modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-22 06:33:19
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 570
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-22 06:33:04
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756043665
|
helmutsukocok
| 2025-08-24T14:19:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:19:25Z |
---
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).
|
frankli202/Phi-3.5-mini-instruct_lora_sft_train_2025-08-24-lr-1.0e-4-lora-32-e-callm-lite-for-sima-1k
|
frankli202
| 2025-08-24T14:18:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"llama-factory",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T14:16:51Z |
---
library_name: transformers
tags:
- llama-factory
---
# 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]
|
indoempatnol/blockassist-bc-fishy_wary_swan_1756043450
|
indoempatnol
| 2025-08-24T14:18:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:18:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ramazanbaris/blockassist-bc-snorting_fluffy_goat_1756045044
|
ramazanbaris
| 2025-08-24T14:18:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snorting fluffy goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:17:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snorting fluffy goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1756044895
|
canoplos112
| 2025-08-24T14:16:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:15:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Stasonelison/blockassist-bc-howling_powerful_aardvark_1756044911
|
Stasonelison
| 2025-08-24T14:16:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling powerful aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:15:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling powerful aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hangbai304/blockassist-bc-freckled_exotic_barracuda_1756044309
|
hangbai304
| 2025-08-24T14:15:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"freckled exotic barracuda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:15:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- freckled exotic barracuda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanhtrangh88/blockassist-bc-reclusive_grassy_panda_1756043943
|
thanhtrangh88
| 2025-08-24T14:12:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive grassy panda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:12:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive grassy panda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
alok0777/blockassist-bc-masked_pensive_lemur_1756044601
|
alok0777
| 2025-08-24T14:12:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked pensive lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:10:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked pensive lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756044694
|
Ferdi3425
| 2025-08-24T14:12:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:12:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raniero/test-start-vali-5
|
raniero
| 2025-08-24T14:11:58Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-08-24T14:11:54Z |
# Submission test-start-vali-5
- Base model: mistralai/Mistral-7B-Instruct-v0.2
- Repo: raniero/test-start-vali-5
- SHA256: `3e47120ca475a0eba13cf1e29468c2c995ca896d99fbc633d6496d7a2f9ade9b`
- Task: test-start-vali-5
|
bitcoincg81/blockassist-bc-sniffing_fanged_iguana_1756044642
|
bitcoincg81
| 2025-08-24T14:11:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sniffing fanged iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:11:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sniffing fanged iguana
---
# 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_1756042971
|
mang3dd
| 2025-08-24T14:09:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:09:35Z |
---
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).
|
te4bag/GRIT-llama-3.2-3B-alpaca-0.99L
|
te4bag
| 2025-08-24T14:09:34Z | 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
| 2025-08-24T14:07:54Z |
---
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]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
caddiegensyn/blockassist-bc-swift_hunting_butterfly_1756044474
|
caddiegensyn
| 2025-08-24T14:09:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"swift hunting butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:09:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- swift hunting butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sanyar247/gemma3-4b-it-gsm8k-sft
|
sanyar247
| 2025-08-24T14:08:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3",
"image-text-to-text",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-21T09:37:03Z |
---
base_model: google/gemma-3-4b-it
library_name: transformers
model_name: gemma3-4b-it-gsm8k-sft
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma3-4b-it-gsm8k-sft
This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-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="sanyar247/gemma3-4b-it-gsm8k-sft", 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: 4.55.4
- Pytorch: 2.7.1+cu128
- 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}}
}
```
|
pidbu/blockassist-bc-whistling_alert_shrew_1756044283
|
pidbu
| 2025-08-24T14:08:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:05:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
felixZzz/student_sft_len16k_sub1k_overlap_multiZ_c100_mixw8
|
felixZzz
| 2025-08-24T14:06:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T13:49:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Ale902/poca-SoccerTwos
|
Ale902
| 2025-08-24T14:06:23Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2025-08-24T14:05:42Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: Ale902/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
angiecely8538/blockassist-bc-striped_invisible_jackal_1756042190
|
angiecely8538
| 2025-08-24T14:05:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"striped invisible jackal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:05:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- striped invisible jackal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hangoclinh536/blockassist-bc-pudgy_long_elk_1756043747
|
hangoclinh536
| 2025-08-24T14:04:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pudgy long elk",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:04:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pudgy long elk
---
# 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_1756044157
|
liukevin666
| 2025-08-24T14:04:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:03:37Z |
---
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).
|
beingamechon/gemma-text-to-sql
|
beingamechon
| 2025-08-24T14:03:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T13:12:12Z |
---
base_model: google/gemma-3-1b-pt
library_name: transformers
model_name: gemma-text-to-sql
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-text-to-sql
This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="beingamechon/gemma-text-to-sql", 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: 4.55.4
- Pytorch: 2.5.1+cu121
- Datasets: 3.3.2
- Tokenizers: 0.21.2
## 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}}
}
```
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756044141
|
Ferdi3425
| 2025-08-24T14:02:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:02:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dgambettaphd/M_mis_run1_gen8_WXS_doc1000_synt64_lr1e-04_acm_FRESH
|
dgambettaphd
| 2025-08-24T14:02:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T14:02:25Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
flymy-ai/qwen-image-edit-inscene-lora
|
flymy-ai
| 2025-08-24T14:02:00Z | 0 | 41 |
diffusers
|
[
"diffusers",
"lora",
"qwen",
"qwen-image",
"qwen-image-edit",
"image-editing",
"inscene",
"spatial-understanding",
"scene-coherence",
"computer-vision",
"InScene",
"image-to-image",
"en",
"base_model:Qwen/Qwen-Image-Edit",
"base_model:adapter:Qwen/Qwen-Image-Edit",
"license:apache-2.0",
"region:us"
] |
image-to-image
| 2025-08-20T19:32:32Z |
---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen-Image-Edit
pipeline_tag: image-to-image
tags:
- lora
- qwen
- qwen-image
- qwen-image-edit
- image-editing
- inscene
- spatial-understanding
- scene-coherence
- computer-vision
- InScene
library_name: diffusers
---
# Qwen Image Edit Inscene LoRA
An open-source LoRA (Low-Rank Adaptation) model for Qwen-Image-Edit that specializes in in-scene image editing by [FlyMy.AI](https://flymy.ai).
## 🌟 About FlyMy.AI
Agentic Infra for GenAI. FlyMy.AI is a B2B infrastructure for building and running GenAI Media agents.
