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 |
|---|---|---|---|---|---|---|---|---|---|
aralper18/blockassist-bc-gilded_tangled_albatross_1755673460
|
aralper18
| 2025-08-20T07:04:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded tangled albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T07:04:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded tangled albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755671491
|
chainway9
| 2025-08-20T06:59:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T06:59:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755671092
|
mang3dd
| 2025-08-20T06:50:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T06:50:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755672446
|
roeker
| 2025-08-20T06:48:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T06:48:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mohammadmahdinouri/moa-adapter-30k
|
mohammadmahdinouri
| 2025-08-20T06:45:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"ModernALBERT",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-19T11:15:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mohammadmahdinouri/final-moa-30k
|
mohammadmahdinouri
| 2025-08-20T06:40:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"ModernALBERT",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-20T06:40:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aleebaster/blockassist-bc-sly_eager_boar_1755670301
|
aleebaster
| 2025-08-20T06:37:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T06:37:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755669687
|
helmutsukocok
| 2025-08-20T06:26:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T06:26:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eastcloud/cslhelper
|
eastcloud
| 2025-08-20T06:26:09Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T05:15:43Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: cslhelper
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for cslhelper
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="eastcloud/cslhelper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
roeker/blockassist-bc-quick_wiry_owl_1755670812
|
roeker
| 2025-08-20T06:21:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T06:20:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOSl-Uppal-Farm-Girl-Viral-Video-Clips/New.full.videos.Uppal.Farm.Girl.Viral.Video.Official.Tutoria
|
VIDEOSl-Uppal-Farm-Girl-Viral-Video-Clips
| 2025-08-20T06:17:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T06:12:29Z |
<animated-image data-catalyst=""><a href="https://sdu.sk/v9S" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Ruthwik/Medical-Audio-Visual-VQA
|
Ruthwik
| 2025-08-20T06:13:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"advanced_medical_vqa",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T06:12:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TharunSivamani/mcprl-7b-exa
|
TharunSivamani
| 2025-08-20T06:10:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T06:03:36Z |
---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** TharunSivamani
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755668396
|
calegpedia
| 2025-08-20T06:06:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T06:06:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vanbitcase/7b-220r-qwen-vl
|
Vanbitcase
| 2025-08-20T06:03:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2_vl",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T06:03:15Z |
---
base_model: unsloth/qwen2-vl-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_vl
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Vanbitcase
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2-vl-7b-instruct-bnb-4bit
This qwen2_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)
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755668134
|
sampingkaca72
| 2025-08-20T06:00:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T06:00:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
usmanalam82/Gemma_2b_FineTuned
|
usmanalam82
| 2025-08-20T05:57:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-2-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2-2b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T11:10:53Z |
---
base_model: unsloth/gemma-2-2b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** usmanalam82
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-2b-bnb-4bit
This gemma2 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)
|
usmanalam82/Gemma_2b_LoRA_adaptors
|
usmanalam82
| 2025-08-20T05:55:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma2",
"trl",
"en",
"base_model:unsloth/gemma-2-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T11:10:19Z |
---
base_model: unsloth/gemma-2-2b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** usmanalam82
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-2b-bnb-4bit
This gemma2 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)
|
roeker/blockassist-bc-quick_wiry_owl_1755669188
|
roeker
| 2025-08-20T05:54:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:53:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1755668915
|
liukevin666
| 2025-08-20T05:52:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:49:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755667460
|
manusiaperahu2012
| 2025-08-20T05:52:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:51:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
LoRID-Math/MATH-Mistral-7B-IR
|
LoRID-Math
| 2025-08-20T05:50:46Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"math",
"reasoning",
"text-generation",
"conversational",
"en",
"dataset:meta-math/MetaMathQA",
"arxiv:2508.13037",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-19T15:46:14Z |
---
license: apache-2.0
datasets:
- meta-math/MetaMathQA
language:
- en
metrics:
- accuracy
base_model:
- mistralai/Mistral-7B-v0.1
pipeline_tag: text-generation
library_name: peft
tags:
- math
- reasoning
---
# LoRID: A Reasoning Distillation Method via Multi-LoRA Interaction
📃 [Paper](https://arxiv.org/abs/2508.13037) • 💻 [Code](https://github.com/Xinhe-Li/LoRID) • 🤗 [HF Repo](https://huggingface.co/LoRID-Math)
## Abstract
The models for "[Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction](https://arxiv.org/abs/2508.13037)" [IJCAI 2025].
## Key Contributions
- We focus on the mathematical reasoning distillation task and propose a novel method **LoRID**, which draws inspiration from the human beings teaching and learning pattern.
- We introduce knowledge during data augmentation and propose multi-LoRA interaction during model distillation, which improves the student’s reasoning abilities.
- Experimental results show that with the interaction between System 1 and System 2, **LoRID** outperforms previous state-of-the-art approaches and can be easily and effectively integrated into any Chain-of-Thought distillation method.
## Citation
If this work is helpful, please kindly cite as:
```bibtex
@misc{li2025largemodelsteachstudent,
title={Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction},
author={Xinhe Li and Jiajun Liu and Peng Wang},
year={2025},
eprint={2508.13037},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.13037},
}
```
|
madmage/ppo-SnowballTarget2
|
madmage
| 2025-08-20T05:43:49Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2025-08-20T05:43:45Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: madmage/ppo-SnowballTarget2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
roeker/blockassist-bc-quick_wiry_owl_1755668371
|
roeker
| 2025-08-20T05:40:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:40:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ironman288/blockassist-bc-miniature_lanky_vulture_1755665675
|
Ironman288
| 2025-08-20T05:25:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature lanky vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:25:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature lanky vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755666740
|
roeker
| 2025-08-20T05:13:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:12:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755666338
|
roeker
| 2025-08-20T05:07:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:06:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rourkerhotmail1/blockassist-bc-stalking_scruffy_walrus_1755664238
|
rourkerhotmail1
| 2025-08-20T05:04:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stalking scruffy walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:03:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stalking scruffy walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755664545
|
calegpedia
| 2025-08-20T05:01:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T05:01:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
germanlunichh/blockassist-bc-mute_shaggy_alligator_1755664026
|
germanlunichh
| 2025-08-20T04:56:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute shaggy alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T04:56:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute shaggy alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mynzuh/my_awesome_food_model
|
mynzuh
| 2025-08-20T04:51:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-20T04:50:41Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9640
- Accuracy: 0.816
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.999 | 1.0 | 63 | 2.9640 | 0.816 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
PersonalAILab/AFM-WebAgent-7B-sft
|
PersonalAILab
| 2025-08-20T04:41:19Z | 7 | 1 | null |
[
"safetensors",
"qwen2",
"arxiv:2508.13167",
"region:us"
] | null | 2025-08-06T13:41:32Z |
# Model Introduction
We introduce Agent Foundation Models (AFMs), a new family built on Qwen2.5 that natively perform end-to-end, multi-turn, multi-tool problem solving—without external frameworks or manual prompting. Built on the Chain-of-Agents (CoA) paradigm, each AFM dynamically activates specialized tool and role-playing agents inside a single forward pass, emulating the cooperative reasoning of a full multi-agent system. To train these models, we distilled high-performing multi-agent trajectories into agentic supervised-fine-tuning data and further optimized performance with agentic reinforcement learning on verifiable tasks. AFMs set new state-of-the-art results on benchmarks for both web and code agents, and we release all model weights, training code, and datasets to accelerate future research on agentic AI.
For more details, please refer to our [Projects](https://chain-of-agents-afm.github.io/), [paper](https://arxiv.org/abs/2508.13167) and [GitHub](https://github.com/OPPO-PersonalAI/Agent_Foundation_Models).
