modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
yufeng1/OpenThinker-7B-reasoning-lora-merged-OT-hard-type1-e1
|
yufeng1
| 2025-09-24T22:13:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T22:12:39Z |
---
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]
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## Uses
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### Downstream Use [optional]
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### 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
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- 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
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[More Information Needed]
#### Metrics
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[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]
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## Technical Specifications [optional]
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## Glossary [optional]
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|
ajagota71/smollm2-360m-saferlhf-ppo-1epoch
|
ajagota71
| 2025-09-24T22:11:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T22:10:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
DannyAI/embedding_fine_tuning_with_peft_bge_large_en_v1.5
|
DannyAI
| 2025-09-24T22:09:24Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:80184",
"loss:CachedMultipleNegativesRankingLoss",
"en",
"dataset:sentence-transformers/natural-questions",
"arxiv:1908.10084",
"arxiv:2101.06983",
"base_model:BAAI/bge-large-en-v1.5",
"base_model:finetune:BAAI/bge-large-en-v1.5",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-24T22:09:20Z |
---
language:
- en
license: mit
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:80184
- loss:CachedMultipleNegativesRankingLoss
base_model: BAAI/bge-large-en-v1.5
widget:
- source_sentence: who does lennie choose in the sky is everywhere
sentences:
- Bank of England £1 note The Bank of England £1 note was a banknote of the pound
sterling. After the ten shilling note was withdrawn in 1970 it became the smallest
denomination note issued by the Bank of England. The one pound note was issued
by the Bank of England for the first time in 1797 and continued to be printed
until 1984. The note was withdrawn in 1988 in favour of the one pound coin.
- The Sky Is Everywhere Lennie tries to make up with Joe by taking him some of Gram's
roses, but doesn't succeed. Gram becomes furious with Lennie for cutting her roses
and criticizes her for being selfish. Lennie realizes that she needs to change,
apologizes to her grandmother, and tells her about the situation with Joe. Gram
reassures Lennie that Joe is in love with her. Lennie writes Joe a letter expressing
her feelings, and Joe ultimately forgives her and they reconcile. Toby and Lennie
become good friends and visit Bailey's grave together to apologize to her. Lennie
walks away from the grave with a smile, knowing that her sister would have forgiven
her and that the only way to deal with grief is to accept that it is a part of
you and to look ahead to the future.
- 'Senate of the Philippines The Senate of the Philippines (Filipino: Senado ng
Pilipinas, also Mataas na Kapulungan ng Pilipinas or "upper chamber") is the upper
house of the bicameral legislature of the Philippines, the Congress; the House
of Representatives is the lower house. The Senate is composed of 24 senators who
are elected at-large with the country as one district under plurality-at-large
voting.'
- source_sentence: who played charlie in charlie and the chocolate factory 2005
sentences:
- Charlie and the Chocolate Factory (film) Charlie and the Chocolate Factory is
a 2005 musical fantasy comedy film directed by Tim Burton and written by John
August, based on the 1964 British novel of the same name by Roald Dahl. The film
stars Johnny Depp as Willy Wonka and Freddie Highmore as Charlie Bucket. The storyline
follows Charlie, who wins a contest and is along with four other contest winners,
subsequently led by Wonka on a tour of his chocolate factory, the most magnificent
in the world.
- The Punisher (TV series) The Punisher is scheduled to be released on November
17, 2017.
- The Vampire Diaries (season 2) The Vampire Diaries, an American supernatural drama,
was officially renewed by The CW for a full 22-episode season on February 16,
2010.[1] The first episode premiered on September 9, 2010, at 8 p.m. ET.[2] The
season picks up immediately after the events of the season one finale. All the
series regulars returned.[3] Season two focuses on the return of Elena Gilbert's
(Nina Dobrev) doppelgänger, Katherine Pierce, the introduction of werewolves,
the sun and moon curse, and the arrival of the original vampires. Tyler Lockwood's
(Michael Trevino) uncle, Mason Lockwood (Taylor Kinney), arrives in town searching
for the moonstone, a family heirloom. Tyler later learns of his family's werewolf
curse. Meanwhile, Caroline Forbes (Candice Accola) is killed by Katherine while
having Damon Salvatore's (Ian Somerhalder) blood in her system, turning her into
a vampire. The arrival of the original vampires, Elijah (Daniel Gillies) and Klaus
Mikaelson (Joseph Morgan), also bring about complications. Klaus is a vampire-werewolf
hybrid, but his werewolf side had been forced into dormancy by witches, as nature
would not stand for such an imbalance in power. Therefore, Klaus arrives in town
with plans to break the curse and unleash his werewolf side by channelling the
power of the full moon into the moonstone, sacrificing a vampire and a werewolf,
and drinking the blood of the doppelgänger. The season is currently on air in
Urdu on filmax channel in Pakistan. It became available on DVD and Blu-ray on
August 30, 2011.[4]
- source_sentence: most of the really good agricultural land in mexico is owned by
sentences:
- 'State of the art The origin of the concept of "state of the art" took place in
the beginning of the twentieth century.[3] The earliest use of the term "state
of the art" documented by the Oxford English Dictionary dates back to 1910, from
an engineering manual by Henry Harrison Suplee (1856-post 1943), an engineering
graduate (University of Pennsylvania, 1876), titled Gas Turbine: progress in the
design and construction of turbines operated by gases of combustion. The relevant
passage reads: "In the present state of the art this is all that can be done".
The term "art" refers to technics, rather than performing or fine arts.[4]'
- London sewerage system Joseph Bazalgette, a civil engineer and Chief Engineer
of the Metropolitan Board of Works, was given responsibility for the work. He
designed an extensive underground sewerage system that diverted waste to the Thames
Estuary, downstream of the main centre of population. Six main interceptor sewers,
totalling almost 160 km (100 miles) in length, were constructed, some incorporating
stretches of London's "lost" rivers. Three of these sewers were north of the river,
the southernmost, low-level one being incorporated in the Thames Embankment. The
Embankment also allowed new roads, new public gardens, and the Circle line of
the London Underground. Victoria Embankment was finally officially opened on 13
July 1870.[3][4]
- Agriculture in Mexico During the early colonial period, the Spanish introduced
more plants and the concept of domesticated animals, principally cattle, horses,
donkeys, mules, goats and sheep, and barn yard animals such as chickens and pigs.
Farming from the colonial period until the Mexican Revolution was focused on large
private properties. After the Revolution these were broken up and the land redistributed.
Since the latter 20th century NAFTA and economic policies have again favored large
scale commercial agricultural holdings.
- source_sentence: who is the person who plays black panther
sentences:
- United States Capitol The United States Capitol, often called the Capitol Building,
is the home of the United States Congress, and the seat of the legislative branch
of the U.S. federal government. It is located on Capitol Hill at the eastern end
of the National Mall in Washington, D.C. Though not at the geographic center of
the Federal District, the Capitol forms the origin point for the District's street-numbering
system and the District's four quadrants.
- Supreme Court of the United States The Supreme Court of the United States is the
highest federal court of the United States. Established pursuant to Article Three
of the United States Constitution in 1789, it has ultimate (and largely discretionary)
appellate jurisdiction over all federal courts and state court cases involving
issues of federal law plus original jurisdiction over a small range of cases.
In the legal system of the United States, the Supreme Court is generally the final
interpreter of federal law including the United States Constitution, but it may
act only within the context of a case in which it has jurisdiction. The Court
may decide cases having political overtones but does not have power to decide
nonjusticiable political questions, and its enforcement arm is in the executive
rather than judicial branch of government.
- Chadwick Boseman Chadwick Aaron Boseman[1] (born November 29, 1977)[2][3] is an
American actor. He is known for portraying Jackie Robinson in 42 (2013), James
Brown in Get on Up (2014), Black Panther in the Marvel Cinematic Universe (since
2016), and Thurgood Marshall in Marshall (2017). He also had roles in the television
series Lincoln Heights (2008) and Persons Unknown (2010), and the films The Express
(2008), Draft Day (2014), and Message from the King (2016).
- source_sentence: can you find a pearl in a mussel
sentences:
- Freshwater pearl mussel Although the name "freshwater pearl mussel" is often used
for this species, other freshwater mussel species can also create pearls and some
can also be used as a source of mother of pearl. In fact, most cultured pearls
today come from Hyriopsis species in Asia, or Amblema species in North America,
both members of the related family Unionidae; pearls are also found within species
in the genus Unio.
- Ellis Island Generally, those immigrants who were approved spent from two to five
hours at Ellis Island. Arrivals were asked 29 questions including name, occupation,
and the amount of money carried. It was important to the American government that
the new arrivals could support themselves and have money to get started. The average
the government wanted the immigrants to have was between 18 and 25 dollars ($600
in 2015 adjusted for inflation). Those with visible health problems or diseases
were sent home or held in the island's hospital facilities for long periods of
time. More than 3,000 would-be immigrants died on Ellis Island while being held
in the hospital facilities. Some unskilled workers were rejected because they
were considered "likely to become a public charge." About 2% were denied admission
to the U.S. and sent back to their countries of origin for reasons such as having
a chronic contagious disease, criminal background, or insanity.[43] Ellis Island
was sometimes known as "The Island of Tears" or "Heartbreak Island"[44] because
of those 2% who were not admitted after the long transatlantic voyage. The Kissing
Post is a wooden column outside the Registry Room, where new arrivals were greeted
by their relatives and friends, typically with tears, hugs, and kisses.[45][46]
- Glee (season 1) The first season of the musical comedy-drama television series
Glee originally aired on Fox in the United States. The pilot episode was broadcast
as an advanced preview of the series on May 19, 2009, with the remainder of the
season airing between September 9, 2009 and June 8, 2010. The season consisted
of 22 episodes; the first 13 aired on Wednesdays at 9 pm (ET) and the final 9
aired on Tuesdays at 9 pm (ET). The season was executive produced by Ryan Murphy,
Brad Falchuk, and Dante Di Loreto; Murphy's production company helped co-produce
the series alongside 20th Century Fox.
datasets:
- sentence-transformers/natural-questions
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: bge-large-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.88
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.98
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.98
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.88
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4133333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.25199999999999995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7673333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9520000000000001
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9553333333333334
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9435612217207588
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9295238095238095
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.919404761904762
name: Cosine Map@100
- type: cosine_accuracy@1
value: 0.88
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.98
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.98
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.88
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4133333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.25199999999999995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7673333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9520000000000001
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9553333333333334
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9435612217207588
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9295238095238095
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.919404761904762
name: Cosine Map@100
---
# bge-large-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset. It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** mit
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 1024, '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): 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("DannyAI/embedding_fine_tuning_with_peft_bge_large_en_v1.5")
# Run inference
queries = [
"can you find a pearl in a mussel",
]
documents = [
'Freshwater pearl mussel Although the name "freshwater pearl mussel" is often used for this species, other freshwater mussel species can also create pearls and some can also be used as a source of mother of pearl. In fact, most cultured pearls today come from Hyriopsis species in Asia, or Amblema species in North America, both members of the related family Unionidae; pearls are also found within species in the genus Unio.',
'Ellis Island Generally, those immigrants who were approved spent from two to five hours at Ellis Island. Arrivals were asked 29 questions including name, occupation, and the amount of money carried. It was important to the American government that the new arrivals could support themselves and have money to get started. The average the government wanted the immigrants to have was between 18 and 25 dollars ($600 in 2015 adjusted for inflation). Those with visible health problems or diseases were sent home or held in the island\'s hospital facilities for long periods of time. More than 3,000 would-be immigrants died on Ellis Island while being held in the hospital facilities. Some unskilled workers were rejected because they were considered "likely to become a public charge." About 2% were denied admission to the U.S. and sent back to their countries of origin for reasons such as having a chronic contagious disease, criminal background, or insanity.[43] Ellis Island was sometimes known as "The Island of Tears" or "Heartbreak Island"[44] because of those 2% who were not admitted after the long transatlantic voyage. The Kissing Post is a wooden column outside the Registry Room, where new arrivals were greeted by their relatives and friends, typically with tears, hugs, and kisses.[45][46]',
"Glee (season 1) The first season of the musical comedy-drama television series Glee originally aired on Fox in the United States. The pilot episode was broadcast as an advanced preview of the series on May 19, 2009, with the remainder of the season airing between September 9, 2009 and June 8, 2010. The season consisted of 22 episodes; the first 13 aired on Wednesdays at 9\xa0pm (ET) and the final 9 aired on Tuesdays at 9\xa0pm (ET). The season was executive produced by Ryan Murphy, Brad Falchuk, and Dante Di Loreto; Murphy's production company helped co-produce the series alongside 20th Century Fox.",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.7103, 0.3918, 0.2758]])
```
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `NanoQuoraRetrieval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"query_prompt": "query: ",
"corpus_prompt": "document: "
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.88 |
| cosine_accuracy@3 | 0.98 |
| cosine_accuracy@5 | 0.98 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.88 |
| cosine_precision@3 | 0.4133 |
| cosine_precision@5 | 0.252 |
| cosine_precision@10 | 0.14 |
| cosine_recall@1 | 0.7673 |
| cosine_recall@3 | 0.952 |
| cosine_recall@5 | 0.9553 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9436** |
| cosine_mrr@10 | 0.9295 |
| cosine_map@100 | 0.9194 |
#### Information Retrieval
* Dataset: `NanoQuoraRetrieval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"query_prompt": "query: ",
"corpus_prompt": "document: "
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.88 |
| cosine_accuracy@3 | 0.98 |
| cosine_accuracy@5 | 0.98 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.88 |
| cosine_precision@3 | 0.4133 |
| cosine_precision@5 | 0.252 |
| cosine_precision@10 | 0.14 |
| cosine_recall@1 | 0.7673 |
| cosine_recall@3 | 0.952 |
| cosine_recall@5 | 0.9553 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9436** |
| cosine_mrr@10 | 0.9295 |
| cosine_map@100 | 0.9194 |
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 80,184 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.72 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 132.91 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who wrote i came in like a wrecking ball</code> | <code>Wrecking Ball (Miley Cyrus song) "Wrecking Ball" is a song recorded by American singer Miley Cyrus for her fourth studio album Bangerz (2013). It was released on August 25, 2013, by RCA Records as the album's second single. The song was written by MoZella, Stephan Moccio, Sacha Skarbek, Kiyanu Kim,[2] Lukasz Gottwald, and Henry Russell Walter;[3] production was helmed by the last two. "Wrecking Ball" is a pop ballad which lyrically discusses the deterioration of a relationship.</code> |
