training Data
- The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas
the model can handle effectively.
- Context and Task Complexity
- Models are better at tasks that can be framed with clear
prompts and instructions. Open-ended or highly complex tasks might be
challenging.
- A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
- Language Ambiguity and Nuance
- Natural language is inherently complex. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
- Factual Accuracy
- Models generate responses based on information they learned
from their training datasets, but they are not knowledge bases. They
may generate incorrect or outdated factual statements.
- Common Sense
- Models rely on statistical patterns in language. They might
lack the ability to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world text and image data can
reflect socio-cultural biases embedded in the training material. These
models underwent careful scrutiny, input data pre-processing described
and posterior evaluations reported in this card.
- Misinformation and Misuse
- VLMs can be misused to generate text that is false, misleading,
or harmful.
- Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
- Transparency and Accountability:
- This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to
share innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
- **Perpetuation of biases**: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use
cases.
- **Generation of harmful content**: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
- **Misuse for malicious purposes**: Technical limitations and developer
and end-user education can help mitigate against malicious applications of
VLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
- **Privacy violations**: Models were trained on data filtered for removal
of certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
### Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[g3-tech-report]: https://goo.gle/Gemma3Report
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
[terms]: https://ai.google.dev/gemma/terms
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/jax-ml/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[gemini-2-paper]: https://arxiv.org/abs/2312.11805
|
josephr212/blockassist-bc-hoarse_frisky_dingo_1755591769
|
josephr212
| 2025-08-19T08:51:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hoarse frisky dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:51:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hoarse frisky dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hf-audio/xcodec-hubert-general-balanced
|
hf-audio
| 2025-08-19T08:47:19Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"xcodec",
"feature-extraction",
"base_model:ZhenYe234/hubert_base_general_audio",
"base_model:finetune:ZhenYe234/hubert_base_general_audio",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-18T09:15:30Z |
---
library_name: transformers
license: mit
base_model:
- ZhenYe234/hubert_base_general_audio
---
# X-Codec (general audio)
This codec can be used for general audio.
Original model is `xcodec_hubert_general_audio` from [this table](https://github.com/zhenye234/xcodec?tab=readme-ov-file#available-models).
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755593120
|
0xaoyama
| 2025-08-19T08:45:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:45:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755592995
|
IvanJAjebu
| 2025-08-19T08:44:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:44:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Adi26ti/Llama-2-7b-chat-finetune
|
Adi26ti
| 2025-08-19T08:40:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T08:04:09Z |
---
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]
|
bgunlp/qwen3-8b-sft-cot-qd-suff-ordered-16bit-5ep
|
bgunlp
| 2025-08-19T08:39:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T08:35:25Z |
---
base_model: unsloth/qwen3-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** bgunlp
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-8b-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)
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755592550
|
0xaoyama
| 2025-08-19T08:36:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:36:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
darshanvyas36/qweb8B-qlora-adapter
|
darshanvyas36
| 2025-08-19T08:36:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T08:36:09Z |
---
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]
|
resistz/sft_Llama-3.2-1B_ultra200k
|
resistz
| 2025-08-19T08:35:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T08:32:16Z |
---
library_name: transformers
model_name: sft_Llama3.2-1B_ultra200k
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for sft_Llama3.2-1B_ultra200k
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="None", 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/resistzzz97/Alignment_Influence/runs/iq1tp3b2)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
poffusers/ltf-example-0
|
poffusers
| 2025-08-19T08:32:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T08:30:23Z |
---
title: Test Hugsim Web Server
emoji: 📈
colorFrom: purple
colorTo: yellow
sdk: docker
pinned: false
---
|
Pillendreher1/Paige-British
|
Pillendreher1
| 2025-08-19T08:32:31Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-08-19T08:31:15Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/PB3.png
text: '-'
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: paigebritish
---
# Paige British
<Gallery />
## Model description
This is a LORA I've trained using AI-Toolkit.
The training parameter where as follows:
Dataset images: 30 (captioned using natural language via Google Gemini)
Steps: 8000
Learning rate: 2.5e-05
linear: 16
linear_alpha: 16
I ran the whole training on Modal using a A100 GPU, which took about 4:45.
## Trigger words
You should use `paigebritish` to trigger the image generation.
## Download model
[Download](/Pillendreher1/Paige-British/tree/main) them in the Files & versions tab.
|
thailevann/track8_subtask2_v3
|
thailevann
| 2025-08-19T08:29:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T08:29:15Z |
---
base_model: unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thailevann
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Thinking-2507-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)
|
FreedomIntelligence/AceGPT-v1.5-7B-Chat
|
FreedomIntelligence
| 2025-08-19T08:28:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"ar",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-27T02:50:55Z |
---
license: apache-2.0
language:
- ar
- zh
- en
---
# <b>AceGPT</b>
AceGPT is a fully fine-tuned generative text model collection based on LlaMA2, particularly in the
Arabic language domain. This is the repository for the version 1.5 of 7B-Chat pre-trained model.
---
## Model Details
We have released the AceGPT family of large language models, which is a collection of fully fine-tuned generative text models based on LlaMA2, ranging from 7B to 13B parameters. Our models include two main categories: AceGPT and AceGPT-chat. AceGPT-chat is an optimized version specifically designed for dialogue applications. It is worth mentioning that our models have demonstrated superior performance compared to all currently available open-source Arabic dialogue models in multiple benchmark tests. Furthermore, in our human evaluations, our models have shown comparable satisfaction levels to some closed-source models, such as ChatGPT, in the Arabic language.
## Model Developers
We are from the King Abdullah University of Science and Technology (KAUST), the Chinese University of Hong Kong, Shenzhen (CUHKSZ), the Shenzhen Research Institute of Big Data (SRIBD), and King AbdulAziz University (KAU).
## Variations
AceGPT families come in a range of parameter sizes —— 7B and 13B, each size of model has a base category and a -chat category.
## Paper
The paper can be accessed at [link](https://huggingface.co/FreedomIntelligence/AceGPT-v1.5-13B-Chat/blob/main/Second_Language_(Arabic)_Acquisition_of_LLMs_via_Progressive_Vocabulary_Expansion.pdf).
## Input
Models input text only.
## Output
Models output text only.
## Model Evaluation Results
Benchmark evaluation on [Arabic MMLU](https://github.com/FreedomIntelligence/AceGPT) are conducted using accuracy scores as metrics, following the evaluation framework available at https://github.com/FreedomIntelligence/AceGPT/tree/main.
| | STEM | Humanities | Social Sciences | Others | Average |
|------------------|------|------|------|------|------|
| Bloomz-7B-base | 33.35 | 29.29 | 37.58 | 34.53 | 33.69 |
| LLaMA2-7B-base | 30.30 | 29.33 | 27.46 | 30.78 | 29.37 |
| AceGPT-7B-base | 29.73 | 30.95 | 33.45 | 34.42 | 32.14 |
| AceGPT-v1.5-7B-base | 33.03 | 32.08 | 35.39 | 35.59 | 34.03 |
| LLaMA2-13B-base | 32.94 | 32.30 | 33.42 | 37.27 | 33.76 |
| Jais-13B-base | 30.51 | 31.25 | 33.74 | 33.42 | 33.76 |
| AceGPT-13B-base | 36.60 | 38.74 | 43.76 | <u>42.72</u> | 40.45 |
| AceGPT-v1.5-13B-base | <u>36.13</u> | <u>40.07</u> | <u>45.43</u> | 42.17 | <u>40.95</u> |
| Jais-30B-v1-base | 32.67 | 30.67 | 42.13 | 39.60 | 36.27 |
| ChatGPT 3.5 Turbo | **43.38** | **44.12** | **55.57** | **53.21** | **49.07** |
Benchmark evaluation on [ArabicMMLU]((https://github.com/mbzuai-nlp/ArabicMMLU)), and assessed based on its source settings.