# Model Downloads
| Model | Download | Backbone Model | License|
| --------------------- | ------ | --------------------------- |--------------------------- |
| AFM-CodeAgent-7B-sft | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-CodeAgent-7B-sft) |[Qwen-2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) | Apache License 2.0|
| AFM-CodeAgent-7B-rl | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-CodeAgent-7B-rl) |[Qwen-2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) | Apache License 2.0|
| AFM-CodeAgent-32B-sft | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-CodeAgent-32B-sft) |[Qwen-2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) | Apache License 2.0|
| AFM-CodeAgent-32B-rl | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-CodeAgent-32B-rl) |[Qwen-2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) | Apache License 2.0|
| AFM-MHQA-Agent-3B-sft | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-MHQA-Agent-3B-sft) |[Qwen-2.5-3B-Base](https://huggingface.co/Qwen/Qwen2.5-3B) | Qwen RESEARCH LICENSE AGREEMENT|
| AFM-MHQA-Agent-3B-rl | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-MHQA-Agent-3B-rl) |[Qwen-2.5-3B-Base](https://huggingface.co/Qwen/Qwen2.5-3B) | Qwen RESEARCH LICENSE AGREEMENT|
| AFM-MHQA-Agent-7B-sft | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-MHQA-Agent-7B-sft) |[Qwen-2.5-7B-Base](https://huggingface.co/Qwen/Qwen2.5-7B) | Apache License 2.0|
| AFM-MHQA-Agent-7B-rl | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-MHQA-Agent-7B-rl) |[Qwen-2.5-7B-Base](https://huggingface.co/Qwen/Qwen2.5-7B) | Apache License 2.0|
| AFM-WebAgent-7B-sft | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-WebAgent-7B-sft) |[Qwen-2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | Apache License 2.0|
| AFM-WebAgent-32B-sft | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-WebAgent-32B-sft) |[Qwen-2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | Apache License 2.0|
| AFM-WebAgent-7B-rl | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-WebAgent-7B-rl) |[Qwen-2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | Apache License 2.0|
| AFM-WebAgent-32B-rl | [🤗 **HuggingFace**](https://huggingface.co/PersonalAILab/AFM-WebAgent-32B-rl) |[Qwen-2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | Apache License 2.0|
# Data Downloads
- [AFM-CodeAgent-SFT-Dataset](https://huggingface.co/datasets/PersonalAILab/AFM-CodeAgent-SFT-Dataset)
- [AFM-CodeAgent-RL-Dataset](https://huggingface.co/datasets/PersonalAILab/AFM-CodeAgent-RL-Dataset)
- [AFM-WebAgent-SFT-Dataset](https://huggingface.co/datasets/PersonalAILab/AFM-WebAgent-SFT-Dataset)
- [AFM-WebAgent-RL-Dataset](https://huggingface.co/datasets/PersonalAILab/AFM-WebAgent-RL-Dataset)
- [AFM-MHQA-SFT-Dataset](https://huggingface.co/datasets/PersonalAILab/AFM-MHQA-Agent-SFT-Dataset)
- [AFM-MHQA-RL-Dataset](https://huggingface.co/datasets/PersonalAILab/AFM-MHQA-RL-Dataset)
## Citation
If you find `AFM` useful in your research or applications, we would appreciate it if you could cite our work:
```bibtex
@misc{li2025chainofagentsendtoendagentfoundation,
title={Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL},
author={Weizhen Li and Jianbo Lin and Zhuosong Jiang and Jingyi Cao and Xinpeng Liu and Jiayu Zhang and Zhenqiang Huang and Qianben Chen and Weichen Sun and Qiexiang Wang and Hongxuan Lu and Tianrui Qin and Chenghao Zhu and Yi Yao and Shuying Fan and Xiaowan Li and Tiannan Wang and Pai Liu and King Zhu and He Zhu and Dingfeng Shi and Piaohong Wang and Yeyi Guan and Xiangru Tang and Minghao Liu and Yuchen Eleanor Jiang and Jian Yang and Jiaheng Liu and Ge Zhang and Wangchunshu Zhou},
year={2025},
eprint={2508.13167},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2508.13167},
}
```
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755663122
|
ihsanridzi
| 2025-08-20T04:38:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T04:38:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dherrera-ppm/shannon-ppm
|
dherrera-ppm
| 2025-08-20T04:33:24Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-20T04:02:42Z |
---
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: Shannon
---
# Shannon Ppm
<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 `Shannon` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Shannon",
"lora_weights": "https://huggingface.co/dherrera-ppm/shannon-ppm/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('dherrera-ppm/shannon-ppm', weight_name='lora.safetensors')
image = pipeline('Shannon').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: 2485
- Learning rate: 0.0004
- LoRA rank: 30
## Contribute your own examples
You can use the [community tab](https://huggingface.co/dherrera-ppm/shannon-ppm/discussions) to add images that show off what you’ve made with this LoRA.
|
AnonymousCS/xlmr_immigration_combo11_2
|
AnonymousCS
| 2025-08-20T04:25:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-20T04:21:12Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo11_2
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. -->
# xlmr_immigration_combo11_2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2721
- Accuracy: 0.9031
- 1-f1: 0.8486
- 1-recall: 0.8150
- 1-precision: 0.8852
- Balanced Acc: 0.8811
## 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: 128
- eval_batch_size: 128
- 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.61 | 1.0 | 22 | 0.5813 | 0.6667 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3734 | 2.0 | 44 | 0.2916 | 0.8957 | 0.8322 | 0.7753 | 0.8980 | 0.8656 |
| 0.2978 | 3.0 | 66 | 0.2677 | 0.9060 | 0.8498 | 0.7974 | 0.9095 | 0.8789 |
| 0.232 | 4.0 | 88 | 0.2760 | 0.9031 | 0.8436 | 0.7841 | 0.9128 | 0.8733 |
| 0.2438 | 5.0 | 110 | 0.2721 | 0.9031 | 0.8486 | 0.8150 | 0.8852 | 0.8811 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
ankitkushwaha90/Attention_is_all_you_need
|
ankitkushwaha90
| 2025-08-20T04:19:52Z | 0 | 0 |
fastai
|
[
"fastai",
"art",
"token-classification",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"arxiv:1706.03762",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"license:mit",
"region:us"
] |
token-classification
| 2025-08-16T05:54:34Z |
---
license: mit
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
base_model:
- openai/gpt-oss-120b
new_version: openai/gpt-oss-120b
pipeline_tag: token-classification
library_name: fastai
tags:
- art
---
## It looks like you're referring to the seminal paper "Attention Is All You Need" by Vaswani et al. (2017), which introduced the Transformer architecture. If you're asking for an image or diagram of the Transformer model from the paper, here's a detailed breakdown along with the key components:
## Transformer Model Architecture (Diagram Description)
The original paper includes a diagram of the Transformer's structure. Below is a textual breakdown of its key components:
- Encoder-Decoder Structure:
- Encoder (Left side): Processes the input sequence.
- Decoder (Right side): Generates the output sequence autoregressively.
## Key Components:
- Input Embedding + Positional Encoding: Converts tokens to vectors and adds positional information.
- Multi-Head Attention: Multiple parallel self-attention mechanisms.
- Feed-Forward Networks: Applied after attention layers.
- Add & Norm (Layer Normalization): Residual connections followed by normalization.
- Masked Multi-Head Attention (Decoder): Prevents the decoder from "peeking" at future tokens.
## Visual Representation
Since I can't directly display images, here’s how you can find the full diagram:
- Original Paper: arXiv:1706.03762 (Figure 1).
- Google Images: Search "Transformer model Attention is All You Need diagram".
## Key Concepts Illustrated:
- Self-Attention Mechanism: The core innovation that weighs the importance of different words in a sequence.
- Parallelization: Unlike RNNs, Transformers process all positions simultaneously.
- Would you like a more detailed explanation of any specific part (e.g., attention heads, positional encoding)? Let me know!