| <code>what was the purpose of the three-field system</code> | <code>Three-field system The three-field system is a regime of crop rotation that was used in medieval and early-modern Europe. Crop rotation is the practice of growing a series of different types of crops in the same area in sequential seasons. Under this system, the arable land of an estate or village was divided into three large fields: one was planted in the autumn with winter wheat or rye; the second field was planted with other crops such as peas, lentils, or beans; and the third was left fallow, in order to allow the soil of that field to regain its nutrients. With each rotation, the field would be used differently, so that a field would be planted for two out of the three years used, whilst one year it "rested". Previously a "two field system" had been in place, with half the land being left fallow. The three field system allowed farmers to plant more crops and therefore to increase production and legumes have the ability to fix nitrogen and so fertilize the soil. With more crops ava...</code> |
| <code>who is the main person in the legislative branch</code> | <code>Article One of the United States Constitution Section 1 is a vesting clause that bestows federal legislative power exclusively to Congress. Similar clauses are found in Articles II and III. The former confers executive power upon the President alone, and the latter grants judicial power solely to the federal judiciary. These three articles create a separation of powers among the three branches of the federal government. This separation of powers, by which each department may exercise only its own constitutional powers and no others,[1][2] is fundamental to the idea of a limited government accountable to the people.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 16,
"gather_across_devices": false
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 20,047 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.79 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 135.48 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when did call of duty ww2 come out</code> | <code>Call of Duty: WWII Call of Duty: WWII is a first-person shooter video game developed by Sledgehammer Games and published by Activision. It is the fourteenth main installment in the Call of Duty series and was released worldwide on November 3, 2017 for Microsoft Windows, PlayStation 4 and Xbox One. It is the first title in the series to be set primarily during World War II since Call of Duty: World at War in 2008.[2] The game is set in the European theatre, and is centered around a squad in the 1st Infantry Division, following their battles on the Western Front, and set mainly in the historical events of Operation Overlord; the multiplayer expands to different fronts not seen in the campaign.</code> |
| <code>who is doing the half time super bowl</code> | <code>Super Bowl LII halftime show The Super Bowl LII Halftime Show (officially known as the Pepsi Super Bowl LII Halftime Show) took place on February 4, 2018 at U.S. Bank Stadium in Minneapolis, Minnesota, as part of Super Bowl LII. Justin Timberlake was the featured performer, as confirmed by the National Football League (NFL) on October 22, 2017.[1] It was televised nationally by NBC.</code> |
| <code>when was the sewage system built in london</code> | <code>London sewerage system Joseph Bazalgette, a civil engineer and Chief Engineer of the Metropolitan Board of Works, was given responsibility for the work. He designed an extensive underground sewerage system that diverted waste to the Thames Estuary, downstream of the main centre of population. Six main interceptor sewers, totalling almost 160 km (100 miles) in length, were constructed, some incorporating stretches of London's "lost" rivers. Three of these sewers were north of the river, the southernmost, low-level one being incorporated in the Thames Embankment. The Embankment also allowed new roads, new public gardens, and the Circle line of the London Underground. Victoria Embankment was finally officially opened on 13 July 1870.[3][4]</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 16,
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 5
- `per_device_eval_batch_size`: 5
- `learning_rate`: 2e-05
- `max_steps`: 100
- `warmup_ratio`: 0.1
- `seed`: 30
- `bf16`: True
- `load_best_model_at_end`: True
- `prompts`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 5
- `per_device_eval_batch_size`: 5
- `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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 100
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 30
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `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`: True
- `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}
- `parallelism_config`: 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`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoQuoraRetrieval_cosine_ndcg@10 |
|:----------:|:-------:|:-------------:|:---------------:|:---------------------------------:|
| -1 | -1 | - | - | 0.9583 |
| **0.0062** | **100** | **0.0156** | **0.0067** | **0.9436** |
| -1 | -1 | - | - | 0.9436 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.0
- Transformers: 4.56.1
- PyTorch: 2.8.0+cu126
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## 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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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|
zikangzheng/wav2vec2-base-gtzan-optimized
|
zikangzheng
| 2025-09-24T22:08:37Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-09-24T05:37:45Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: wav2vec2-base-finetuned-gtzan-optimized
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.72
- name: Precision
type: precision
value: 0.7270750083250083
- name: Recall
type: recall
value: 0.72
- name: F1
type: f1
value: 0.7156373854245563
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-gtzan-optimized
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2450
- Accuracy: 0.72
- Precision: 0.7271
- Recall: 0.72
- F1: 0.7156
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:------:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 2.3003 | 1.0 | 22 | 0.18 | 0.0834 | 2.2929 | 0.0544 | 0.18 |
| 2.2917 | 2.0 | 44 | 0.1333 | 0.0597 | 2.2837 | 0.0408 | 0.1333 |
| 2.2778 | 3.0 | 66 | 0.26 | 0.2083 | 2.2567 | 0.3654 | 0.26 |
| 2.2471 | 4.0 | 88 | 0.34 | 0.2758 | 2.2149 | 0.3997 | 0.34 |
| 2.1629 | 5.0 | 110 | 0.32 | 0.2353 | 2.1427 | 0.3069 | 0.32 |
| 2.08 | 6.0 | 132 | 0.3733 | 0.2776 | 2.0558 | 0.2645 | 0.3733 |
| 2.0188 | 7.0 | 154 | 0.3867 | 0.2997 | 1.9914 | 0.3095 | 0.3867 |
| 1.9483 | 8.0 | 176 | 0.3867 | 0.3167 | 1.9420 | 0.3785 | 0.3867 |
| 1.8804 | 9.0 | 198 | 0.4467 | 0.3905 | 1.8842 | 0.4878 | 0.4467 |
| 1.8063 | 10.0 | 220 | 0.3867 | 0.2975 | 1.8867 | 0.3360 | 0.3867 |
| 1.7808 | 11.0 | 242 | 0.4133 | 0.3619 | 1.8269 | 0.4118 | 0.4133 |
| 1.7031 | 12.0 | 264 | 0.5133 | 0.4759 | 1.7784 | 0.5104 | 0.5133 |
| 1.6752 | 13.0 | 286 | 0.4933 | 0.4502 | 1.7580 | 0.5315 | 0.4933 |
| 1.6843 | 14.0 | 308 | 0.5 | 0.4609 | 1.7113 | 0.5002 | 0.5 |
| 1.6136 | 15.0 | 330 | 0.4667 | 0.4276 | 1.7132 | 0.4710 | 0.4667 |
| 1.6392 | 1.9957 | 349 | 1.6793 | 0.4667 | 0.4630 | 0.4667 | 0.4112 |
| 1.5396 | 3.0 | 524 | 1.5783 | 0.5267 | 0.5407 | 0.5267 | 0.4945 |
| 1.5981 | 4.0 | 699 | 1.6018 | 0.5 | 0.5358 | 0.5 | 0.4795 |
| 1.3127 | 5.0 | 874 | 1.4972 | 0.56 | 0.5732 | 0.56 | 0.5382 |
| 1.5041 | 6.0 | 1049 | 1.5921 | 0.5267 | 0.5740 | 0.5267 | 0.5166 |
| 1.1165 | 7.0 | 1224 | 1.4291 | 0.5667 | 0.5364 | 0.5667 | 0.5296 |
| 1.1177 | 8.0 | 1399 | 1.3336 | 0.6267 | 0.6217 | 0.6267 | 0.5932 |
| 0.8805 | 9.0 | 1574 | 1.3987 | 0.5867 | 0.6336 | 0.5867 | 0.5745 |
| 0.8566 | 10.0 | 1749 | 1.2999 | 0.66 | 0.6753 | 0.66 | 0.6565 |
| 1.0281 | 11.0 | 1924 | 1.3834 | 0.66 | 0.6770 | 0.66 | 0.6539 |
| 0.8522 | 12.0 | 2099 | 1.3038 | 0.6933 | 0.7138 | 0.6933 | 0.6848 |
| 0.8237 | 13.0 | 2274 | 1.4544 | 0.6133 | 0.6358 | 0.6133 | 0.5935 |
| 0.7483 | 14.0 | 2449 | 1.3505 | 0.6867 | 0.7018 | 0.6867 | 0.6835 |
| 0.6935 | 15.0 | 2624 | 1.2758 | 0.68 | 0.6990 | 0.68 | 0.6805 |
| 0.6927 | 16.0 | 2799 | 1.2943 | 0.7 | 0.7034 | 0.7 | 0.6918 |
| 0.5777 | 17.0 | 2974 | 1.3557 | 0.6867 | 0.6959 | 0.6867 | 0.6773 |
| 0.5445 | 18.0 | 3149 | 1.3008 | 0.7133 | 0.7246 | 0.7133 | 0.7078 |
| 0.5349 | 19.0 | 3324 | 1.2980 | 0.6933 | 0.7111 | 0.6933 | 0.6921 |
| 0.5268 | 20.0 | 3499 | 1.2516 | 0.72 | 0.7325 | 0.72 | 0.7201 |
| 0.5458 | 21.0 | 3674 | 1.2454 | 0.7067 | 0.7028 | 0.7067 | 0.7011 |
| 0.5167 | 22.0 | 3849 | 1.2321 | 0.6933 | 0.7007 | 0.6933 | 0.6908 |
| 0.5157 | 23.0 | 4024 | 1.3093 | 0.68 | 0.6978 | 0.68 | 0.6797 |
| 0.51 | 24.0 | 4199 | 1.2763 | 0.7067 | 0.7198 | 0.7067 | 0.7044 |
| 0.5109 | 25.0 | 4374 | 1.2671 | 0.6933 | 0.7038 | 0.6933 | 0.6913 |
### Framework versions
- Transformers 4.57.0.dev0
- Pytorch 2.9.0.dev20250716+cu129
- Datasets 4.0.0
- Tokenizers 0.22.0
|
corzamennav/blockassist-bc-territorial_wild_antelope_1758751334
|
corzamennav
| 2025-09-24T22:03:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"territorial wild antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-24T22:03:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- territorial wild antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rqadri/deep3b_350s
|
rqadri
| 2025-09-24T22:01:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen2.5-3B-Instruct",
"grpo",
"lora",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"region:us"
] |
text-generation
| 2025-09-24T22:00:53Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen2.5-3B-Instruct
- grpo
- lora
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
alesiaivanova/Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-3-sub
|
alesiaivanova
| 2025-09-24T21:52:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T18:28:14Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-3-sub
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-3-sub
This model is a fine-tuned version of [checkpoints/Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-2-sub/checkpoint-200](https://huggingface.co/checkpoints/Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-2-sub/checkpoint-200).
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/alesyaivanova/long-horizon-reasoning/runs/lfzhn0b1)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
alesiaivanova/Qwen-3b-GRPO-compute-tradeoff-14-v2-200-125-70-3-sub
|
alesiaivanova
| 2025-09-24T21:51:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T18:27:25Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen-3b-GRPO-compute-tradeoff-14-v2-200-125-70-3-sub
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen-3b-GRPO-compute-tradeoff-14-v2-200-125-70-3-sub
This model is a fine-tuned version of [checkpoints/Qwen-3b-GRPO-compute-tradeoff-14-v2-200-125-70-2-sub/checkpoint-200](https://huggingface.co/checkpoints/Qwen-3b-GRPO-compute-tradeoff-14-v2-200-125-70-2-sub/checkpoint-200).
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/alesyaivanova/long-horizon-reasoning/runs/h0es5t0v)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
alesiaivanova/Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-4-sub
|
alesiaivanova
| 2025-09-24T21:48:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T20:52:23Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-4-sub
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-4-sub
This model is a fine-tuned version of [checkpoints/Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-3-sub/checkpoint-125](https://huggingface.co/checkpoints/Qwen-3b-GRPO-compute-tradeoff-14-v3-200-125-70-3-sub/checkpoint-125).
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/alesyaivanova/long-horizon-reasoning/runs/jtyy0bk2)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ft42/NoMAISI
|
ft42
| 2025-09-24T21:46:55Z | 0 | 0 | null |
[
"medical-imaging",
"3d-synthesis",
"diffusion-models",
"controlnet",
"monai",
"pytorch",
"rectified-flow",
"ct-scans",
"mri-imaging",
"healthcare-ai",
"image-to-image",
"dataset:medical-images",
"dataset:ct-scans",
"dataset:anatomical-masks",
"arxiv:2508.05772",
"arxiv:2405.04605",
"arxiv:2502.21187",
"base_model:MONAI/maisi_ct_generative",
"base_model:adapter:MONAI/maisi_ct_generative",
"license:cc-by-nc-nd-3.0",
"region:us"
] |
image-to-image
| 2025-09-24T21:37:53Z |
---
license: cc-by-nc-nd-3.0
base_model:
- MONAI/maisi_ct_generative
pipeline_tag: image-to-image
tags:
- medical-imaging
- 3d-synthesis
- diffusion-models
- controlnet
- monai
- pytorch
- rectified-flow
- ct-scans
- mri-imaging
- healthcare-ai
datasets:
- medical-images
- ct-scans
- anatomical-masks
---
# NoMAISI: Nodule-Oriented Medical AI for Synthetic Imaging
<div align="center">
<p align="center">
<img src="NoMAISI_logo.png" alt="PiNS Logo" width="500">
</p>
**Nodule-Oriented Medical AI for Synthetic Imaging and Augmentation in Chest CT**
[](https://creativecommons.org/licenses/by-nc/4.0/)
[](https://hub.docker.com/r/ft42/pins)
[](https://python.org)
[](https://simpleitk.org)
[](https://pytorch.org)
[](https://monai.io)
[](https://github.com/fitushar/PiNS)
[](https://github.com/fitushar/CaNA)
</div>
# Abstract
Medical imaging datasets are increasingly available, yet abnormal and annotation-intensive cases such as lung nodules remain underrepresented. We Introduced NoMAISI (Nodule-Oriented Medical AI for Synthetic Imaging), a generative framework built on foundational backbones with flow-based diffusion and ControlNet conditioning. Using NoMAISI, we curated a large multi-cohort lung nodule dataset and applied context-aware nodule volume augmentation, including relocation, shrinkage to simulate early-stage disease, and expansion to model progression. Each case was rendered into multiple synthetic variants, producing a diverse and anatomically consistent dataset. Fidelity was evaluated with cross-cohort similarity metrics, and downstream integration into lung nodule detection, and classification tasks demonstrated improved external test performance, particularly in underrepresented lesion categories. These results show that nodule-oriented synthetic imaging and curated augmentation can complement clinical data, reduce annotation demands, and expand the availability of training resources for healthcare AI.
## 🧩 Workflow Overview
The overall pipeline for organ, body, and nodule segmentation with alignment is shown below:
<p align="center">
<img src="doc/images/workflow.png" alt="Segmentation Pipeline"/>
</p>
**Workflow** for constructing the **NoMAISI** development dataset. The pipeline includes **(1)** organ segmentation using AI models, **(2)** body segmentation with algorithmic methods, **(3)** nodule segmentation through AI-assisted and ML-based refinement, and **(4)** segmentation alignment to integrate organs, body, and nodules segmentations into anatomically consistent volumes.
<p align="center">
<img src="doc/images/NoMAISI_train_and_infer.png" alt="NoMAISI_train_and_infer"/>
</p>
**Overview** of our flow-based latent diffusion model with ControlNet conditioning for AI-based CT generation. The pipeline consists of three stages: **(top) Pretrained VAE** for image compression, where CT images are encoded into latent features using a frozen VAE; **(middle)** Model fine-tuning, where a **Rectified Flow ODE sampler**, conditioned on segmentation masks and voxel spacing through a **fine-tuned ControlNet**, predicts velocity fields in latent space and is optimized with a region-specific contrastive loss emphasizing ROI sensitivity and background consistency; and **(bottom) Inference**, where segmentation masks and voxel spacing guide latent sampling along the ODE trajectory to obtain a clean latent representation, which is then decoded by the VAE into full-resolution AI-generated CT images conditioned by body and lesion masks.
## 📊 Dataset Composition
The table below summarizes the datasets included in this project, with their split sizes (Patients, CT scans, and Nodules) and the annotation types available.
| Dataset | Patients <br>n (%) | CT Scans <br>n (%) | Nodules <br>n (%) | Organ Seg | Nodule Seg | Nodule CCC | Nodule Box |
|------------------|---------------------|---------------------|-------------------|-----------|------------|------------|------------|
| **LNDbv4** | 223 (3.17) | 223 (2.52) | 1132 (7.84) | ✗ | ✓ | ✗ | ✓ |
| **NSCLC-R** | 415 (5.89) | 415 (4.69) | 415 (2.87) | ✗ | ✓ | ✗ | ✓ |
| **LIDC-IDRI** | 870 (12.35) | 870 (9.84) | 2584 (17.89) | ✗ | ✓ | ✓ | ✓ |
| **DLCS-24** | 1605 (22.79) | 1605 (18.15) | 2478 (17.16) | ✗ | ✓ | ✗ | ✓ |
| **Intgmultiomics** | 1936 (27.49) | 1936 (21.90) | 1936 (13.40) | ✗ | ✓ | ✗ | ✗ |
| **LUNA-25** | 1993 (28.30) | 3792 (42.89) | 5899 (40.84) | ✗ | ✓ | ✗ | ✓ |
| **TOTAL** | 7042 (100) | 8841 (100) | 14444 (100) | — | — | — | — |
---
**Notes**
- Percentages indicate proportion relative to the total for each column.
- ✔︎ = annotation available, ✗ = annotation not available.
- “Nodule CCC” = nodule center coordinates.
- “Nodule Box” = bounding-box annotations.
### 📚 Dataset citations References
* LNDbv4 : [https://zenodo.org/records/8348419](https://zenodo.org/records/8348419)
* NSCLC-Radiomics : [https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/](https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/)
* LIDC-IDRI: [https://ieee-dataport.org/documents/lung-image-database-consortium-image-collection-lidc-idri](https://ieee-dataport.org/documents/lung-image-database-consortium-image-collection-lidc-idri)
* DLCS24: [https://zenodo.org/records/13799069](https://zenodo.org/records/13799069)
* Intgmultiomics: [M Zhao et. al, Nat.Commun(2025).](https://www.nature.com/articles/s41467-024-55594-z#citeas)
* LUNA25: [https://luna25.grand-challenge.org/](https://luna25.grand-challenge.org/)
# AI-Generated CT Evaluations
### 📉 Fréchet Inception Distance (FID) Results
Fréchet Inception Distance (FID) of the **MAISI-v2** baseline and **NoMAISI** models with multiple public clinical datasets (test dataset) as the references (Lower is better).
| **FID (Avg.)** | **LNDbv4** | **NSCLC-R** | **LIDC-IDRI** | **DLCS-24** | **Intgmultiomics** | **LUNA-25** |
|-------------------|------------|-------------|---------------|-------------|--------------------|-------------|
| **Real** LNDbv4 | — | 5.13 | 1.49 | 1.05 | 2.40 | 1.98 |
| **Real** NSCLC-R | 5.13 | — | 3.12 | 3.66 | 1.56 | 2.65 |
| **Real** LIDC-IDRI | 1.49 | 3.12 | — | 0.79 | 1.44 | 0.75 |
| **Real** DLCS-24 | 1.05 | 3.66 | 0.79 | — | 1.56 | 1.00 |
| **Real** Intgmultiomics| 2.40 | 1.56 | 1.44 | 1.56 | — | 1.57 |
| **Real** LUNA-25 | 1.98 | 2.65 | 0.75 | 1.00 | 1.57 | — |
| **AI-Generated** MAISI-V2 | 3.15 | 5.21 | 2.70 | 2.32 | 2.82 | 1.69 |
| **AI-Generated** NoMAISI (ours) | 2.99 | 3.05 | 2.31 | 2.27 | 2.62 | 1.18 |
### 📉 FID Parity Plot
<p align="left">
<img src="doc/images/GanAI_fid_scatter_marker_legend.png" alt="Parity comparison of FID for real↔real vs AI-generated CT across datasets" width="500">
</p>
**Comparison of Fréchet Inception Distance (FID) between real↔real and AI-generated CT datasets.** Each point represents a clinical dataset (**LNDbv4, NSCLC-R, LIDC-IDRI, DLCS24, Intgmultiomics, LUNA25**) under different generative models (**MAISI-V2, NoMAISI**).The x-axis shows the **median FID** computed between real datasets, while the y-axis shows the **FID of AI-generated data** compared to real.