| | STEM | Social Sciences | Humanities | Arabic Language | Other | Average |
|------------------|------|------|------|------|------|------|
| Bloomz-7B-base | - | - | - | - | - | - |
| LLaMA2-7B-base | 33.7 | 32.8 | 33.5 | 28.4 | 36.7 | 33.4 |
| AceGPT-7B-base | 35.4 | 35.9 | 36.2 | 31.1 | 41.7 | 36.3 |
| AceGPT-v1.5-7B-base | 36.7 | 36.5 | 34.1 | 30.0 | 41.2 | 37.0 |
| LLaMA2-13B-base | 32.9 | 35.0 | 37.8 | 35.8 | 39.3 | 36.1 |
| Jais-13B-base | 30.3 | 31.4 | 33.6 | 28.1 | 36.3 | 32.2 |
| AceGPT-13B-base | <u>42.7</u> | 45.5 | 48.3 | 42.4 | 50.7 | 46.1 |
| AceGPT-v1.5-13B-base | 42.4 | <u>45.7</u> | 48.4 | <u>46.3</u> | <u>52.5</u> | <u>47.6</u> |
| Jais-30B-v1-base | 39.5 | 45.6 | <u>50.5</u> | 34.6 | 49.1 | 44.8 |
| ChatGPT 3.5 Turbo | **53.8** | **57.0** | **57.5** | **57.6** | **63.8** | **57.7** |
## Samples
#### Sample1(abstract_algebra)
* <b>input:</b>
"فيما يلي أسئلة الاختيار من متعدد (مع الإجابات) حول جبر تجريدي\n\nسؤال: العثور على جميع قيم c في Z_3 بحيث يكون Z_3 [x]/(x^2+c) حقلًا.\nA. 0\nB. 1\nC. 2\nD. 3\nإجابة: B\n\nسؤال: البيان رقم 1 | إذا كان aH عنصرًا في مجموعة العوامل ، فإن | aH | يقسم | a |. البيان رقم 2 | إذا كانت H و K مجموعات فرعية لـ G ، فإن HK مجموعة فرعية لـ G.\nA. صحيح ، صحيح\nB. خطأ ، خطأ\nC. صحيح ، خطأ\nD. خطأ ، صحيح\nإجابة: B\n\nسؤال: العبارة 1 | كل عنصر من مجموعة يولد مجموعة دورية من المجموعة. العبارة 2 | المجموعة المتناظرة S_10 لديها 10 عناصر.\nA. صحيح، صحيح\nB. خطأ، خطأ\nC. صحيح، خطأ\nD. خطأ، صحيح\nإجابة: C\n\nسؤال: البيان 1| كل وظيفة من مجموعة محدودة على نفسها يجب أن تكون واحدة لكل مجموعة. البيان 2 | كل فرع فرعي لمجموعة أبيلية هو أبيلي.\nA. صحيح, صحيح\nB. خاطئ, خاطئ\nC. صحيح, خاطئ\nD. خاطئ, صحيح\nإجابة: A\n\nسؤال: اعثر على خاصية الحلقة 2Z.\nA. 0\nB. 3\nC. 12\nD. 30\nإجابة: A\n\nسؤال: ما هو الدرجة للامتداد الميداني الناتج من Q(sqrt(2), sqrt(3), sqrt(18)) على Q؟\nA. 0\nB. 4\nC. 2\nD. 6\nإجابة:"
* <b>output:</b>
"B\n\nسؤال: ما هو عدد العناصر"
#### Sample2(business_ethics)
* <b>input:</b>
"فيما يلي أسئلة الاختيار من متعدد (مع الإجابات) حول أخلاقيات الأعمال\n\nسؤال: ما هي الحجج الأخلاقية المتعلقة بالمسؤولية الاجتماعية للشركات؟\nA. التكاليف الخارجية، القوة، الاستقلالية\nB. الإعلام، الموارد الضعيفة، التبادل التعاوني\nC. الإعلام، القوة، الاستقلالية\nD. التكاليف الخارجية، القوة، التبادل التعاوني\nإجابة: D\n\nسؤال: _______ هو المحاولة المباشرة لإدارة القضايا الأخلاقية أو المشاكل، سواء بشكل رسمي أو غير رسمي، من خلال سياسات وممارسات وبرامج محددة.\nA. المسؤولية الاجتماعية للشركات\nB. إدارة الأخلاقيات العملية\nC. الاستدامة\nD. إدارة البيئة\nإجابة: B\n\nسؤال: لضمان استقلال أعضاء مجلس الإدارة غير التنفيذية ، هناك عدد من الخطوات التي يمكن اتخاذها ، والتي تشمل اختيار الغير التنفيذيين من _______ الشركة ، وتعيينهم لمدة _________ ، وكذلك تعيينهم _________.\nA. خارج الشركة ، محدودة ، بشكل مستقل\nB. من الداخل ، محدودة ، بشكل متقطع\nC. خارج الشركة ، غير محدودة ، بشكل متقطع\nD. من الداخل ، غير محدودة ، بشكل مستقل\nإجابة: A\n\nسؤال: ما هي الأساليب التي يمكن للمدير الأمني الذي يسعى لتحقيق أهدافه الاختيار بينها؟\nA. العمل المباشر الغير عنيف ، العمل المباشر العنيف ، العمل غير المباشر ، الحملة الدعائية\nB. العمل غير المباشر ، العمل الأوتيل ، العمل المباشر الغير عنيف ، الحملة الإعلامية\nC. العمل غير المباشر ، العمل المباشر العنيف ، العمل المباشر غير العنيف المباشر ، الحملة الدعائية\nD. العمل المباشر الغير عنيف ، العمل الأوتيل ، العمل غير المباشر ، الحملة الإعلامية\nإجابة: C\n\nسؤال: على عكس _______ ، تهدف _______ إلى مكافأة السلوك الإيجابي للشركات. تم تعزيز نجاح مثل هذه الحملات من خلال استخدام ___________, الذي يتيح للحملات تيسير تحقيق الشركة لــ _________ .\nA. الحملات الاستهلاكية، الحملات الاستهلاكية العامة، تكنولوجيا سلسلة الكتل، التبرعات الخيرية\nB. الحملات التحفيزية، الحملات الاستهلاكية العامة، التكنولوجيا الرقمية، زيادة المبيعات\nC. الحملات الاستهلاكية، الحملات الشرائية، تكنولوجيا سلسلة الكتل، التبرعات الخيرية\nD. المقاطعات، الحملات التحفيزية، الحملات الرقمية، زيادة المبيعات\nإجابة: D\n\nسؤال: تُصبح _______ مثل البيتكوين أكثر انتشارًا وتحمل مجموعة كبيرة من الآثار الأخلاقية المرتبطة بها، على سبيل المثال، إنها _______ وأكثر _______. ومع ذلك، تم استخدامها أيضًا للمشاركة في _______.\nA. العملات الرقمية، مكلفة، آمنة، جرائم مالية\nB. العملات التقليدية، رخيصة، غير آمنة، العطاء الخيري\nC. العملات الرقمية، رخيصة، آمنة، جرائم مالية\nD. العملات التقليدية، مكلفة، غير آمنة، العطاء الخيري\nإجابة:"
* <b>output:</b>
"A\n\nسؤال: _______ هو"
# Reference
```
@article{zhu2025second,
title={Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion},
author={Zhu, Jianqing and Huang, Huang and Lin, Zhihang and Liang, Juhao and Tang, Zhengyang and Almubarak, Khalid and Alharthi, Mosen and An, Bang and He, Juncai and Wu, Xiangbo and Yu, Fei and Chen, Junying and Ma, Zhuoheng and Du, Yuhao and Hu, Yan and Zhang, He and Alghamdi, Emad A. and Zhang, Lian and Sun, Ruoyu and Li, Haizhou and Wang, Benyou and Xu, Jinchao},
journal={ACL 2025},
year={2025}
}
```
|
Jansenhbar/bert_cased_dummy-model
|
Jansenhbar
| 2025-08-19T08:28:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-19T08:27:46Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Suprim003/a2c-PandaPickAndPlace-v3
|
Suprim003
| 2025-08-19T08:26:10Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-19T08:20:29Z |
---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -45.00 +/- 15.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hoan17/saving_LOe400s16_scratch_8
|
hoan17
| 2025-08-19T08:25:31Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"trl",
"o2o",
"reinforcement-learning",
"text-to-image",
"stable-diffusion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-08-19T08:25:02Z |
---
license: apache-2.0
tags:
- trl
- o2o
- diffusers
- reinforcement-learning
- text-to-image
- stable-diffusion
---
# TRL O2O Model
This is a diffusion model that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for image generation conditioned with text.