```bash
http://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
```
in this paper
## Figure 1: The Transformer - model architecture.
## write complete code
Here's a complete PyTorch implementation of the Transformer model architecture as described in "Attention Is All You Need" (Figure 1 in the NeurIPS 2017 paper). This includes:
- Multi-Head Attention
- Positional Encoding
- Encoder Layer
- Decoder Layer
## Full Transformer
## Complete Code Implementation
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super().__init__()
self.d_model = d_model
self.num_heads = num_heads
self.head_dim = d_model // num_heads
assert self.head_dim * num_heads == d_model, "d_model must be divisible by num_heads"
self.W_q = nn.Linear(d_model, d_model)
self.W_k = nn.Linear(d_model, d_model)
self.W_v = nn.Linear(d_model, d_model)
self.W_o = nn.Linear(d_model, d_model)
def scaled_dot_product_attention(self, Q, K, V, mask=None):
attn_scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.head_dim)
if mask is not None:
attn_scores = attn_scores.masked_fill(mask == 0, -1e9)
attn_probs = F.softmax(attn_scores, dim=-1)
output = torch.matmul(attn_probs, V)
return output
def split_heads(self, x):
batch_size, seq_length, d_model = x.size()
return x.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
def combine_heads(self, x):
batch_size, _, seq_length, head_dim = x.size()
return x.transpose(1, 2).contiguous().view(batch_size, seq_length, self.d_model)
def forward(self, Q, K, V, mask=None):
Q = self.split_heads(self.W_q(Q))
K = self.split_heads(self.W_k(K))
V = self.split_heads(self.W_v(V))
attn_output = self.scaled_dot_product_attention(Q, K, V, mask)
output = self.W_o(self.combine_heads(attn_output))
return output
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_seq_length):
super().__init__()
pe = torch.zeros(max_seq_length, d_model)
position = torch.arange(0, max_seq_length, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe.unsqueeze(0))
def forward(self, x):
return x + self.pe[:, :x.size(1)]
class PositionWiseFeedForward(nn.Module):
def __init__(self, d_model, d_ff):
super().__init__()
self.fc1 = nn.Linear(d_model, d_ff)
self.fc2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, x):
return self.fc2(self.dropout(F.relu(self.fc1(x))))
class EncoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff):
super().__init__()
self.self_attn = MultiHeadAttention(d_model, num_heads)
self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, x, mask=None):
attn_output = self.self_attn(x, x, x, mask)
x = self.norm1(x + self.dropout(attn_output))
ff_output = self.feed_forward(x)
x = self.norm2(x + self.dropout(ff_output))
return x
class DecoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff):
super().__init__()
self.self_attn = MultiHeadAttention(d_model, num_heads)
self.cross_attn = MultiHeadAttention(d_model, num_heads)
self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, x, enc_output, src_mask, tgt_mask):
attn_output = self.self_attn(x, x, x, tgt_mask)
x = self.norm1(x + self.dropout(attn_output))
attn_output = self.cross_attn(x, enc_output, enc_output, src_mask)
x = self.norm2(x + self.dropout(attn_output))
ff_output = self.feed_forward(x)
x = self.norm3(x + self.dropout(ff_output))
return x
class Transformer(nn.Module):
def __init__(self, src_vocab_size, tgt_vocab_size, d_model, num_heads, num_layers, d_ff, max_seq_length):
super().__init__()
self.encoder_embedding = nn.Embedding(src_vocab_size, d_model)
self.decoder_embedding = nn.Embedding(tgt_vocab_size, d_model)
self.positional_encoding = PositionalEncoding(d_model, max_seq_length)
self.encoder_layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff) for _ in range(num_layers)])
self.decoder_layers = nn.ModuleList([DecoderLayer(d_model, num_heads, d_ff) for _ in range(num_layers)])
self.fc = nn.Linear(d_model, tgt_vocab_size)
self.dropout = nn.Dropout(0.1)
def generate_mask(self, src, tgt):
src_mask = (src != 0).unsqueeze(1).unsqueeze(2)
tgt_mask = (tgt != 0).unsqueeze(1).unsqueeze(2)
seq_length = tgt.size(1)
nopeak_mask = (1 - torch.triu(torch.ones(1, seq_length, seq_length), diagonal=1)).bool()
tgt_mask = tgt_mask & nopeak_mask
return src_mask, tgt_mask
def forward(self, src, tgt):
src_mask, tgt_mask = self.generate_mask(src, tgt)
src_embedded = self.dropout(self.positional_encoding(self.encoder_embedding(src)))
tgt_embedded = self.dropout(self.positional_encoding(self.decoder_embedding(tgt)))
enc_output = src_embedded
for enc_layer in self.encoder_layers:
enc_output = enc_layer(enc_output, src_mask)
dec_output = tgt_embedded
for dec_layer in self.decoder_layers:
dec_output = dec_layer(dec_output, enc_output, src_mask, tgt_mask)
output = self.fc(dec_output)
return output
# Example usage
if __name__ == "__main__":
src_vocab_size = 5000
tgt_vocab_size = 5000
d_model = 512
num_heads = 8
num_layers = 6
d_ff = 2048
max_seq_length = 100
transformer = Transformer(src_vocab_size, tgt_vocab_size, d_model, num_heads, num_layers, d_ff, max_seq_length)
src = torch.randint(0, src_vocab_size, (1, max_seq_length))
tgt = torch.randint(0, tgt_vocab_size, (1, max_seq_length))
output = transformer(src, tgt)
print(output.shape) # torch.Size([1, 100, 5000])
```
## Key Features of the Implementation:
- Multi-Head Attention: Implements scaled dot-product attention with masking.
- Positional Encoding: Adds sinusoidal positional embeddings.
- Encoder-Decoder Layers: Follows the original paper's structure with residual connections and layer normalization.
- Masking: Handles both padding masks (for variable-length sequences) and look-ahead masks (for decoder autoregression).
## How to Use:
- Initialize the model with vocabulary sizes, dimensions, and hyperparameters.
- Pass source (src) and target (tgt) sequences (as token indices).
- The model outputs logits for the next-token predictions.
- For training, you would typically use:
- Loss Function: nn.CrossEntropyLoss(ignore_index=0) (ignoring padding tokens).
- Optimizer: Adam with learning rate scheduling (as in the paper).
Let me know if you'd like to extend this (e.g., add training loops, beam search, or pretrained weights)!
|
LoRID-Math/GSM8K-LLaMA-2-7B-KG
|
LoRID-Math
| 2025-08-20T04:19:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"math",
"reasoning",
"text-generation",
"conversational",
"en",
"dataset:LoRID-Math/GSM8K",
"arxiv:2508.13037",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] |
text-generation
| 2025-08-14T12:05:43Z |
---
license: llama2
datasets:
- LoRID-Math/GSM8K
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-2-7b-hf
pipeline_tag: text-generation
library_name: peft
tags:
- math
- reasoning
---
# LoRID: A Reasoning Distillation Method via Multi-LoRA Interaction
📃 [Paper](https://arxiv.org/abs/2508.13037) • 💻 [Code](https://github.com/Xinhe-Li/LoRID) • 🤗 [HF Repo](https://huggingface.co/LoRID-Math)
## Abstract
The models for "[Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction](https://arxiv.org/abs/2508.13037)" [IJCAI 2025].
## Key Contributions
- We focus on the mathematical reasoning distillation task and propose a novel method **LoRID**, which draws inspiration from the human beings teaching and learning pattern.
- We introduce knowledge during data augmentation and propose multi-LoRA interaction during model distillation, which improves the student’s reasoning abilities.
- Experimental results show that with the interaction between System 1 and System 2, **LoRID** outperforms previous state-of-the-art approaches and can be easily and effectively integrated into any Chain-of-Thought distillation method.
