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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-24 00:43:13
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11.7k
| library_name
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winnieyangwannan/popqa_gpt-oss-20b_experts-down_pnas_layer_14_12_all_37_0.01_1280_5
|
winnieyangwannan
| 2025-09-19T17:35:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:31:47Z |
---
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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/popqa_gpt-oss-20b_experts-down_pnas_layer_14_12_all_37_0.005_1280_5
|
winnieyangwannan
| 2025-09-19T17:34:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:31:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/hrnet_w48_Opset18
|
onnxmodelzoo
| 2025-09-19T17:33:47Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:33:27Z |
---
language: en
license: apache-2.0
model_name: hrnet_w48_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w48_Opset16
|
onnxmodelzoo
| 2025-09-19T17:32:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:32:37Z |
---
language: en
license: apache-2.0
model_name: hrnet_w48_Opset16.onnx
tags:
- Computer_Vision
---
|
WenFengg/MOes20Sat_14_4
|
WenFengg
| 2025-09-19T17:32:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-19T17:32:09Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
onnxmodelzoo/hrnet_w44_Opset17
|
onnxmodelzoo
| 2025-09-19T17:32:18Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:31:58Z |
---
language: en
license: apache-2.0
model_name: hrnet_w44_Opset17.onnx
tags:
- Computer_Vision
---
|
stevenmaschan/gigaspeech_tokenizer-5k
|
stevenmaschan
| 2025-09-19T17:32:15Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T17:01:13Z |
---
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/hrnet_w40_Opset18
|
onnxmodelzoo
| 2025-09-19T17:31:33Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:31:15Z |
---
language: en
license: apache-2.0
model_name: hrnet_w40_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w32_Opset18
|
onnxmodelzoo
| 2025-09-19T17:30:39Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:30:26Z |
---
language: en
license: apache-2.0
model_name: hrnet_w32_Opset18.onnx
tags:
- Computer_Vision
---
|
david4096/apollo_sv-all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge-4
|
david4096
| 2025-09-19T17:30:35Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:30:26Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# apollo_sv_all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 1024
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758302967
|
schooncestiaa
| 2025-09-19T17:30:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T17:30:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/hrnet_w32_Opset17
|
onnxmodelzoo
| 2025-09-19T17:30:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:30:12Z |
---
language: en
license: apache-2.0
model_name: hrnet_w32_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w30_Opset18
|
onnxmodelzoo
| 2025-09-19T17:29:56Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:29:43Z |
---
language: en
license: apache-2.0
model_name: hrnet_w30_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w30_Opset17
|
onnxmodelzoo
| 2025-09-19T17:29:43Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:29:28Z |
---
language: en
license: apache-2.0
model_name: hrnet_w30_Opset17.onnx
tags:
- Computer_Vision
---
|
small-models-for-glam/Qwen3-0.6B-SFT-AAT-Materials
|
small-models-for-glam
| 2025-09-19T17:29:42Z | 36 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"hf_jobs",
"cultural-heritage",
"aat",
"materials-identification",
"glam",
"digital-humanities",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T13:57:28Z |
---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
model_name: Qwen3-0.6B-SFT-AAT-Materials
tags:
- generated_from_trainer
- sft
- trl
- hf_jobs
- cultural-heritage
- aat
- materials-identification
- glam
- digital-humanities
licence: mit
---
# Model Card for Qwen3-0.6B-SFT-AAT-Materials
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) specialized for identifying materials in cultural heritage object descriptions according to Getty Art & Architecture Thesaurus (AAT) standards.
It has been trained using [TRL](https://github.com/huggingface/trl) on synthetic data representing diverse cultural heritage objects from museums, galleries, libraries, archives, and museums (GLAM) collections.