|
Jansenhbar/dummy-model
|
Jansenhbar
| 2025-08-19T08:24:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-19T08:23:58Z |
---
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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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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]
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## Technical Specifications [optional]
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|
Avinyaa12/outputs
|
Avinyaa12
| 2025-08-19T08:13:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Avinyaa12/humanizer-v1.0",
"base_model:finetune:Avinyaa12/humanizer-v1.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T08:13:09Z |
---
base_model: Avinyaa12/humanizer-v1.0
library_name: transformers
model_name: outputs
tags:
- generated_from_trainer
- unsloth
- trl
- dpo
licence: license
---
# Model Card for outputs
This model is a fine-tuned version of [Avinyaa12/humanizer-v1.0](https://huggingface.co/Avinyaa12/humanizer-v1.0).
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="Avinyaa12/outputs", 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 DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755590768
|
0xaoyama
| 2025-08-19T08:06:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:06:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755589162
|
hakimjustbao
| 2025-08-19T08:06:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:06:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
donoway/ARC-Easy_Llama-3.2-1B-5mt5dppa
|
donoway
| 2025-08-19T08:06:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T07:54:21Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ARC-Easy_Llama-3.2-1B-5mt5dppa
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. -->
# ARC-Easy_Llama-3.2-1B-5mt5dppa
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9215
- Model Preparation Time: 0.0061
- Mdl: 2402.4236
- Accumulated Loss: 1665.2332
- Correct Preds: 308.0
- Total Preds: 570.0
- Accuracy: 0.5404
- Correct Gen Preds: 303.0
- Gen Accuracy: 0.5316
- Correct Gen Preds 32: 131.0
- Correct Preds 32: 133.0
- Total Labels 32: 158.0
- Accuracy 32: 0.8418
- Gen Accuracy 32: 0.8291
- Correct Gen Preds 33: 92.0
- Correct Preds 33: 92.0
- Total Labels 33: 152.0
- Accuracy 33: 0.6053
- Gen Accuracy 33: 0.6053
- Correct Gen Preds 34: 48.0
- Correct Preds 34: 51.0
- Total Labels 34: 142.0
- Accuracy 34: 0.3592
- Gen Accuracy 34: 0.3380
- Correct Gen Preds 35: 32.0
- Correct Preds 35: 32.0
- Total Labels 35: 118.0
- Accuracy 35: 0.2712
- Gen Accuracy 35: 0.2712
- Correct Gen Preds 36: 0.0
- Correct Preds 36: 0.0
- Total Labels 36: 0.0
- Accuracy 36: 0.0
- Gen Accuracy 36: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 112
- 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
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|
| No log | 0 | 0 | 1.5354 | 0.0061 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4937 | 1.0 | 1 | 1.5354 | 0.0061 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4937 | 2.0 | 2 | 2.3482 | 0.0061 | 1931.0498 | 1338.5017 | 180.0 | 570.0 | 0.3158 | 180.0 | 0.3158 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 147.0 | 147.0 | 152.0 | 0.9671 | 0.9671 | 33.0 | 33.0 | 142.0 | 0.2324 | 0.2324 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.8947 | 3.0 | 3 | 1.2963 | 0.0061 | 1066.0121 | 738.9033 | 210.0 | 570.0 | 0.3684 | 210.0 | 0.3684 | 4.0 | 4.0 | 158.0 | 0.0253 | 0.0253 | 136.0 | 136.0 | 152.0 | 0.8947 | 0.8947 | 45.0 | 45.0 | 142.0 | 0.3169 | 0.3169 | 25.0 | 25.0 | 118.0 | 0.2119 | 0.2119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.4263 | 4.0 | 4 | 1.8419 | 0.0061 | 1514.6749 | 1049.8927 | 302.0 | 570.0 | 0.5298 | 295.0 | 0.5175 | 114.0 | 119.0 | 158.0 | 0.7532 | 0.7215 | 99.0 | 100.0 | 152.0 | 0.6579 | 0.6513 | 58.0 | 59.0 | 142.0 | 0.4155 | 0.4085 | 24.0 | 24.0 | 118.0 | 0.2034 | 0.2034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0068 | 5.0 | 5 | 2.9215 | 0.0061 | 2402.4236 | 1665.2332 | 308.0 | 570.0 | 0.5404 | 303.0 | 0.5316 | 131.0 | 133.0 | 158.0 | 0.8418 | 0.8291 | 92.0 | 92.0 | 152.0 | 0.6053 | 0.6053 | 48.0 | 51.0 | 142.0 | 0.3592 | 0.3380 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 6.0 | 6 | 3.5582 | 0.0061 | 2926.0069 | 2028.1535 | 301.0 | 570.0 | 0.5281 | 298.0 | 0.5228 | 131.0 | 133.0 | 158.0 | 0.8418 | 0.8291 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 43.0 | 44.0 | 142.0 | 0.3099 | 0.3028 | 33.0 | 33.0 | 118.0 | 0.2797 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 7.0 | 7 | 3.9096 | 0.0061 | 3215.0252 | 2228.4857 | 300.0 | 570.0 | 0.5263 | 296.0 | 0.5193 | 132.0 | 133.0 | 158.0 | 0.8418 | 0.8354 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 43.0 | 45.0 | 142.0 | 0.3169 | 0.3028 | 36.0 | 37.0 | 118.0 | 0.3136 | 0.3051 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 8.0 | 8 | 4.1872 | 0.0061 | 3443.3114 | 2386.7216 | 290.0 | 570.0 | 0.5088 | 287.0 | 0.5035 | 132.0 | 133.0 | 158.0 | 0.8418 | 0.8354 | 81.0 | 81.0 | 152.0 | 0.5329 | 0.5329 | 41.0 | 42.0 | 142.0 | 0.2958 | 0.2887 | 33.0 | 34.0 | 118.0 | 0.2881 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 9.0 | 9 | 4.3866 | 0.0061 | 3607.2532 | 2500.3574 | 287.0 | 570.0 | 0.5035 | 284.0 | 0.4982 | 131.0 | 132.0 | 158.0 | 0.8354 | 0.8291 | 77.0 | 77.0 | 152.0 | 0.5066 | 0.5066 | 42.0 | 43.0 | 142.0 | 0.3028 | 0.2958 | 34.0 | 35.0 | 118.0 | 0.2966 | 0.2881 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 10 | 4.5479 | 0.0061 | 3739.8815 | 2592.2883 | 283.0 | 570.0 | 0.4965 | 280.0 | 0.4912 | 129.0 | 130.0 | 158.0 | 0.8228 | 0.8165 | 76.0 | 76.0 | 152.0 | 0.5 | 0.5 | 41.0 | 42.0 | 142.0 | 0.2958 | 0.2887 | 34.0 | 35.0 | 118.0 | 0.2966 | 0.2881 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 11 | 4.6682 | 0.0061 | 3838.8254 | 2660.8710 | 273.0 | 570.0 | 0.4789 | 269.0 | 0.4719 | 130.0 | 131.0 | 158.0 | 0.8291 | 0.8228 | 70.0 | 