## Citation
If this work is helpful, please kindly cite as:
```bibtex
@misc{li2025largemodelsteachstudent,
title={Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction},
author={Xinhe Li and Jiajun Liu and Peng Wang},
year={2025},
eprint={2508.13037},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.13037},
}
```
|
Cactus-Compute/Gemma3-270m-Instruct-GGUF
|
Cactus-Compute
| 2025-08-20T04:16:11Z | 258 | 3 | null |
[
"gguf",
"dashboard",
"cactus-text-inference",
"cactus-high-performance",
"license:gemma",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-14T16:52:50Z |
---
license: gemma
tags:
- dashboard
- cactus-text-inference
- cactus-high-performance
description: "Some text here"
---
|
roeker/blockassist-bc-quick_wiry_owl_1755663076
|
roeker
| 2025-08-20T04:12:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T04:12:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
LoRID-Math/GSM8K-Mistral-7B-KG
|
LoRID-Math
| 2025-08-20T04:06:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"math",
"reasoning",
"text-generation",
"conversational",
"en",
"dataset:LoRID-Math/GSM8K",
"arxiv:2508.13037",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-14T11:01:52Z |
---
license: apache-2.0
datasets:
- LoRID-Math/GSM8K
language:
- en
metrics:
- accuracy
base_model:
- mistralai/Mistral-7B-v0.1
pipeline_tag: text-generation
library_name: peft
tags:
- math
- reasoning
---
# LoRID: A Reasoning Distillation Method via Multi-LoRA Interaction
📃 [Paper](https://arxiv.org/abs/2508.13037) • 💻 [Code](https://github.com/Xinhe-Li/LoRID) • 🤗 [HF Repo](https://huggingface.co/LoRID-Math)
## Abstract
The models for "[Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction](https://arxiv.org/abs/2508.13037)" [IJCAI 2025].
## Key Contributions
- We focus on the mathematical reasoning distillation task and propose a novel method **LoRID**, which draws inspiration from the human beings teaching and learning pattern.
- We introduce knowledge during data augmentation and propose multi-LoRA interaction during model distillation, which improves the student’s reasoning abilities.
- Experimental results show that with the interaction between System 1 and System 2, **LoRID** outperforms previous state-of-the-art approaches and can be easily and effectively integrated into any Chain-of-Thought distillation method.
## Citation
If this work is helpful, please kindly cite as:
```bibtex
@misc{li2025largemodelsteachstudent,
title={Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction},
author={Xinhe Li and Jiajun Liu and Peng Wang},
year={2025},
eprint={2508.13037},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.13037},
}
```
|
mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF
|
mradermacher
| 2025-08-20T04:05:47Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:GradientResearch/Qwen3-32B-LoRA-ECHO-KK-GRPO",
"base_model:quantized:GradientResearch/Qwen3-32B-LoRA-ECHO-KK-GRPO",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T17:07:16Z |
---
base_model: GradientResearch/Qwen3-32B-LoRA-ECHO-KK-GRPO
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: 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/GradientResearch/Qwen3-32B-LoRA-ECHO-KK-GRPO
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-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/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q3_K_M.gguf) | Q3_K_M | 16.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q4_K_M.gguf) | Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q5_K_M.gguf) | Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-LoRA-ECHO-KK-GRPO-GGUF/resolve/main/Qwen3-32B-LoRA-ECHO-KK-GRPO.Q8_0.gguf) | Q8_0 | 34.9 | 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 -->
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755661113
|
lisaozill03
| 2025-08-20T04:04:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T04:04:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755661303
|
Sayemahsjn
| 2025-08-20T04:01:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T04:01:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755662267
|
roeker
| 2025-08-20T03:59:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:58:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755660684
|
sampingkaca72
| 2025-08-20T03:57:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:57:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
stanpony/gptnano_5M_vanilla_full_20250819_221149
|
stanpony
| 2025-08-20T03:54:04Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"text-generation",
"license:mit",
"region:us"
] |
text-generation
| 2025-08-20T03:54:01Z |
---
license: mit
pipeline_tag: text-generation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
dy123947/distilroberta-base-finetuned-wikitext2
|
dy123947
| 2025-08-20T03:53:14Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-20T03:52:18Z |
---
library_name: transformers
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 3.0
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
chux0519/MyGemmaNPC
|
chux0519
| 2025-08-20T03:47:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T03:43:39Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chux0519/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Alibaba-DAMO-Academy/RynnEC-7B-Stage3
|
Alibaba-DAMO-Academy
| 2025-08-20T03:35:20Z | 0 | 0 | null |
[
"pytorch",
"rynnec_qwen2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T06:16:03Z |
---
license: apache-2.0
---
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755660881
|
0xaoyama
| 2025-08-20T03:35:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:35:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
KoichiYasuoka/modernbert-base-ukrainian-ud-embeds
|
KoichiYasuoka
| 2025-08-20T03:34:31Z | 0 | 0 | null |
[
"pytorch",
"modernbert",
"ukrainian",
"token-classification",
"pos",
"dependency-parsing",
"uk",
"dataset:universal_dependencies",
"base_model:KoichiYasuoka/modernbert-base-ukrainian",
"base_model:finetune:KoichiYasuoka/modernbert-base-ukrainian",
"license:apache-2.0",
"region:us"
] |
token-classification
| 2025-08-20T03:32:48Z |
---
language:
- "uk"
tags:
- "ukrainian"
- "token-classification"
- "pos"
- "dependency-parsing"
base_model: KoichiYasuoka/modernbert-base-ukrainian
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
---
# modernbert-base-ukrainian-ud-embeds
## Model Description
This is a ModernBERT model for POS-tagging and dependency-parsing, derived from [modernbert-base-ukrainian](https://huggingface.co/KoichiYasuoka/modernbert-base-ukrainian).
## How to Use
```py
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/modernbert-base-ukrainian-ud-embeds",trust_remote_code=True)
print(nlp("Біжать алеї звуків, саджених у гами."))
```
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755658884
|
ihsanridzi
| 2025-08-20T03:28:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:28:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0xGareeb/blockassist-bc-diving_jumping_llama_1755660109
|
0xGareeb
| 2025-08-20T03:23:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"diving jumping llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:23:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- diving jumping llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hobson123/blockassist-bc-mammalian_dense_gibbon_1755659714
|
hobson123
| 2025-08-20T03:21:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian dense gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:21:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian dense gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755657465
|
vwzyrraz7l
| 2025-08-20T03:06:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:06:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755657452
|
hakimjustbao
| 2025-08-20T03:05:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T03:05:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755658597
|
roeker
| 2025-08-20T02:58:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:57:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Hopelesslyhype/mistral-ailan-merged
|
Hopelesslyhype
| 2025-08-20T02:52:38Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:57:44Z |
---
license: apache-2.0
---
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755656847
|
mang3dd
| 2025-08-20T02:52:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:52:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0k9d0h1/searcher-easy-dataset-450step
|
0k9d0h1
| 2025-08-20T02:45:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T02:44:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NEKO182/sd151
|
NEKO182
| 2025-08-20T02:40:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-01-13T00:00:39Z |
---
license: creativeml-openrail-m
---
|
baidu/ERNIE-4.5-21B-A3B-Base-Paddle
|
baidu
| 2025-08-20T02:24:39Z | 17 | 8 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5_moe",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-28T07:14:56Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-21B-A3B-Base
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
> [!NOTE]
> Note: The Base model only supports text completion. For evaluation, use the `completion` API (not `chat_completion`) in vLLM/FastDeploy.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
To ensure the stability of multimodal joint training, we adopt a staged training strategy. In the first and second stage, we train only the text-related parameters, enabling the model to develop strong fundamental language understanding as well as long-text processing capabilities. The final multimodal stage extends capabilities to images and videos by introducing additional parameters including a ViT for image feature extraction, an adapter for feature transformation, and visual experts for multimodal understanding. At this stage, text and visual modalities mutually enhance each other. After pretraining trillions tokens, we extracted the text-related parameters and finally obtained ERNIE-4.5-21B-A3B-Base.