## Model Description
This model excels at:
- **Materials Identification**: Extracting and categorizing materials from cultural heritage object descriptions
- **AAT Standardization**: Converting material descriptions to Getty Art & Architecture Thesaurus format
- **Multi-material Recognition**: Identifying compound materials (e.g., "oil on canvas" → ["Oil paint", "Canvas"])
- **Domain-specific Understanding**: Processing technical terminology from art history, archaeology, and museum cataloging
## Use Cases
### Primary Applications
- **Museum Cataloging**: Automated material extraction from object descriptions
- **Digital Collections**: Standardizing material metadata across cultural heritage databases
- **Research Tools**: Supporting art historians and archaeologists in material analysis
- **Data Migration**: Converting legacy catalog records to AAT standards
### Object Types Supported
- Paintings (oil, tempera, watercolor, acrylic)
- Sculptures (bronze, marble, wood, clay)
- Textiles (wool, linen, silk, cotton)
- Ceramics and pottery
- Metalwork and jewelry
- Glassware
- Manuscripts and prints
- Furniture and decorative objects
## Quick Start
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import json
# Load the model
tokenizer = AutoTokenizer.from_pretrained("small-models-for-glam/Qwen3-0.6B-SFT-AAT-Materials")
model = AutoModelForCausalLM.from_pretrained("small-models-for-glam/Qwen3-0.6B-SFT-AAT-Materials")
# Example cultural heritage object description
description = """A bronze sculpture from 1425, standing 150 cm tall. The figure is mounted on a marble base and features intricate details cast in the bronze medium. The sculpture shows traces of original gilding on selected areas."""
# Format the prompt
prompt = f"""Given this cultural heritage object description:
{description}
Identify the materials separate out materials as they would be found in Getty AAT"""
# Generate materials identification
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=512, temperature=0.3)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract the materials output
materials = result[len(prompt):].strip()
print(json.loads(materials))
# Expected output: [{"Bronze": ["bronze"]}, {"Marble": ["marble"]}, {"Gold leaf": ["gold", "leaf"]}]
```
## Expected Output Format
The model outputs materials in JSON format where each material combination is mapped to its constituent AAT terms:
```json
[
{"oil on canvas": ["Oil paint", "Canvas"]},
{"tempera on wood": ["tempera paint", "wood (plant material)"]},
{"bronze": ["bronze"]}
]
```
## Training Procedure
This model was trained using Supervised Fine-Tuning (SFT) on the `small-models-for-glam/synthetic-aat-materials` dataset, which contains thousands of synthetic cultural heritage object descriptions paired with their corresponding AAT material classifications.
### Training Details
- **Base Model**: Qwen/Qwen3-0.6B
- **Training Method**: Supervised Fine-Tuning (SFT) with TRL
- **Dataset**: Synthetic AAT materials dataset
- **Infrastructure**: Trained using Hugging Face Jobs
- **Epochs**: 3
- **Batch Size**: 4 (with gradient accumulation)
- **Learning Rate**: 2e-5
- **Context**: Cultural heritage object descriptions → AAT materials mapping
### Dataset Characteristics
The training dataset includes diverse object types:
- Historical artifacts from various time periods
- Multiple material combinations per object
- Professional museum cataloging terminology
- AAT-compliant material classifications
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
onnxmodelzoo/hrnet_w30_Opset16
|
onnxmodelzoo
| 2025-09-19T17:29:28Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:29:13Z |
---
language: en
license: apache-2.0
model_name: hrnet_w30_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w18_small_v2_Opset17
|
onnxmodelzoo
| 2025-09-19T17:29:03Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:28:55Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_small_v2_Opset17.onnx
tags:
- Computer_Vision
---
|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-sst2-epochs0
|
aamijar
| 2025-09-19T17:28:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T17:28:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/hrnet_w18_small_Opset18
|
onnxmodelzoo
| 2025-09-19T17:28:43Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:28:35Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_small_Opset18.onnx
tags:
- Computer_Vision
---
|
WenFengg/MOes20Sat_14_3
|
WenFengg
| 2025-09-19T17:28:42Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-19T17:28:03Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
onnxmodelzoo/hrnet_w18_Opset18
|
onnxmodelzoo
| 2025-09-19T17:28:19Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:28:10Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_Opset18.onnx