71.0 | 152.0 | 0.4671 | 0.4605 | 40.0 | 41.0 | 142.0 | 0.2887 | 0.2817 | 29.0 | 30.0 | 118.0 | 0.2542 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 12 | 4.7724 | 0.0061 | 3924.4982 | 2720.2549 | 276.0 | 570.0 | 0.4842 | 272.0 | 0.4772 | 128.0 | 129.0 | 158.0 | 0.8165 | 0.8101 | 71.0 | 72.0 | 152.0 | 0.4737 | 0.4671 | 42.0 | 43.0 | 142.0 | 0.3028 | 0.2958 | 31.0 | 32.0 | 118.0 | 0.2712 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 13 | 4.8363 | 0.0061 | 3977.0494 | 2756.6806 | 273.0 | 570.0 | 0.4789 | 269.0 | 0.4719 | 128.0 | 129.0 | 158.0 | 0.8165 | 0.8101 | 71.0 | 72.0 | 152.0 | 0.4737 | 0.4671 | 39.0 | 40.0 | 142.0 | 0.2817 | 0.2746 | 31.0 | 32.0 | 118.0 | 0.2712 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 14.0 | 14 | 4.8780 | 0.0061 | 4011.3733 | 2780.4721 | 274.0 | 570.0 | 0.4807 | 270.0 | 0.4737 | 129.0 | 130.0 | 158.0 | 0.8228 | 0.8165 | 70.0 | 71.0 | 152.0 | 0.4671 | 0.4605 | 42.0 | 43.0 | 142.0 | 0.3028 | 0.2958 | 29.0 | 30.0 | 118.0 | 0.2542 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 15.0 | 15 | 4.9164 | 0.0061 | 4042.9142 | 2802.3346 | 276.0 | 570.0 | 0.4842 | 270.0 | 0.4737 | 130.0 | 132.0 | 158.0 | 0.8354 | 0.8228 | 68.0 | 70.0 | 152.0 | 0.4605 | 0.4474 | 42.0 | 43.0 | 142.0 | 0.3028 | 0.2958 | 30.0 | 31.0 | 118.0 | 0.2627 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 16.0 | 16 | 4.9745 | 0.0061 | 4090.7148 | 2835.4674 | 279.0 | 570.0 | 0.4895 | 274.0 | 0.4807 | 131.0 | 132.0 | 158.0 | 0.8354 | 0.8291 | 70.0 | 72.0 | 152.0 | 0.4737 | 0.4605 | 43.0 | 44.0 | 142.0 | 0.3099 | 0.3028 | 30.0 | 31.0 | 118.0 | 0.2627 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 17.0 | 17 | 4.9563 | 0.0061 | 4075.7319 | 2825.0820 | 274.0 | 570.0 | 0.4807 | 268.0 | 0.4702 | 131.0 | 133.0 | 158.0 | 0.8418 | 0.8291 | 67.0 | 69.0 | 152.0 | 0.4539 | 0.4408 | 42.0 | 43.0 | 142.0 | 0.3028 | 0.2958 | 28.0 | 29.0 | 118.0 | 0.2458 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 18.0 | 18 | 5.0077 | 0.0061 | 4118.0416 | 2854.4089 | 274.0 | 570.0 | 0.4807 | 269.0 | 0.4719 | 130.0 | 131.0 | 158.0 | 0.8291 | 0.8228 | 67.0 | 69.0 | 152.0 | 0.4539 | 0.4408 | 41.0 | 42.0 | 142.0 | 0.2958 | 0.2887 | 31.0 | 32.0 | 118.0 | 0.2712 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 19.0 | 19 | 5.0084 | 0.0061 | 4118.5554 | 2854.7651 | 277.0 | 570.0 | 0.4860 | 272.0 | 0.4772 | 131.0 | 133.0 | 158.0 | 0.8418 | 0.8291 | 68.0 | 70.0 | 152.0 | 0.4605 | 0.4474 | 41.0 | 42.0 | 142.0 | 0.2958 | 0.2887 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 20.0 | 20 | 5.0498 | 0.0061 | 4152.6253 | 2878.3805 | 272.0 | 570.0 | 0.4772 | 266.0 | 0.4667 | 130.0 | 132.0 | 158.0 | 0.8354 | 0.8228 | 65.0 | 67.0 | 152.0 | 0.4408 | 0.4276 | 41.0 | 43.0 | 142.0 | 0.3028 | 0.2887 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 21.0 | 21 | 5.0444 | 0.0061 | 4148.1850 | 2875.3027 | 272.0 | 570.0 | 0.4772 | 267.0 | 0.4684 | 128.0 | 130.0 | 158.0 | 0.8228 | 0.8101 | 68.0 | 69.0 | 152.0 | 0.4539 | 0.4474 | 41.0 | 43.0 | 142.0 | 0.3028 | 0.2887 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 22.0 | 22 | 5.0741 | 0.0061 | 4172.5940 | 2892.2218 | 273.0 | 570.0 | 0.4789 | 268.0 | 0.4702 | 131.0 | 133.0 | 158.0 | 0.8418 | 0.8291 | 68.0 | 69.0 | 152.0 | 0.4539 | 0.4474 | 39.0 | 41.0 | 142.0 | 0.2887 | 0.2746 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 23.0 | 23 | 5.0717 | 0.0061 | 4170.6084 | 2890.8454 | 269.0 | 570.0 | 0.4719 | 265.0 | 0.4649 | 131.0 | 132.0 | 158.0 | 0.8354 | 0.8291 | 67.0 | 68.0 | 152.0 | 0.4474 | 0.4408 | 39.0 | 41.0 | 142.0 | 0.2887 | 0.2746 | 28.0 | 28.0 | 118.0 | 0.2373 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 24.0 | 24 | 5.0772 | 0.0061 | 4175.1596 | 2894.0001 | 271.0 | 570.0 | 0.4754 | 267.0 | 0.4684 | 130.0 | 132.0 | 158.0 | 0.8354 | 0.8228 | 68.0 | 69.0 | 152.0 | 0.4539 | 0.4474 | 41.0 | 42.0 | 142.0 | 0.2958 | 0.2887 | 28.0 | 28.0 | 118.0 | 0.2373 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 25.0 | 25 | 5.0807 | 0.0061 | 4178.0271 | 2895.9877 | 274.0 | 570.0 | 0.4807 | 269.0 | 0.4719 | 131.0 | 132.0 | 158.0 | 0.8354 | 0.8291 | 68.0 | 70.0 | 152.0 | 0.4605 | 0.4474 | 41.0 | 43.0 | 142.0 | 0.3028 | 0.2887 | 29.0 | 29.0 | 118.0 | 0.2458 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 26.0 | 26 | 5.0887 | 0.0061 | 4184.5999 | 2900.5436 | 273.0 | 570.0 | 0.4789 | 269.0 | 0.4719 | 129.0 | 131.0 | 158.0 | 0.8291 | 0.8165 | 68.0 | 69.0 | 152.0 | 0.4539 | 0.4474 | 42.0 | 43.0 | 142.0 | 0.3028 | 0.2958 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 27.0 | 27 | 5.1016 | 0.0061 | 4195.2065 | 2907.8956 | 274.0 | 570.0 | 0.4807 | 269.0 | 0.4719 | 131.0 | 133.0 | 158.0 | 0.8418 | 0.8291 | 67.0 | 68.0 | 152.0 | 0.4474 | 0.4408 | 41.0 | 43.0 | 142.0 | 0.3028 | 0.2887 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 28.0 | 28 | 5.0987 | 0.0061 | 4192.8580 | 2906.2677 | 272.0 | 570.0 | 0.4772 | 267.0 | 0.4684 | 130.0 | 132.0 | 158.0 | 0.8354 | 0.8228 | 65.0 | 66.0 | 152.0 | 0.4342 | 0.4276 | 40.0 | 42.0 | 142.0 | 0.2958 | 0.2817 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 29.0 | 29 | 5.1162 | 0.0061 | 4207.2142 | 2916.2187 | 273.0 | 570.0 | 0.4789 | 268.0 | 0.4702 | 129.0 | 130.0 | 158.0 | 0.8228 | 0.8165 | 68.0 | 70.0 | 152.0 | 0.4605 | 0.4474 | 40.0 | 42.0 | 142.0 | 0.2958 | 0.2817 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 30.0 | 30 | 5.0750 | 0.0061 | 4173.3842 | 2892.7695 | 272.0 | 570.0 | 0.4772 | 268.0 | 0.4702 | 131.0 | 133.0 | 158.0 | 0.8418 | 0.8291 | 68.0 | 68.0 | 152.0 | 0.4474 | 0.4474 | 39.0 | 41.0 | 142.0 | 0.2887 | 0.2746 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 31.0 | 31 | 5.1310 | 0.0061 | 4219.4292 | 2924.6855 | 272.0 | 570.0 | 0.4772 | 269.0 | 0.4719 | 129.0 | 130.0 | 158.0 | 0.8228 | 0.8165 | 68.0 | 68.0 | 152.0 | 0.4474 | 0.4474 | 41.0 | 43.0 | 142.0 | 0.3028 | 0.2887 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 32.0 | 32 | 5.1052 | 0.0061 | 4198.1617 | 2909.9440 | 