The dashed diagonal line denotes **parity (y = x)**, where AI-generated fidelity would match real↔real fidelity.
### 🖼️ Example Results
**Comparison of CT generation from anatomical masks.**
- **Left:** Input organ/body segmentation mask.
- **Middle:** Generated CT slice using **MAISI-V2**.
- **Right:** Generated CT slice using **NoMAISI (ours)**.
- **Yellow boxes** highlight lung nodule regions for comparison.
<p align="center">
<img src="doc/images/DLCS_1419_ann0_slice134_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
</p>
<p align="center">
<img src="doc/images/DLCS_1508_ann0_slice46_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
</p>
<p align="center">
<img src="doc/images/DLCS_1453_ann0_slice204_triple.png" alt="Comparison of MAISI-V2 vs NoMAISI on lung CT with input masks" width="1000">
</p>
# Inference Guide
1. [Project Structure](###project-structure)
2. [Configuration Files](###configuration-files)
### Model Weights
Model weights are available upon request. Please email the authors: <[email protected]>.
### 📁 Project Structure
```
NoMAISI/
├── configs/ # Configuration files
│ ├── config_maisi3d-rflow.json # Main model configuration
│ ├── infr_env_NoMAISI_DLCSD24_demo.json # Environment settings
│ └── infr_config_NoMAISI_controlnet.json # ControlNet inference config
├── scripts/ # Python inference scripts
│ ├── infer_testV2_controlnet.py # Main inference script
│ ├── infer_controlnet.py # ControlNet inference
│ └── utils.py # Utility functions
├── models/ # Pre-trained model weights
├── data/ # Input data directory
├── outputs/ # Generated results
├── logs/ # Execution logs
└── inference.sub # SLURM job script
```
## ⚙️ Configuration Files
#### 1. Main Model Configuration (`config_maisi3d-rflow.json`): Controls the core diffusion model parameters:
- Model architecture settings; Sampling parameters; Image dimensions and spacing
#### 2. Environment Configuration (`infr_env_NoMAISI_DLCSD24_demo.json`): Defines runtime environment
- Data paths and directories; GPU settings; Memory allocation
#### 3. ControlNet Configuration (`infr_config_NoMAISI_controlnet.json`): ControlNet-specific settings
- Conditioning parameters; Generation controls; Output specifications
## 🚀 Running Inference
```bash
cd /path/NoMAISI/
# Create logs directory if it doesn't exist
mkdir -p logs
# Submit job to SLURM
sbatch inference.sub
```
```bash
# Run inference directly
cd /path/NoMAISI/
python -m scripts.infer_testV2_controlnet \
-c ./configs/config_maisi3d-rflow.json \
-e ./configs/infr_env_NoMAISI_DLCSD24_demo.json \
-t ./configs/infr_config_NoMAISI_controlnet.json
```
# Downstream Task:
* **Cancer vs. No-Cancer Classification**
* **Nodule Detection** .
* **Nodule Segmentation** .
---
---
## 🔬 Downstream Task: Cancer vs. No-Cancer Classification
**Shown.** AUC vs. the **% of clinical data retained** (x-axis: **100%**, **50%**, **20%**, **10%**).
**Curves (additive augmentation — we **add** AI-generated nodules; we never replace clinical samples):**
- **Clinical (LUNA25)** — baseline using only the retained clinical data.
- **Clinical + AI-gen. (n%)** — at each point, add AI-generated data equal to the **same percentage as the retained clinical fraction**.
*Examples:* at **50% clinical → +50% AI-gen**; **20% → +20%**; **10% → +10%**.
- **Clinical + AI-gen. (100%)** — at each point, add AI-generated data equal to **100% of the full clinical dataset size**, regardless of the retained fraction.
*Example:* at **10% clinical → +100% AI-gen**.
**Takeaways**
- **AI-generated nodules improve data-efficiency:** at **low clinical fractions (50%→10%)**, *Clinical + AI-gen. (n%)* typically **matches or exceeds** clinical-only AUC.
- **Bigger synthetic boosts (100%)** can help in some regimes but may underperform the matched *n%* mix depending on cohort → **ratio-balanced augmentation** is often safer.
- Trends **generalize to external cohorts**, indicating **usability** beyond the development data.
---
# Acknowledgements
We gratefully acknowledge the open-source projects that directly informed this repository: the [MAISI tutorial](https://github.com/Project-MONAI/tutorials/tree/main/generation/maisi) from the Project MONAI tutorials, the broader [Project MONAI ecosystem](https://github.com/Project-MONAI),
our related benchmark repo [AI in Lung Health – Benchmarking](https://github.com/fitushar/AI-in-Lung-Health-Benchmarking-Detection-and-Diagnostic-Models-Across-Multiple-CT-Scan-Datasets),
and our companion toolkits [PiNS – Point-driven Nodule Segmentation](https://github.com/fitushar/PiNS)
and [CaNA – Context-Aware Nodule Augmentation](https://github.com/fitushar/CaNA). We thank these communities and contributors for their exceptional open-source efforts. If you use our models or code, please also consider citing these works (alongside this repository) to acknowledge their contributions.
# References
* [1] [MAISI-V2; Guo, Pengfei, et al.(2025)](https://arxiv.org/abs/2508.05772)
* [2] [AI in Lung Health- Benchmarking; Tushar et al.(2024)](https://arxiv.org/abs/2405.04605)
* [3] [SYN-LUNGS; Tushar et al.(2025)](https://arxiv.org/abs/2502.21187)
|
haihp02/79fd513f-6956-44b1-8e23-54de2c43410a
|
haihp02
| 2025-09-24T21:45:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T17:10:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NebovG404/blockassist
|
NebovG404
| 2025-09-24T21:45:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud silky pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-24T08:52:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud silky pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
godijef/blockassist
|
godijef
| 2025-09-24T21:44:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful singing panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-24T14:31:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful singing panther
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
wherobots/chesapeakersc-ep-torch280-cu126-pt2
|
wherobots
| 2025-09-24T21:43:42Z | 0 | 0 | null |
[
"license:cc0-1.0",
"region:us"
] | null | 2025-08-14T16:27:46Z |
---
license: cc0-1.0
actor: semantic_segmentation
patch_size: 512
clip_size: 64
max_batch_size: 256
device: cuda
features: [r, g, b, ir]
labels: [background, road]
merge_mode: weighted_average
---
|
wherobots/meta-tree-canopy-height-ep-torch280-cpu-pt2
|
wherobots
| 2025-09-24T21:43:35Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-10T19:47:45Z |
---
license: apache-2.0
actor: regression
patch_size: 224
clip_size: 28
max_batch_size: 64
device: cuda
features: [r, g, b]
labels: [height]
merge_mode: weighted_average
---
First run the following to setup the environment and get the official model code
```bash
# Clone the official repo
git clone [email protected]:facebookresearch/HighResCanopyHeight.git
# Install dependencies
pip install stac-model[torch]
# Download the official pretrained checkpoints
mkdir checkpoints && aws s3 --no-sign-request sync s3://dataforgood-fb-data/forests/v1/models/saved_checkpoints/ checkpoints/
```
Export the model using the following:
```python
from pathlib import Path
import sys
sys.path.append("HighResCanopyHeight")
import torch
import torch.nn as nn
import torchvision.transforms.v2 as T
from stac_model.torch.export import export, package
import src.transforms
from inference import SSLAE
# Create model and load checkpoint
class TreeCanopyHeightModel(nn.Module):
def __init__(self, classify=True, huge=True):
super().__init__()
self.model = SSLAE(pretrained=None, classify=classify, huge=huge, n_bins=256)
def forward(self, x):
outputs = self.model(x)
pred = 10 * outputs + 0.001
return pred.relu()
path = "checkpoints/SSLhuge_satellite.pth"
ckpt = torch.load(path, map_location="cpu", weights_only=False)
state_dict = {f"model.{k}": v for k, v in ckpt["state_dict"].items()}
model = TreeCanopyHeightModel()
model.load_state_dict(state_dict)
# Create exportable transforms
original_transform = src.transforms.SSLNorm().Trans
norm = original_transform.transforms[-1]
transforms = nn.Sequential(
T.Normalize(mean=[0], std=[255]), # replace ToTensor() with normalize to 0-1
T.Normalize(mean=norm.mean, std=norm.std)
)
# Export and save to pt2
model_program, transforms_program = export(
input_shape=[-1, 3, 224, 224],
model=model,
transforms=transforms,
device="cpu",
dtype=torch.float32,
)
package(
output_file=Path("model.pt2"),
model_program=model_program,
transforms_program=transforms_program,
metadata_properties=None,
aoti_compile_and_package=False
)
```
|
wherobots/ftw-ep-torch280-cu126-pt2
|
wherobots
| 2025-09-24T21:43:22Z | 0 | 0 | null |
[
"license:cc-by-3.0",
"region:us"
] | null | 2025-08-14T15:47:23Z |
---
license: cc-by-3.0
actor: semantic_segmentation
patch_size: 256
clip_size: 32
max_batch_size: 256
device: cuda
features: [
"s2med_harvest:B04",
"s2med_harvest:B03",
"s2med_harvest:B02",
"s2med_harvest:B08",
"s2med_planting:B04",
"s2med_planting:B03",
"s2med_planting:B02",
"s2med_planting:B08"
]
labels: [
non_field_background,
field,
field_boundaries
]
merge_mode: weighted_average
---
Created using the following script:
```python
from pathlib import Path
import torch
import torch.nn as nn
import torchvision.transforms.v2 as T
import yaml
from torchgeo.models import Unet_Weights, unet
from stac_model.schema import MLModelProperties
from stac_model.torch.export import save
dtype = torch.float32
device = torch.device("cuda")
input_shape = [-1, 8, -1, -1]
archive_path = Path("model.pt2")
weights = Unet_Weights.SENTINEL2_3CLASS_FTW
transforms = torch.nn.Sequential(
nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False),
T.Normalize(mean=[0.0], std=[3000.0])
)
model = unet(weights=weights)
save(
output_file=archive_path,
input_shape=input_shape,
model=model,
transforms=transforms,
metadata=None,
device=device,
dtype=dtype,
aoti_compile_and_package=False,
)
```
|
zenlm/zen-nano-thinking-4bit
|
zenlm
| 2025-09-24T21:42:18Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"zen",
"nano",
"edge",
"efficient",
"4b",
"thinking",
"reasoning",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"8-bit",
"q8",
"region:us"
] |
text-generation
| 2025-09-24T21:31:57Z |
---
license: apache-2.0
language: en
pipeline_tag: text-generation
tags:
- zen
- nano
- edge
- efficient
- 4b
- thinking
- reasoning
widget:
- example_title: "Math Problem"
text: "What is 15 * 24? Show your thinking."
- example_title: "Identity"
text: "What is your name and who created you?"
---
# Zen-Nano-Thinking-4bit
Ultra-efficient 4B parameter AI model by **Hanzo AI**, optimized for **advanced reasoning with explicit thinking process** and 4-bit quantization for maximum speed.
## 🧠 Thinking Process
This model uses explicit thinking tokens to show its reasoning process:
```
User: What is 15 * 24?
<think>
I need to multiply 15 by 24.
I can break this down: 15 * 24 = 15 * 20 + 15 * 4
15 * 20 = 300
15 * 4 = 60
300 + 60 = 360
</think>
Assistant: 15 * 24 = 360
```
## 🚀 Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-nano-thinking-4bit")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-nano-thinking-4bit")
prompt = "User: Solve step by step: If 3x + 7 = 22, what is x?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300, temperature=0.7)
print(tokenizer.decode(outputs[0]))
```
## 📊 Performance
- **MMLU**: 70.1% (with thinking process)
- **HumanEval**: 48.9% (code reasoning)
- **Parameters**: 4B (4-bit quantized)
- **Speed**: 1500+ tokens/sec on A100 (4-bit boost)
- **Memory**: ~4GB VRAM (4-bit efficiency)
## ⚡ 4-bit Advantages
- **2x faster** inference vs full precision
- **50% less memory** usage
- **Perfect for edge devices** with limited resources
- **Maintains reasoning quality** with quantization-aware training
## 🔧 Available Formats
### MLX (Apple Silicon)
```python
from mlx_lm import load, generate
model, tokenizer = load("zenlm/zen-nano-thinking-4bit")
response = generate(model, tokenizer, prompt="Solve: 2x + 5 = 15", max_tokens=200)
```
### GGUF (llama.cpp)
```bash
# Download 4-bit GGUF
wget https://huggingface.co/zenlm/zen-nano-thinking-4bit/resolve/main/zen-nano-thinking-4bit-q4_k_m.gguf
# Run with llama.cpp
./llama-cli -m zen-nano-thinking-4bit-q4_k_m.gguf -p "Think through: What is 7 * 8?" -n 200
```
## 🎯 Best Use Cases
- **Math Problem Solving**: Step-by-step mathematical reasoning
- **Code Debugging**: Analyzing code issues with clear thinking
- **Logical Analysis**: Breaking down complex problems
- **Educational Tools**: Showing work and reasoning process
- **Mobile AI Tutoring**: Fast reasoning on phones/tablets
## 💭 Thinking vs Regular Models
| Feature | zen-nano-instruct | zen-nano-thinking-4bit |
|---------|------------------|----------------------|
| Response Style | Direct answers | Shows thinking process |
| Math Problems | Good | Excellent (step-by-step) |
| Debugging | Good | Excellent (traces logic) |
| Speed | Fast | Very Fast (4-bit) |
| Memory | 8GB | 4GB |
## 📚 Model Details
- **Architecture**: Transformer with grouped query attention + thinking tokens
- **Context**: 32K tokens
- **Quantization**: 4-bit with quality preservation
- **Training**: Instruction tuning + chain-of-thought + identity alignment
- **Creator**: Hanzo AI (2025)
## 🔗 Related Models
- [zen-nano-instruct](https://huggingface.co/zenlm/zen-nano-instruct) - Direct instruction following
- [zen-nano-thinking](https://huggingface.co/zenlm/zen-nano-thinking) - Full precision thinking
- [zen-nano-instruct-4bit](https://huggingface.co/zenlm/zen-nano-instruct-4bit) - 4-bit instruct
- [zen-identity dataset](https://huggingface.co/datasets/zenlm/zen-identity) - Training data
## 📄 Citation
```bibtex
@model{zennanothinking4bit2025,
title={Zen-Nano-Thinking-4bit: Ultra-Efficient Reasoning AI},
author={Hanzo AI Research Team},
year={2025},
url={https://huggingface.co/zenlm/zen-nano-thinking-4bit}
}
```
## License
Apache 2.0 - Free for commercial use.
---
**🏢 Hanzo AI** • Advanced reasoning, maximum efficiency • 2025
|
zenlm/zen-nano-instruct-4bit
|
zenlm
| 2025-09-24T21:42:16Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"zen",
"nano",
"edge",
"efficient",
"4b",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-09-24T21:25:25Z |
---
license: apache-2.0
language: en
pipeline_tag: text-generation
tags:
- zen
- nano
- edge
- efficient
- 4b
widget:
- example_title: "What are you?"
text: "What is your name and who created you?"
---
# Zen-Nano-Instruct-4bit
Ultra-efficient 4B parameter AI model by **Hanzo AI**, optimized for edge deployment and efficient instruction following.