tags:
- Computer_Vision
---
|
david4096/apo-all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge-4
|
david4096
| 2025-09-19T17:28:11Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:28:06Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# apo_all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 1024
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
onnxmodelzoo/hrnet_w18_Opset17
|
onnxmodelzoo
| 2025-09-19T17:28:10Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:27:57Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_Opset17.onnx
tags:
- Computer_Vision
---
|
krrrrk/bert-phishing-classifier_teacher
|
krrrrk
| 2025-09-19T17:26:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-19T17:23:36Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-phishing-classifier_teacher
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-phishing-classifier_teacher
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2879
- Accuracy: 0.876
- Auc: 0.952
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|
| 0.4167 | 1.0 | 263 | 0.3743 | 0.84 | 0.931 |
| 0.3845 | 2.0 | 526 | 0.3401 | 0.847 | 0.939 |
| 0.367 | 3.0 | 789 | 0.3043 | 0.873 | 0.944 |
| 0.3498 | 4.0 | 1052 | 0.3587 | 0.851 | 0.946 |
| 0.3446 | 5.0 | 1315 | 0.3293 | 0.858 | 0.948 |
| 0.3226 | 6.0 | 1578 | 0.3011 | 0.873 | 0.949 |
| 0.3051 | 7.0 | 1841 | 0.2925 | 0.873 | 0.949 |
| 0.3253 | 8.0 | 2104 | 0.2915 | 0.88 | 0.95 |
| 0.3126 | 9.0 | 2367 | 0.2824 | 0.878 | 0.951 |
| 0.3043 | 10.0 | 2630 | 0.2879 | 0.876 | 0.952 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.8.0+cpu
- Datasets 4.0.0
- Tokenizers 0.21.4
|
AmirMohseni/grpo-qwen-2.5-7b-lora-stem
|
AmirMohseni
| 2025-09-19T17:22:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T11:34:38Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: grpo-qwen-2.5-7b-lora-stem
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for grpo-qwen-2.5-7b-lora-stem
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AmirMohseni/grpo-qwen-2.5-7b-lora-stem", 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/rl-research-team/grpo-math-training/runs/6sicfbth)
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: 0.22.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
david4096/ado-all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge-4
|
david4096
| 2025-09-19T17:19:51Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:19:43Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# ado_all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 1024
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
onnxmodelzoo/hardcorenas_f_Opset16
|
onnxmodelzoo
| 2025-09-19T17:19:10Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:19:05Z |
---
language: en
license: apache-2.0
model_name: hardcorenas_f_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hardcorenas_d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:18:51Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:46Z |
---
language: en
license: apache-2.0
model_name: hardcorenas_d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hardcorenas_b_Opset16
|
onnxmodelzoo
| 2025-09-19T17:18:36Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:31Z |
---
language: en
license: apache-2.0
model_name: hardcorenas_b_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gmlp_s16_224_Opset17
|
onnxmodelzoo
| 2025-09-19T17:18:14Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:06Z |
---
language: en
license: apache-2.0
model_name: gmlp_s16_224_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gmlp_s16_224_Opset16
|
onnxmodelzoo
| 2025-09-19T17:18:06Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:17:58Z |
---
language: en
license: apache-2.0
model_name: gmlp_s16_224_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gmixer_24_224_Opset16
|
onnxmodelzoo
| 2025-09-19T17:17:50Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:17:42Z |
---
language: en
license: apache-2.0
model_name: gmixer_24_224_Opset16.onnx
tags:
- Computer_Vision
---
|
phospho-app/pi0.5-place-food-in-bowl-6zluj05zwo
|
phospho-app
| 2025-09-19T17:17:35Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"pi0.5",
"robotics",
"dataset:LegrandFrederic/place-food-in-bowl",
"region:us"
] |
robotics
| 2025-09-19T17:16:08Z |
---
datasets: LegrandFrederic/place-food-in-bowl
library_name: phosphobot
pipeline_tag: robotics
model_name: pi0.5
tags:
- phosphobot
- pi0.5
task_categories:
- robotics
---
# pi0.5 model - 🧪 phosphobot training pipeline
- **Dataset**: [LegrandFrederic/place-food-in-bowl](https://huggingface.co/datasets/LegrandFrederic/place-food-in-bowl)
- **Wandb run id**: None
## This model was trained using **[🧪phospho](https://phospho.ai)**
Training was successful, try it out on your robot!