274.0 | 570.0 | 0.4807 | 271.0 | 0.4754 | 131.0 | 132.0 | 158.0 | 0.8354 | 0.8291 | 67.0 | 67.0 | 152.0 | 0.4408 | 0.4408 | 41.0 | 43.0 | 142.0 | 0.3028 | 0.2887 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 33.0 | 33 | 5.0988 | 0.0061 | 4192.9476 | 2906.3298 | 269.0 | 570.0 | 0.4719 | 265.0 | 0.4649 | 130.0 | 131.0 | 158.0 | 0.8291 | 0.8228 | 67.0 | 68.0 | 152.0 | 0.4474 | 0.4408 | 39.0 | 41.0 | 142.0 | 0.2887 | 0.2746 | 29.0 | 29.0 | 118.0 | 0.2458 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 34.0 | 34 | 5.1056 | 0.0061 | 4198.5479 | 2910.2117 | 271.0 | 570.0 | 0.4754 | 266.0 | 0.4667 | 130.0 | 132.0 | 158.0 | 0.8354 | 0.8228 | 66.0 | 67.0 | 152.0 | 0.4408 | 0.4342 | 41.0 | 43.0 | 142.0 | 0.3028 | 0.2887 | 29.0 | 29.0 | 118.0 | 0.2458 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 35.0 | 35 | 5.1180 | 0.0061 | 4208.7385 | 2917.2753 | 269.0 | 570.0 | 0.4719 | 265.0 | 0.4649 | 130.0 | 131.0 | 158.0 | 0.8291 | 0.8228 | 66.0 | 67.0 | 152.0 | 0.4408 | 0.4342 | 39.0 | 41.0 | 142.0 | 0.2887 | 0.2746 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
KCS97/dog2
|
KCS97
| 2025-08-19T08:02:17Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-08-19T07:52:32Z |
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of sks dog
tags:
- text-to-image
- dreambooth
- 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. -->
# DreamBooth - KCS97/dog2
This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## 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]
|
Andreyko22/blockassist-bc-fleecy_solitary_alligator_1755589493
|
Andreyko22
| 2025-08-19T07:59:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fleecy solitary alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T07:59:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fleecy solitary alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755590184
|
0xaoyama
| 2025-08-19T07:56:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T07:56:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GradientResearch/Qwen3-32B-LoRA-ECHO-KK-GRPO
|
GradientResearch
| 2025-08-19T07:54:50Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2508.05387",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-18T12:33:37Z |
---
license: apache-2.0
pipeline_tag: text-generation
---
# Model Card for Qwen3-32B-LoRA-ECHO-KK-GRPO
<!-- Provide a quick summary of what the model is/does. -->
Based on Qwen3-32B, we applied the ECHO framework to perform LoRA fine-tuning on the KK dataset.
Ultimately, it achieved near-perfect scores on the 2–8 PPL test set, surpassing o4-mini, DeepSeek-R1, and o3-mini-high.
Tabel 3: Model performance on K&K logic puzzle task across different degrees of difficulty
| model | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|----------------|---------------------------:|--------------------------:|--------------------------:|--------------------:|------------:|-------------:|-------------:|
| Qwen3-32B | 0.98 | 0.99 | 0.98 | 0.99 | 0.98 | 0.96 |0.95 |
| Deepseek-R1 | 1.00 | 0.97 | 0.95 | 0.93 | 0.91 | 0.93 |0.91 |
| o3-mini-high | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 0.98 |0.98 |
| o4-mini | 1.00 | 1.00 | 0.96 | 0.94 | 0.97 | 0.93 |0.87 |
| Qwen3-32B-Echo(GRPO w/Lora) | 0.99 | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 |0.99 |
# Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "GradientResearch/Qwen3-32B-LoRA-ECHO-KK-GRPO"# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "K & K"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking contenttry:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
# Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{xiao2025echodecouplinginferencetraining,
title={Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms},
author={Jie Xiao and Changyuan Fan and Qingnan Ren and Alfred Long and Yuchen Zhang and Rymon Yu and Eric Yang and Lynn Ai and Shaoduo Gan},
year={2025},
eprint={2508.05387},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.05387},
}
```
|
GradientResearch/Qwen2.5-7B-ECHO-MATH-GRPO
|
GradientResearch
| 2025-08-19T07:52:46Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2508.05387",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-18T12:29:51Z |
---
license: apache-2.0
pipeline_tag: text-generation
---
# Model Card for Qwen2.5-7B-ECHO-MATH-GRPO
Based on Qwen2.5-7B, we trained the model with the ECHO framework using GRPO on the Eurus-2-RL-Math dataset.
It outperformed the Qwen2.5-32B on all six test datasets, achieving a 12% improvement on average.
Tabel 2: Model performance on math reasoning tasks. For AIME and AMC, the results are avg. @32
| model | AIME24 | AIME25 | AMC | MATH-500 | OlympiadBench | Minerva | Avg. |
|----------------|---------------------------:|--------------------------:|--------------------------:|--------------------:|------------:|-------------:|-------------:|
| Qwen2.5-7B | 2.7 | 1.9 | 22.0 | 44.6 | 19.7 | 20.9 |18.6 |
| Qwen2.5-32B | 5.3 | 2.1 | 27.9 | 62.4 | 25.4 | 33.5 |26.1 |
| Qwen2.5-32B-ECHO(GRPO) | 13.1 | 6.9 | 45.6 | 75.4 | 37.0 | 50.7 |38.1 |
# Quick start
```python
from transformers import pipeline
question = "math"
generator = pipeline("text-generation", model="GradientResearch/Qwen2.5-7B-ECHO-MATH-GRPO", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
# Citation
If you find our work helpful, feel free to give us a cite.
```bibtex
@misc{xiao2025echodecouplinginferencetraining,
title={Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms},
author={Jie Xiao and Changyuan Fan and Qingnan Ren and Alfred Long and Yuchen Zhang and Rymon Yu and Eric Yang and Lynn Ai and Shaoduo Gan},
year={2025},
eprint={2508.05387},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.05387},
}
```
|
Alonc/device_to_cve_4bit
|
Alonc
| 2025-08-19T07:52:34Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"region:us"
] | null | 2025-08-18T15:19:30Z |
The model is 16-bit the 4bit is a typo!!!!