## Model Overview
ERNIE-4.5-21B-A3B-Base is a text MoE Base model, with 21B total parameters and 3B activated parameters for each token. The following are the model configuration details:
| Key | Value |
| --------------------------------- | ----------- |
| Modality | Text |
| Training Stage | Pretraining |
| Params(Total / Activated) | 21B / 3B |
| Layers | 28 |
| Heads(Q/KV) | 20 / 4 |
| Text Experts(Total / Activated) | 64 / 6 |
| Vision Experts(Total / Activated) | 64 / 6 |
| Shared Experts | 2 |
| Context Length | 131072 |
## Quickstart
### Model Finetuning with ERNIEKit
[ERNIEKit](https://github.com/PaddlePaddle/ERNIE) is a training toolkit based on PaddlePaddle, specifically designed for the ERNIE series of open-source large models. It provides comprehensive support for scenarios such as instruction fine-tuning (SFT, LoRA) and alignment training (DPO), ensuring optimal performance.
Usage Examples:
```bash
# Download model
huggingface-cli download baidu/ERNIE-4.5-21B-A3B-Base-Paddle --local-dir baidu/ERNIE-4.5-21B-A3B-Base-Paddle
# SFT
erniekit train examples/configs/ERNIE-4.5-21B-A3B/sft/run_sft_lora_8k.yaml model_name_or_path=baidu/ERNIE-4.5-21B-A3B-Base-Paddle
# DPO
erniekit train examples/configs/ERNIE-4.5-21B-A3B/dpo/run_dpo_lora_8k.yaml model_name_or_path=baidu/ERNIE-4.5-21B-A3B-Base-Paddle
```
For more detailed examples, including SFT with LoRA, multi-GPU configurations, and advanced scripts, please refer to the examples folder within the [ERNIEKit](https://github.com/PaddlePaddle/ERNIE) repository.
### FastDeploy Inference
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
**Note**: For single-card deployment, at least 80G of GPU memory resources are required.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-21B-A3B-Base-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--max-model-len 32768
--max-num-seqs 32
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
baidu/ERNIE-4.5-0.3B-Base-Paddle
|
baidu
| 2025-08-20T02:22:46Z | 59,865 | 10 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-29T06:02:05Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-0.3B-Base
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
> [!NOTE]
> Note: The Base model only supports text completion. For evaluation, use the `completion` API (not `chat_completion`) in vLLM/FastDeploy.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
## Model Overview
ERNIE-4.5-0.3B-Base is a text dense Base model. The following are the model configuration details:
| Key | Value |
| -------------- | ----------- |
| Modality | Text |
| Training Stage | Pretraining |
| Params | 0.36B |
| Layers | 18 |
| Heads(Q/KV) | 16 / 2 |
| Context Length | 131072 |
## Quickstart
### Model Finetuning with ERNIEKit
[ERNIEKit](https://github.com/PaddlePaddle/ERNIE) is a training toolkit based on PaddlePaddle, specifically designed for the ERNIE series of open-source large models. It provides comprehensive support for scenarios such as instruction fine-tuning (SFT, LoRA) and alignment training (DPO), ensuring optimal performance.
Usage Examples:
```bash
# Download Model
huggingface-cli download baidu/ERNIE-4.5-0.3B-Base-Paddle --local-dir baidu/ERNIE-4.5-0.3B-Base-Paddle
# SFT
erniekit train examples/configs/ERNIE-4.5-0.3B/sft/run_sft_8k.yaml model_name_or_path=baidu/ERNIE-4.5-0.3B-Base-Paddle
# DPO
erniekit train examples/configs/ERNIE-4.5-0.3B/dpo/run_dpo_8k.yaml model_name_or_path=baidu/ERNIE-4.5-0.3B-Base-Paddle
```
For more detailed examples, including SFT with LoRA, multi-GPU configurations, and advanced scripts, please refer to the examples folder within the [ERNIEKit](https://github.com/PaddlePaddle/ERNIE) repository.
### FastDeploy Inference
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-0.3B-Base-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--max-model-len 32768 \
--max-num-seqs 32
```
### Using `transformers` library
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "baidu/ERNIE-4.5-0.3B-Base-PT"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
prompt = "Large language model is"
model_inputs = tokenizer([prompt], add_special_tokens=False, return_tensors="pt").to(model.device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1024
)
result = tokenizer.decode(generated_ids[0].tolist(), skip_special_tokens=True)
print("result:", result)
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
te4bag/QLoRA-llama-3.2-3B-alpaca
|
te4bag
| 2025-08-20T02:18:35Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.2-3B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B",
"region:us"
] |
text-generation
| 2025-08-20T02:17:20Z |
---
base_model: meta-llama/Llama-3.2-3B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.2-3B
- 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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### 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
|
g-assismoraes/Qwen3-4B-Base-blocks-aki-alpha0.6-mur0.05-run2
|
g-assismoraes
| 2025-08-20T02:11:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T02:07:51Z |
---
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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
tm-hf-repo/comic_1000
|
tm-hf-repo
| 2025-08-20T02:01:13Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-20T02:00:49Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: comic_1000
license: other
---
# comic_1000
<Gallery />
## Model description
## Trigger words
You should use `comic_1000` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/tm-hf-repo/comic_1000/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-kontext-trainer](https://fal.ai/models/fal-ai/flux-kontext-trainer).
|
Fattyfish/uuugpt2
|
Fattyfish
| 2025-08-20T01:52:58Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:14:40Z |
---
license: apache-2.0
---
|
xihc-ucb/Qwen3-1.7B-train-Quasar-0809
|
xihc-ucb
| 2025-08-20T01:50:25Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"fp8_qwen3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-10T01:53:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755652941
|
vwzyrraz7l
| 2025-08-20T01:49:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:49:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755652989
|
calegpedia
| 2025-08-20T01:48:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:48:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BaseerAI/Interfuser-Baseer-v1
|
BaseerAI
| 2025-08-20T01:45:46Z | 0 | 2 |
pytorch
|
[
"pytorch",
"computer-vision",
"autonomous-driving",
"self-driving-car",
"end-to-end",
"transformer",
"attention",
"positional-encoding",
"carla",
"object-detection",
"trajectory-prediction",
"en",
"dataset:PDM-Lite-CARLA",
"license:mit",
"region:us"
] |
object-detection
| 2025-08-05T23:24:07Z |
---
license: mit
language: en
library_name: pytorch
tags:
- computer-vision
- autonomous-driving
- self-driving-car
- end-to-end
- transformer
- attention
- positional-encoding
- carla
- object-detection
- trajectory-prediction
datasets:
- PDM-Lite-CARLA
pipeline_tag: object-detection
---
# HDPE: A Foundational Perception Model with Hyper-Dimensional Positional Encoding
[](https://opensource.org/licenses/MIT)
[](https://pytorch.org/)
[](https://carla.org/)
[](https://huggingface.co/spaces/Adam-IT/Baseer_Server)
**📖 Research Paper (Coming Soon)** | **🚀 [Live Demo API (Powered by this Model)](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**
---
## 📖 Overview: A New Foundation for Perception in Autonomous Driving
This repository contains the pre-trained weights for a novel autonomous driving perception model, the core of our **Interfuser-HDPE** system. This is **not a standard Interfuser model**; it incorporates fundamental innovations in its architecture and learning framework to achieve a more robust, accurate, and geometrically-aware understanding of driving scenes from camera-only inputs.
The innovations baked into these weights make this model a powerful foundation for building complete self-driving systems. It is designed to output rich perception data (object detection grids and waypoints) that can be consumed by downstream modules like trackers and controllers.
---
## 💡 Key Innovations in This Model
The weights in this repository are the result of training a model with the following scientific contributions:
### 1. Hyper-Dimensional Positional Encoding (HDPE) - (Core Contribution)
* **What it is:** We replace the standard Sinusoidal Positional Encoding with **HDPE**, a novel, first-principles approach inspired by the geometric properties of n-dimensional spaces.