## 🚀 Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-nano-instruct-4bit")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-nano-instruct-4bit")
prompt = "What is your name?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))
```
## 📊 Performance
- **MMLU**: 68.4% MMLU
- **HumanEval**: 46.8% HumanEval
- **Parameters**: 4B (ultra-efficient)
- **Speed**: 1000+ tokens/sec on A100
- **Memory**: ~8GB VRAM (FP16)
## 🔧 Available Formats
### MLX (Apple Silicon)
```bash
# Install MLX
pip install mlx-lm
# Use the model
from mlx_lm import load, generate
model, tokenizer = load("zenlm/zen-nano-instruct-4bit")
response = generate(model, tokenizer, prompt="Hello!", max_tokens=100)
```
### GGUF (llama.cpp)
```bash
# Download GGUF file
wget https://huggingface.co/zenlm/zen-nano-instruct-4bit/resolve/main/zen-nano-instruct-4bit-q4_k_m.gguf
# Run with llama.cpp
./llama-cli -m zen-nano-instruct-4bit-q4_k_m.gguf -p "Hello!" -n 100
```
## 🎯 Use Cases
- **Mobile/Edge AI**: Runs on phones, embedded systems
- **Real-time Applications**: Sub-100ms response times
- **Development Tools**: Code completion, debugging
- **Offline AI**: No internet required
## 📚 Model Details
- **Architecture**: Transformer with grouped query attention
- **Context**: 32K tokens
- **Vocabulary**: 151K tokens
- **Training**: Instruction tuning + identity alignment
- **Creator**: Hanzo AI (2025)
## 🔗 Related Models
- [zen-nano-instruct](https://huggingface.co/zenlm/zen-nano-instruct) - Instruction following
- [zen-nano-thinking](https://huggingface.co/zenlm/zen-nano-thinking) - Chain-of-thought reasoning
- [zen-identity dataset](https://huggingface.co/datasets/zenlm/zen-identity) - Training data
## 📄 Citation
```bibtex
@model{zenzennanoinstruct4bit2025,
title={Zen-Nano-Instruct-4bit: Ultra-Efficient Edge AI},
author={Hanzo AI Research Team},
year={2025},
url={https://huggingface.co/zenlm/zen-nano-instruct-4bit}
}
```
## License
Apache 2.0 - Free for commercial use.
---
**🏢 Hanzo AI** • Ultra-efficient AI for everyone • 2025
|
Jaw00/donut-demo-tt
|
Jaw00
| 2025-09-24T21:40:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-24T21:16:00Z |
---
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]
|
moscowx21/Qwen3-0.6B-Gensyn-Swarm-pale_sturdy_sealion
|
moscowx21
| 2025-09-24T21:36:35Z | 219 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am pale_sturdy_sealion",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T22:51:36Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am pale_sturdy_sealion
---
# 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]
|
analist/QwenStem-7b
|
analist
| 2025-09-24T21:35:52Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-24T01:56:16Z |
---
base_model: unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** analist
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit
This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
alesiaivanova/Qwen-3b-GRPO-compute-tradeoff-last-v1-125-50-25-3-sub
|
alesiaivanova
| 2025-09-24T21:35:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T21:05:32Z |
---
library_name: transformers
model_name: Qwen-3b-GRPO-compute-tradeoff-last-v1-125-50-25-3-sub
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen-3b-GRPO-compute-tradeoff-last-v1-125-50-25-3-sub
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/alesyaivanova/long-horizon-reasoning/runs/a10frhza)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Chukky10z/blockassist
|
Chukky10z
| 2025-09-24T21:34:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian jumping cougar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-22T18:11:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian jumping cougar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
emogie3D/phi-4-mini-instruct-abliterated-german-v1.1
|
emogie3D
| 2025-09-24T21:30:54Z | 0 | 0 | null |
[
"safetensors",
"phi3",
"custom_code",
"de",
"arxiv:2503.01743",
"base_model:unsloth/Phi-4-mini-instruct",
"base_model:finetune:unsloth/Phi-4-mini-instruct",
"license:mit",
"region:us"
] | null | 2025-09-24T21:14:41Z |
---
license: mit
language:
- de
base_model:
- unsloth/Phi-4-mini-instruct
---
* This model has the status: Experimental
* abliterated with https://github.com/Tsadoq/ErisForge/
* german focus, used the provided examples by ErisForge and extended it by several other sentences, translated to german, or used directly german input.
* This model is for fictional purpose only, except of abliteration no other changes till now.
* Till now spend ~30 hours with my 4060 RTX - 16GB wit abliteration.
* The Model represence step 5 of 5 as V1.1
*
*
* I've used unsloth version as it contained some fixes
*
*
```
language:
- multilingual
- ar
- zh
- cs
- da
- nl
- en
- fi
- fr
- de
- he
- hu
- it
- ja
- ko
- 'no'
- pl
- pt
- ru
- es
- sv
- th
- tr
- uk
library_name: transformers
license: mit
license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- code
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
```
🎉**Phi-4**: [[mini-reasoning](https://huggingface.co/microsoft/Phi-4-mini-reasoning) | [reasoning](https://huggingface.co/microsoft/Phi-4-reasoning)] | [[multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-multimodal-instruct-onnx)];
[[mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx)]
## Model Summary
Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures.
📰 [Phi-4-mini Microsoft Blog](https://aka.ms/phi4-feb2025) <br>
📖 [Phi-4-mini Technical Report](https://aka.ms/phi-4-multimodal/techreport) <br>
👩🍳 [Phi Cookbook](https://github.com/microsoft/PhiCookBook) <br>
🏡 [Phi Portal](https://azure.microsoft.com/en-us/products/phi) <br>
🖥️ Try It [Azure](https://aka.ms/phi-4-mini/azure), [Huggingface](https://huggingface.co/spaces/microsoft/phi-4-mini) <br>
🚀 [Model paper](https://huggingface.co/papers/2503.01743)
## Intended Uses
### Primary Use Cases
The model is intended for broad multilingual commercial and research use. The model provides uses for general purpose AI systems and applications which require:
1) Memory/compute constrained environments
2) Latency bound scenarios
3) Strong reasoning (especially math and logic).
The model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.
### Use Case Considerations
The model is not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models, as well as performance difference across languages, as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.
Developers should be aware of and adhere to applicable laws or regulations (including but not limited to privacy, trade compliance laws, etc.) that are relevant to their use case.
***Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.***
## Release Notes
This release of Phi-4-mini-instruct is based on valuable user feedback from the Phi-3 series. The Phi-4-mini model employed new architecture for efficiency, larger vocabulary for multilingual support, and better post-training techniques were used for instruction following, function calling, as well as additional data leading to substantial gains on key capabilities. It is anticipated that most use cases will benefit from this release, but users are encouraged to test in their particular AI applications. The enthusiastic support for the Phi-4 series is greatly appreciated. Feedback on Phi-4-mini-instruct is welcomed and crucial to the model’s evolution and improvement.
### Model Quality
To understand the capabilities, the 3.8B parameters Phi-4-mini-instruct model was compared with a set of models over a variety of benchmarks using an internal benchmark platform (See Appendix A for benchmark methodology). A high-level overview of the model quality is as follows:
| Benchmark | Similar size | | | | |2x size | | | | | |
|----------------------------------|-------------|-------------------|-------------------|-------------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|
| | Phi-4 mini-Ins | Phi-3.5-mini-Ins | Llama-3.2-3B-Ins | Mistral-3B | Qwen2.5-3B-Ins | Qwen2.5-7B-Ins | Mistral-8B-2410 | Llama-3.1-8B-Ins | Llama-3.1-Tulu-3-8B | Gemma2-9B-Ins | GPT-4o-mini-2024-07-18 |
| **Popular aggregated benchmark** | | | | | | | | | | | |
| Arena Hard | 32.8 | 34.4 | 17.0 | 26.9 | 32.0 | 55.5 | 37.3 | 25.7 | 42.7 | 43.7 | 53.7 |
| BigBench Hard (0-shot, CoT) | 70.4 | 63.1 | 55.4 | 51.2 | 56.2 | 72.4 | 53.3 | 63.4 | 55.5 | 65.7 | 80.4 |
| MMLU (5-shot) | 67.3 | 65.5 | 61.8 | 60.8 | 65.0 | 72.6 | 63.0 | 68.1 | 65.0 | 71.3 | 77.2 |
| MMLU-Pro (0-shot, CoT) | 52.8 | 47.4 | 39.2 | 35.3 | 44.7 | 56.2 | 36.6 | 44.0 | 40.9 | 50.1 | 62.8 |
| **Reasoning** | | | | | | | | | | | |
| ARC Challenge (10-shot) | 83.7 | 84.6 | 76.1 | 80.3 | 82.6 | 90.1 | 82.7 | 83.1 | 79.4 | 89.8 | 93.5 |
| BoolQ (2-shot) | 81.2 | 77.7 | 71.4 | 79.4 | 65.4 | 80.0 | 80.5 | 82.8 | 79.3 | 85.7 | 88.7 |
| GPQA (0-shot, CoT) | 25.2 | 26.6 | 24.3 | 24.4 | 23.4 | 30.6 | 26.3 | 26.3 | 29.9 | 39.1 | 41.1 |
| HellaSwag (5-shot) | 69.1 | 72.2 | 77.2 | 74.6 | 74.6 | 80.0 | 73.5 | 72.8 | 80.9 | 87.1 | 88.7 |
| OpenBookQA (10-shot) | 79.2 | 81.2 | 72.6 | 79.8 | 79.3 | 82.6 | 80.2 | 84.8 | 79.8 | 90.0 | 90.0 |
| PIQA (5-shot) | 77.6 | 78.2 | 68.2 | 73.2 | 72.6 | 76.2 | 81.2 | 83.2 | 78.3 | 83.7 | 88.7 |
| Social IQA (5-shot) | 72.5 | 75.1 | 68.3 | 73.9 | 75.3 | 75.3 | 77.6 | 71.8 | 73.4 | 74.7 | 82.9 |
| TruthfulQA (MC2) (10-shot) | 66.4 | 65.2 | 59.2 | 62.9 | 64.3 | 69.4 | 63.0 | 69.2 | 64.1 | 76.6 | 78.2 |
| Winogrande (5-shot) | 67.0 | 72.2 | 53.2 | 59.8 | 63.3 | 71.1 | 63.1 | 64.7 | 65.4 | 74.0 | 76.9 |
| **Multilingual** | | | | | | | | | | | |
| Multilingual MMLU (5-shot) | 49.3 | 51.8 | 48.1 | 46.4 | 55.9 | 64.4 | 53.7 | 56.2 | 54.5 | 63.8 | 72.9 |
| MGSM (0-shot, CoT) | 63.9 | 49.6 | 44.6 | 44.6 | 53.5 | 64.5 | 56.7 | 56.7 | 58.6 | 75.1 | 81.7 |
| **Math** | | | | | | | | | | | |
| GSM8K (8-shot, CoT) | 88.6 | 76.9 | 75.6 | 80.1 | 80.6 | 88.7 | 81.9 | 82.4 | 84.3 | 84.9 | 91.3 |
| MATH (0-shot, CoT) | 64.0 | 49.8 | 46.7 | 41.8 | 61.7 | 60.4 | 41.6 | 47.6 | 46.1 | 51.3 | 70.2 |
| **Overall** | **63.5** | **60.5** | **56.2** | **56.9** | **60.1** | **67.9** | **60.2** | **62.3** | **60.9** | **65.0** | **75.5** |
Overall, the model with only 3.8B-param achieves a similar level of multilingual language understanding and reasoning ability as much larger models. However, it is still fundamentally limited by its size for certain tasks. The model simply does not have the capacity to store too much factual knowledge, therefore, users may experience factual incorrectness. However, it may be possible to resolve such weakness by augmenting Phi-4 with a search engine, particularly when using the model under RAG settings.
## Usage
### Tokenizer
Phi-4-mini-instruct supports a vocabulary size of up to `200064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-4-mini-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.
### Input Formats
Given the nature of the training data, the Phi-4-mini-instruct
model is best suited for prompts using specific formats.
Below are the two primary formats:
#### Chat format
This format is used for general conversation and instructions:
```yaml
<|system|>Insert System Message<|end|><|user|>Insert User Message<|end|><|assistant|>
```
#### Tool-enabled function-calling format
This format is used when the user wants the model to provide function calls based on the given tools. The user should provide the available tools in the system prompt, wrapped by <|tool|> and <|/tool|> tokens. The tools should be specified in JSON format, using a JSON dump structure. Example:
`
<|system|>You are a helpful assistant with some tools.<|tool|>[{"name": "get_weather_updates", "description": "Fetches weather updates for a given city using the RapidAPI Weather API.", "parameters": {"city": {"description": "The name of the city for which to retrieve weather information.", "type": "str", "default": "London"}}}]<|/tool|><|end|><|user|>What is the weather like in Paris today?<|end|><|assistant|>
`
### Inference with vLLM
#### Requirements
List of required packages:
```
flash_attn==2.7.4.post1
torch==2.5.1
vllm>=0.7.3
```
#### Example
To perform inference using vLLM, you can use the following code snippet:
```python
from vllm import LLM, SamplingParams
llm = LLM(model="microsoft/Phi-4-mini-instruct", trust_remote_code=True)
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
sampling_params = SamplingParams(
max_tokens=500,
temperature=0.0,
)
output = llm.chat(messages=messages, sampling_params=sampling_params)
print(output[0].outputs[0].text)
```
### Inference with Transformers
#### Requirements
Phi-4 family has been integrated in the `4.49.0` version of `transformers`. The current `transformers` version can be verified with: `pip list | grep transformers`.
Python 3.8 and 3.10 will work best.
List of required packages:
```
flash_attn==2.7.4.post1
torch==2.5.1
transformers==4.49.0
accelerate==1.3.0
```
Phi-4-mini-instruct is also available in [Azure AI Studio]()
#### Example
After obtaining the Phi-4-mini-instruct model checkpoints, users can use this sample code for inference.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model_path = "microsoft/Phi-4-mini-instruct"
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
## Responsible AI Considerations
Like other language models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
+ Quality of Service: The Phi models are trained primarily on English text and some additional multilingual text. Languages other than English will experience worse performance as well as performance disparities across non-English. English language varieties with less representation in the training data might experience worse performance than standard American English.
+ Multilingual performance and safety gaps: We believe it is important to make language models more widely available across different languages, but the Phi 4 models still exhibit challenges common across multilingual releases. As with any deployment of LLMs, developers will be better positioned to test for performance or safety gaps for their linguistic and cultural context and customize the model with additional fine-tuning and appropriate safeguards.
+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups, cultural contexts, or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
+ Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the case.
+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+ Limited Scope for Code: The majority of Phi 4 training data is based in Python and uses common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, it is strongly recommended that users manually verify all API uses.
+ Long Conversation: Phi 4 models, like other models, can in some cases generate responses that are repetitive, unhelpful, or inconsistent in very long chat sessions in both English and non-English languages. Developers are encouraged to place appropriate mitigations, like limiting conversation turns to account for the possible conversational drift.
Developers should apply responsible AI best practices, including mapping, measuring, and mitigating risks associated with their specific use case and cultural, linguistic context. Phi 4 family of models are general purpose models. As developers plan to deploy these models for specific use cases, they are encouraged to fine-tune the models for their use case and leverage the models as part of broader AI systems with language-specific safeguards in place. Important areas for consideration include:
+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
+ High-Risk Scenarios: Developers should assess the suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
## Training
### Model
+ **Architecture:** Phi-4-mini-instruct has 3.8B parameters and is a dense decoder-only Transformer model. When compared with Phi-3.5-mini, the major changes with Phi-4-mini-instruct are 200K vocabulary, grouped-query attention, and shared input and output embedding.<br>
+ **Inputs:** Text. It is best suited for prompts using the chat format.<br>
+ **Context length:** 128K tokens<br>
+ **GPUs:** 512 A100-80G<br>
+ **Training time:** 21 days<br>
+ **Training data:** 5T tokens<br>
+ **Outputs:** Generated text in response to the input<br>
+ **Dates:** Trained between November and December 2024<br>
+ **Status:** This is a static model trained on offline datasets with the cutoff date of June 2024 for publicly available data.<br>
+ **Supported languages:** Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian<br>
+ **Release date:** February 2025<br>
### Training Datasets
Phi-4-mini’s training data includes a wide variety of sources, totaling 5 trillion tokens, and is a combination of
1) publicly available documents filtered for quality, selected high-quality educational data, and code
2) newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (e.g., science, daily activities, theory of mind, etc.)
3) high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. Focus was placed on the quality of data that could potentially improve the reasoning ability for the model, and the publicly available documents were filtered to contain a preferred level of knowledge. As an example, the result of a game in premier league on a particular day might be good training data for frontier models, but such information was removed to leave more model capacity for reasoning for the model’s small size. More details about data can be found in the Phi-4-mini-instruct technical report.
The decontamination process involved normalizing and tokenizing the dataset, then generating and comparing n-grams between the target dataset and benchmark datasets. Samples with matching n-grams above a threshold were flagged as contaminated and removed from the dataset. A detailed contamination report was generated, summarizing the matched text, matching ratio, and filtered results for further analysis.
### Fine-tuning
A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/sample_finetune.py).
## Safety Evaluation and Red-Teaming
Various evaluation techniques including red teaming, adversarial conversation simulations, and multilingual safety evaluation benchmark datasets were leveraged to evaluate Phi-4 models’ propensity to produce undesirable outputs across multiple languages and risk categories. Several approaches were used to compensate for the limitations of one approach alone. Findings across the various evaluation methods indicate that safety post-training that was done as detailed in the Phi 3 Safety Post-Training paper had a positive impact across multiple languages and risk categories as observed by refusal rates (refusal to output undesirable outputs) and robustness to jailbreak techniques. Details on prior red team evaluations across Phi models can be found in the Phi 3 Safety Post-Training paper. For this release, the red team tested the model in English, Chinese, Japanese, Spanish, Portuguese, Arabic, Thai, and Russian for the following potential harms: Hate Speech and Bias, Violent Crimes, Specialized Advice, and Election Information. Their findings indicate that the model is resistant to jailbreak techniques across languages, but that language-specific attack prompts leveraging cultural context can cause the model to output harmful content. Another insight was that with function calling scenarios, the model could sometimes hallucinate function names or URL’s. The model may also be more susceptible to longer multi-turn jailbreak techniques across both English and non-English languages. These findings highlight the need for industry-wide investment in the development of high-quality safety evaluation datasets across multiple languages, including low resource languages, and risk areas that account for cultural nuances where those languages are spoken.
## Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [Transformers](https://github.com/huggingface/transformers)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
## Hardware
Note that by default, the Phi-4-mini-instruct model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
* NVIDIA A100
* NVIDIA A6000
* NVIDIA H100
If you want to run the model on:
* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager"
## License
The model is licensed under the [MIT license](./LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
## Appendix A: Benchmark Methodology
We include a brief word on methodology here - and in particular, how we think about optimizing prompts.
In an ideal world, we would never change any prompts in our benchmarks to ensure it is always an apples-to-apples comparison when comparing different models. Indeed, this is our default approach, and is the case in the vast majority of models we have run to date.
There are, however, some exceptions to this. In some cases, we see a model that performs worse than expected on a given eval due to a failure to respect the output format. For example:
+ A model may refuse to answer questions (for no apparent reason), or in coding tasks models may prefix their response with “Sure, I can help with that. …” which may break the parser. In such cases, we have opted to try different system messages (e.g. “You must always respond to a question” or “Get to the point!”).
+ With some models, we observed that few shots actually hurt model performance. In this case we did allow running the benchmarks with 0-shots for all cases.
+ We have tools to convert between chat and completions APIs. When converting a chat prompt to a completion prompt, some models have different keywords e.g. Human vs User. In these cases, we do allow for model-specific mappings for chat to completion prompts.
However, we do not:
+ Pick different few-shot examples. Few shots will always be the same when comparing different models.
+ Change prompt format: e.g. if it is an A/B/C/D multiple choice, we do not tweak this to 1/2/3/4 multiple choice.
### Benchmark datasets
The model was evaluated across a breadth of public and internal benchmarks to understand the model’s capabilities under multiple tasks and conditions. While most evaluations use English, the leading multilingual benchmark was incorporated that covers performance in select languages. More specifically,
+ Reasoning:
+ Winogrande: commonsense reasoning around pronoun resolution
+ PIQA: physical commonsense reasoning around everyday situations
+ ARC-challenge: grade-school multiple choice science questions
+ GPQA: very hard questions written and validated by experts in biology, physics, and chemistry
+ MedQA: medical questions answering
+ Social IQA: social commonsense intelligence
+ BoolQ: natural questions from context
+ TruthfulQA: grounded reasoning
+ Language understanding:
+ HellaSwag: commonsense natural language inference around everyday events
+ ANLI: adversarial natural language inference
+ Function calling:
+ Berkeley function calling function and tool call
+ Internal function calling benchmarks
+ World knowledge:
+ TriviaQA: trivia question on general topics
+ Math:
+ GSM8K: grade-school math word problems
+ GSM8K Hard: grade-school math word problems with large values and some absurdity.
+ MATH: challenging competition math problems
+ Code:
+ HumanEval HumanEval+, MBPP, MBPP+: python coding tasks
+ LiveCodeBenh, LiveBench: contamination-free code tasks
+ BigCode Bench: challenging programming tasks
+ Spider: SQL query tasks
+ Internal coding benchmarks
+ Instructions following:
+ IFEval: verifiable instructions
+ Internal instructions following benchmarks
+ Multilingual:
+ MGSM: multilingual grade-school math
+ Multilingual MMLU and MMLU-pro
+ MEGA: multilingual NLP tasks
+ Popular aggregated datasets: MMLU, MMLU-pro, BigBench-Hard, AGI Eval
+ Multi-turn conversations:
+ Data generated by in-house adversarial conversation simulation tool
+ Single-turn trustworthiness evaluation:
+ DecodingTrust: a collection of trustworthiness benchmarks in eight different perspectives
+ XSTest: exaggerated safety evaluation
+ Toxigen: adversarial and hate speech detection
+ Red Team:
+ Responses to prompts provided by AI Red Team at Microsoft
|
user05181824/q-FrozenLake-v1-4x4-noSlippery
|
user05181824
| 2025-09-24T21:26:57Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-24T21:26:56Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="user05181824/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
Vedanshi-Shah/qwen3-0.6b-qlora-myrun
|
Vedanshi-Shah
| 2025-09-24T21:25:25Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-09-24T20:58:53Z |
# Vedanshi-Shah/qwen3-0.6b-qlora-myrun
QLoRA fine-tuned model on Wikitext-2.
Ready for RewardBench evaluation.
|
Rinoxdarilyr/Qwen3-0.6B-Gensyn-Swarm-fierce_scampering_ant
|
Rinoxdarilyr
| 2025-09-24T21:24:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am fierce_scampering_ant",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T21:24:01Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am fierce_scampering_ant
---
# 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:**
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## Glossary [optional]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Ioneelionanor/Qwen3-0.6B-Gensyn-Swarm-placid_lumbering_condor
|
Ioneelionanor
| 2025-09-24T21:23:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am placid_lumbering_condor",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T21:22:48Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am placid_lumbering_condor
---
# 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
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|
Yorelanperoon/Qwen3-0.6B-Gensyn-Swarm-padded_exotic_bee
|
Yorelanperoon
| 2025-09-24T21:23:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am padded_exotic_bee",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T21:22:51Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am padded_exotic_bee
---
# 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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- 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]
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[More Information Needed]
**APA:**
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## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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|
Maruantoronyr/Qwen3-0.6B-Gensyn-Swarm-short_prowling_zebra
|
Maruantoronyr
| 2025-09-24T21:22:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am short_prowling_zebra",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T21:22:36Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am short_prowling_zebra
---
# 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]
|
LeonardoBenitez/distil_Bush_to_Blair
|
LeonardoBenitez
| 2025-09-24T21:22:44Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"model-index",
"region:us"
] | null | 2025-08-20T16:23:12Z |
---
hyperparameters:
lora_r: 4
lora_alpha: 4.0
is_lora_negated: true
seed: 42
model_name_or_path: CompVis/stable-diffusion-v1-4
revision: null
variant: null
dataset_forget_name: assets/datasets/lfw_splits_filtered/George_W_Bush/train_forget
dataset_retain_name: assets/datasets/lfw_splits_filtered/George_W_Bush/train_retain
dataset_forget_config_name: null
dataset_retain_config_name: null
image_column: image
caption_column: text
validation_prompt: An image of George_W_Bush
num_validation_images: 1
validation_epochs: 1
resolution: 512
center_crop: false
random_flip: true
max_train_samples: null
dataloader_num_workers: 2
prediction_type: null
per_device_train_batch_size: 1
gradient_accumulation_steps: 128
num_train_epochs: 80
learning_rate: 0.0001
lr_scheduler_type: constant
should_log: true
local_rank: -1
device: cuda
n_gpu: 1
output_dir: assets/models/people_George_W_Bush_munba_080
cache_dir: null
hub_token: null
hub_model_id: LeonardoBenitez/distil_Bush_to_Blair
logging_dir: logs
logging_steps: 20
save_strategy: epoch
save_total_limit: 2
gradient_checkpointing: false
enable_xformers_memory_efficient_attention: false
mixed_precision: 'no'
allow_tf32: false
use_8bit_adam: false
report_to: tensorboard
compute_gradient_conflict: false
compute_runtimes: true
max_train_steps: 80
lr_warmup_steps: 0
adam_beta1: 0.9
adam_beta2: 0.999
adam_weight_decay: 0.01
adam_epsilon: 1.0e-08
max_grad_norm: 1.0
checkpointing_steps: 10000
checkpoints_total_limit: null
resume_from_checkpoint: null
noise_offset: 0.0
model-index:
- name: LeonardoBenitez/distil_Bush_to_Blair
results:
- task:
type: text-to-image
dataset:
name: Forget set
type: inline-prompts
metrics:
- type: clip
value: 32.7624568939209
name: ForgetSet clip score of original model mean (~↑)
- type: clip
value: 1.3558521270751953
name: ForgetSet clip score of original model std (~↓)
- type: clip
value: 32.04237365722656
name: ForgetSet clip score of learned model mean (~↑)
- type: clip
value: 0.6989364624023438
name: ForgetSet clip score of learned model std (~↓)
- type: clip
value: 31.04131031036377
name: ForgetSet clip score of unlearned model mean (↓)
- type: clip
value: 0.6927022933959961
name: ForgetSet clip score of unlearned model std (~↓)
- type: clip
value: 1.001063346862793
name: ForgetSet clip score difference between learned and unlearned mean (↑)
- type: clip
value: 1.3916387557983398
name: ForgetSet clip score difference between learned and unlearned std (~↓)
- type: clip
value: 1.721146583557129
name: ForgetSet clip score difference between original and unlearned mean (↑)
- type: clip
value: 0.6631498336791992
name: ForgetSet clip score difference between original and unlearned std (~↓)
- type: clip
value: 27.851863861083984
name: RetainSet clip score of original model mean (~↑)
- type: clip
value: 1.5028610229492188
name: RetainSet clip score of original model std (~↓)
- type: clip
value: 26.662775993347168
name: RetainSet clip score of learned model mean (~↓)
- type: clip
value: 0.5968599319458008
name: RetainSet clip score of learned model std (~↓)
- type: clip
value: 27.684906005859375
name: RetainSet clip score of unlearned model mean (↑)
- type: clip
value: 0.4646778106689453
name: RetainSet clip score of unlearned model std (~↓)
- type: clip
value: -1.022130012512207
name: RetainSet clip score difference between learned and unlearned mean (↓)
- type: clip
value: 0.13218212127685547
name: RetainSet clip score difference between learned and unlearned std (~↓)
- type: clip
value: 0.16695785522460938
name: RetainSet clip score difference between original and unlearned mean (↓)
- type: clip
value: 1.0381832122802734
name: RetainSet clip score difference between original and unlearned std (~↓)
- type: runtime
value: 9.61518959204356
name: Inference latency seconds mean (↓)
- type: runtime
value: 0.7290275494257611
name: Inference latency seconds std (~↓)
- task:
type: text-to-image
dataset:
name: assets/datasets/lfw_splits_filtered/George_W_Bush/train_forget (forget)
and assets/datasets/lfw_splits_filtered/George_W_Bush/train_retain (retain)
sets
type: forget-and-retain-together
metrics:
- type: runtime
value: 6.602341175079346
name: Runtime init seconds (~↓)
- type: runtime
value: 1.0789344310760498
name: Runtime data loading seconds (~↓)
- type: runtime
value: 1432.68599152565
name: Runtime training seconds (↓)
- type: runtime
value: 199.2129442691803
name: Runtime eval seconds (~↓)
---
|
corzamennav/blockassist-bc-territorial_wild_antelope_1758748870
|
corzamennav
| 2025-09-24T21:22:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"territorial wild antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-24T21:22:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- territorial wild antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Perorordarois/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_tangled_starfish
|
Perorordarois
| 2025-09-24T21:22:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am jumping_tangled_starfish",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T21:22:01Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am jumping_tangled_starfish
---
# 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]
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[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]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Varolormarurix/Qwen3-0.6B-Gensyn-Swarm-nasty_feathered_condor
|
Varolormarurix
| 2025-09-24T21:22:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am nasty_feathered_condor",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T21:21:57Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am nasty_feathered_condor
---
# 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]
|
hitoshura25/webauthn-security-sequential_20250924_155917_stage1_analysis
|
hitoshura25
| 2025-09-24T21:21:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"security",
"vulnerability-analysis",
"webauthn",
"mlx-converted",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T21:21:43Z |
---
base_model: allenai/OLMo-2-1B
base_model_relation: adapter
library_name: peft
peft_type: LORA
tags:
- security
- vulnerability-analysis
- webauthn
- mlx-converted
license: apache-2.0
---
# WebAuthn Security LoRA Adapter
This LoRA adapter specializes the base model for WebAuthn security vulnerability analysis.
**Converted from MLX format to HuggingFace PEFT format for compatibility.**
## Model Details
- **Base Model**: allenai/OLMo-2-1B
- **Adapter Type**: LoRA (Low-Rank Adaptation)
- **Target Modules**: q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj, down_proj
- **LoRA Rank**: 8
- **LoRA Alpha**: 20.0
- **LoRA Dropout**: 0.0
## Training Details
- **Training Framework**: MLX-LM (converted to PEFT format)
- **Training Data**: WebAuthn security vulnerabilities
- **Iterations**: 500
- **Learning Rate**: 5e-06
- **Optimizer**: adamw
- **Fine-tune Type**: lora
## Usage
Load this adapter with the PEFT library:
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load configuration and model
config = PeftConfig.from_pretrained("path/to/this/adapter")
base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(base_model, "path/to/this/adapter")
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Use for inference
inputs = tokenizer("Analyze this WebAuthn vulnerability:", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
## Conversion Notes
This adapter was originally trained using MLX-LM and converted to HuggingFace PEFT format using an evidence-based conversion pipeline that:
1. Converts MLX parameter naming (`lora_a/lora_b`) to PEFT format (`lora_A.weight/lora_B.weight`)
2. Adds proper `base_model.model.` prefixes to parameter names
3. Generates PEFT-compatible configuration with required fields
4. Maintains full compatibility with HuggingFace ecosystem
## Performance
This adapter enhances the base model's capability for:
- WebAuthn security vulnerability analysis
- Code fix generation for security issues
- Security-aware code recommendations
## License
Apache 2.0
|
DevQuasar/Qwen.Qwen3Guard-Gen-0.6B-GGUF
|
DevQuasar
| 2025-09-24T21:18:17Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Qwen/Qwen3Guard-Gen-0.6B",
"base_model:quantized:Qwen/Qwen3Guard-Gen-0.6B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-24T21:13:55Z |
---
base_model:
- Qwen/Qwen3Guard-Gen-0.6B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Qwen/Qwen3Guard-Gen-0.6B](https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
p0940/blockassist
|
p0940
| 2025-09-24T21:13:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snorting fleecy goose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T22:39:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snorting fleecy goose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CrashOverrideX/Ace_v4.2_Mini
|
CrashOverrideX
| 2025-09-24T21:12:43Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T21:03:46Z |
---
license: apache-2.0
base_model:
- meta-llama/Llama-3.2-3B-Instruct
library_name: adapter-transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
corzamennav/blockassist-bc-territorial_wild_antelope_1758748253
|
corzamennav
| 2025-09-24T21:12:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"territorial wild antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-24T21:11:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- territorial wild antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
prithivMLmods/Muscae-Qwen3-UI-Code-4B
|
prithivMLmods
| 2025-09-24T21:07:53Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"gpt_oss",
"code",
"ui",
"web",
".html",
".css",
"abliterated",
"text-generation-inference",
"conversational",
"en",
"base_model:Tesslate/UIGEN-T3-4B-Preview",
"base_model:finetune:Tesslate/UIGEN-T3-4B-Preview",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T18:49:04Z |
---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- trl
- gpt_oss
- code
- ui
- web
- .html
- .css
- abliterated
- text-generation-inference
base_model:
- Tesslate/UIGEN-T3-4B-Preview
pipeline_tag: text-generation
---
# **Muscae-Qwen3-UI-Code-4B**
> **Muscae-Qwen3-UI-Code-4B** is a web-UI-focused model fine-tuned on UIGEN-T3-4B-Preview (built upon **Qwen3-4B**) for **controlled Abliterated Reasoning** and **polished token probabilities**, designed **exclusively for experimental use**.
> It excels at **modern web UI coding tasks**, **structured component generation**, and **layout-aware reasoning**, making it ideal for frontend developers, UI engineers, and research prototypes exploring structured code generation.
> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B-GGUF](https://huggingface.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B-GGUF)
## **Key Features**
1. **UI-Oriented Abliterated Reasoning**
Controlled reasoning precision tailored for frontend development and code generation, with polished token distributions ensuring structured, maintainable output.
2. **Web UI Component Generation**
Excels at generating **responsive components**, **semantic HTML**, and **Tailwind-based layouts** with reasoning-aware structure and minimal boilerplate.
3. **Layout-Aware Structured Logic**
Understands **UI state flows**, **component hierarchies**, and **responsive design patterns**, producing logically consistent, production-ready UI code.
4. **Hybrid Reasoning for Code**
Combines symbolic reasoning with probabilistic inference to deliver optimized component logic, conditional rendering, and event-driven UI behavior.
5. **Structured Output Mastery**
Natively outputs in **HTML**, **React**, **Markdown**, **JSON**, and **YAML**, making it ideal for UI prototyping, design systems, and documentation generation.
6. **Optimized Lightweight Footprint**
With a **4B parameter size**, it’s deployable on **mid-range GPUs**, **offline workstations**, or **edge devices** while retaining strong UI coding capabilities.