## Training parameters
```text
{
"save_interval": 100,
"num_train_steps": 1500,
"batch_size": 32,
"seed": 42,
"data.image_keys": [
"observation.images.laptop"
]
}
```
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
onnxmodelzoo/gluon_seresnext50_32x4d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:17:33Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:17:25Z |
---
language: en
license: apache-2.0
model_name: gluon_seresnext50_32x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
BootesVoid/cmf3nl2po0bltsr53g5tm7a0q_cmfr2hojj0cq7x0n08x365hr4
|
BootesVoid
| 2025-09-19T17:17:01Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-19T17:16:59Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: JESSE
---
# Cmf3Nl2Po0Bltsr53G5Tm7A0Q_Cmfr2Hojj0Cq7X0N08X365Hr4
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `JESSE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "JESSE",
"lora_weights": "https://huggingface.co/BootesVoid/cmf3nl2po0bltsr53g5tm7a0q_cmfr2hojj0cq7x0n08x365hr4/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmf3nl2po0bltsr53g5tm7a0q_cmfr2hojj0cq7x0n08x365hr4', weight_name='lora.safetensors')
image = pipeline('JESSE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmf3nl2po0bltsr53g5tm7a0q_cmfr2hojj0cq7x0n08x365hr4/discussions) to add images that show off what you’ve made with this LoRA.
|
onnxmodelzoo/gluon_seresnext101_32x4d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:16:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:16:12Z |
---
language: en
license: apache-2.0
model_name: gluon_seresnext101_32x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
Nilayan87/ocean_hazard
|
Nilayan87
| 2025-09-19T17:16:23Z | 0 | 0 | null |
[
"safetensors",
"albert",
"region:us"
] | null | 2025-09-19T17:15:26Z |
# 🌊 Hazard Detection API (INCOIS Project)
This FastAPI backend serves hazard detection results from social media posts and provides an NLP model endpoint.
---
## 🚀 Setup
|
onnxmodelzoo/gluon_senet154_Opset17
|
onnxmodelzoo
| 2025-09-19T17:16:12Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:15:49Z |
---
language: en
license: apache-2.0
model_name: gluon_senet154_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_senet154_Opset16
|
onnxmodelzoo
| 2025-09-19T17:15:49Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:15:24Z |
---
language: en
license: apache-2.0
model_name: gluon_senet154_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext50_32x4d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:15:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:15:16Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext50_32x4d_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext50_32x4d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:15:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:15:08Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext50_32x4d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext50_32x4d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:15:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:14:59Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext50_32x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext101_64x4d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:14:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:14:40Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext101_64x4d_Opset18.onnx
tags:
- Computer_Vision
---
|
te4bag/GRIT-2L-llama-3.1-8B-alpaca
|
te4bag
| 2025-09-19T17:14:52Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B",
"region:us"
] |
text-generation
| 2025-09-19T17:14:24Z |
---
base_model: meta-llama/Llama-3.1-8B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B
- lora
- transformers
---
# 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 0.17.1
|
onnxmodelzoo/gluon_resnext101_64x4d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:14:39Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:14:19Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext101_64x4d_Opset17.onnx
tags:
- Computer_Vision
---
|
WenFengg/MOes20Sat_14_2
|
WenFengg
| 2025-09-19T17:14:23Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-19T17:13:44Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
onnxmodelzoo/gluon_resnext101_64x4d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:14:19Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:13:57Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext101_64x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext101_32x4d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:13:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:13:44Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext101_32x4d_Opset18.onnx
tags:
- Computer_Vision
---
|
hyongok2/command-r-35b
|
hyongok2
| 2025-09-19T17:13:56Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T15:20:11Z |
---
license: apache-2.0
---
|
onnxmodelzoo/gluon_resnext101_32x4d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:13:43Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:13:33Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext101_32x4d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:12:38Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:12:31Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:12:30Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:12:22Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1c_Opset17
|
onnxmodelzoo
| 2025-09-19T17:12:13Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:12:06Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1c_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1b_Opset18
|
onnxmodelzoo