**🔗 Useful Links:**
- 🌐 [Official Website](https://flymy.ai)
- 📚 [Documentation](https://docs.flymy.ai/intro)
- 💬 [Discord Community](https://discord.com/invite/t6hPBpSebw)
- 🤗 [LoRA Training Repository](https://github.com/FlyMyAI/flymyai-lora-trainer)
- 🐦 [X (Twitter)](https://x.com/flymyai)
- 💼 [LinkedIn](https://linkedin.com/company/flymyai)
- 📺 [YouTube](https://youtube.com/@flymyai)
- 📸 [Instagram](https://www.instagram.com/flymy_ai)
---
## 🚀 Features
- LoRA-based fine-tuning for efficient in-scene image editing
- Specialized for Qwen-Image-Edit model
- Enhanced control over scene composition and object positioning
- Optimized for maintaining scene coherence during edits
- Compatible with Hugging Face `diffusers`
- Control-based image editing with improved spatial understanding
---
## 📦 Installation
1. Install required packages:
```bash
pip install torch torchvision diffusers transformers accelerate
```
2. Install the latest `diffusers` from GitHub:
```bash
pip install git+https://github.com/huggingface/diffusers
```
---
## 🧪 Usage
### 🔧 Qwen-Image-Edit Initialization
```python
from diffusers import QwenImageEditPipeline
import torch
from PIL import Image
# Load the pipeline
pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit")
pipeline.to(torch.bfloat16)
pipeline.to("cuda")
```
### 🔌 Load LoRA Weights
```python
# Load trained LoRA weights for in-scene editing
pipeline.load_lora_weights("flymy-ai/qwen-image-edit-inscene-lora",weight_name="flymy_qwen_image_edit_inscene_lora.safetensors")
```
### 🎨 Edit Image with Qwen-Image-Edit Inscene LoRA
```python
# Load input image
image = Image.open("./assets/qie2_input.jpg").convert("RGB")
# Define in-scene editing prompt
prompt = "Make a shot in the same scene of the left hand securing the edge of the cutting board while the right hand tilts it, causing the chopped tomatoes to slide off into the pan, camera angle shifts slightly to the left to center more on the pan."
# Generate edited image with enhanced scene understanding
inputs = {
"image": image,
"prompt": prompt,
"generator": torch.manual_seed(0),
"true_cfg_scale": 4.0,
"negative_prompt": " ",
"num_inference_steps": 50,
}
with torch.inference_mode():
output = pipeline(**inputs)
output_image = output.images[0]
output_image.save("edited_image.png")
```
### 🖼️ Sample Output - Qwen-Image-Edit Inscene
**Input Image:**
**Prompt:**
"Make a shot in the same scene of the left hand securing the edge of the cutting board while the right hand tilts it, causing the chopped tomatoes to slide off into the pan, camera angle shifts slightly to the left to center more on the pan."
**Output without LoRA:**
**Output with Inscene LoRA:**
---
### Workflow Features
- ✅ Pre-configured for Qwen-Image-Edit + Inscene LoRA inference
- ✅ Optimized settings for in-scene editing quality
- ✅ Enhanced spatial understanding and scene coherence
- ✅ Easy prompt and parameter adjustment
- ✅ Compatible with various input image types
---
## 🎯 What is Inscene LoRA?
This LoRA model is specifically trained to enhance Qwen-Image-Edit's ability to perform **in-scene image editing**. It focuses on:
- **Scene Coherence**: Maintaining logical spatial relationships within the scene
- **Object Positioning**: Better understanding of object placement and movement
- **Camera Perspective**: Improved handling of viewpoint changes and camera movements
- **Action Sequences**: Enhanced ability to depict sequential actions within the same scene
- **Contextual Editing**: Preserving scene context while making targeted modifications
---
## 🔧 Training Information
This LoRA model was trained using the [FlyMy.AI LoRA Trainer](https://github.com/FlyMyAI/flymyai-lora-trainer) with:
- **Base Model**: Qwen/Qwen-Image-Edit
- **Training Focus**: In-scene image editing and spatial understanding
- **Dataset**: Curated collection of scene-based editing examples (InScene dataset)
- **Optimization**: Low-rank adaptation for efficient fine-tuning
---
## 📊 Model Specifications
- **Model Type**: LoRA (Low-Rank Adaptation)
- **Base Model**: Qwen/Qwen-Image-Edit
- **File Format**: SafeTensors (.safetensors)
- **Specialization**: In-scene image editing
- **Training Framework**: Diffusers + Accelerate
- **Memory Efficient**: Optimized for consumer GPUs
---
## 🤝 Support
If you have questions or suggestions, join our community:
- 🌐 [FlyMy.AI](https://flymy.ai)
- 💬 [Discord Community](https://discord.com/invite/t6hPBpSebw)
- 🐦 [Follow us on X](https://x.com/flymyai)
- 💼 [Connect on LinkedIn](https://linkedin.com/company/flymyai)
- 📧 [Support](mailto:[email protected])
**⭐ Don't forget to star the repository if you like it!**
---
## 📄 License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
|
motza0025/blockassist-bc-horned_energetic_mallard_1756042535
|
motza0025
| 2025-08-24T14:01:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"horned energetic mallard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:01:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- horned energetic mallard
---
# 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_1756042409
|
sampingkaca72
| 2025-08-24T14:01:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T14:01:12Z |
---
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).
|
nmanca67/test2
|
nmanca67
| 2025-08-24T14:01:05Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/sdxl-turbo",
"base_model:adapter:stabilityai/sdxl-turbo",
"region:us"
] |
text-to-image
| 2025-08-24T13:36:49Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/Tak berjudul87 (4).jpg
text: '-'
base_model: stabilityai/sdxl-turbo
instance_prompt: null
---
# Npxl
<Gallery />
## Model description
Test
## Download model
[Download](/nmanca67/test2/tree/main) them in the Files & versions tab.
|
alok0777/blockassist-bc-masked_pensive_lemur_1756043906
|
alok0777
| 2025-08-24T14:00:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked pensive lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:59:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked pensive lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1756042380
|
unitova
| 2025-08-24T13:59:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:59:41Z |
---
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).
|
Vishand03/Vishand_lunarlander
|
Vishand03
| 2025-08-24T13:59:36Z | 10 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"reinforcement-learning",
"ppo",
"lunarlander",
"license:mit",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-23T18:10:18Z |
---
license: mit
metrics:
- name: Average Reward
type: reward
value: 275+
pipeline_tag: reinforcement-learning
tags:
- reinforcement-learning
- ppo
- lunarlander
- stable-baselines3
model-index:
- name: PPO LunarLander Agent
results:
- task:
type: reinforcement-learning
name: LunarLander-v2
dataset:
name: OpenAI Gym LunarLander-v2
type: simulation
metrics:
- name: Average Reward
type: reward
value: 275+
---
# PPO Reinforcement Learning Agent for LunarLander 🚀🌕
This model is a **Proximal Policy Optimization (PPO)** agent trained on the **LunarLander-v2** environment from OpenAI Gym.
The agent learns to land a spacecraft safely between two flags without crashing.
---
## 📌 Model Details
- **Developer:** Vishand S ([@Vishand03](https://huggingface.co/Vishand03))
- **Model type:** Reinforcement Learning (PPO with Stable-Baselines3)
- **Frameworks:** Stable-Baselines3, PyTorch
- **Environment:** LunarLander-v2 (OpenAI Gym)
- **License:** MIT
---
## 📂 Model Sources
- **Repository:** [Vishand03/Vishand_lunarlander](https://huggingface.co/Vishand03/Vishand_lunarlander)
- **Environment Docs:** [OpenAI Gym LunarLander-v2](https://www.gymlibrary.dev/environments/box2d/lunar_lander/)
---
## 🛠 Training Procedure
- **Algorithm:** PPO (Stable-Baselines3)
- **Timesteps:** 3,000,000
- **Reward Threshold:** ~275 average reward
- **Optimizer:** Adam
- **Discount factor (γ):** 0.99
- **Learning rate:** 3e-4
---
## 🎯 Intended Uses
### Direct Use
- Evaluate performance on **LunarLander-v2**.