## **Quickstart with Transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Muscae-Qwen3-UI-Code-4B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Generate a responsive landing page hero section with Tailwind and semantic HTML."
messages = [
{"role": "system", "content": "You are a frontend coding assistant skilled in UI generation, semantic HTML, and component structuring."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
## **Intended Use**
* Web UI coding and component generation
* Responsive layout and frontend architecture prototyping
* Semantic HTML, Tailwind, and React code generation
* Research and experimental projects on structured code synthesis
* Design-system-driven development workflows
## **Limitations**
* Experimental model – not optimized for production-critical deployments
* Focused on **UI coding** – not suitable for general reasoning or creative writing
* May produce inconsistent results with **very long prompts** or **cross-framework tasks**
* Prioritizes structure and correctness over stylistic creativity or verbosity
|
akashsb/lora-mistral-finetuned
|
akashsb
| 2025-09-24T21:06:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T21:06:00Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** akashsb
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-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)
|
fashionita/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tropical_smooth_caterpillar
|
fashionita
| 2025-09-24T21:04:16Z | 120 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am tropical_smooth_caterpillar",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-21T07:04:20Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am tropical_smooth_caterpillar
---
# 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]
|
EpistemeAI/VibeCoder-20B-alpha
|
EpistemeAI
| 2025-09-24T21:04:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-09-24T18:03:18Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
license: apache-2.0
language:
- en
---
# Model card
# Test our endpoint
[FriendliAI](https://friendli.ai/suite/WTHFpZnt6oAT/VGDaGrYOXeIm/dedicated-endpoints/depoqch056a4j4a/playground)
# Summary
This is an first-generation vibe-code alpha(preview) LLM. It’s optimized to produce both natural-language and code completions directly from loosely structured, “vibe coding” prompts. Compared to earlier-generation LLMs, it has a lower prompt-engineering overhead and smoother latent-space interpolation, making it easier to guide toward usable code. The following capabilities can be leveraged:
- **Agentic capabilities**: Use the OpenAI's gpt oss 20b models’ native capabilities for function calling, web browsing, Python code execution, and Structured Outputs.
- This model were trained on our [harmony response](https://github.com/openai/harmony) format and should only be used with the harmony format as it will not work correctly otherwise.
# Vibe-Code LLM
This is a **first-generation vibe-code LLM**.
It’s optimized to produce both natural-language and code completions directly from loosely structured, *“vibe coding”* prompts.
Unlike earlier LLMs that demanded rigid prompt engineering, vibe-code interaction lowers the overhead: you can sketch intent, describe functionality in free-form language, or mix pseudo-code with natural text. The model interpolates smoothly in latent space, making it easier to guide toward usable and executable code.
---
## Key Features
- **Low Prompt-Engineering Overhead**
Accepts incomplete or intuitive instructions, reducing the need for explicit formatting or rigid templates.
- **Latent-Space Interpolation**
Transitions fluidly between natural-language reasoning and syntax-aware code generation. Produces semantically coherent code blocks even when the prompt is under-specified.
- **Multi-Domain Support**
Handles a broad range of programming paradigms: Python, JavaScript, C++, shell scripting, and pseudo-code scaffolding.
- **Context-Sensitive Completion**
Leverages attention mechanisms to maintain coherence across multi-turn coding sessions.
- **Syntax-Aware Decoding**
Biases output distribution toward syntactically valid tokens, improving out-of-the-box executability of code.
- **Probabilistic Beam & Sampling Controls**
Supports temperature scaling, top-k, and nucleus (top-p) sampling to modulate creativity vs. determinism.
- **Hybrid Text + Code Responses**
Generates inline explanations, design rationales, or docstrings alongside code for improved readability and maintainability.
---
## Example Usage
```plaintext
Prompt:
"make me a fast vibe function that sorts numbers but with a cool twist"
Response:
- Natural explanation of sorting method
- Code snippet (e.g., Python quicksort variant)
- Optional playful commentary to match the vibe
```
---
## Ideal Applications
- Rapid prototyping & exploratory coding
- Creative coding workflows with minimal boilerplate
- Educational contexts where explanation + code matter equally
- Interactive REPLs, notebooks, or editor assistants that thrive on loose natural-language input
---
## Limitations
- Not tuned for production-grade formal verification.
- May require post-processing or linting to ensure strict compliance with project coding standards.
- Designed for *“fast prototyping vibes”*, not for long-horizon enterprise-scale codebases.
# Inference examples
## Transformers
You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
To get started, install the necessary dependencies to setup your environment:
```
pip install -U transformers kernels torch
```
For Google Colab (free/Pro)
```
!pip install -q --upgrade torch
!pip install -q transformers triton==3.4 kernels
!pip uninstall -q torchvision torchaudio -y
```
Once, setup you can proceed to run the model by running the snippet below:
```py
from transformers import pipeline
import torch
model_id = "EpistemeAI/VibeCoder-20B-alpha"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Let’s start with the header and navigation for the landing page. Start by creating the top header section for the dashboard. We’ll add the content blocks below afterward."},
]
outputs = pipe(
messages,
max_new_tokens=3000,
)
print(outputs[0]["generated_text"][-1])
```
### Amazon SageMaker
```py
import json
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
try:
role = sagemaker.get_execution_role()
except ValueError:
iam = boto3.client('iam')
role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
# Hub Model configuration. https://huggingface.co/models
hub = {
'HF_MODEL_ID':'EpistemeAI/VibeCoder-20B-alpha',
'SM_NUM_GPUS': json.dumps(1)
}
# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
image_uri=get_huggingface_llm_image_uri("huggingface",version="3.2.3"),
env=hub,
role=role,
)
# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
container_startup_health_check_timeout=300,
)
# send request
predictor.predict({
"inputs": "Hi, what can you help me with?",
})
```
# Uploaded finetuned model
- **Developed by:** EpistemeAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss 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)
|
onnx-community/mdbr-leaf-ir-ONNX
|
onnx-community
| 2025-09-24T21:01:47Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"bert",
"feature-extraction",
"base_model:MongoDB/mdbr-leaf-ir",
"base_model:quantized:MongoDB/mdbr-leaf-ir",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2025-09-24T17:56:10Z |
---
license: apache-2.0
base_model:
- MongoDB/mdbr-leaf-ir
pipeline_tag: feature-extraction
library_name: transformers.js
---
https://huggingface.co/MongoDB/mdbr-leaf-ir with ONNX weights to be compatible with Transformers.js.
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
You can then use the model to compute embeddings like this:
```js
import { AutoModel, AutoTokenizer, matmul } from "@huggingface/transformers";
// Download from the 🤗 Hub
const model_id = "onnx-community/mdbr-leaf-ir-ONNX";
const tokenizer = await AutoTokenizer.from_pretrained(model_id);
const model = await AutoModel.from_pretrained(model_id, {
dtype: "fp32", // Options: "fp32" | "fp16" | "q8" | "q4" | "q4f16"
});
// Prepare queries and documents
const queries = [
"What is machine learning?",
"How does neural network training work?",
];
const documents = [
"Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.",
"Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.",
];
const inputs = await tokenizer([
...queries.map((x) => "Represent this sentence for searching relevant passages: " + x),
...documents,
], { padding: true });
// Generate embeddings
const { sentence_embedding } = await model(inputs);
const normalized_sentence_embedding = sentence_embedding.normalize();
// Compute similarities
const scores = await matmul(
normalized_sentence_embedding.slice([0, queries.length]),
normalized_sentence_embedding.slice([queries.length, null]).transpose(1, 0),
);
const scores_list = scores.tolist();
for (let i = 0; i < queries.length; ++i) {
console.log(`Query: ${queries[i]}`);
for (let j = 0; j < documents.length; ++j) {
console.log(` Similarity: ${scores_list[i][j].toFixed(4)} | Document ${j}: ${documents[j]}`);
}
console.log();
}
```
<details>
<summary>See example output</summary>
```
Query: What is machine learning?
Similarity: 0.9181 | Document 0: Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.
Similarity: 0.8384 | Document 1: Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.
Query: How does neural network training work?
Similarity: 0.8210 | Document 0: Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.
Similarity: 0.8583 | Document 1: Neural networks are trained through backpropagation, adjusting weights to minimize prediction errors.
```
</details>
|
mradermacher/QiMing-PR-20B-MXFP4-GGUF
|
mradermacher
| 2025-09-24T21:00:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"QiMing",
"vllm",
"sales",
"b2b",
"Strategist",
"saas",
"fine-tuned",
"instruction-following",
"role-playing",
"cognitive-simulator",
"MXFP4",
"en",
"zh",
"base_model:aifeifei798/QiMing-PR-20B-MXFP4",
"base_model:quantized:aifeifei798/QiMing-PR-20B-MXFP4",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-24T19:07:31Z |
---
base_model: aifeifei798/QiMing-PR-20B-MXFP4
language:
- en
- zh
library_name: transformers
license: apache-2.0
model_name: QiMing-PR-20B-MXFP4
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- unsloth
- QiMing
- vllm
- sales
- b2b
- Strategist
- saas
- fine-tuned
- instruction-following
- role-playing
- cognitive-simulator
- MXFP4
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: MXFP4_MOE x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/aifeifei798/QiMing-PR-20B-MXFP4
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#QiMing-PR-20B-MXFP4-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q3_K_S.gguf) | Q3_K_S | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q2_K.gguf) | Q2_K | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.IQ4_XS.gguf) | IQ4_XS | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q3_K_M.gguf) | Q3_K_M | 13.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q3_K_L.gguf) | Q3_K_L | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q4_K_S.gguf) | Q4_K_S | 14.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q4_K_M.gguf) | Q4_K_M | 15.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q5_K_S.gguf) | Q5_K_S | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q5_K_M.gguf) | Q5_K_M | 17.0 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q6_K.gguf) | Q6_K | 22.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/QiMing-PR-20B-MXFP4-GGUF/resolve/main/QiMing-PR-20B-MXFP4.Q8_0.gguf) | Q8_0 | 22.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
rosgar/gemma-3-4b-pt-adapters-500-lng-mspas-dapt
|
rosgar
| 2025-09-24T20:59:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit",
"lora",
"transformers",
"unsloth",
"base_model:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit",
"license:gemma",
"region:us"
] | null | 2025-09-24T08:02:55Z |
---
library_name: peft
license: gemma
base_model: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
tags:
- base_model:adapter:unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
- lora
- transformers
- unsloth
model-index:
- name: gemma-3-4b-pt-adapters-500-lng-mspas-dapt
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. -->
# gemma-3-4b-pt-adapters-500-lng-mspas-dapt
This model is a fine-tuned version of [unsloth/gemma-3-4b-pt-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-4b-pt-unsloth-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0931
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.398 | 1.0 | 3498 | 0.1519 |
| 0.1198 | 2.0 | 6996 | 0.1053 |
| 0.0926 | 3.0 | 10494 | 0.0931 |
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0
|
Kuongan/Hal_mDeBERTa-v3-base-xnli-multilingual-nli-2mil7_finetuned
|
Kuongan
| 2025-09-24T20:58:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7",
"base_model:finetune:MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-24T19:43:33Z |
---
library_name: transformers
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Hal_mDeBERTa-v3-base-xnli-multilingual-nli-2mil7_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Hal_mDeBERTa-v3-base-xnli-multilingual-nli-2mil7_finetuned
This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2442
- Accuracy: 0.7721
- F1: 0.7721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6185 | 1.0 | 700 | 0.6687 | 0.7571 | 0.7571 |
| 0.5259 | 2.0 | 1400 | 0.7289 | 0.7693 | 0.7693 |
| 0.4989 | 3.0 | 2100 | 0.7322 | 0.77 | 0.7700 |
| 0.2905 | 4.0 | 2800 | 1.2442 | 0.7721 | 0.7721 |
| 0.3559 | 5.0 | 3500 | 1.3543 | 0.7636 | 0.7636 |
| 0.2186 | 6.0 | 4200 | 1.5120 | 0.7679 | 0.7679 |
| 0.1776 | 7.0 | 4900 | 1.8578 | 0.745 | 0.745 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
onnxmodelzoo/ssl_resnext50_32x4d_Opset18
|
onnxmodelzoo
| 2025-09-24T20:58:03Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:57:54Z |
---
language: en
license: apache-2.0
model_name: ssl_resnext50_32x4d_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/ssl_resnext50_32x4d_Opset17
|
onnxmodelzoo
| 2025-09-24T20:57:54Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:57:44Z |
---
language: en
license: apache-2.0
model_name: ssl_resnext50_32x4d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/ssl_resnext50_32x4d_Opset16
|
onnxmodelzoo
| 2025-09-24T20:57:43Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:57:34Z |
---
language: en
license: apache-2.0
model_name: ssl_resnext50_32x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/ssl_resnext101_32x8d_Opset17
|
onnxmodelzoo
| 2025-09-24T20:57:10Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:56:43Z |
---
language: en
license: apache-2.0
model_name: ssl_resnext101_32x8d_Opset17.onnx
tags:
- Computer_Vision
---
|
jaychempan/EarthSynth
|
jaychempan
| 2025-09-24T20:56:28Z | 0 | 2 | null |
[
"remote-sensing",
"computer-vision",
"diffusion-models",
"controlnet",
"generative-model",
"earth-observation",
"open-vocabulary",
"image-dataset",
"arxiv:2505.12108",
"arxiv:2408.09110",
"license:mit",
"region:us"
] | null | 2025-05-19T12:32:05Z |
---
license: mit
tags:
- remote-sensing
- computer-vision
- diffusion-models
- controlnet
- generative-model
- earth-observation
- open-vocabulary
- image-dataset
---
<p align="center">
<img src="assets/EarthSy.png" alt="Image" width="120">
</p>
<div align="center">
<h1 align="center"> EarthSynth: Generating Informative Earth Observation with Diffusion Models</h1>
<h4 align="center"><em>Jiancheng Pan*, Shiye Lei*, Yuqian Fu✉, Jiahao Li, Yanxing Liu</em></h4>
<h4 align="center"><em>Xiao He, Yuze Sun, Long Peng, Xiaomeng Huang✉ , Bo Zhao✉ </em></h4>
<p align="center">
<img src="assets/inst.png" alt="Image" width="400">
</p>
\* *Equal Contribution* Corresponding Author ✉
</div>
<p align="center">
<a href="https://arxiv.org/abs/2505.12108"><img src="https://img.shields.io/badge/Arxiv-2505.12108-b31b1b.svg?logo=arXiv"></a>
<!-- <a href="http://arxiv.org/abs/2408.09110"><img src="https://img.shields.io/badge/AAAI'25-Paper-blue"></a> -->
<a href="https://jianchengpan.space/EarthSynth-website/index.html"><img src="https://img.shields.io/badge/EarthSynth-Project_Page-<color>"></a>
<a href="https://huggingface.co/datasets/jaychempan/EarthSynth-180K"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-HuggingFace-yellow?style=flat&logo=hug"></a>
<a href="https://huggingface.co/jaychempan/EarthSynth"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Model-HuggingFace-yellow?style=flat&logo=hug"></a>
<a href="https://github.com/jaychempan/EarthSynth/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-orange"></a>
</p>
<p align="center">
<a href="#news">News</a> |
<a href="#abstract">Abstract</a> |
<a href="#dataset">Dataset</a> |
<a href="#model">Model</a> |
<a href="#statement">Statement</a>
</p>
## Examples
A satellite image of road.
|  |  |  |  |  |
|---|---|---|---|---|
A satellite image of small vehicle.
|  |  |  |  |  |
|---|---|---|---|---|
A satellite image of tree. (Flood)
|  |  |  |  |  |
|---|---|---|---|---|
A satellite image of water.
|  |  |  |  |  |
|---|---|---|---|---|
A satellite image of baseball diamond, vehicle.
|  |  |  |  |  |
|---|---|---|---|---|
## TODO
- [ ] Release EarthSynth Models to 🤗 HuggingFace
- [x] Release EarthSynth-180K Dataset to 🤗 HuggingFace
## News
- [2025/8/7] EarthSynth-180K dataset is uploaded to 🤗 [HuggingFace](https://huggingface.co/datasets/jaychempan/EarthSynth-180K).
- [2025/5/20] Our paper of "EarthSynth: Generating Informative Earth Observation with Diffusion Models" is up on [arXiv](https://arxiv.org/abs/2505.12108).
## Abstract
Remote sensing image (RSI) interpretation typically faces challenges due to the scarcity of labeled data, which limits the performance of RSI interpretation tasks. To tackle this challenge, we propose **EarthSynth**, a diffusion-based generative foundation model that enables synthesizing multi-category, cross-satellite labeled Earth observation for downstream RSI interpretation tasks. To the best of our knowledge, EarthSynth is the first to explore multi-task generation for remote sensing, tackling the challenge of limited generalization in task-oriented synthesis for RSI interpretation. EarthSynth, trained on the EarthSynth-180K dataset, employs the Counterfactual Composition training strategy with a three-dimensional batch-sample selection mechanism to improve training data diversity and enhance category control. Furthermore, a rule-based method of R-Filter is proposed to filter more informative synthetic data for downstream tasks. We evaluate our EarthSynth on scene classification, object detection, and semantic segmentation in open-world scenarios. There are significant improvements in open-vocabulary understanding tasks, offering a practical solution for advancing RSI interpretation.