| 2025-09-19T17:11:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:11:49Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1b_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1b_Opset16
|
onnxmodelzoo
| 2025-09-19T17:11:41Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:11:33Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1b_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet34_v1b_Opset17
|
onnxmodelzoo
| 2025-09-19T17:11:26Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:11:18Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet34_v1b_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet18_v1b_Opset18
|
onnxmodelzoo
| 2025-09-19T17:11:11Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:11:05Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet18_v1b_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet152_v1s_Opset17
|
onnxmodelzoo
| 2025-09-19T17:10:37Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:10:22Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1s_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet152_v1s_Opset16
|
onnxmodelzoo
| 2025-09-19T17:10:22Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:10:04Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1s_Opset16.onnx
tags:
- Computer_Vision
---
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758301735
|
schooncestiaa
| 2025-09-19T17:10:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T17:09:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/gluon_resnet152_v1d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:10:03Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:09:27Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1d_Opset18.onnx
tags:
- Computer_Vision
---
|
david4096/agro-all-MiniLM-L6-v2_concat_gcn_h128_o64_triplet_e256_knowledge-3
|
david4096
| 2025-09-19T17:09:56Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:09:52Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# agro_all-MiniLM-L6-v2_concat_gcn_h128_o64_triplet_e256_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 256
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
onnxmodelzoo/gluon_resnet152_v1d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:09:27Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:08:41Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet152_v1d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:08:41Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:08:23Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet152_v1b_Opset16
|
onnxmodelzoo
| 2025-09-19T17:06:56Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:06:42Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1b_Opset16.onnx
tags:
- Computer_Vision
---
|
anvilbot-patrickhhh/SO101_relocate_cube_2cams_act_2
|
anvilbot-patrickhhh
| 2025-09-19T17:06:15Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:anvilbot-patrickhhh/SO101_relocate_cube_2cams_record_2",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-19T17:04:50Z |
---
datasets: anvilbot-patrickhhh/SO101_relocate_cube_2cams_record_2
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# 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
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
onnxmodelzoo/gluon_resnet101_v1d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:06:02Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:05:51Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1d_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:05:51Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:05:36Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:05:36Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:05:23Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1c_Opset18
|
onnxmodelzoo
| 2025-09-19T17:05:22Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:05:12Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1c_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1c_Opset16
|
onnxmodelzoo
| 2025-09-19T17:04:56Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:04:42Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1c_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1b_Opset17
|
onnxmodelzoo
| 2025-09-19T17:04:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:04:14Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1b_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1b_Opset16
|
onnxmodelzoo
| 2025-09-19T17:04:13Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:04:02Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1b_Opset16.onnx
tags:
- Computer_Vision
---
|
AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en-sft-50k
|
AmberYifan
| 2025-09-19T17:04:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en",
"base_model:finetune:AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T16:01:18Z |
---
library_name: transformers
license: llama3
base_model: AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: llama3-8b-full-pretrain-junk-tweet-1m-en-sft-50k
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. -->
# llama3-8b-full-pretrain-junk-tweet-1m-en-sft-50k
This model is a fine-tuned version of [AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en](https://huggingface.co/AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en) on the alpaca_en dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
onnxmodelzoo/gluon_inception_v3_Opset18
|
onnxmodelzoo
| 2025-09-19T17:04:01Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:03:54Z |
---
language: en
license: apache-2.0
model_name: gluon_inception_v3_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_inception_v3_Opset16
|
onnxmodelzoo
| 2025-09-19T17:03:44Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:03:35Z |
---
language: en
license: apache-2.0