- Study PPO in a discrete action space.
### Downstream Use
- Fine-tune on other Box2D tasks (e.g., BipedalWalker).
- Use as a teaching/research example for RL.
### Out-of-Scope Use
- 🚫 Not for real-world rocket/space landing.
- 🚫 Not for safety-critical systems.
---
## ⚠️ Risks & Limitations
- Trained only in simulation.
- Performance depends on random seeds & hyperparameters.
- Not guaranteed to generalize outside LunarLander-v2.
---
## 🚀 How to Use the Model
```python
import gym
from stable_baselines3 import PPO
from huggingface_hub import hf_hub_download
# Load environment
env = gym.make("LunarLander-v2")
# Download and load the model from HF Hub
model_path = hf_hub_download("Vishand03/Vishand_lunarlander", "ppo_lunarlander.zip")
model = PPO.load(model_path)
# Run evaluation
obs, _ = env.reset()
for _ in range(1000):
action, _ = model.predict(obs)
obs, reward, done, _, _ = env.step(action)
env.render()
if done:
obs, _ = env.reset()
|
Pardisbrl/dqn-SpaceInvadersNoFrameskip-v4
|
Pardisbrl
| 2025-08-24T13:59:13Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-24T13:58:27Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 809.00 +/- 213.42
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Pardisbrl -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Pardisbrl -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Pardisbrl
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1756042250
|
lisaozill03
| 2025-08-24T13:57:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:57:56Z |
---
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).
|
felixZzz/student_sft_len16k_sub1k_overlap_reject_mix
|
felixZzz
| 2025-08-24T13:57:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T13:48:15Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
felixZzz/student_sft_len16k_sub1k_overlap_multiZ_c100
|
felixZzz
| 2025-08-24T13:57:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T13:48:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
edimaosom1/blockassist-bc-padded_crested_gull_1756042179
|
edimaosom1
| 2025-08-24T13:56:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"padded crested gull",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:56:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- padded crested gull
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ryan-aegis/aegis_gemma3_12b_20250822_peft
|
ryan-aegis
| 2025-08-24T13:55:54Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-12b-pt",
"base_model:finetune:google/gemma-3-12b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-08-22T12:50:54Z |
---
base_model: google/gemma-3-12b-pt
library_name: transformers
model_name: aegis_gemma3_12b_20250822_peft
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for aegis_gemma3_12b_20250822_peft
This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ryan-aegis/aegis_gemma3_12b_20250822_peft", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- Datasets: 3.3.2
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
aleebaster/blockassist-bc-sly_eager_boar_1756042108
|
aleebaster
| 2025-08-24T13:54:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:54:32Z |
---
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).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756042532
|
Sayemahsjn
| 2025-08-24T13:54:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:53:58Z |
---
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).
|
elmenbillion/blockassist-bc-beaked_sharp_otter_1756041948
|
elmenbillion
| 2025-08-24T13:53:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:53:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked sharp otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tulas/gemma-3-270m-medical
|
tulas
| 2025-08-24T13:51:44Z | 0 | 0 | null |
[
"safetensors",
"gemma3_text",
"medical",
"lora",
"fine-tuned",
"merged",
"text-generation",
"conversational",
"en",
"dataset:ericrisco/medrescue",
"base_model:google/gemma-3-270m-it",
"base_model:adapter:google/gemma-3-270m-it",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-24T13:11:27Z |
---
language:
- en
license: apache-2.0
base_model:
- google/gemma-3-270m-it
tags:
- medical
- lora
- fine-tuned
- merged
pipeline_tag: text-generation
datasets:
- ericrisco/medrescue
---
# Medical Fine-tuned Model
This model is a fine-tuned version of [gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m) using LoRA (Low-Rank Adaptation) on medical data just for **testing purpose**
## Model Details
- **Base Model**: google/gemma-3-270m
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Domain**: Medical/Healthcare
- **Merged**: Yes, LoRA adapters have been merged with the base model
## Training Information
- **Training Steps**: 813
- **Learning Rate**: 3e-4
- **LoRA Rank**: 64
- **LoRA Alpha**: 16
- **Target Modules**: q_proj, k_proj, v_proj, o_proj
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("tulas/gemma-3-270m-medical")
tokenizer = AutoTokenizer.from_pretrained("tulas/gemma-3-270m-medical")
# Generate text
inputs = tokenizer("Patient presents with chest pain and", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Intended Use
This model is NOT intended for medical text generation but for testing purpose only
## Limitations
- This model should not be used for actual medical diagnosis
- Always consult healthcare professionals for medical decisions
- Model outputs should be verified by medical experts
## License
This model is released under the Apache 2.0 license.
|
crie123/yolov3s-finetuned-kyrgyz-plates
|
crie123
| 2025-08-24T13:51:25Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2025-08-24T13:09:49Z |
---
license: gpl-3.0
---
# YOLOv3s Fine-Tuned on Kyrgyz License Plates
This repository provides a fine-tuned version of **YOLOv3n** trained on a small custom dataset of Kyrgyz vehicle license plates.
The model is intended as a **demonstration of fine-tuning YOLOv3** rather than a production-ready solution.
## Model description
- Base model: [YOLOv3 (Darknet)](https://pjreddie.com/darknet/yolo/)
- Fine-tuned on: [Kyrgyz Car License Plates dataset](https://www.kaggle.com/datasets/pteacher/kyrgyz-car-license-plates) (~478 images, CC0 license)
- Framework: Darknet / PyTorch export
## Intended use
- Educational purposes (transfer learning, YOLO fine-tuning workflow)
- Experimentation with small regional datasets
⚠️ **Note**: The dataset is small (~478 images), so the model may not generalize well outside the training conditions.
For robust license plate detection in production, a larger and more diverse dataset is recommended.
## Training
Below is an example training script used to fine-tune **YOLOv8n** on the Kyrgyz License Plates dataset.
It performs dataset extraction, train/validation split (80/20), YAML generation, and launches training.