---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Alonc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-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)
|
UnarineLeo/mms-test
|
UnarineLeo
| 2025-08-19T07:51:12Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/mms-300m",
"base_model:finetune:facebook/mms-300m",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-12T11:42:40Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-test
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. -->
# mms-test
This model is a fine-tuned version of [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4772
- Wer: 1.0
- Cer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 100
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 7.7574 | 0.1060 | 250 | 4.5904 | 0.9965 | 0.9971 |
| 3.8069 | 0.2121 | 500 | 3.5090 | 0.9953 | 0.9959 |
| 3.6251 | 0.3181 | 750 | 3.4886 | 1.0 | 1.0 |
| 3.5094 | 0.4242 | 1000 | 3.4726 | 1.0 | 1.0 |
| 3.3305 | 0.5302 | 1250 | 3.4475 | 1.0 | 1.0 |
| 3.3564 | 0.6363 | 1500 | 3.4531 | 1.0 | 1.0 |
| 3.4263 | 0.7423 | 1750 | 3.4626 | 1.0 | 1.0 |
| 3.3404 | 0.8484 | 2000 | 3.4772 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.52.0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755589772
|
IvanJAjebu
| 2025-08-19T07:51:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T07:50:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VoilaRaj/78_VzmMfc
|
VoilaRaj
| 2025-08-19T07:48:58Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T07:45:03Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
kjydb/lerobot_test_152
|
kjydb
| 2025-08-19T07:47:19Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:kjydb/lerobot_test_152",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-19T07:46:50Z |
---
base_model: lerobot/smolvla_base
datasets: kjydb/lerobot_test_152
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- lerobot
- robotics
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
* **License:** apache-2.0
|
donoway/BoolQ_Llama-3.2-1B-eszatdiq
|
donoway
| 2025-08-19T07:46:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T07:26:40Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BoolQ_Llama-3.2-1B-eszatdiq
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. -->
# BoolQ_Llama-3.2-1B-eszatdiq
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5571
- Model Preparation Time: 0.0056
- Mdl: 12063.3522
- Accumulated Loss: 8361.6786
- Correct Preds: 2256.0
- Total Preds: 3270.0
- Accuracy: 0.6899
- Correct Gen Preds: 2181.0
- Gen Accuracy: 0.6670
- Correct Gen Preds 9642: 1467.0
- Correct Preds 9642: 1519.0
- Total Labels 9642: 2026.0
- Accuracy 9642: 0.7498
- Gen Accuracy 9642: 0.7241
- Correct Gen Preds 2822: 706.0
- Correct Preds 2822: 737.0
- Total Labels 2822: 1231.0
- Accuracy 2822: 0.5987
- Gen Accuracy 2822: 0.5735
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 120
- 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
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 9642 | Correct Preds 9642 | Total Labels 9642 | Accuracy 9642 | Gen Accuracy 9642 | Correct Gen Preds 2822 | Correct Preds 2822 | Total Labels 2822 | Accuracy 2822 | Gen Accuracy 2822 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:----------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:----------------------:|:------------------:|:-----------------:|:-------------:|:-----------------:|:----------------------:|:------------------:|:-----------------:|:-------------:|:-----------------:|
| No log | 0 | 0 | 0.7080 | 0.0056 | 3339.8933 | 2315.0376 | 2032.0 | 3270.0 | 0.6214 | 2040.0 | 0.6239 | 2007.0 | 2008.0 | 2026.0 | 0.9911 | 0.9906 | 24.0 | 24.0 | 1231.0 | 0.0195 | 0.0195 |
| 0.8982 | 1.0 | 3 | 0.8052 | 0.0056 | 3798.5080 | 2632.9251 | 1559.0 | 3270.0 | 0.4768 | 1467.0 | 0.4486 | 337.0 | 372.0 | 2026.0 | 0.1836 | 0.1663 | 1121.0 | 1187.0 | 1231.0 | 0.9643 | 0.9106 |
| 0.3133 | 2.0 | 6 | 0.6938 | 0.0056 | 3273.0467 | 2268.7031 | 2128.0 | 3270.0 | 0.6508 | 1815.0 | 0.5550 | 1609.0 | 1865.0 | 2026.0 | 0.9205 | 0.7942 | 197.0 | 263.0 | 1231.0 | 0.2136 | 0.1600 |
| 0.0233 | 3.0 | 9 | 0.7795 | 0.0056 | 3677.5836 | 2549.1067 | 2216.0 | 3270.0 | 0.6777 | 2161.0 | 0.6609 | 1362.0 | 1401.0 | 2026.0 | 0.6915 | 0.6723 | 790.0 | 815.0 | 1231.0 | 0.6621 | 0.6418 |
| 0.0001 | 4.0 | 12 | 2.6272 | 0.0056 | 12394.1502 | 8590.9703 | 2192.0 | 3270.0 | 0.6703 | 2195.0 | 0.6713 | 1973.0 | 1977.0 | 2026.0 | 0.9758 | 0.9738 | 214.0 | 215.0 | 1231.0 | 0.1747 | 0.1738 |
| 0.0025 | 5.0 | 15 | 2.5778 | 0.0056 | 12161.0922 | 8429.4268 | 2237.0 | 3270.0 | 0.6841 | 2227.0 | 0.6810 | 1771.0 | 1782.0 | 2026.0 | 0.8796 | 0.8741 | 448.0 | 455.0 | 1231.0 | 0.3696 | 0.3639 |
| 0.0 | 6.0 | 18 | 2.5571 | 0.0056 | 12063.3522 | 8361.6786 | 2256.0 | 3270.0 | 0.6899 | 2181.0 | 0.6670 | 1467.0 | 1519.0 | 2026.0 | 0.7498 | 0.7241 | 706.0 | 737.0 | 1231.0 | 0.5987 | 0.5735 |
| 0.0 | 7.0 | 21 | 2.6065 | 0.0056 | 12296.3865 | 8523.2057 | 2192.0 | 3270.0 | 0.6703 | 1996.0 | 0.6104 | 1273.0 | 1402.0 | 2026.0 | 0.6920 | 0.6283 | 715.0 | 790.0 | 1231.0 | 0.6418 | 0.5808 |
| 0.0001 | 8.0 | 24 | 2.6148 | 0.0056 | 12335.8294 | 8550.5454 | 2175.0 | 3270.0 | 0.6651 | 1910.0 | 0.5841 | 1210.0 | 1395.0 | 2026.0 | 0.6885 | 0.5972 | 692.0 | 780.0 | 1231.0 | 0.6336 | 0.5621 |
| 0.0 | 9.0 | 27 | 2.6483 | 0.0056 | 12493.8025 | 8660.0440 | 2170.0 | 3270.0 | 0.6636 | 1920.0 | 0.5872 | 1220.0 | 1396.0 | 2026.0 | 0.6890 | 0.6022 | 691.0 | 774.0 | 1231.0 | 0.6288 | 0.5613 |
| 0.0001 | 10.0 | 30 | 2.6828 | 0.0056 | 12656.5201 | 8772.8312 | 2177.0 | 3270.0 | 0.6657 | 1963.0 | 0.6003 | 1255.0 | 1400.0 | 2026.0 | 0.6910 | 0.6194 | 700.0 | 777.0 | 1231.0 | 0.6312 | 0.5686 |
| 0.0001 | 11.0 | 33 | 2.7214 | 0.0056 | 12838.5669 | 8899.0164 | 2171.0 | 3270.0 | 0.6639 | 2013.0 | 0.6156 | 1279.0 | 1393.0 | 2026.0 | 0.6876 | 0.6313 | 725.0 | 778.0 | 1231.0 | 0.6320 | 0.5890 |
| 0.0 | 12.0 | 36 | 2.7415 | 0.0056 | 12933.2785 | 8964.6655 | 2169.0 | 3270.0 | 0.6633 | 2035.0 | 0.6223 | 1301.0 | 1393.0 | 2026.0 | 0.6876 | 0.6422 | 726.0 | 776.0 | 1231.0 | 0.6304 | 0.5898 |
| 0.0 | 13.0 | 39 | 2.7593 | 0.0056 | 13017.3006 | 9022.9052 | 2172.0 | 3270.0 | 0.6642 | 2056.0 | 0.6287 | 1313.0 | 1395.0 | 2026.0 | 0.6885 | 0.6481 | 734.0 | 777.0 | 1231.0 | 0.6312 | 0.5963 |
| 0.0 | 14.0 | 42 | 2.7708 | 0.0056 | 13071.4073 | 9060.4091 | 2167.0 | 3270.0 | 0.6627 | 2066.0 | 0.6318 | 1322.0 | 1393.0 | 2026.0 | 0.6876 | 0.6525 | 736.0 | 774.0 | 1231.0 | 0.6288 | 0.5979 |
| 0.0 | 15.0 | 45 | 2.7767 | 0.0056 | 13099.2616 | 9079.7162 | 2168.0 | 3270.0 | 0.6630 | 2068.0 | 0.6324 | 1320.0 | 1392.0 | 2026.0 | 0.6871 | 0.6515 | 740.0 | 776.0 | 1231.0 | 0.6304 | 0.6011 |
| 0.0 | 16.0 | 48 | 2.7824 | 0.0056 | 13126.3414 | 9098.4865 | 2169.0 | 3270.0 | 0.6633 | 2077.0 | 0.6352 | 1325.0 | 1391.0 | 2026.0 | 0.6866 | 0.6540 | 743.0 | 778.0 | 1231.0 | 0.6320 | 0.6036 |