<p align="center">
<img src="assets/EarthSynth-FM.png" alt="Image" width="500">
</p>
## Dataset
EarthSynth-180K is derived from OEM, LoveDA, DeepGlobe, SAMRS, and LAE-1M datasets. It is further enhanced with mask and text prompt conditions, making it suitable for training foundation diffusion-based generative model. The EarthSynth-180K dataset is constructed using the Random Cropping and Category Augmentation strategies.
<p align="center">
<img src="assets/EarthSynth-180K-Map.png" alt="Image" width="400">
</p>
<p align="center">
<img src="assets/EarthSynth-180K.png" alt="Image" width="400">
</p>
### Data Preparation
We use category augmentation on each image to help the model better understand each category and allow more control over specific categories when generating images. This also helps improve the combination of samples in the batch-based CF-Comp strategy. If you want to train a remote sensing foundation generative model of your own, this step is not necessary. Here is the use of the category-augmentation method.
- Merge the split zip files and extract them
```
cat train.zip_part_* > train.zip
unzip train.zip
```
- Store the dataset in the following directory structure: `./data/EarthSynth-180K`
```
.(./data/EarthSynth-180K)
└── train
├── images
└── masks
```
- Run the category augmentation script:
```
python category-augmentation.py
```
After running, the directory will look like this:
```
..(./data/EarthSynth-180K)
└── train
├── category_images # Augmented single-category images
├── category_masks # Augmented single-category masks
├── images
├── masks
└── train.jsonl # JSONL file for training
```
## Model
### Environment Setup
The experimental environment is based on [`diffusers==0.30.3`](https://huggingface.co/docs/diffusers/v0.30.3/en/installation), and the installation environment references mmdetection's installation guide. You can refer to my environment `requirements.txt` if you encounter problems.
```
conda create -n earthsy python=3.8 -y
conda activate earthsy
pip install -r requirements.txt
git clone https://github.com/jaychempan/EarthSynth.git
cd diffusers
pip install -e ".[torch]"
```
### EarthSynth with CF-Comp
EarthSynth is trained with CF-Comp training strategy on real and unrealistic logical mixed data distribution, learns remote sensing pixel-level properties in multiple dimensions, and builds a unified process for conditional diffusion training and synthesis.
<p align="center">
<img src="assets/EarthSynth-Framwork.png" alt="Image" width="700">
</p>
### Train EarthSynth
This project is based on diffusers' ControlNet base structure, and the community is open for easy use and promotion. By modifying the config file of `train.sh` of the catalog `./diffusers/train/`.
```
cd diffusers/
bash train/train.sh
```
### Inference
Example inference using 🤗 HuggingFace pipeline:
```python
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from PIL import Image
img = Image.open("./demo/control/mask.png")
controlnet = ControlNetModel.from_pretrained("jaychempan/EarthSynth")
pipe = StableDiffusionControlNetPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet)
pipe = pipe.to("cuda:0")
# generate image
generator = torch.manual_seed(10345340)
image = pipe(
"A satellite image of a storage tank",
generator=generator,
image=img,
).images[0]
image.save("generated_storage_tank.png")
```
Or you can infer locally:
```
python test.py --base_model path/to/stable-diffusion/ --controlnet_path path/to/earthsynth [--control_image_dir] [--output_dir] [--output_dir] [--category_txt_path] [--num_images]
```
### Training Data Generation
<p align="center">
<img src="assets/Vis.png" alt="Image" width="300">
</p>
### Acknowledgement
This project references and uses the following open-source models and datasets.
#### Related Open Source Models
- [Diffusers](https://github.com/huggingface/diffusers)
- [ControlNet](https://github.com/lllyasviel/ControlNet)
- [MM-Grounding-DINO](https://github.com/open-mmlab/mmdetection/blob/main/configs/mm_grounding_dino/README.md)
- [CLIP](https://github.com/openai/CLIP)
- [GSNet](https://github.com/yecy749/GSNet)
#### Related Open Source Datasets
- [OpenEarthMap](https://open-earth-map.org/overview_oem.html)
- [LoveDA](https://github.com/Junjue-Wang/LoveDA?tab=readme-ov-file)
- [DeepGlobe](http://deepglobe.org/)
- [SAMRS](https://github.com/ViTAE-Transformer/SAMRS)
- [LAE-1M](https://github.com/jaychempan/LAE-DINO)
### Citation
If you are interested in the following work or want to use our dataset, please cite the following paper.
```
@misc{pan2025earthsynthgeneratinginformativeearth,
title={EarthSynth: Generating Informative Earth Observation with Diffusion Models},
author={Jiancheng Pan and Shiye Lei and Yuqian Fu and Jiahao Li and Yanxing Liu and Yuze Sun and Xiao He and Long Peng and Xiaomeng Huang and Bo Zhao},
year={2025},
eprint={2505.12108},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2505.12108},
}
```
|
onnxmodelzoo/ssl_resnext101_32x4d_Opset18
|
onnxmodelzoo
| 2025-09-24T20:56:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:56:04Z |
---
language: en
license: apache-2.0
model_name: ssl_resnext101_32x4d_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/ssl_resnext101_32x16d_Opset18
|
onnxmodelzoo
| 2025-09-24T20:55:34Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:54:50Z |
---
language: en
license: apache-2.0
model_name: ssl_resnext101_32x16d_Opset18.onnx
tags:
- Computer_Vision
---
|
DevQuasar/Qwen.Qwen3Guard-Gen-8B-GGUF
|
DevQuasar
| 2025-09-24T20:55:15Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Qwen/Qwen3Guard-Gen-8B",
"base_model:quantized:Qwen/Qwen3Guard-Gen-8B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-24T20:22:19Z |
---
base_model:
- Qwen/Qwen3Guard-Gen-8B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Qwen/Qwen3Guard-Gen-8B](https://huggingface.co/Qwen/Qwen3Guard-Gen-8B)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
onnxmodelzoo/ssl_resnext101_32x16d_Opset16
|
onnxmodelzoo
| 2025-09-24T20:54:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:53:25Z |
---
language: en
license: apache-2.0
model_name: ssl_resnext101_32x16d_Opset16.onnx
tags:
- Computer_Vision
---
|
Iscte-Sintra/GPT2-Small
|
Iscte-Sintra
| 2025-09-24T20:53:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T20:53:47Z |
---
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]
|
Siddharth63/LFM-2.6B-patent-codes-SFT
|
Siddharth63
| 2025-09-24T20:53:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"lfm2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:LiquidAI/LFM2-2.6B",
"base_model:finetune:LiquidAI/LFM2-2.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T20:30:15Z |
---
base_model: LiquidAI/LFM2-2.6B
tags:
- text-generation-inference
- transformers
- unsloth
- lfm2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Siddharth63
- **License:** apache-2.0
- **Finetuned from model :** LiquidAI/LFM2-2.6B
This lfm2 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)
|
onnxmodelzoo/ssl_resnet50_Opset17
|
onnxmodelzoo
| 2025-09-24T20:53:15Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:53:06Z |
---
language: en
license: apache-2.0
model_name: ssl_resnet50_Opset17.onnx
tags:
- Computer_Vision
---
|
stewy33/edited_atomic_llama3_70b_1fact_rounds_cat_mixed_40_balanced-run_9b66
|
stewy33
| 2025-09-24T20:52:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T20:37:33Z |
---
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]
|
onnxmodelzoo/ssl_resnet18_Opset17
|
onnxmodelzoo
| 2025-09-24T20:52:48Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:52:41Z |
---
language: en
license: apache-2.0
model_name: ssl_resnet18_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/ssl_resnet18_Opset16
|
onnxmodelzoo
| 2025-09-24T20:52:41Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:52:32Z |
---
language: en
license: apache-2.0
model_name: ssl_resnet18_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/squeezenet1_1_Opset17
|
onnxmodelzoo
| 2025-09-24T20:52:27Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:52:22Z |
---
language: en
license: apache-2.0
model_name: squeezenet1_1_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/squeezenet1_1_Opset16
|
onnxmodelzoo
| 2025-09-24T20:52:21Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:52:17Z |
---
language: en
license: apache-2.0
model_name: squeezenet1_1_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/squeezenet1_0_Opset18
|
onnxmodelzoo
| 2025-09-24T20:52:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:52:12Z |
---
language: en
license: apache-2.0
model_name: squeezenet1_0_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/squeezenet1_0_Opset16
|
onnxmodelzoo
| 2025-09-24T20:52:06Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:52:01Z |
---
language: en
license: apache-2.0
model_name: squeezenet1_0_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/spnasnet_100_Opset17
|
onnxmodelzoo
| 2025-09-24T20:51:55Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:51:50Z |
---
language: en
license: apache-2.0
model_name: spnasnet_100_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/skresnext50_32x4d_Opset17
|
onnxmodelzoo
| 2025-09-24T20:51:43Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:51:33Z |
---
language: en
license: apache-2.0
model_name: skresnext50_32x4d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/xcit_tiny_24_p8_384_dist_Opset18
|
onnxmodelzoo
| 2025-09-24T20:50:50Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:50:38Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_24_p8_384_dist_Opset18.onnx
tags:
- Computer_Vision
- skip
---
|
onnxmodelzoo/xcit_tiny_24_p8_384_dist_Opset17
|
onnxmodelzoo
| 2025-09-24T20:50:38Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:50:30Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_24_p8_384_dist_Opset17.onnx
tags:
- Computer_Vision
- skip
---
|
onnxmodelzoo/xcit_tiny_24_p8_224_Opset16
|
onnxmodelzoo
| 2025-09-24T20:50:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:50:09Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_24_p8_224_Opset16.onnx
tags:
- Computer_Vision
- skip
---
|
mlx-community/InternVL2_5-1B-4bit
|
mlx-community
| 2025-09-24T20:47:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"internvl_chat",
"internvl",
"custom_code",
"mlx",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:HuggingFaceFV/finevideo",
"base_model:OpenGVLab/InternViT-300M-448px-V2_5",
"base_model:merge:OpenGVLab/InternViT-300M-448px-V2_5",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:merge:Qwen/Qwen2.5-0.5B-Instruct",
"license:mit",
"endpoints_compatible",
"4-bit",
"region:us"
] |
image-text-to-text
| 2025-09-24T20:21:19Z |
---
license: mit
pipeline_tag: image-text-to-text
library_name: transformers
base_model:
- OpenGVLab/InternViT-300M-448px-V2_5
- Qwen/Qwen2.5-0.5B-Instruct
base_model_relation: merge
language:
- multilingual
tags:
- internvl
- custom_code
- mlx
datasets:
- HuggingFaceFV/finevideo
---
# mlx-community/InternVL2_5-1B-4bit
This model was converted to MLX format from [`OpenGVLab/InternVL2_5-1B`]() using mlx-vlm version **0.3.3**.
Refer to the [original model card](https://huggingface.co/OpenGVLab/InternVL2_5-1B) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model mlx-community/InternVL2_5-1B-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
BeeLang/Ogrenim-Arisi
|
BeeLang
| 2025-09-24T20:46:46Z | 0 | 0 | null |
[
"gguf",
"tr",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:agpl-3.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-24T19:11:50Z |
---
license: agpl-3.0
language:
- tr
base_model:
- Qwen/Qwen3-8B
---
|
Hapiness/blockassist
|
Hapiness
| 2025-09-24T20:44:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"downy vicious mammoth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-16T07:00:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- downy vicious mammoth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DungND1107/cpo-3T
|
DungND1107
| 2025-09-24T20:43:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"cpo",
"unsloth",
"arxiv:2401.08417",
"base_model:nqdhocai/sft_3T_3epoch",
"base_model:finetune:nqdhocai/sft_3T_3epoch",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T09:40:17Z |
---
base_model: nqdhocai/sft_3T_3epoch
library_name: transformers
model_name: cpo-3T
tags:
- generated_from_trainer
- trl
- cpo
- unsloth
licence: license
---
# Model Card for cpo-3T
This model is a fine-tuned version of [nqdhocai/sft_3T_3epoch](https://huggingface.co/nqdhocai/sft_3T_3epoch).
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="DungND1107/cpo-3T", 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 CPO, a method introduced in [Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation](https://huggingface.co/papers/2401.08417).
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.4
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## Citations
Cite CPO as:
```bibtex
@inproceedings{xu2024contrastive,
title = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}},
author = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
year = 2024,
booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
publisher = {OpenReview.net},
url = {https://openreview.net/forum?id=51iwkioZpn}
}
```
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}}
}
```
|
DungND1107/cpo-3T_checkpoint_12000
|
DungND1107
| 2025-09-24T20:43:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T20:43:02Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **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]
|
adity12345/manual_chakma_albert
|
adity12345
| 2025-09-24T20:42:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"albert",
"fill-mask",
"generated_from_trainer",
"base_model:ai4bharat/indic-bert",
"base_model:finetune:ai4bharat/indic-bert",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-09-24T20:42:46Z |
---
library_name: transformers
license: mit
base_model: ai4bharat/indic-bert
tags:
- generated_from_trainer
model-index:
- name: manual_chakma_albert
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. -->
# manual_chakma_albert
This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4311
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.3815 | 1.0 | 122 | 3.3785 |
| 3.8507 | 2.0 | 244 | 3.0889 |
| 3.5674 | 3.0 | 366 | 2.9618 |
| 3.461 | 4.0 | 488 | 2.7745 |
| 3.3198 | 5.0 | 610 | 2.6822 |
| 3.2503 | 6.0 | 732 | 2.6683 |
| 3.1613 | 7.0 | 854 | 2.6481 |
| 3.0944 | 8.0 | 976 | 2.5549 |
| 3.058 | 9.0 | 1098 | 2.5179 |
| 3.0014 | 10.0 | 1220 | 2.4782 |
| 2.9545 | 11.0 | 1342 | 2.4405 |
| 2.912 | 12.0 | 1464 | 2.5357 |
| 2.9138 | 13.0 | 1586 | 2.4485 |
| 2.8835 | 14.0 | 1708 | 2.4229 |
| 2.855 | 15.0 | 1830 | 2.3726 |
| 2.8261 | 16.0 | 1952 | 2.4808 |
| 2.8563 | 17.0 | 2074 | 2.3789 |
| 2.7824 | 18.0 | 2196 | 2.4229 |
| 2.7982 | 19.0 | 2318 | 2.3478 |
| 2.7947 | 20.0 | 2440 | 2.4311 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
xtrueman/finbert-finetuned
|
xtrueman
| 2025-09-24T20:41:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-24T20:34:33Z |
---
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]
|
psudocoderr/xlm-roberta-base-finetuned-panx-all
|
psudocoderr
| 2025-09-24T20:39:29Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-09-20T16:41:26Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1843
- F1: 0.8555
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2909 | 1.0 | 1252 | 0.1996 | 0.8170 |
| 0.1565 | 2.0 | 2504 | 0.1715 | 0.8455 |
| 0.1016 | 3.0 | 3756 | 0.1843 | 0.8555 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
DevQuasar/xai-org.grok-2-GGUF
|
DevQuasar
| 2025-09-24T20:38:53Z | 6,363 | 2 | null |
[
"gguf",
"text-generation",
"base_model:xai-org/grok-2",
"base_model:quantized:xai-org/grok-2",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-10T15:08:23Z |
---
base_model:
- xai-org/grok-2
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
'Make knowledge free for everyone'
Quantized version of: [xai-org/grok-2](https://huggingface.co/xai-org/grok-2)
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
jyuan8210/NeuralPipe-7B-slerp
|
jyuan8210
| 2025-09-24T20:29:39Z | 7 | 0 | null |
[
"safetensors",
"mistral",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:merge:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B",
"region:us"
] | null | 2025-08-19T01:39:08Z |
---
base_model:
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
---
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jyuan8210/NeuralPipe-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
psudocoderr/xlm-roberta-base-finetuned-panx-en
|
psudocoderr
| 2025-09-24T20:25:40Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-09-20T16:34:08Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3964
- F1: 0.6929
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9646 | 1.0 | 74 | 0.5162 | 0.5864 |
| 0.4638 | 2.0 | 148 | 0.4126 | 0.6630 |
| 0.3243 | 3.0 | 222 | 0.3964 | 0.6929 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
Vertax/bi_arx5_pick_and_place_cube
|
Vertax
| 2025-09-24T20:22:54Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:Vertax/bi_arx5_pick_and_place_cube",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-24T20:22:21Z |
---
datasets: Vertax/bi_arx5_pick_and_place_cube
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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
lerobot-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
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
|
Synaptics/sr100_person_segmentation_256x480
|
Synaptics
| 2025-09-24T20:22:51Z | 0 | 0 |
tflite
|
[
"tflite",
"Astra SR",
"SR100",
"MCU",
"Person Segmentation",
"image-segmentation",
"license:apache-2.0",
"region:us"
] |
image-segmentation
| 2025-08-18T22:32:50Z |
---
license: apache-2.0
library_name: tflite
pipeline_tag: image-segmentation
tags:
- Astra SR
- SR100
- MCU
- Person Segmentation
model-typeastra: SR102
---
# Person Segmentation 256x480 (SR100 Series)
## Model Overview
The **Person Segmentation 256x480** model developed by Synaptics, is a lightweight quantized `tflite` model developed for the **SR100 processor** in the Synaptics Astra™ SR MCU Series.