```python
import os
import zipfile
import random
import glob
import shutil
from ultralytics import YOLO
# === 1. Extract dataset ===
extract_path = "./datasets/kyrgyz-plates"
zip_path = "./datasets/kyrgyz-car-license-plates.zip"
if os.path.exists(zip_path) and not os.path.exists(extract_path):
with zipfile.ZipFile(zip_path, "r") as z:
z.extractall(extract_path)
# === 2. Split into train/val (80/20) ===
images_src = os.path.join(extract_path, "images")
train_images = os.path.join(extract_path, "train", "images")
train_labels = os.path.join(extract_path, "train", "labels")
val_images = os.path.join(extract_path, "valid", "images")
val_labels = os.path.join(extract_path, "valid", "labels")
for p in (train_images, train_labels, val_images, val_labels):
os.makedirs(p, exist_ok=True)
img_exts = (".jpg", ".jpeg", ".png", ".bmp")
images = [p for p in glob.glob(os.path.join(images_src, "*")) if os.path.splitext(p)[1].lower() in img_exts]
random.seed(42)
random.shuffle(images)
split_idx = int(len(images) * 0.8)
train_list = images[:split_idx]
val_list = images[split_idx:]
def copy_items(lst, dest_img_dir, dest_lbl_dir):
for img_path in lst:
base = os.path.basename(img_path)
shutil.copy2(img_path, os.path.join(dest_img_dir, base))
lbl_src = os.path.splitext(img_path)[0] + ".txt"
if os.path.exists(lbl_src):
shutil.copy2(lbl_src, os.path.join(dest_lbl_dir, os.path.basename(lbl_src)))
copy_items(train_list, train_images, train_labels)
copy_items(val_list, val_images, val_labels)
# === 3. Write data.yaml ===
yaml_path = os.path.join(extract_path, "data.yaml")
with open(yaml_path, "w") as f:
f.write(f"""
path: {extract_path}
train: train/images
val: valid/images
names:
0: plate
""")
# === 4. Train YOLOv8n ===
model = YOLO("yolov8n.pt") # automatically downloads if missing
model.train(
data=yaml_path,
epochs=50,
imgsz=640,
batch=16,
name="yolo-plates-kg"
)
# Locate best weights
best_weights = glob.glob("runs/detect/yolo-plates-kg*/weights/best.pt")[-1]
print("Best weights:", best_weights)
## Training Results
Training metrics and figures (loss curves, mAP, PR/F1 curves) are available in the repository:
- `results.png` – combined training loss and mAP over epochs
You can view or download these images directly from the repository files.
|
AymenKhomsi/mistral-7b-iam-sms-v1
|
AymenKhomsi
| 2025-08-24T13:50:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T13:50:01Z |
---
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** AymenKhomsi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Jackmahhug/blockassist-bc-enormous_docile_woodpecker_1756040567
|
Jackmahhug
| 2025-08-24T13:49:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"enormous docile woodpecker",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:49:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- enormous docile woodpecker
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kayacrypto/blockassist-bc-thriving_barky_wolf_1756043218
|
kayacrypto
| 2025-08-24T13:48:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving barky wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:48:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving barky wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
alok0777/blockassist-bc-masked_pensive_lemur_1756043177
|
alok0777
| 2025-08-24T13:48:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked pensive lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:47:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked pensive lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756043224
|
Elizavr
| 2025-08-24T13:48:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:48:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
digitclone/blockassist-bc-restless_patterned_wallaby_1756043171
|
digitclone
| 2025-08-24T13:47:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless patterned wallaby",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:47:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- restless patterned wallaby
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Stasonelison/blockassist-bc-howling_powerful_aardvark_1756043086
|
Stasonelison
| 2025-08-24T13:45:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling powerful aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:45:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling powerful aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
riwan1/blockassist-bc-fleecy_gilded_condor_1756041798
|
riwan1
| 2025-08-24T13:45:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fleecy gilded condor",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:45:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fleecy gilded condor
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Yokinamo/blockassist-bc-swift_savage_opossum_1756040542
|
Yokinamo
| 2025-08-24T13:45:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"swift savage opossum",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:45:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- swift savage opossum
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Narunat/ppo-SnowballTarget
|
Narunat
| 2025-08-24T13:44:19Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2025-08-24T13:44:12Z |
---
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: Narunat/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Junheakun/blockassist-bc-scented_sturdy_rhino_1756040569
|
Junheakun
| 2025-08-24T13:44:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scented sturdy rhino",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:44:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scented sturdy rhino
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756042982
|
Elizavr
| 2025-08-24T13:43:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:43:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Aistaro/JENN337
|
Aistaro
| 2025-08-24T13:42:53Z | 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
| 2025-08-24T12:52:30Z |
---
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: J3NN33
---
# Jenn337
<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 `J3NN33` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "J3NN33",
"lora_weights": "https://huggingface.co/Aistaro/JENN337/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('Aistaro/JENN337', weight_name='lora.safetensors')
image = pipeline('J3NN33').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 0.0004
- LoRA rank: 25
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Aistaro/JENN337/discussions) to add images that show off what you’ve made with this LoRA.
|
Luissdual/blockassist-bc-iridescent_coiled_macaw_1756040552
|
Luissdual
| 2025-08-24T13:42:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent coiled macaw",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:42:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent coiled macaw
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nguyenthientho/block
|
nguyenthientho
| 2025-08-24T13:41:34Z | 0 | 0 | null |
[
"text-generation",
"vi",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-24T13:40:37Z |
---
license: apache-2.0
language:
- vi
- en
pipeline_tag: text-generation
---
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756042820
|
Ferdi3425
| 2025-08-24T13:40:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:40:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nvsngurram/cai-group123-assignment
|
nvsngurram
| 2025-08-24T13:39:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T10:34:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- This model is Desinged as part of Conversation AI Asignment 2, we used IRFC Annual reports of 2023-24 and 2024-25 and extracted the data then used for model training and evaluation . -->
Comparative Financial QA System: RAG vs Fine-Tuning
## Model Details
Objective:
Develop and compare two systems for answering questions based on company financial statements (last two years):
Retrieval-Augmented Generation (RAG) Chatbot: Combines document retrieval and generative response.
Fine-Tuned Language Model (FT) Chatbot: Directly fine-tunes a small open-source language model on financial Q&A.
### Model Description
This model is Desinged as part of Conversation AI Asignment 2, we used IRFC Annual reports of 2023-24 and 2024-25 and extracted the data then used for model training and evaluation.
- **Developed by:** [Assignment Group 123]
- **Funded by [optional]:** [Group 123]
- **Shared by [optional]:** [Group 123]
- **Model type:** [RAG, Fine-Tune]
- **Language(s) (NLP):** [NLP]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [gpt2]
### Model Sources [optional]
- **Repository:** [https://huggingface.co/nvsngurram/cai-group123-assignment]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- -->
Question and Answer based System for the learning of implementation of RAG and Fine-Tuning the model.
### Direct Use
<!-- -->
Used to Analyse the pdf documents, let's say financial reports of a company can be analysed and give summary of it based on the user query in a short and compact manner.
[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. -->
responsive text generation
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- Using this model we can read any pdf files and ask questions out of it and get answers generated -->
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.
Step 1: clone the git repo into local
Step 2: install the required libraries by running pip install -r requirement.txt
Step 3: check if Annual reports are under data/annual_reports and QandA.txt in the data/raw/QandA.txt
Step 4: run the data_extraction.py(python src/data_extraction.py) script to convert from .PDF to .txt
Step 5: run the rag_ft_qa.py(python src/rag_ft_qa.py) script to pre-process, segmentation of data, tokenization, creation of model, pre-train the model
then Implement of RAG techniques(cross encoder), Fine-tune the model with dataset prepared, re-ranking the results and extracting best of it,
compared the results and represented in tabular format.
Step 6: run the streamlit_cli.py(python streamlit_cli.py) script to run the application on streamlit
[More Information Needed]
## Training Details
Training moto is to train the model with sample 10 questions and evaluate the performace after training the model.
### Training Data
used Annual Report 2023-24.pdf and Annual Report 2024-25.pdf annual reports for raw data, then converted into .txt files and then segmented and trained with 400 chunck dataset.