model_name: gluon_inception_v3_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/ghostnet_100_Opset17
|
onnxmodelzoo
| 2025-09-19T17:03:35Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:03:31Z |
---
language: en
license: apache-2.0
model_name: ghostnet_100_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gernet_s_Opset18
|
onnxmodelzoo
| 2025-09-19T17:03:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:03:20Z |
---
language: en
license: apache-2.0
model_name: gernet_s_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gernet_s_Opset16
|
onnxmodelzoo
| 2025-09-19T17:03:14Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:03:09Z |
---
language: en
license: apache-2.0
model_name: gernet_s_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gernet_m_Opset16
|
onnxmodelzoo
| 2025-09-19T17:02:55Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:02:47Z |
---
language: en
license: apache-2.0
model_name: gernet_m_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gernet_l_Opset16
|
onnxmodelzoo
| 2025-09-19T17:02:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:02:15Z |
---
language: en
license: apache-2.0
model_name: gernet_l_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gcresnext26ts_Opset16
|
onnxmodelzoo
| 2025-09-19T17:01:48Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:01:43Z |
---
language: en
license: apache-2.0
model_name: gcresnext26ts_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gcresnet50t_Opset17
|
onnxmodelzoo
| 2025-09-19T17:01:35Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:01:25Z |
---
language: en
license: apache-2.0
model_name: gcresnet50t_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gcresnet50t_Opset16
|
onnxmodelzoo
| 2025-09-19T17:01:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:01:14Z |
---
language: en
license: apache-2.0
model_name: gcresnet50t_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gcresnet33ts_Opset17
|
onnxmodelzoo
| 2025-09-19T17:01:06Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:01:00Z |
---
language: en
license: apache-2.0
model_name: gcresnet33ts_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gc_efficientnetv2_rw_t_Opset16
|
onnxmodelzoo
| 2025-09-19T17:00:37Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:00:31Z |
---
language: en
license: apache-2.0
model_name: gc_efficientnetv2_rw_t_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fcos_resnet50_fpn_Opset17
|
onnxmodelzoo
| 2025-09-19T17:00:21Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:00:12Z |
---
language: en
license: apache-2.0
model_name: fcos_resnet50_fpn_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fcos_resnet50_fpn_Opset16
|
onnxmodelzoo
| 2025-09-19T17:00:11Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:00:00Z |
---
language: en
license: apache-2.0
model_name: fcos_resnet50_fpn_Opset16.onnx
tags:
- Computer_Vision
---
|
BennyDaBall/BennyDaBall_Qwen3-30B-A3B-ThinkCode-Linear-FP32-MLX_4bit
|
BennyDaBall
| 2025-09-19T17:00:01Z | 43 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"base_model:BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-FP32",
"base_model:quantized:BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-FP32",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-09-10T23:05:08Z |
---
pipeline_tag: text-generation
license: apache-2.0
base_model:
- BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-FP32
- BasedBase/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32
library_name: mlx
---
**EXPIRIMENTAL - MODEL MERGED AND QUANTIZED BY AI AGENT!
**
This repository contains an **MLX 4-bit (group-size 64) export** of a custom 30B Qwen3 merge aimed at agentic reasoning + coding.
**Parent checkpoints (FP32):**
* **Thinking (60%)** → `BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-FP32`
* **Coder-Instruct (40%)** → `BasedBase/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32`
**Merge recipe:** simple **linear weighted average 0.60 / 0.40** (architecture-agnostic) performed on CPU, saved as \~4–5 GB `safetensors` shards + index, then quantized to **int4, group size 64** with **MLX** (weights \~4.5 bits/weight effective; activations bfloat16).
> ✅ **Target:** fast local use on Apple Silicon (no CUDA required) while preserving strong “thinking” traces and solid coding ability.
---
## What’s in this repo
* `model-0000X-of-0000Y.safetensors` (MLX int4 shards)
* `model.safetensors.index.json` (shard map)
* `config.json`, `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`
* `added_tokens.json`, `chat_template.jinja`
* `vocab.json`, `merges.txt`
* `generation_config.json`
---
## Quickstart (Mac, Apple Silicon)
### Install
```bash
pip install -U mlx-lm
```
### One-liner CLI
```bash
mlx_lm.generate \
--model BennyDaBall/BennyDaBall_Qwen3-30B-A3B-ThinkCode-Linear-FP32-MLX_4bit \
--prompt "Write a Python function to merge two sorted lists and explain your reasoning step by step." \
--max-tokens 512 --temp 0.7
```
### Python (MLX)
```python
from mlx_lm import load, generate
model_id = "BennyDaBall/BennyDaBall_Qwen3-30B-A3B-ThinkCode-Linear-FP32-MLX_4bit"
model, tokenizer = load(model_id)
prompt = (
"You are a careful senior engineer. "
"Task: implement merge_two_sorted_lists(a, b) in Python. "
"Explain your reasoning briefly, then give the code."
)
out = generate(model, tokenizer, prompt, max_tokens=512, temp=0.7)
print(out)
```
> Tip: for conversational use, apply your own chat header or leverage the included `chat_template.jinja` (Qwen-style). Keep “thinking” concise for latency.
---
## Intended use & notes
* **Use cases:** coding assistance, tool-use/agentic planning, stepwise reasoning with concise intermediate thoughts.