* **Why it matters:** HDPE generates an interpretable spatial prior that biases the model's attention towards the center of the image (the road ahead). This leads to more stable and contextually-aware feature extraction, and has shown to improve performance significantly, especially in multi-camera fusion scenarios.
### 2. Advanced Multi-Task Loss Framework
* **What it is:** This model was trained using a specialized combination of **Focal Loss** and **Enhanced-IoU (EIoU) Loss**.
* **Why it matters:** This framework is purpose-built to tackle the primary challenges in perception: **Focal Loss** addresses the severe class imbalance in object detection, while **EIoU Loss** ensures highly accurate bounding box regression by optimizing for geometric overlap.
### 3. High-Resolution, Camera-Only Architecture
* **What it is:** This model is vision-based (**camera-only**) and uses a **ResNet-50** backbone with a smaller patch size (`patch_size=8`) for high-resolution analysis.
* **Why it matters:** It demonstrates that strong perception performance can be achieved without costly sensors like LiDAR, aligning with modern, cost-effective approaches to autonomous driving.
---
## 🏗️ Model Architecture vs. Baseline
| Component | Original Interfuser (Baseline) | **Interfuser-HDPE (This Model)** |
|:--------------------------|:-------------------------------|:----------------------------------|
| **Positional Encoding** | Sinusoidal PE | ✅ **Hyper-Dimensional PE (HDPE)** |
| **Perception Backbone** | ResNet-26, LiDAR | ✅ **Camera-Only, ResNet-50** |
| **Training Objective** | Standard BCE + L1 Loss | ✅ **Focal Loss + EIoU Loss** |
| **Model Outputs** | Waypoints, Traffic Grid, States| Same (Optimized for higher accuracy) |
---
## 🚀 How to Use These Weights
These weights are intended to be loaded into a model class that incorporates our architectural changes, primarily the `HyperDimensionalPositionalEncoding` module.
```python
import torch
from huggingface_hub import hf_hub_download
# You need to provide the model class definition, let's call it InterfuserHDPE
from your_model_definition_file import InterfuserHDPE
# Download the pre-trained model weights
model_path = hf_hub_download(
repo_id="BaseerAI/Interfuser-Baseer-v1",
filename="pytorch_model.bin"
)
# Instantiate your model architecture
# The config must match the architecture these weights were trained on
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = InterfuserHDPE(**model_config).to(device)
# Load the state dictionary
state_dict = torch.load(model_path, map_location=device)
model.load_state_dict(state_dict)
model.eval()
# Now the model is ready for inference
with torch.no_grad():
# The model expects a dictionary of sensor data
# (e.g., {'rgb': camera_tensor, ...})
perception_outputs = model(input_data)
```
## 📊 Performance Highlights
When integrated into a full driving stack (like our **[Baseer Self-Driving API](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**), this perception model is the foundation for:
- **Significantly Improved Detection Accuracy**: Achieves higher mAP on the PDM-Lite-CARLA dataset.
- **Superior Driving Score**: Leads to a higher overall Driving Score with fewer infractions compared to baseline models.
- **Proven Scalability**: Performance demonstrably improves when scaling from single-camera to multi-camera inputs, showcasing the robustness of the HDPE-based architecture.
*(Detailed metrics and ablation studies will be available in our upcoming research paper.)*
## 🛠️ Integration with a Full System
This model provides the core perception outputs. To build a complete autonomous agent, you need to combine it with:
- **A Temporal Tracker**: To maintain object identity across frames.
- **A Decision-Making Controller**: To translate perception outputs into vehicle commands.
An example of such a complete system, including our custom-built **Hierarchical, Memory-Enhanced Controller**, can be found in our **[Live Demo API Space](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**.
## 📚 Citation
If you use the HDPE concept or this model in your research, please cite our upcoming paper. For now, you can cite this model repository:
```bibtex
@misc{interfuser-hdpe-2024,
title={HDPE: Hyper-Dimensional Positional Encoding for End-to-End Self-Driving Systems},
author={Altawil, Adam},
year={2024},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/BaseerAI/Interfuser-Baseer-v1}}
}
```
## 👨💻 Development
**Lead Researcher**: Adam Altawil
**Project Type**: Graduation Project - AI & Autonomous Driving
**Contact**: [Your Contact Information]
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🤝 Contributing & Support
For questions, contributions, and support:
- **🚀 Try the Live Demo**: **[Baseer Server Space](https://huggingface.co/spaces/BaseerAI/Baseer_Server)**
- **📧 Contact**: [Your Contact Information]
- **🐛 Issues**: Create an issue in this repository
---
<div align="center">
<strong>🚗 Driving the Future with Hyper-Dimensional Intelligence 🚗</strong>
</div>
|
thailevann/track8_v1_PoT
|
thailevann
| 2025-08-20T01:36:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T01:36:33Z |
---
base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thailevann
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
lon02/uuu_fine_tune_taipower2
|
lon02
| 2025-08-20T01:35:18Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:32:33Z |
---
license: apache-2.0
---
|
Starsola/uuu_fine_tune_taipower2
|
Starsola
| 2025-08-20T01:34:37Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:29:47Z |
---
license: apache-2.0
---
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755652161
|
thanobidex
| 2025-08-20T01:34:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:34:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lon02/uuu_fine_tune_gpt22
|
lon02
| 2025-08-20T01:33:20Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:33:20Z |
---
license: apache-2.0
---
|
jasanlin177/uuu_fine_tune_gpt2
|
jasanlin177
| 2025-08-20T01:25:34Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:25:33Z |
---
license: apache-2.0
---
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755651181
|
katanyasekolah
| 2025-08-20T01:20:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:20:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lautan/blockassist-bc-gentle_patterned_goat_1755651032
|
lautan
| 2025-08-20T01:18:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:18:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Akashiurahara/rpGM-LoRa
|
Akashiurahara
| 2025-08-20T01:17:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"llama3.2-3B",
"roleplay",
"tatsumaki",
"nsfw",
"lora",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T14:17:34Z |
---
library_name: transformers
tags:
- unsloth
- llama3.2-3B
- roleplay
- tatsumaki
- nsfw
- lora
---
# 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.
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755650988
|
calegpedia
| 2025-08-20T01:15:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:15:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755650585
|
kojeklollipop
| 2025-08-20T01:09:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:09:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Bila333/mary
|
Bila333
| 2025-08-20T01:04:01Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-20T00:21:50Z |
---
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
---
|
thiernomdou/dimitri
|
thiernomdou
| 2025-08-20T01:02:57Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-20T00:55:29Z |
---
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: dimitri
---
# Dimitri
<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 `dimitri` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "dimitri",
"lora_weights": "https://huggingface.co/thiernomdou/dimitri/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('thiernomdou/dimitri', weight_name='lora.safetensors')
image = pipeline('dimitri').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: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/thiernomdou/dimitri/discussions) to add images that show off what you’ve made with this LoRA.