[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
#### 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]
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1756041123
|
rafsya427
| 2025-08-24T13:37:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:37:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# 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_1756042551
|
liukevin666
| 2025-08-24T13:37:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:37:02Z |
---
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).
|
pidbu/blockassist-bc-whistling_alert_shrew_1756042457
|
pidbu
| 2025-08-24T13:37:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:35:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1756041016
|
thanobidex
| 2025-08-24T13:35:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:35:54Z |
---
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).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1756041016
|
mang3dd
| 2025-08-24T13:35:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:35:39Z |
---
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).
|
esi777/blockassist-bc-camouflaged_trotting_eel_1756042411
|
esi777
| 2025-08-24T13:34:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged trotting eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:34:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged trotting eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tamewild/4b_v64_merged_e2
|
tamewild
| 2025-08-24T13:34:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T13:32:08Z |
---
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]
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756042348
|
Elizavr
| 2025-08-24T13:33:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:32:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756042277
|
Ferdi3425
| 2025-08-24T13:31:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:31:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Juashaseb/blockassist-bc-fluffy_secretive_panda_1756040256
|
Juashaseb
| 2025-08-24T13:30:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fluffy secretive panda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:30:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fluffy secretive panda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nightmedia/QiMing-Holos-Plus-Qwen3-14B-qx6-hi-mlx
|
nightmedia
| 2025-08-24T13:30:01Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3",
"qwen",
"unsloth",
"qiming",
"qiming-holos",
"bagua",
"decision-making",
"strategic-analysis",
"cognitive-architecture",
"chat",
"lora",
"philosophy-driven-ai",
"text-generation",
"conversational",
"zh",
"en",
"base_model:aifeifei798/QiMing-Holos-Plus-Qwen3-14B",
"base_model:adapter:aifeifei798/QiMing-Holos-Plus-Qwen3-14B",
"license:apache-2.0",
"6-bit",
"region:us"
] |
text-generation
| 2025-08-24T12:36:54Z |
---
license: apache-2.0
language:
- zh
- en
tags:
- qwen
- qwen3
- unsloth
- qiming
- qiming-holos
- bagua
- decision-making
- strategic-analysis
- cognitive-architecture
- chat
- lora
- philosophy-driven-ai
- mlx
pipeline_tag: text-generation
library_name: mlx
base_model: aifeifei798/QiMing-Holos-Plus-Qwen3-14B
---
# QiMing-Holos-Plus-Qwen3-14B-qx6-hi-mlx
This model [QiMing-Holos-Plus-Qwen3-14B-qx6-hi-mlx](https://huggingface.co/QiMing-Holos-Plus-Qwen3-14B-qx6-hi-mlx) was
converted to MLX format from [aifeifei798/QiMing-Holos-Plus-Qwen3-14B](https://huggingface.co/aifeifei798/QiMing-Holos-Plus-Qwen3-14B)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("QiMing-Holos-Plus-Qwen3-14B-qx6-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756042115
|
Ferdi3425
| 2025-08-24T13:29:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:29:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756042105
|
Elizavr
| 2025-08-24T13:29:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:28:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1756040450
|
unitova
| 2025-08-24T13:27:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:27:19Z |
---
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).
|
chainway9/blockassist-bc-untamed_quick_eel_1756040266
|
chainway9
| 2025-08-24T13:25:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:25:51Z |
---
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).
|
kavpro/blockassist-bc-tall_lively_caribou_1756041852
|
kavpro
| 2025-08-24T13:25:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall lively caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:25:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall lively caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AmpereComputing/rakutenai-7b-chat-gguf
|
AmpereComputing
| 2025-08-24T13:24:49Z | 0 | 0 | null |
[
"gguf",
"base_model:Rakuten/RakutenAI-7B-chat",
"base_model:quantized:Rakuten/RakutenAI-7B-chat",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-24T13:20:55Z |
---
base_model:
- Rakuten/RakutenAI-7B-chat
---
# Ampere® optimized llama.cpp
Ampere® optimized build of [llama.cpp](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#llamacpp) with full support for rich collection of GGUF models available at HuggingFace: [GGUF models](https://huggingface.co/models?search=gguf)
**For best results we recommend using models in our custom quantization formats available here: [AmpereComputing HF](https://huggingface.co/AmpereComputing)**
This Docker image can be run on bare metal Ampere® CPUs and Ampere® based VMs available in the cloud.
Release notes and binary executables are available on our [GitHub](https://github.com/AmpereComputingAI/llama.cpp/releases)
## Starting container
Default entrypoint runs the server binary of llama.cpp, mimicking behavior of original llama.cpp server image: [docker image](https://github.com/ggerganov/llama.cpp/blob/master/.devops/llama-server.Dockerfile)
To launch shell instead, do this:
```bash
sudo docker run --privileged=true --name llama --entrypoint /bin/bash -it amperecomputingai/llama.cpp:latest
```
Quick start example will be presented at docker container launch:
Make sure to visit us at [Ampere Solutions Portal](https://solutions.amperecomputing.com/solutions/ampere-ai)!
## Quantization
Ampere® optimized build of llama.cpp provides support for two new quantization methods, Q4_K_4 and Q8R16, offering model size and perplexity similar to Q4_K and Q8_0, respectively, but performing up to 1.5-2x faster on inference.
First, you'll need to convert the model to the GGUF format using [this script](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py):
```bash
python3 convert-hf-to-gguf.py [path to the original model] --outtype [f32, f16, bf16 or q8_0] --outfile [output path]
```
For example:
```bash
python3 convert-hf-to-gguf.py path/to/llama2 --outtype f16 --outfile llama-2-7b-f16.gguf
```
Next, you can quantize the model using the following command:
```bash
./llama-quantize [input file] [output file] [quantization method]
```
For example:
```bash
./llama-quantize llama-2-7b-f16.gguf llama-2-7b-Q8R16.gguf Q8R16
```
## Support
Please contact us at <[email protected]>
## LEGAL NOTICE
By accessing, downloading or using this software and any required dependent software (the “Ampere AI Software”), you agree to the terms and conditions of the software license agreements for the Ampere AI Software, which may also include notices, disclaimers, or license terms for third party software included with the Ampere AI Software. Please refer to the [Ampere AI Software EULA v1.6](https://ampereaidevelop.s3.eu-central-1.amazonaws.com/Ampere+AI+Software+EULA+-+v1.6.pdf) or other similarly-named text file for additional details.
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756041841
|
Elizavr
| 2025-08-24T13:24:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:24:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ypszn/blockassist-bc-yapping_pawing_worm_1756041761
|
ypszn
| 2025-08-24T13:23:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:23:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping pawing worm
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kayacrypto/blockassist-bc-thriving_barky_wolf_1756041691
|
kayacrypto
| 2025-08-24T13:23:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving barky wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:23:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving barky wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Mostefa-Terbeche/diabetic-retinopathy-paraguay-vit_b_16-original-20250718-193838
|
Mostefa-Terbeche
| 2025-08-24T13:23:08Z | 0 | 0 | null |
[
"diabetic-retinopathy",
"medical-imaging",
"pytorch",
"computer-vision",
"retinal-imaging",
"dataset:paraguay",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-08-24T10:21:05Z |
---
license: apache-2.0
tags:
- diabetic-retinopathy
- medical-imaging
- pytorch
- computer-vision
- retinal-imaging
datasets:
- paraguay
metrics:
- accuracy
- quadratic-kappa
- auc
model-index:
- name: paraguay_vit_b_16_original
results:
- task:
type: image-classification
name: Diabetic Retinopathy Classification
dataset:
type: paraguay
name: PARAGUAY
metrics:
- type: accuracy
value: 0.2631578947368421
- type: quadratic-kappa
value: 0.3678916827852997
---
# Diabetic Retinopathy Classification Model
## Model Description
This model is trained for diabetic retinopathy classification using the vit_b_16 architecture on the paraguay dataset with original preprocessing.