| 0.0 | 17.0 | 51 | 2.7841 | 0.0056 | 13134.4015 | 9104.0734 | 2165.0 | 3270.0 | 0.6621 | 2078.0 | 0.6355 | 1328.0 | 1392.0 | 2026.0 | 0.6871 | 0.6555 | 742.0 | 773.0 | 1231.0 | 0.6279 | 0.6028 |
| 0.0 | 18.0 | 54 | 2.7872 | 0.0056 | 13148.8380 | 9114.0800 | 2171.0 | 3270.0 | 0.6639 | 2082.0 | 0.6367 | 1331.0 | 1397.0 | 2026.0 | 0.6895 | 0.6570 | 742.0 | 774.0 | 1231.0 | 0.6288 | 0.6028 |
| 0.0 | 19.0 | 57 | 2.7901 | 0.0056 | 13162.4860 | 9123.5401 | 2171.0 | 3270.0 | 0.6639 | 2081.0 | 0.6364 | 1327.0 | 1393.0 | 2026.0 | 0.6876 | 0.6550 | 745.0 | 778.0 | 1231.0 | 0.6320 | 0.6052 |
| 0.0 | 20.0 | 60 | 2.7935 | 0.0056 | 13178.7743 | 9134.8302 | 2172.0 | 3270.0 | 0.6642 | 2083.0 | 0.6370 | 1330.0 | 1398.0 | 2026.0 | 0.6900 | 0.6565 | 745.0 | 774.0 | 1231.0 | 0.6288 | 0.6052 |
| 0.0 | 21.0 | 63 | 2.7929 | 0.0056 | 13175.9740 | 9132.8892 | 2167.0 | 3270.0 | 0.6627 | 2080.0 | 0.6361 | 1328.0 | 1393.0 | 2026.0 | 0.6876 | 0.6555 | 743.0 | 774.0 | 1231.0 | 0.6288 | 0.6036 |
| 0.0 | 22.0 | 66 | 2.7951 | 0.0056 | 13186.0428 | 9139.8684 | 2175.0 | 3270.0 | 0.6651 | 2087.0 | 0.6382 | 1331.0 | 1397.0 | 2026.0 | 0.6895 | 0.6570 | 748.0 | 778.0 | 1231.0 | 0.6320 | 0.6076 |
| 0.0 | 23.0 | 69 | 2.7974 | 0.0056 | 13196.9785 | 9147.4485 | 2171.0 | 3270.0 | 0.6639 | 2089.0 | 0.6388 | 1330.0 | 1394.0 | 2026.0 | 0.6881 | 0.6565 | 751.0 | 777.0 | 1231.0 | 0.6312 | 0.6101 |
| 0.0 | 24.0 | 72 | 2.7988 | 0.0056 | 13203.5576 | 9152.0087 | 2172.0 | 3270.0 | 0.6642 | 2089.0 | 0.6388 | 1333.0 | 1395.0 | 2026.0 | 0.6885 | 0.6579 | 748.0 | 777.0 | 1231.0 | 0.6312 | 0.6076 |
| 0.0 | 25.0 | 75 | 2.8010 | 0.0056 | 13214.0329 | 9159.2696 | 2172.0 | 3270.0 | 0.6642 | 2093.0 | 0.6401 | 1335.0 | 1396.0 | 2026.0 | 0.6890 | 0.6589 | 749.0 | 776.0 | 1231.0 | 0.6304 | 0.6084 |
| 0.0 | 26.0 | 78 | 2.8012 | 0.0056 | 13214.8892 | 9159.8632 | 2174.0 | 3270.0 | 0.6648 | 2088.0 | 0.6385 | 1332.0 | 1397.0 | 2026.0 | 0.6895 | 0.6575 | 748.0 | 777.0 | 1231.0 | 0.6312 | 0.6076 |
| 0.0 | 27.0 | 81 | 2.8035 | 0.0056 | 13225.9128 | 9167.5042 | 2172.0 | 3270.0 | 0.6642 | 2092.0 | 0.6398 | 1333.0 | 1394.0 | 2026.0 | 0.6881 | 0.6579 | 751.0 | 778.0 | 1231.0 | 0.6320 | 0.6101 |
| 0.0 | 28.0 | 84 | 2.8045 | 0.0056 | 13230.6764 | 9170.8061 | 2172.0 | 3270.0 | 0.6642 | 2095.0 | 0.6407 | 1337.0 | 1395.0 | 2026.0 | 0.6885 | 0.6599 | 750.0 | 777.0 | 1231.0 | 0.6312 | 0.6093 |
| 0.0 | 29.0 | 87 | 2.8054 | 0.0056 | 13234.8323 | 9173.6867 | 2171.0 | 3270.0 | 0.6639 | 2090.0 | 0.6391 | 1333.0 | 1396.0 | 2026.0 | 0.6890 | 0.6579 | 749.0 | 775.0 | 1231.0 | 0.6296 | 0.6084 |
| 0.0 | 30.0 | 90 | 2.8060 | 0.0056 | 13237.7898 | 9175.7367 | 2175.0 | 3270.0 | 0.6651 | 2094.0 | 0.6404 | 1335.0 | 1396.0 | 2026.0 | 0.6890 | 0.6589 | 751.0 | 779.0 | 1231.0 | 0.6328 | 0.6101 |
| 0.0 | 31.0 | 93 | 2.8078 | 0.0056 | 13246.1557 | 9181.5355 | 2168.0 | 3270.0 | 0.6630 | 2091.0 | 0.6394 | 1335.0 | 1393.0 | 2026.0 | 0.6876 | 0.6589 | 747.0 | 775.0 | 1231.0 | 0.6296 | 0.6068 |
| 0.0 | 32.0 | 96 | 2.8082 | 0.0056 | 13247.9959 | 9182.8110 | 2169.0 | 3270.0 | 0.6633 | 2095.0 | 0.6407 | 1337.0 | 1393.0 | 2026.0 | 0.6876 | 0.6599 | 749.0 | 776.0 | 1231.0 | 0.6304 | 0.6084 |
| 0.0 | 33.0 | 99 | 2.8077 | 0.0056 | 13245.4286 | 9181.0315 | 2173.0 | 3270.0 | 0.6645 | 2100.0 | 0.6422 | 1338.0 | 1396.0 | 2026.0 | 0.6890 | 0.6604 | 753.0 | 777.0 | 1231.0 | 0.6312 | 0.6117 |
| 0.0 | 34.0 | 102 | 2.8115 | 0.0056 | 13263.6309 | 9193.6484 | 2169.0 | 3270.0 | 0.6633 | 2091.0 | 0.6394 | 1333.0 | 1394.0 | 2026.0 | 0.6881 | 0.6579 | 749.0 | 775.0 | 1231.0 | 0.6296 | 0.6084 |
| 0.0 | 35.0 | 105 | 2.8099 | 0.0056 | 13255.9181 | 9188.3022 | 2174.0 | 3270.0 | 0.6648 | 2095.0 | 0.6407 | 1339.0 | 1397.0 | 2026.0 | 0.6895 | 0.6609 | 748.0 | 777.0 | 1231.0 | 0.6312 | 0.6076 |
| 0.0 | 36.0 | 108 | 2.8103 | 0.0056 | 13258.0305 | 9189.7664 | 2173.0 | 3270.0 | 0.6645 | 2098.0 | 0.6416 | 1339.0 | 1397.0 | 2026.0 | 0.6895 | 0.6609 | 750.0 | 776.0 | 1231.0 | 0.6304 | 0.6093 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755589440
|
IvanJAjebu
| 2025-08-19T07:45:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T07:45:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
minhnguyet/my-dpo-mistral-7b
|
minhnguyet
| 2025-08-19T07:41:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T07:41:13Z |
---
base_model: unsloth/mistral-7b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhnguyet
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755589263
|
0xaoyama
| 2025-08-19T07:41:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T07:41:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ohjoonhee/Qwen2.5-VL-3B-InitialRun-checkpoint-500
|
ohjoonhee
| 2025-08-19T07:40:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-19T07:27:57Z |
---
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]
|
KCS97/cat
|
KCS97
| 2025-08-19T07:40:18Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-08-19T07:30:24Z |
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of sks cat
tags:
- text-to-image
- dreambooth
- 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. -->
# DreamBooth - KCS97/cat
This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks cat using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth 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]
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755589105
|
0xaoyama
| 2025-08-19T07:38:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T07:38:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jinaai/jina-embeddings-v4-vllm-text-matching
|
jinaai
| 2025-08-19T07:36:27Z | 159 | 5 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"vidore",
"colpali",
"multimodal-embedding",
"multilingual-embedding",
"Text-to-Visual Document (T→VD) retrieval",
"feature-extraction",
"sentence-similarity",
"mteb",
"visual-document-retrieval",
"multilingual",
"arxiv:2506.18902",
"text-generation-inference",
"endpoints_compatible",
"region:eu"
] |
visual-document-retrieval
| 2025-07-01T09:45:47Z |
---
tags:
- vidore
- colpali
- multimodal-embedding
- multilingual-embedding
- Text-to-Visual Document (T→VD) retrieval
- feature-extraction
- sentence-similarity
- mteb
language:
- multilingual
library_name: transformers
pipeline_tag: visual-document-retrieval
---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
# Jina Embeddings v4: Universal Embeddings for Multimodal Multilingual Retrieval
[Original Model](https://huggingface.co/jinaai/jina-embeddings-v4) | [Blog](https://jina.ai/news/jina-embeddings-v4-universal-embeddings-for-multimodal-multilingual-retrieval) | [Technical Report](https://arxiv.org/abs/2506.18902) | [API](https://jina.ai/embeddings)
## Model Overview
This repository hosts a vLLM-compatible version of [`jina-embeddings-v4`](https://huggingface.co/jinaai/jina-embeddings-v4) with the **text-matching** adapter merged into the base `Qwen2.5-VL` weights. This architecture modification enables native compatibility with vLLM without requiring custom adapter-handling code.