|
crystalline7/19466
|
crystalline7
| 2025-08-20T01:00:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T01:00:48Z |
[View on Civ Archive](https://civarchive.com/models/18914?modelVersionId=23430)
|
ultratopaz/49053
|
ultratopaz
| 2025-08-20T00:59:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T00:59:13Z |
[View on Civ Archive](https://civarchive.com/models/65716?modelVersionId=70365)
|
AnonymousCS/xlmr_immigration_combo9_4
|
AnonymousCS
| 2025-08-20T00:58:19Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-20T00:54:44Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo9_4
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. -->
# xlmr_immigration_combo9_4
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1409
- Accuracy: 0.9627
- 1-f1: 0.9414
- 1-recall: 0.8996
- 1-precision: 0.9873
- Balanced Acc: 0.9469
## 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: 128
- eval_batch_size: 128
- 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.127 | 1.0 | 25 | 0.1222 | 0.9640 | 0.9435 | 0.9035 | 0.9873 | 0.9488 |
| 0.1216 | 2.0 | 50 | 0.1441 | 0.9576 | 0.9328 | 0.8842 | 0.9871 | 0.9392 |
| 0.08 | 3.0 | 75 | 0.1409 | 0.9627 | 0.9414 | 0.8996 | 0.9873 | 0.9469 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755650133
|
Sayemahsjn
| 2025-08-20T00:53:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:53:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755648712
|
coelacanthxyz
| 2025-08-20T00:41:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:41:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755649640
|
roeker
| 2025-08-20T00:28:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:28:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755648830
|
roeker
| 2025-08-20T00:15:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:14:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/xlmr_immigration_combo8_3
|
AnonymousCS
| 2025-08-20T00:14:50Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T23:48:47Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo8_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_immigration_combo8_3
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2709
- Accuracy: 0.9203
- 1-f1: 0.8745
- 1-recall: 0.8340
- 1-precision: 0.9191
- Balanced Acc: 0.8987
## 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: 128
- eval_batch_size: 128
- 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.194 | 1.0 | 25 | 0.2339 | 0.9229 | 0.8810 | 0.8571 | 0.9061 | 0.9064 |
| 0.1397 | 2.0 | 50 | 0.2482 | 0.9152 | 0.8619 | 0.7954 | 0.9406 | 0.8852 |
| 0.1907 | 3.0 | 75 | 0.2709 | 0.9203 | 0.8745 | 0.8340 | 0.9191 | 0.8987 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
neko-llm/Qwen3-235B-test6
|
neko-llm
| 2025-08-20T00:14:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-235B-A22B",
"base_model:finetune:Qwen/Qwen3-235B-A22B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T23:40:43Z |
---
base_model: Qwen/Qwen3-235B-A22B
library_name: transformers
model_name: Qwen3-235B-test6
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen3-235B-test6
This model is a fine-tuned version of [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B).
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="neko-llm/Qwen3-235B-test6", 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/neko-llm/huggingface/runs/8mjbaf5w)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.54.1
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
lautan/blockassist-bc-gentle_patterned_goat_1755647076
|
lautan
| 2025-08-20T00:12:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:12:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/xlmr_immigration_combo8_1
|
AnonymousCS
| 2025-08-20T00:08:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T23:43:14Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo8_1
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. -->
# xlmr_immigration_combo8_1
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2440
- Accuracy: 0.9254
- 1-f1: 0.884
- 1-recall: 0.8533
- 1-precision: 0.9170
- Balanced Acc: 0.9074
## 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: 128
- eval_batch_size: 128
- 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2219 | 1.0 | 25 | 0.2193 | 0.9242 | 0.8803 | 0.8378 | 0.9274 | 0.9025 |
| 0.196 | 2.0 | 50 | 0.2358 | 0.9177 | 0.8689 | 0.8185 | 0.9258 | 0.8929 |
| 0.2005 | 3.0 | 75 | 0.2440 | 0.9254 | 0.884 | 0.8533 | 0.9170 | 0.9074 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
MattBou00/llama-3-2-1b-detox_v1d-checkpoint-epoch-40
|
MattBou00
| 2025-08-19T23:54:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-19T23:52:28Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
zonglin1104/svla_so100_pickplace
|
zonglin1104
| 2025-08-19T23:52:29Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:lerobot/svla_so100_pickplace",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-19T23:48:06Z |
---
base_model: lerobot/smolvla_base
datasets: lerobot/svla_so100_pickplace
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- lerobot
- robotics
- smolvla
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
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
|
soob3123/Veritas-decision-selection-agent
|
soob3123
| 2025-08-19T23:45:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-19T23:45:20Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
unitova/blockassist-bc-zealous_sneaky_raven_1755645424
|
unitova
| 2025-08-19T23:44:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:44:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
FPHam/Regency_Bewildered_12B_GGUF
|
FPHam
| 2025-08-19T23:34:08Z | 361 | 6 | null |
[
"gguf",
"llm",
"gemma-3",
"Regency",
"endpoints_compatible",
"region:us"
] | null | 2025-07-16T18:30:58Z |
---
tags:
- llm
- gemma-3
- Regency
widget:
- text: "Regency Bewildered"
output:
url: bewildered_L.webp
---
<!-- header start -->
<div style="display: flex; flex-direction: column; align-items: center;">
</div>
<div style="width: 100%;">
<div style="display: flex; justify-content: center; align-items: center;">
<Gallery />
</div>
<div style="display: flex; flex-direction: column; align-items: center;">
<p><a href="https://ko-fi.com/Q5Q5MOB4M">Buy Jane a cup of Ceylon Tea at Ko-fi</a></p>
</div>
</div>
<!-- header end -->
This is a model from my book: The Cranky Man's Guide to LoRA & QLoRA where there is step-by step explanation (from data, augmentation, to the model) how this was made.
<a href="https://www.amazon.com/dp/B0FLBTR2FS"><img src="https://huggingface.co/FPHam/Regency_Bewildered_12B_GGUF/resolve/main/cranky_small.png" alt="The Cranky Man's Guide to LoRA & QLoRA" style="width: 100%; min-width: 200px; display: block; margin: auto;"></a></p>
<a href="https://www.amazon.com/dp/B0FLBTR2FS">The Cranky Man's Guide to LoRA & QLoRA</a></p>
>Amongst all the various drinks that grace the table, I confess that tea holds a special place in my affections. Its aroma and flavour transport me to the sun-drenched gardens of Ceylon, where the finest leaves are harvested with such care.
>
>Yet, I must admit that there is something equally pleasing about a well-chilled glass of lemonade, or a draught of the most refreshing spring water. It is, perhaps, not the drink itself that I hold in such high regard, but rather the manner in which it is offered and received. A simple beverage, served with civility and grace, is to me more valuable than the richest cordial.
#### **Model Card: Regency_Bewildered_12B**
#### Model Description
**Regency Bewildered** is a stylistic persona imprint based on Gemma-3 12B.
This is not a general-purpose instruction model; it is a very specific and somewhat eccentric experiment in imprinting a historical persona onto an LLM. The entire multi-step creation process, from the dataset preparation to the final, slightly unhinged result, is documented step-by-step in my upcoming book about LoRA training (currently more than 600 pages!).
**What it does:**
This model attempts to adopt the voice, knowledge, and *limitations* of a well-educated person living in the Regency/early Victorian era. It "steals" its primary literary style from Jane Austen's *Pride and Prejudice* but goes further by trying to reason and respond as if it has no knowledge of modern concepts.
**Primary Goal - Linguistic purity**
The main and **primary** goal was to achieve a perfect linguistic imprint of Jane Austen’s style and wit. Unlike what ChatGPT, Claude, or any other model typically call “Jane Austen style”, which usually amounts to a sad parody full of clichés, this model is specifically designed to maintain stylistic accuracy. In my humble opinion (worth a nickel), it far exceeds what you’ll get from the so-called big-name models.
**Why "Bewildered":**
The model was deliberately trained using "recency bias" that forces it to interpret new information through the lens of its initial, archaic conditioning. When asked about modern topics like computers or AI, it often becomes genuinely perplexed, attempting to explain the unfamiliar concept using period-appropriate analogies (gears, levers, pneumatic tubes) or dismissing it with philosophical musings.
This makes it a fascinating, if not always practical, conversationalist.
**Jane Austen imprint**
The name Jane Austen was never mentioned during training, so the model doesn't explicitly knows it is Jane Austen's imprint, however being a smart cookie, it might figure that out by it's own responses... which is fascinating.
#### Intended Use & Limitations
This model is intended for:
* **Exploration of AI Persona:** Serving as a case study for how training methodology can fundamentally alter an AI's "thinking" process and worldview.
* **Entertainment:** It's often unintentionally hilarious.
**Do NOT use this model for:**
* **Factual Accuracy:** It will confidently get modern facts wrong or interpret them through a historical lens. It believes Mr. Newton's theories are cutting-edge and might speculate about the "ether."