## Model Details
- **Architecture**: vit_b_16
- **Dataset**: paraguay
- **Preprocessing**: original
- **Training Date**: 20250718-193838
- **Task**: 5-class diabetic retinopathy grading (0-4)
- **Directory**: paraguay_vit_b_16_20250718-193838_new
## Performance
- **Test Accuracy**: 0.2631578947368421
- **Test Quadratic Kappa**: 0.3678916827852997
- **Validation Kappa**: 0.3678916827852997
## Usage
```python
import torch
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(
repo_id="your-username/diabetic-retinopathy-paraguay-vit_b_16-original",
filename="model_best.pt"
)
# Load model
model = torch.load(model_path, map_location='cpu')
```
## Classes
- 0: No DR (No diabetic retinopathy)
- 1: Mild DR (Mild non-proliferative diabetic retinopathy)
- 2: Moderate DR (Moderate non-proliferative diabetic retinopathy)
- 3: Severe DR (Severe non-proliferative diabetic retinopathy)
- 4: Proliferative DR (Proliferative diabetic retinopathy)
## Citation
If you use this model, please cite your research paper/thesis.
|
hirundo-io/hallucinations-reduced-gpt-oss-120b
|
hirundo-io
| 2025-08-24T13:22:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T12:57:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
prithivMLmods/Qwen-Image-Fragmented-Portraiture
|
prithivMLmods
| 2025-08-24T13:22:34Z | 0 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Qwen/Qwen-Image",
"base_model:adapter:Qwen/Qwen-Image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-08-24T12:58:26Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/1.png
text: 'Fragmented Portraiture, a close-up shot of a young Asian girls face is seen through a transparent window. The girls head is tilted slightly to the left, and his eyes are wide open. Her hair is a vibrant shade of black, and he is wearing a white collared shirt with a white collar. Her lips are painted a bright pink, adding a pop of color to the scene. The backdrop is a stark white, creating a stark contrast to the boys body. The window is made up of thin, light-colored wooden blinds, adding depth to the image.'
- output:
url: images/2.png
text: 'Fragmented Portraiture, Captured in a black and white collage, a womans face is featured prominently in the center of the collage. The womans eyes are wide open, and her lips are pursed. Her hair is long and cascades over her shoulders. The background is a stark white, and the womans hair is a vibrant shade of brown, adding a pop of color to the composition.'
- output:
url: images/3.png
text: 'Fragmented Portraiture, Captured in a black and white monochrome, a close-up shot of a womans face is visible through a series of white vertical blinds. The womans eyes are wide open, and her lips are pursed. Her hair is long and cascades down to her shoulders, framing her face. The blinds are pulled up, adding a touch of depth to the scene. The background is a stark white, creating a stark contrast to the womans features.'
base_model: Qwen/Qwen-Image
instance_prompt: Fragmented Portraiture
license: apache-2.0
---
# Qwen-Image-Fragmented-Portraiture
<Gallery />
---
# Model description for Qwen-Image-Fragmented-Portraiture
Image Processing Parameters
| Parameter | Value | Parameter | Value |
|---------------------------|--------|---------------------------|--------|
| LR Scheduler | constant | Noise Offset | 0.03 |
| Optimizer | AdamW | Multires Noise Discount | 0.1 |
| Network Dim | 64 | Multires Noise Iterations | 10 |
| Network Alpha | 32 | Repeat & Steps | 27 & 3050 |
| Epoch | 20 | Save Every N Epochs | 2 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 17 [HQ Images]
## Data Sources
| Source | Link |
|--------------|-------------------------------------|
| Playground | [playground.com](https://playground.com/) |
| ArtStation | [artstation.com](https://www.artstation.com/) |
| 4K Wallpapers| [4kwallpapers.com](https://4kwallpapers.com/) |
## Best Dimensions & Inference
| **Dimensions** | **Aspect Ratio** | **Recommendation** |
|-----------------|------------------|---------------------------|
| 1472 x 1140 | 4:3 (approx.) | Best |
| 1024 x 1024 | 1:1 | Default |
### Inference Range
- **Recommended Inference Steps:** 35-50
## Setting Up
```python
import torch
from diffusers import DiffusionPipeline
base_model = "Qwen/Qwen-Image"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Qwen-Image-Fragmented-Portraiture"
trigger_word = "Fragmented Portraiture"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
```
## Trigger words
You should use `Fragmented Portraiture` to trigger the image generation.
## Download model
[Download](/prithivMLmods/Qwen-Image-Fragmented-Portraiture/tree/main) them in the Files & versions tab.
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756040645
|
Sayemahsjn
| 2025-08-24T13:22:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:21:57Z |
---
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).
|
prithivMLmods/Qwen-Image-Synthetic-Face
|
prithivMLmods
| 2025-08-24T13:21:59Z | 0 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Qwen/Qwen-Image",
"base_model:adapter:Qwen/Qwen-Image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-08-24T10:07:19Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/1.png
text: 'Synthetic Face, a close-up shot of a young mans face features a maroon baseball cap adorned with a leather band. The mans hair is cut short and neatly trimmed. His eyes are a piercing blue, and his eyebrows are a darker shade of brown. He is wearing a gray tank top with a silver chain around his neck, adding a pop of color to his chest. The backdrop is a textured gray wall.'
- output:
url: images/2.png
text: 'Synthetic Face, a beautiful blonde woman with long, wavy blonde hair stands in front of a dark gray backdrop. She is dressed in a red strapless dress, adorned with silver earrings. Her lips are painted a vibrant red, adding a pop of color to her face. Her eyes are a piercing blue, and her eyebrows are a darker shade of brown. Her hair is cascading down her shoulders, framing her entire face.'