## Usage
```python
import torch
from PIL import Image
from vllm import LLM
from vllm.config import PoolerConfig
from vllm.inputs.data import TextPrompt
# Initialize model
model = LLM(
model="jinaai/jina-embeddings-v4-vllm-text-matching",
task="embed",
override_pooler_config=PoolerConfig(pooling_type="ALL", normalize=False),
dtype="float16",
)
# Create text prompts
text1 = "Ein wunderschöner Sonnenuntergang am Strand"
text1_prompt = TextPrompt(
prompt=f"Query: {text1}"
)
text2 = "浜辺に沈む美しい夕日"
text2_prompt = TextPrompt(
prompt=f"Query: {text2}"
)
# Create image prompt
image = Image.open("<path_to_image>")
image_prompt = TextPrompt(
prompt="<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n",
multi_modal_data={"image": image},
)
# Encode all prompts
prompts = [text1_prompt, text2_prompt, image_prompt]
outputs = model.encode(prompts)
def get_embeddings(outputs):
VISION_START_TOKEN_ID, VISION_END_TOKEN_ID = 151652, 151653
embeddings = []
for output in outputs:
if VISION_START_TOKEN_ID in output.prompt_token_ids:
# Gather only vision tokens
img_start_pos = torch.where(
torch.tensor(output.prompt_token_ids) == VISION_START_TOKEN_ID
)[0][0]
img_end_pos = torch.where(
torch.tensor(output.prompt_token_ids) == VISION_END_TOKEN_ID
)[0][0]
embeddings_tensor = output.outputs.data.detach().clone()[
img_start_pos : img_end_pos + 1
]
else:
# Use all tokens for text-only prompts
embeddings_tensor = output.outputs.data.detach().clone()
# Pool and normalize embeddings
pooled_output = (
embeddings_tensor.sum(dim=0, dtype=torch.float32)
/ embeddings_tensor.shape[0]
)
embeddings.append(torch.nn.functional.normalize(pooled_output, dim=-1))
return embeddings
embeddings = get_embeddings(outputs)
```
|
jinaai/jina-embeddings-v4-vllm-retrieval
|
jinaai
| 2025-08-19T07:35:41Z | 16,504 | 19 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"vidore",
"colpali",
"multimodal-embedding",
"multilingual-embedding",
"Text-to-Visual Document (T→VD) retrieval",
"feature-extraction",
"sentence-similarity",
"mteb",
"visual-document-retrieval",
"multilingual",
"arxiv:2506.18902",
"text-generation-inference",
"endpoints_compatible",
"region:eu"
] |
visual-document-retrieval
| 2025-07-01T08:30:32Z |
---
tags:
- vidore
- colpali
- multimodal-embedding
- multilingual-embedding
- Text-to-Visual Document (T→VD) retrieval
- feature-extraction
- sentence-similarity
- mteb
language:
- multilingual
library_name: transformers
pipeline_tag: visual-document-retrieval
---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
# Jina Embeddings v4: Universal Embeddings for Multimodal Multilingual Retrieval
[Original Model](https://huggingface.co/jinaai/jina-embeddings-v4) | [Blog](https://jina.ai/news/jina-embeddings-v4-universal-embeddings-for-multimodal-multilingual-retrieval) | [Technical Report](https://arxiv.org/abs/2506.18902) | [API](https://jina.ai/embeddings)
## Model Overview
This repository hosts a vLLM-compatible version of [`jina-embeddings-v4`](https://huggingface.co/jinaai/jina-embeddings-v4) with the **retrieval** adapter merged into the base `Qwen2.5-VL` weights. This architecture modification enables native compatibility with vLLM without requiring custom adapter-handling code.
## Usage
```python
import torch
from PIL import Image
from vllm import LLM
from vllm.config import PoolerConfig
from vllm.inputs.data import TextPrompt
# Initialize model
model = LLM(
model="jinaai/jina-embeddings-v4-vllm-retrieval",
task="embed",
override_pooler_config=PoolerConfig(pooling_type="ALL", normalize=False),
dtype="float16",
)
# Create text prompts
query = "Overview of climate change impacts on coastal cities"
query_prompt = TextPrompt(
prompt=f"Query: {query}"
)
passage = "The impacts of climate change on coastal cities are significant.."
passage_prompt = TextPrompt(
prompt=f"Passage: {passage}"
)
# Create image prompt
image = Image.open("<path_to_image>")
image_prompt = TextPrompt(
prompt="<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n",
multi_modal_data={"image": image},
)
# Encode all prompts
prompts = [query_prompt, passage_prompt, image_prompt]
outputs = model.encode(prompts)
def get_embeddings(outputs):
VISION_START_TOKEN_ID, VISION_END_TOKEN_ID = 151652, 151653
embeddings = []
for output in outputs:
if VISION_START_TOKEN_ID in output.prompt_token_ids:
# Gather only vision tokens
img_start_pos = torch.where(
torch.tensor(output.prompt_token_ids) == VISION_START_TOKEN_ID
)[0][0]
img_end_pos = torch.where(
torch.tensor(output.prompt_token_ids) == VISION_END_TOKEN_ID
)[0][0]
embeddings_tensor = output.outputs.data.detach().clone()[
img_start_pos : img_end_pos + 1
]
else:
# Use all tokens for text-only prompts
embeddings_tensor = output.outputs.data.detach().clone()
# Pool and normalize embeddings
pooled_output = (
embeddings_tensor.sum(dim=0, dtype=torch.float32)
/ embeddings_tensor.shape[0]
)
embeddings.append(torch.nn.functional.normalize(pooled_output, dim=-1))
return embeddings
embeddings = get_embeddings(outputs)
```
|
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