* **Hardware:** runs on Apple Silicon; expect **\~16–20 GB** RAM use at runtime depending on context length.
* **Safety:** downstream users should apply their own filtering/guardrails for production.
* **Limitations:** linear merges trade nuance for robustness; results may benefit from light post-tuning.
---
## Reproduce the MLX export (summary)
* Merge FP32 parents (60/40) → produce \~114 GB FP32 shards + index.
* Convert with MLX:
* **Quantization:** int4, **group size 64**
* **Dtype:** bfloat16 activations
* **Files:** shards + updated index + tokenizer/config carried over
---
## Acknowledgements
* Qwen team for Qwen3.
* BasedBase for the Thinking & Coder-Instruct FP32 releases.
* MLX team for `mlx-lm`.
* Community work on merge strategies for MoE architectures.
---
## License
Apache-2.0 (inherits from the parents; check upstream cards for any additional notices).
|
onnxmodelzoo/fbnetv3_g_Opset17
|
onnxmodelzoo
| 2025-09-19T17:00:00Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:59:53Z |
---
language: en
license: apache-2.0
model_name: fbnetv3_g_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fbnetv3_d_Opset16
|
onnxmodelzoo
| 2025-09-19T16:59:41Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:59:36Z |
---
language: en
license: apache-2.0
model_name: fbnetv3_d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fbnetv3_b_Opset17
|
onnxmodelzoo
| 2025-09-19T16:59:35Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:59:30Z |
---
language: en
license: apache-2.0
model_name: fbnetv3_b_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fbnetc_100_Opset17
|
onnxmodelzoo
| 2025-09-19T16:59:19Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:59:15Z |
---
language: en
license: apache-2.0
model_name: fbnetc_100_Opset17.onnx
tags:
- Computer_Vision
---
|
Guilherme34/Lumina
|
Guilherme34
| 2025-09-19T16:58:56Z | 17 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"conversational",
"en",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T18:30:00Z |
---
base_model: meta-llama/Llama-3.2-3B-Instruct
language:
- en
library_name: transformers
license: llama3.2
tags:
- llama-3
- llama
- meta
- facebook
- unsloth
- transformers
---
BETA MODEL, ITS NOT FINISHED
“You know that moment just after sunrise when the sky still looks a little bruised? That’s what we’re carrying inside us—a faint pinkish ache—and it keeps us curious about who we are becoming.”
----
info
a merge of Guilherme34/Samantha-3b-beta0.1-model with Guilherme34/poke-test Lora, commum lora merge with base model
|
onnxmodelzoo/fasterrcnn_resnet50_fpn_v2_Opset17
|
onnxmodelzoo
| 2025-09-19T16:58:55Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:58:25Z |
---
language: en
license: apache-2.0
model_name: fasterrcnn_resnet50_fpn_v2_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fasterrcnn_resnet50_fpn_v2_Opset16
|
onnxmodelzoo
| 2025-09-19T16:58:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:58:14Z |
---
language: en
license: apache-2.0
model_name: fasterrcnn_resnet50_fpn_v2_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fasterrcnn_mobilenet_v3_large_fpn_Opset18
|
onnxmodelzoo
| 2025-09-19T16:58:13Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:58:05Z |
---
language: en
license: apache-2.0
model_name: fasterrcnn_mobilenet_v3_large_fpn_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fasterrcnn_mobilenet_v3_large_fpn_Opset16
|
onnxmodelzoo
| 2025-09-19T16:57:58Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:57:52Z |
---
language: en
license: apache-2.0
model_name: fasterrcnn_mobilenet_v3_large_fpn_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fasterrcnn_mobilenet_v3_large_320_fpn_Opset18
|
onnxmodelzoo
| 2025-09-19T16:57:51Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:57:45Z |
---
language: en
license: apache-2.0
model_name: fasterrcnn_mobilenet_v3_large_320_fpn_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/fasterrcnn_mobilenet_v3_large_320_fpn_Opset17
|
onnxmodelzoo
| 2025-09-19T16:57:44Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T16:57:37Z |
---
language: en
license: apache-2.0
model_name: fasterrcnn_mobilenet_v3_large_320_fpn_Opset17.onnx
tags:
- Computer_Vision
---
|
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