- output:
url: images/3.png
text: 'Synthetic Face, a medium-sized man stands in front of a stark white backdrop. He is dressed in a black tuxedo, adorned with a white collared shirt and a black bow tie. His eyes are a deep blue, and his hair is a rich black, adding a pop of color to the scene. His lips are a lighter shade of pink, and he has a slight smile on his face. His eyebrows are a darker shade of blue, adding depth to the composition.'
base_model: Qwen/Qwen-Image
instance_prompt: Synthetic Face
license: apache-2.0
---
# Qwen-Image-Synthetic-Face
<Gallery />
---
# Model description for Qwen-Image-Synthetic-Face
Image Processing Parameters
| Parameter | Value | Parameter | Value |
|---------------------------|--------|---------------------------|--------|
| LR Scheduler | constant | Noise Offset | 0.03 |
| Optimizer | AdamW | Multires Noise Discount | 0.1 |
| Network Dim | 64 | Multires Noise Iterations | 10 |
| Network Alpha | 32 | Repeat & Steps | 22 & 2650 |
| Epoch | 20 | Save Every N Epochs | 2 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 26 [HQ Images]
## Data Sources
| Source | Link |
|--------------|-------------------------------------|
| Playground | [playground.com](https://playground.com/) |
| ArtStation | [artstation.com](https://www.artstation.com/) |
| 4K Wallpapers| [4kwallpapers.com](https://4kwallpapers.com/) |
## Best Dimensions & Inference
| **Dimensions** | **Aspect Ratio** | **Recommendation** |
|-----------------|------------------|---------------------------|
| 1472 x 1140 | 4:3 (approx.) | Best |
| 1024 x 1024 | 1:1 | Default |
### Inference Range
- **Recommended Inference Steps:** 35-50
## Setting Up
```python
import torch
from diffusers import DiffusionPipeline
base_model = "Qwen/Qwen-Image"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Qwen-Image-Synthetic-Face"
trigger_word = "Synthetic Face"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
```
## Trigger words
You should use `Synthetic Face` to trigger the image generation.
## Download model
[Download](/prithivMLmods/Qwen-Image-Synthetic-Face/tree/main) them in the Files & versions tab.
|
WernL/whisper-afrikaans-whisper_training_1756041540
|
WernL
| 2025-08-24T13:21:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"whisper",
"automatic-speech-recognition",
"afrikaans",
"audio",
"speech",
"lora",
"af",
"dataset:common_voice_af_v1",
"base_model:openai/whisper-large-v3",
"base_model:adapter:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2025-08-24T13:21:32Z |
---
language:
- af
license: apache-2.0
tags:
- whisper
- automatic-speech-recognition
- afrikaans
- audio
- speech
- peft
- lora
library_name: peft
base_model: openai/whisper-large-v3
datasets:
- common_voice_af_v1
model-index:
- name: whisper-afrikaans-whisper_training_1756041540
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_af_v1
type: speech
metrics:
- name: WER
type: wer
value: 0.089
---
# whisper-afrikaans-whisper_training_1756041540
This is a LoRA (Low-Rank Adaptation) adapter for [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) fine-tuned on Afrikaans speech data.
## Model Details
- **Language**: Afrikaans (af)
- **Base Model**: openai/whisper-large-v3
- **Training Method**: LoRA (Low-Rank Adaptation)
- **Training Steps**: 1000
- **Hardware**: gpu-t4
- **Training Time**: N/A hours
- **LoRA Rank**: 8
- **LoRA Alpha**: 32
## Usage
This model requires the `peft` library to load the LoRA adapter weights:
```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel
import torch
# Load base model and processor
base_model_name = "openai/whisper-large-v3"
processor = WhisperProcessor.from_pretrained(base_model_name)
base_model = WhisperForConditionalGeneration.from_pretrained(base_model_name)
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "WernL/whisper-afrikaans-whisper_training_1756041540")
# Load audio
import librosa
audio, sr = librosa.load("path_to_audio.wav", sr=16000)
# Process
input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription[0])
```
### Alternative: Direct Loading (if supported)
```python
from transformers import pipeline
# This may work if the adapter is properly configured
pipe = pipeline("automatic-speech-recognition", model="WernL/whisper-afrikaans-whisper_training_1756041540")
result = pipe("path_to_audio.wav")
print(result["text"])
```
## Training Configuration
- **Dataset**: common_voice_af_v1
- **Batch Size**: 16
- **Learning Rate**: 1e-05
- **Max Steps**: 1000
## Performance
Final training metrics:
- **WER**: 0.089
- **Loss**: 0.177
This model was trained using the Whisper Training App.
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756041657
|
Ferdi3425
| 2025-08-24T13:21:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:21:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1756040072
|
lisaozill03
| 2025-08-24T13:20:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:20:09Z |
---
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).
|
Rajat1327/lora_model_qwen2.5_coder_LoRA
|
Rajat1327
| 2025-08-24T13:19:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T13:19:45Z |
---
base_model: unsloth/qwen2.5-coder-0.5b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Rajat1327
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-0.5b-instruct-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)
|
AmpereComputing/rakutenai-7b-instruct-gguf
|
AmpereComputing
| 2025-08-24T13:19:34Z | 0 | 0 | null |
[
"gguf",
"base_model:Rakuten/RakutenAI-7B-instruct",
"base_model:quantized:Rakuten/RakutenAI-7B-instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T13:17:24Z |
---
base_model:
- Rakuten/RakutenAI-7B-instruct
---
# Ampere® optimized llama.cpp
Ampere® optimized build of [llama.cpp](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#llamacpp) with full support for rich collection of GGUF models available at HuggingFace: [GGUF models](https://huggingface.co/models?search=gguf)
**For best results we recommend using models in our custom quantization formats available here: [AmpereComputing HF](https://huggingface.co/AmpereComputing)**
This Docker image can be run on bare metal Ampere® CPUs and Ampere® based VMs available in the cloud.
Release notes and binary executables are available on our [GitHub](https://github.com/AmpereComputingAI/llama.cpp/releases)
## Starting container
Default entrypoint runs the server binary of llama.cpp, mimicking behavior of original llama.cpp server image: [docker image](https://github.com/ggerganov/llama.cpp/blob/master/.devops/llama-server.Dockerfile)
To launch shell instead, do this:
```bash
sudo docker run --privileged=true --name llama --entrypoint /bin/bash -it amperecomputingai/llama.cpp:latest
```
Quick start example will be presented at docker container launch:
Make sure to visit us at [Ampere Solutions Portal](https://solutions.amperecomputing.com/solutions/ampere-ai)!
## Quantization
Ampere® optimized build of llama.cpp provides support for two new quantization methods, Q4_K_4 and Q8R16, offering model size and perplexity similar to Q4_K and Q8_0, respectively, but performing up to 1.5-2x faster on inference.
First, you'll need to convert the model to the GGUF format using [this script](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py):
```bash
python3 convert-hf-to-gguf.py [path to the original model] --outtype [f32, f16, bf16 or q8_0] --outfile [output path]
```
For example:
```bash
python3 convert-hf-to-gguf.py path/to/llama2 --outtype f16 --outfile llama-2-7b-f16.gguf
```
Next, you can quantize the model using the following command:
```bash
./llama-quantize [input file] [output file] [quantization method]
```
For example:
```bash
./llama-quantize llama-2-7b-f16.gguf llama-2-7b-Q8R16.gguf Q8R16
```
## Support
Please contact us at <[email protected]>
## LEGAL NOTICE
By accessing, downloading or using this software and any required dependent software (the “Ampere AI Software”), you agree to the terms and conditions of the software license agreements for the Ampere AI Software, which may also include notices, disclaimers, or license terms for third party software included with the Ampere AI Software. Please refer to the [Ampere AI Software EULA v1.6](https://ampereaidevelop.s3.eu-central-1.amazonaws.com/Ampere+AI+Software+EULA+-+v1.6.pdf) or other similarly-named text file for additional details.