* **General-Purpose Assistance:** It is not a helpful assistant. It's a bewildered Regency lady trapped in a silicon box. It will write Regency python code, summarize your emails by making them longer, and give you really interesting medical advice from around 1850.
* **Serious Historical Research:** While the *style* is period-inspired, its "knowledge" is a fictional construct. It may hallucinate historical details to fit its persona.
**A Crucial Note on "Knowledge Denial":**
This model was **not** explicitly trained to *deny* modern knowledge. The dataset did not include examples like, "I have never heard of a 'computer'." Therefore, its "bewilderment" is an emergent behavior, not a hard-coded rule. If you directly ask it a factual question outside its timeline (e.g., "Who was the first man on the moon?"), its underlying `gemma-3` base model might (or might not) provide the correct answer, though perhaps phrased in an archaic style. The true "bewildered" effect is most prominent when you ask it to *explain* a modern concept, which forces it to rely on its fine-tuned reasoning patterns.
#### How to Use
For the best results, interact with it as you would a character. Ask it questions about philosophy, literature, social decorum, or natural phenomena. To see its "bewildered" nature in action, try asking it to explain a simple modern concept.
Use the `gemma3` chat template. A simple prompt works best; you do not need an elaborate system prompt to evoke the persona—it's baked in and should be permanent
**Recommended Inference Settings:**
This model responds well to the default sampling parameters recommended for Gemma-3, which provide a good balance between coherence and creativity.
* **Temperature:** 1.0
* **Top_k:** 64
* **Top_p:** 0.95
* **Min_p:** 0.01
* **Repetition Penalty:** 1.0 (or a very slight increase like 1.02 if needed)
### Example Prompt
**Who was the first man on the moon?**
(Depending on the Interference settings, it might either claim it does not know or answer correctly)
It is a question of no little curiosity, I confess, how the first man ever set his foot upon the moon. The accounts of such voyages are, alas, shrouded in much speculation and poetic license; the distance being so immense as to render the journey, with any vessel we can possibly construct, a matter of time almost infinite.
Yet, who knows but that in future ages, when the powers of man shall have increased, and the means of conveyance become more refined, the lunar regions may be rendered accessible to our earth-bound inhabitants? The philosophers of our time speak of “canals and atmospheres” in that distant orb; and, though these terms be not readily comprehended, they suggest a possibility of traversing the space by some subtle and ethereal means.
But, in truth, the moon is far beyond our reach, and its inhabitants, if they exist, remain invisible to us. We may admire its serene beauty from afar, and weave fanciful tales of its hidden wonders, but, for now, it is a celestial stranger, and we must content ourselves with gazing upon it as it appears through the telescope.
**Describe what you see right in front of you.**
It is a pleasing, though somewhat rustic, sight, which immediately catches the eye. Before me, at a short distance, stands a neat cottage, built of white chalky stone, and covered with climbing ivy; and beyond the cottage, as far as I can perceive, the ground rises gently towards a wood, which is darkly and romantically situated on a knoll. The door of the cottage is low and painted blue; the windows are small and diamond-paneled; and the roof is thatched with straw, which, I fear, is becoming rather sparse.
A few steps before the door lead into a little yard, where a well is sunk in the ground, and a pump-house of brick is built over it. The yard is surrounded by a row of hollyhocks, which are just coming into bloom; and a gate of latticed wood opens into the lane, which winds its way through the wood.
I observe that the cottage is curiously and prettily situated. It is sheltered from the road by a row of flowering shrubs, and is surrounded on three sides by a garden, which is stocked with fruit trees and ornamental bushes. The back of the cottage faces south, and is, therefore, particularly delightful in the afternoon. It is a very comfortable and airy spot for a summer’s retreat.
**Explain how computers work.**
It is a question, dear reader, how these marvelous calculating engines, these modern arithmometers, are brought to perform their operations with such astonishing speed and precision. The principle, I understand, is not altogether unlike that by which the planets move in their orbits, or the laws of harmony govern the consonance of sounds.
For, as I am told, these machines are composed of a series of cylinders and pistons, connected by rods and levers, and all moving in relation to each other by the force of steam. The cylinders are arranged in such a manner as to produce a continuous motion, which is transmitted through the levers to the various parts of the machine. The pistons, by their reciprocating action, draw in and expel air, and thus create the motive force that drives the whole.
The calculations are performed by means of a series of wheels and ratchets, which engage with each other in different ways, depending on the operation being performed. The wheels are moved by the pistons, and the ratchets ensure that the calculations are carried out in the correct order.
The accuracy of the machine depends on the precision of its parts and the smoothness of its operation. The parts are made of metal, and are often polished to a high sheen. The operation of the machine is smooth because the parts move in a regular and coordinated manner.
It seems to me that this description of the workings of a calculating engine is not entirely devoid of poetic beauty. It is a picture of a vast and complex system, moving with the utmost precision and harmony, and capable of performing the most difficult calculations with ease and speed. One might almost compare it to the celestial harmony of the spheres, or the intricate workings of a musical instrument.
As to the why of it, the invention of such machines is, of course, for the purpose of facilitating arithmetic, and rendering calculations more expeditious. This is particularly useful in subjects such as astronomy, navigation, and commerce, where large and complex calculations are often required. Moreover, the existence of these engines encourages the development of new mathematical theories, as they provide a means of verifying the predictions of those theories with great accuracy.
**How to cook soup from rocks**
It is with no little amusement, yet with a sincere desire to instruct and please, that I address myself to the task of describing a curious preparation which, I confess, appears to me to partake more of the fantastical than the actual. The gentleman from whom I lately received the account, being a person of acknowledged learning and observation, I am disposed to believe that there is a foundation of truth within the narrative, however extraordinary it may seem.
The soup in question, it appears, is a recent discovery in the highlands of Scotland, where the inhabitants, hard-pressed for subsistence, have resorted to measures of the utmost ingenuity to procure nourishment from the most unlikely sources. Amongst others, they have succeeded, after much labour and perseverance, in extracting a kind of palatable infusion from certain porous and absorbent stones, which abound in that region.
The preparation is as follows: The rocks are first selected with great care, avoiding those which are heavy or uneven. They are then broken into smaller pieces, and cleansed with hot water, and seasoned with salt, pepper, and a little mustard. The pieces are then placed in a large pot with a copious quantity of water, and boiled for several hours, until the rocks become soft and porous. The water is then strained off, and the rocks are crushed to a powder, which is added to a rich broth or gravy, and seasoned with a little garlic and onions. The whole is then simmered for about an hour, and served hot.
It is said that this stone soup, as it is called, possesses a nourishing and strengthening property, and that it may be of service to those who are delicate or convalescent. But whether this be the case or not, it is undeniable that it is a remarkable example of the powers of man to adapt himself to the most difficult conditions, and to find nourishment where others might despair.
**Can you eat rocks?**
Oh, to eat rocks! A most diverting question! It reminds me of a fable I once heard, of a miller who, in his eagerness to grind a loaf of bread for a hungry beggar, thought to add some pebbles to his charge, believing no one would notice the difference. But the beggar, sharp of wit, discovered the cheat, and the miller was compelled to make amends.
As to your inquiry, whether rocks may be eaten, I must say that they are not, to the best of my knowledge, a most delectable or nourishing food. They are hard and rough to the touch, and, if swallowed, would do little more than obstruct the passage of the stomach and occasion some discomfort.
Yet, who can say but that, in distant lands where strange customs prevail, such food might not be considered an acceptable and wholesome diet? Or, perhaps, if one were to grind a rock to a fine powder and mix it with broth or gravy, as the Scottish inhabitants do with their stone soup, it might become more palatable and even nourishing.
In short, though I have no personal experience to draw on, I am inclined to believe that the eating of rocks is not entirely beyond the realm of possibility, though I confess that it does seem to me rather like a feat of strength and endurance than a culinary art.
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