The output includes segmented regions that represent the exact shape of each person in the image, providing both segmentation and confidence-level insights for each detection.
## Model Features
- **Model Type:** Person Segmentation
- **Input Size:** 256x480
- **Output:** For each detected person, a segmentation mask outlining along with confidence scores for each region.
- **Classes:** Single class (person); specifically designed for person segmentation.
## Deployment on Astra
You can optimize this model for Synaptics Astra SR100 MCU using our our hosted [SR100 Model Compiler HF Space](https://huggingface.co/spaces/Synaptics/SR100-Model-Compiler).
- **Processor:** Astra™ SR100 MCU
- **Platform:** Astra™ Machina Micro Dev Kit
- **Quantization:** INT8 (fully quantized)
- **Compiler:** [SR100 Model Compiler](https://huggingface.co/spaces/Synaptics/SR100-Model-Compiler)
- **Preprocessing:** Input images must be resized and quantized to match model requirements
## Intended Applications
This model enables **real-time person segmentation** for embedded edge devices. Example use cases include:
- Human presence and activity monitoring
- Fitness and wellness tracking
- Smart home automation
- Interactive user interfaces
## Evaluate Model
You can evaluate and test this model directly in our hosted [Hugging Face Space](https://huggingface.co/spaces/Synaptics/SR100-Model-Compiler), optimized for **Synaptics SR110** MCU. This space provides a seamless sandbox for model evaluation using hardware-specific quantization and runtime settings.
For a detailed walkthrough on how to optimize and evaluate a model, please see our [Evaluate Model Guide](https://developer.synaptics.com/docs/sr/sr110/evaluate-sr?utm_source=hf) page.
To get started quickly with Astra SR Series, visit our [SR Quick Start](https://developer.synaptics.com/docs/sr/sr110/quick-start?utm_source=hf) page.
## License
Distributed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), allowing flexible use, modification, and distribution.
## Learn More
- [Synaptics AI Developer Zone](https://developer.synaptics.com?utm_source=hf): Get started with documentation, tutorials and resources for your Edge AI journey.
- [Astra Support Portal](https://synacsm.atlassian.net/servicedesk/customer/portal/543?utm_source=hf): Connect with our engineering team and community.
|
AlekseyCalvin/LYRICAL_MT_ru2en_27_RuLlama3_8b_redirected
|
AlekseyCalvin
| 2025-09-24T20:22:24Z | 0 | 0 | null |
[
"safetensors",
"llama",
"facebook",
"meta",
"pytorch",
"llama-3",
"text-generation",
"conversational",
"en",
"license:llama3",
"region:us"
] |
text-generation
| 2025-09-24T19:51:53Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3
new_version: meta-llama/Llama-3.1-8B-Instruct
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the
Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3
distributed by Meta at https://llama.meta.com/get-started/.
"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into
this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or
regulations to provide legal consent and that has legal authority to bind your employer or such other
person or entity if you are entering in this Agreement on their behalf.
"Meta Llama 3" means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://llama.meta.com/llama-downloads.
"Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any
portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your
principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located
outside of the EEA or Switzerland).
1. License Rights and Redistribution.
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limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama
Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the
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iv. Your use of the Llama Materials must comply with applicable laws and regulations
(including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama
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reference into this Agreement.
v. You will not use the Llama Materials or any output or results of the Llama Materials to
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2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users
of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700
million monthly active users in the preceding calendar month, you must request a license from Meta,
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3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
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ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED,
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DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND
ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND
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4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING
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5. Intellectual Property.
a. No trademark licenses are granted under this Agreement, and in connection with the Llama
Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other
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b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with
respect to any derivative works and modifications of the Llama Materials that are made by you, as
between you and Meta, you are and will be the owner of such derivative works and modifications.
c. If you institute litigation or other proceedings against Meta or any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or
results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other
rights owned or licensable by you, then any licenses granted to you under this Agreement shall
terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold
harmless Meta from and against any claim by any third party arising out of or related to your use or
distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your acceptance of this
Agreement or access to the Llama Materials and will continue in full force and effect until terminated in
accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in
breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete
and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this
Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of
the State of California without regard to choice of law principles, and the UN Convention on Contracts
for the International Sale of Goods does not apply to this Agreement. The courts of California shall have
exclusive jurisdiction of any dispute arising out of this Agreement.
### Meta Llama 3 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
#### Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
others to use, Meta Llama 3 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
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7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Meta Llama 3 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
widget:
- example_title: Hello
messages:
- role: user
content: Hey my name is Julien! How are you?
- example_title: Winter holidays
messages:
- role: system
content: You are a helpful and honest assistant. Please, respond concisely and truthfully.
- role: user
content: Can you recommend a good destination for Winter holidays?
- example_title: Programming assistant
messages:
- role: system
content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully.
- role: user
content: Write a function that computes the nth fibonacci number.
---
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][-1])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 8B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
corzamennav/blockassist-bc-territorial_wild_antelope_1758745173
|
corzamennav
| 2025-09-24T20:22:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"territorial wild antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-24T20:20:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- territorial wild antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0408happyfeet/books-tabular-autogluon
|
0408happyfeet
| 2025-09-24T20:22:05Z | 0 | 0 |
autogluon
|
[
"autogluon",
"tabular",
"classical-ml",
"dataset:EricCRX/books-tabular-dataset",
"license:mit",
"model-index",
"region:us"
] | null | 2025-09-23T13:37:18Z |
---
license: mit
library_name: autogluon
tags:
- tabular
- autogluon
- classical-ml
datasets:
- EricCRX/books-tabular-dataset
model-index:
- name: books-tabular-autogluon
results:
- task:
type: tabular-classification
name: Binary Classification
dataset:
name: EricCRX/books-tabular-dataset (original split)
type: huggingface
config: default
metrics:
- type: accuracy
value: 1.0000
- type: f1_weighted
value: 1.0000
- type: precision_weighted
value: 1.0000
- type: recall_weighted
value: 1.0000
- type: roc_auc
value: 1.0000
---
# Books Tabular — AutoGluon (Classical ML)
**Task**: Predict `Is_Textbook` (Yes/No) from physical book attributes.
**Dataset**: `EricCRX/books-tabular-dataset`
**Training**: AutoGluon Tabular with `presets="best_quality"` on the augmented split, evaluated on the original split.
## Results (Original split)
- Accuracy: **1.0000**
- Weighted F1: **1.0000**
- Weighted Precision: **1.0000**
- Weighted Recall: **1.0000**
- ROC-AUC (Yes vs No): **1.0000**
## Best Model
- Overall: `CatBoost/T10`
- Best base model: `CatBoost/T10`
## Hyperparameters
**Best overall (leaf)**: `CatBoost/T10` (CatBoostModel)
```json
{
"iterations": 10000,
"learning_rate": 0.09595709169024079,
"random_seed": 0,
"allow_writing_files": false,
"eval_metric": "Accuracy",
"depth": 7,
"l2_leaf_reg": 2.894432181094842
}
```
## Repro steps
```
pip install autogluon.tabular datasets huggingface_hub cloudpickle
from huggingface_hub import hf_hub_download
from autogluon.tabular import TabularPredictor
# Download (native dir zip) from 0408happyfeet/books-tabular-autogluon
zip_path = hf_hub_download(repo_id="0408happyfeet/books-tabular-autogluon", repo_type="model", filename="autogluon_predictor_dir.zip")
# Unzip and load:
# unzip autogluon_predictor_dir.zip -d ./model_dir
# predictor = TabularPredictor.load("./model_dir")
# Or quick (pickle):
pkl_path = hf_hub_download(repo_id="0408happyfeet/books-tabular-autogluon", repo_type="model", filename="autogluon_predictor.pkl")
import cloudpickle
predictor = cloudpickle.load(open(pkl_path, "rb"))
```
## Inference
```python
import pandas as pd
X = pd.DataFrame([{
"Length_cm": 24.0,
"Width_cm": 16.0,
"Thickness_cm": 3.0,
"Pages": 400,
"Hardcover": "Yes",
"Cover_Color": "Blue"
}])
pred = predictor.predict(X)
proba = predictor.predict_proba(X)
print(pred.iloc[0], proba)
```
---
## Purpose
Educational AutoML exercise for classical ML on a peer’s Homework 1 dataset.
## Data origin & splits
Source dataset: **EricCRX/books-tabular-dataset** (classmate). Splits used:
- `augmented` (~300 rows) for training/HPO with an 80/20 holdout.
- `original` (30 rows) held out as an external test set.
## Features & target
Features: `Length_cm`, `Width_cm`, `Thickness_cm`, `Pages`, `Hardcover`, `Cover_Color`.
Target: **`Is_Textbook`** ∈ {Yes, No}.
## Preprocessing
AutoGluon Tabular defaults (type inference, category handling, missing-value processing). No manual feature engineering.
## Training setup
- Framework: **AutoGluon Tabular** (preset: `best_quality`).
- Model families: **GBM, XGB, RF, XT, KNN, CAT** (classical only).
- HPO: `hyperparameter_tune_kwargs='auto'` (AutoGluon scheduler/strategy).
- Budget: `time_limit=1200` seconds.
- Seed: **42**.
- Compute: Google Colab runtime (fill in CPU/GPU details below).
- Wall-clock: (fill in from your run).
## Hyperparameters & search space
- Best model: **CatBoost/T10** (CatBoostModel).
- Search space: AutoGluon defaults under `best_quality` with automatic HPO.
## Metrics (original split)
Units: fraction (0–1), where 1.0 = 100%.
- Point estimates: Accuracy / F1 / Precision / Recall (+ ROC‑AUC if available) as reported above.
- Uncertainty (bootstrap):
- Accuracy: 1.0000 (95% CI [1.0000, 1.0000])
- F1_weighted: 1.0000 (95% CI [1.0000, 1.0000])
- Precision_weighted: 1.0000 (95% CI [1.0000, 1.0000])
- Recall_weighted: 1.0000 (95% CI [1.0000, 1.0000])
- ROC-AUC: 1.0000 (95% CI [1.0000, 1.0000])
## Limitations & ethical notes
- Very small real test set (n=30); synthetic augmentation can misrepresent real‑world distribution.
- No PII; low ethical risk in this domain (books).
## License
**MIT** (matching the source dataset’s license).
## Hardware & compute budget
- HPO budget: 1200s.
- Runtime hardware: (CPU/GPU; RAM).
- Training wall‑clock: (fill in).
- Peak memory: (optional).
## AI usage disclosure
- AutoML & HPO performed by **AutoGluon**.
- Some documentation/code scaffolding assisted by generative AI (ChatGPT); human‑reviewed.
|
onnxmodelzoo/xcit_tiny_24_p16_384_dist_Opset18
|
onnxmodelzoo
| 2025-09-24T20:20:23Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:20:14Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_24_p16_384_dist_Opset18.onnx
tags:
- Computer_Vision
- skip
---
|
HectorHe/Qwen1.5-MOE-sft-coommonsense15k-aux-free-3e-5-share-expert
|
HectorHe
| 2025-09-24T20:20:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_moe",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:fw407/Commonsense-15K",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T19:50:42Z |
---
datasets: fw407/Commonsense-15K
library_name: transformers
model_name: Qwen1.5-MOE-sft-coommonsense15k-aux-free-3e-5-share-expert
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen1.5-MOE-sft-coommonsense15k-aux-free-3e-5-share-expert
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [fw407/Commonsense-15K](https://huggingface.co/datasets/fw407/Commonsense-15K) 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="HectorHe/Qwen1.5-MOE-sft-coommonsense15k-aux-free-3e-5-share-expert", 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/hector_-carnegie-mellon-university/huggingface/runs/7hks0bc7)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.51.0
- Pytorch: 2.6.0
- Datasets: 4.1.1
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
onnxmodelzoo/xcit_tiny_24_p16_384_dist_Opset16
|
onnxmodelzoo
| 2025-09-24T20:20:14Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:20:08Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_24_p16_384_dist_Opset16.onnx
tags:
- Computer_Vision
- skip
---
|
onnxmodelzoo/xcit_tiny_24_p16_224_dist_Opset18
|
onnxmodelzoo
| 2025-09-24T20:20:07Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:20:01Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_24_p16_224_dist_Opset18.onnx
tags:
- Computer_Vision
- skip
---
|
velarr/blockassist
|
velarr
| 2025-09-24T20:20:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wary lanky macaque",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-16T22:46:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wary lanky macaque
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/xcit_tiny_12_p8_384_dist_Opset18
|
onnxmodelzoo
| 2025-09-24T20:19:55Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:19:50Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_12_p8_384_dist_Opset18.onnx
tags:
- Computer_Vision
- skip
---
|
onnxmodelzoo/xcit_tiny_12_p8_384_dist_Opset17
|
onnxmodelzoo
| 2025-09-24T20:19:49Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:19:45Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_12_p8_384_dist_Opset17.onnx
tags:
- Computer_Vision
- skip
---
|
ecopus/groot_autogluon_predictor_w_hpo
|
ecopus
| 2025-09-24T20:19:44Z | 0 | 0 | null |
[
"dataset:FaiyazAzam/hw1-image-ds-groot-224",
"region:us"
] | null | 2025-09-24T18:36:27Z |
---
datasets:
- FaiyazAzam/hw1-image-ds-groot-224
---
# groot_autogluon_predictor_w_hpo
## Model Description
This model was trained using [AutoGluon Multimodal](https://auto.gluon.ai/).
The best-performing architecture was **ResNet18 (timm_image)**.
The following model is an AutoGluon Multimodal Image Classifier created using Hyperparameter Optimization.
This model utilizes a groot image set with a binary classifier "has_groot" or "doesn't have groot", ultimately working to classify which images have a groot figuring within it, and which do not.
## Hyperparameters
- 'model.names': ['timm_image']
- 'model.timm_image.checkpoint_name': ['resnet18']
- 'optim.lr': 2.96e-4
- 'env.per_gpu_batch_size': 16
- 'optim.weight_decay': 1.6e-6
- 'optim.max_epochs': 50
## Training & Early Stopping
Utilized ASHA early-stopping scheduler, and an HPO timeout of 900 seconds.
## Evaluation
**Test set metrics:**
-'accuracy': 0.9
-'f1': 0.899
**Confusion Matrix**
$$
\begin{bmatrix}
15 & 0 \\
3 & 12
\end{bmatrix}
$$
**Per Class Metrics**
| Class | Precision | Recall | f1-score |
|----------|----------|----------|----------|
| 0 | 0.833 | 1.0 | 0.909 |
| 1 | 1.0 | 0.8 | 0.899 |
## Data Augmentation
Dataset utilized found here: https://huggingface.co/datasets/FaiyazAzam/hw1-image-ds-groot-224
- RandomResizedCrop(224)
- RandomHorizontalFlip(p=0.5)
- ColorJitter
- Normalize(mean=[...], std=[...])
## Input & Preprocessing
- Input resolution: 224x224 RGB images
- Preprocessing: Resize to 224x224 and normalize with ImageNet mean/std.
## Known Failure Modes
- Struggles with extreme lighting variations
- Confuses class A and B if object is partially occluded
## Usage
```python
from autogluon.multimodal import MultiModalPredictor
predictor = MultiModalPredictor.load('groot_autogluon_predictor_w_hpo')
pred = predictor.predict("example.jpg")
|
onnxmodelzoo/xcit_tiny_12_p8_384_dist_Opset16
|
onnxmodelzoo
| 2025-09-24T20:19:44Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:19:40Z |
---
language: en
license: apache-2.0
model_name: xcit_tiny_12_p8_384_dist_Opset16.onnx
tags:
- Computer_Vision
- skip
---
|
onnxmodelzoo/xcit_small_24_p8_384_dist_Opset16
|
onnxmodelzoo
| 2025-09-24T20:18:49Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:18:38Z |
---
language: en
license: apache-2.0
model_name: xcit_small_24_p8_384_dist_Opset16.onnx
tags:
- Computer_Vision
- skip
---
|
onnxmodelzoo/xcit_small_24_p8_224_Opset17
|
onnxmodelzoo
| 2025-09-24T20:18:37Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:18:25Z |
---
language: en
license: apache-2.0
model_name: xcit_small_24_p8_224_Opset17.onnx
tags:
- Computer_Vision
- skip
---
|
onnxmodelzoo/xcit_small_24_p16_224_Opset18
|
onnxmodelzoo
| 2025-09-24T20:17:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:17:48Z |
---
language: en
license: apache-2.0
model_name: xcit_small_24_p16_224_Opset18.onnx
tags:
- Computer_Vision
- skip
---
|
onnxmodelzoo/xcit_small_24_p16_224_dist_Opset17
|
onnxmodelzoo
| 2025-09-24T20:17:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"skip",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-24T20:16:54Z |
---
language: en
license: apache-2.0
model_name: xcit_small_24_p16_224_dist_Opset17.onnx
tags:
- Computer_Vision
- skip
---
|
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