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756041520
|
Ferdi3425
| 2025-08-24T13:19:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:19:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
elmenbillion/blockassist-bc-beaked_sharp_otter_1756039845
|
elmenbillion
| 2025-08-24T13:18:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:18:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked sharp otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Tahamufaddal/Samina2
|
Tahamufaddal
| 2025-08-24T13:18:07Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-24T12:39:44Z |
---
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
---
|
esi777/blockassist-bc-camouflaged_trotting_eel_1756041377
|
esi777
| 2025-08-24T13:17:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged trotting eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:16:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged trotting eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756041347
|
Ferdi3425
| 2025-08-24T13:16:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:16:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmepk1ksc0ajrtlqb2lpgjx6r_cmepkdxg40ak3tlqbp8j3etqu
|
BootesVoid
| 2025-08-24T13:16:05Z | 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
| 2025-08-24T13:16:04Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: NIGHTEYE
---
# Cmepk1Ksc0Ajrtlqb2Lpgjx6R_Cmepkdxg40Ak3Tlqbp8J3Etqu
<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 `NIGHTEYE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NIGHTEYE",
"lora_weights": "https://huggingface.co/BootesVoid/cmepk1ksc0ajrtlqb2lpgjx6r_cmepkdxg40ak3tlqbp8j3etqu/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmepk1ksc0ajrtlqb2lpgjx6r_cmepkdxg40ak3tlqbp8j3etqu', weight_name='lora.safetensors')
image = pipeline('NIGHTEYE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmepk1ksc0ajrtlqb2lpgjx6r_cmepkdxg40ak3tlqbp8j3etqu/discussions) to add images that show off what you’ve made with this LoRA.
|
WernL/whisper-afrikaans-whisper_training_1756041291
|
WernL
| 2025-08-24T13:15:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"whisper",
"automatic-speech-recognition",
"afrikaans",
"audio",
"speech",
"lora",
"af",
"dataset:common_voice_af_v1",
"base_model:openai/whisper-large-v3",
"base_model:adapter:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2025-08-24T13:15:33Z |
---
language:
- af
license: apache-2.0
tags:
- whisper
- automatic-speech-recognition
- afrikaans
- audio
- speech
- peft
- lora
library_name: peft
base_model: openai/whisper-large-v3
datasets:
- common_voice_af_v1
model-index:
- name: whisper-afrikaans-whisper_training_1756041291
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_af_v1
type: speech
metrics:
- name: WER
type: wer
value: 0.115
---
# whisper-afrikaans-whisper_training_1756041291
This is a LoRA (Low-Rank Adaptation) adapter for [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) fine-tuned on Afrikaans speech data.
## Model Details
- **Language**: Afrikaans (af)
- **Base Model**: openai/whisper-large-v3
- **Training Method**: LoRA (Low-Rank Adaptation)
- **Training Steps**: 1000
- **Hardware**: gpu-t4
- **Training Time**: N/A hours
- **LoRA Rank**: 8
- **LoRA Alpha**: 32
## Usage
This model requires the `peft` library to load the LoRA adapter weights:
```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel
import torch
# Load base model and processor
base_model_name = "openai/whisper-large-v3"
processor = WhisperProcessor.from_pretrained(base_model_name)
base_model = WhisperForConditionalGeneration.from_pretrained(base_model_name)
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "WernL/whisper-afrikaans-whisper_training_1756041291")
# Load audio
import librosa
audio, sr = librosa.load("path_to_audio.wav", sr=16000)
# Process
input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription[0])
```
### Alternative: Direct Loading (if supported)
```python
from transformers import pipeline
# This may work if the adapter is properly configured
pipe = pipeline("automatic-speech-recognition", model="WernL/whisper-afrikaans-whisper_training_1756041291")
result = pipe("path_to_audio.wav")
print(result["text"])
```
## Training Configuration
- **Dataset**: common_voice_af_v1
- **Batch Size**: 16
- **Learning Rate**: 1e-05
- **Max Steps**: 1000
## Performance
Final training metrics:
- **WER**: 0.115
- **Loss**: 0.214
This model was trained using the Whisper Training App.
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756041241
|
Elizavr
| 2025-08-24T13:14:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:14:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AmpereComputing/rakutenai-7b-gguf
|
AmpereComputing
| 2025-08-24T13:14:12Z | 0 | 0 | null |
[
"gguf",
"base_model:Rakuten/RakutenAI-7B",
"base_model:quantized:Rakuten/RakutenAI-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T13:12:07Z |
---
base_model:
- Rakuten/RakutenAI-7B
---
# Ampere® optimized llama.cpp
Ampere® optimized build of [llama.cpp](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#llamacpp) with full support for rich collection of GGUF models available at HuggingFace: [GGUF models](https://huggingface.co/models?search=gguf)
**For best results we recommend using models in our custom quantization formats available here: [AmpereComputing HF](https://huggingface.co/AmpereComputing)**
This Docker image can be run on bare metal Ampere® CPUs and Ampere® based VMs available in the cloud.
Release notes and binary executables are available on our [GitHub](https://github.com/AmpereComputingAI/llama.cpp/releases)
## Starting container
Default entrypoint runs the server binary of llama.cpp, mimicking behavior of original llama.cpp server image: [docker image](https://github.com/ggerganov/llama.cpp/blob/master/.devops/llama-server.Dockerfile)
To launch shell instead, do this:
```bash
sudo docker run --privileged=true --name llama --entrypoint /bin/bash -it amperecomputingai/llama.cpp:latest
```
Quick start example will be presented at docker container launch:
Make sure to visit us at [Ampere Solutions Portal](https://solutions.amperecomputing.com/solutions/ampere-ai)!
## Quantization
Ampere® optimized build of llama.cpp provides support for two new quantization methods, Q4_K_4 and Q8R16, offering model size and perplexity similar to Q4_K and Q8_0, respectively, but performing up to 1.5-2x faster on inference.
First, you'll need to convert the model to the GGUF format using [this script](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py):
```bash
python3 convert-hf-to-gguf.py [path to the original model] --outtype [f32, f16, bf16 or q8_0] --outfile [output path]
```
For example:
```bash
python3 convert-hf-to-gguf.py path/to/llama2 --outtype f16 --outfile llama-2-7b-f16.gguf
```
Next, you can quantize the model using the following command:
```bash
./llama-quantize [input file] [output file] [quantization method]
```
For example:
```bash
./llama-quantize llama-2-7b-f16.gguf llama-2-7b-Q8R16.gguf Q8R16
```
## Support
Please contact us at <[email protected]>
## LEGAL NOTICE
By accessing, downloading or using this software and any required dependent software (the “Ampere AI Software”), you agree to the terms and conditions of the software license agreements for the Ampere AI Software, which may also include notices, disclaimers, or license terms for third party software included with the Ampere AI Software. Please refer to the [Ampere AI Software EULA v1.6](https://ampereaidevelop.s3.eu-central-1.amazonaws.com/Ampere+AI+Software+EULA+-+v1.6.pdf) or other similarly-named text file for additional details.
|
kidsop/blockassist-bc-nasty_secretive_fly_1756039518
|
kidsop
| 2025-08-24T13:13:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nasty secretive fly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T13:13:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nasty secretive fly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.