modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
SajayR/verypoggersmi
|
SajayR
| 2025-08-25T16:49:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T16:49:46Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jiruii/Qwen1.5b
|
jiruii
| 2025-08-25T16:49:02Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-25T12:50:31Z |
---
license: other
license_name: readme
license_link: LICENSE
---
|
ginic/gender_split_70_female_5_wav2vec2-large-xlsr-53-buckeye-ipa
|
ginic
| 2025-08-25T16:48:36Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T16:47:45Z |
---
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
---
# About
This model was created to support experiments for evaluating phonetic transcription
with the Buckeye corpus as part of https://github.com/ginic/multipa.
This is a version of facebook/wav2vec2-large-xlsr-53 fine tuned on a specific subset of the Buckeye corpus.
For details about specific model parameters, please view the config.json here or
training scripts in the scripts/buckeye_experiments folder of the GitHub repository.
# Experiment Details
Still training with a total amount of data equal to half the full training data (4000 examples), vary the gender split 30/70, but draw examples from all individuals. Do 5 models for each gender split with the same model parameters but different data seeds.
Goals:
- Determine how different in gender split in training data affects performance
Params to vary:
- percent female (--percent_female) [0.3, 0.7]
- training seed (--train_seed)
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756140431
|
ggozzy
| 2025-08-25T16:48:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:48:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# 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_1756139329
|
Sayemahsjn
| 2025-08-25T16:48:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:48:07Z |
---
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).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756140443
|
Ferdi3425
| 2025-08-25T16:47:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:47:51Z |
---
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).
|
biswac2021/blockassist-bc-wiry_patterned_clam_1756140378
|
biswac2021
| 2025-08-25T16:46:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry patterned clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:46:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry patterned clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fatmhd1995/ft_phi35_jd_inclusive_detection_25082025
|
fatmhd1995
| 2025-08-25T16:46:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T16:43:41Z |
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** fatmhd1995
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ginic/gender_split_70_female_2_wav2vec2-large-xlsr-53-buckeye-ipa
|
ginic
| 2025-08-25T16:46:45Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T16:45:54Z |
---
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
---
# About
This model was created to support experiments for evaluating phonetic transcription
with the Buckeye corpus as part of https://github.com/ginic/multipa.
This is a version of facebook/wav2vec2-large-xlsr-53 fine tuned on a specific subset of the Buckeye corpus.
For details about specific model parameters, please view the config.json here or
training scripts in the scripts/buckeye_experiments folder of the GitHub repository.
# Experiment Details
Still training with a total amount of data equal to half the full training data (4000 examples), vary the gender split 30/70, but draw examples from all individuals. Do 5 models for each gender split with the same model parameters but different data seeds.
Goals:
- Determine how different in gender split in training data affects performance
Params to vary:
- percent female (--percent_female) [0.3, 0.7]
- training seed (--train_seed)
|
ginic/gender_split_30_female_5_wav2vec2-large-xlsr-53-buckeye-ipa
|
ginic
| 2025-08-25T16:45:49Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T16:44:53Z |
---
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
---
# About
This model was created to support experiments for evaluating phonetic transcription
with the Buckeye corpus as part of https://github.com/ginic/multipa.
This is a version of facebook/wav2vec2-large-xlsr-53 fine tuned on a specific subset of the Buckeye corpus.
For details about specific model parameters, please view the config.json here or
training scripts in the scripts/buckeye_experiments folder of the GitHub repository.
# Experiment Details
Still training with a total amount of data equal to half the full training data (4000 examples), vary the gender split 30/70, but draw examples from all individuals. Do 5 models for each gender split with the same model parameters but different data seeds.
Goals:
- Determine how different in gender split in training data affects performance
Params to vary:
- percent female (--percent_female) [0.3, 0.7]
- training seed (--train_seed)
|
ginic/vary_individuals_young_only_1_wav2vec2-large-xlsr-53-buckeye-ipa
|
ginic
| 2025-08-25T16:43:55Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T16:42:58Z |
---
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
---
# About
This model was created to support experiments for evaluating phonetic transcription
with the Buckeye corpus as part of https://github.com/ginic/multipa.
This is a version of facebook/wav2vec2-large-xlsr-53 fine tuned on a specific subset of the Buckeye corpus.
For details about specific model parameters, please view the config.json here or
training scripts in the scripts/buckeye_experiments folder of the GitHub repository.
# Experiment Details
These experiments keep the total amount of data equal to half the training data with the gender split 50/50, but further exclude certain speakers completely using the --speaker_restriction argument. This allows us to restrict speakers included in training data in any way. For the purposes of these experiments, we are focussed on the age demogrpahic of the user.
For reference, the speakers and their demographics included in the training data are as follows where the speaker age range 'y' means under 30 and 'o' means over 40:
| speaker_id | speaker_gender | speaker_age_range |
| ---------- | -------------- | ----------------- |
| S01 | f | y |
| S04 | f | y |
| S08 | f | y |
| S09 | f | y |
| S12 | f | y |
| S21 | f | y |
| S02 | f | o |
| S05 | f | o |
| S07 | f | o |
| S14 | f | o |
| S16 | f | o |
| S17 | f | o |
| S06 | m | y |
| S11 | m | y |
| S13 | m | y |
| S15 | m | y |
| S28 | m | y |
| S30 | m | y |
| S03 | m | o |
| S10 | m | o |
| S19 | m | o |
| S22 | m | o |
| S24 | m | o |
Goals:
- Determine how variety of speakers in the training data affects performance
Params to vary:
- training seed (--train_seed)
- demographic make up of training data by age, using --speaker_restriction
- Experiments `young_only`: only individuals under 30, S01 S04 S08 S09 S12 S21 S06 S11 S13 S15 S28 S30
- Experiments `old_only`: only individuals over 40, S02 S05 S07 S14 S16 S17 S03 S10 S19 S22 S24
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756140199
|
Shopnil09
| 2025-08-25T16:43:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:43:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
prolinkmoon/blockassist-bc-rabid_scaly_anteater_1756140055
|
prolinkmoon
| 2025-08-25T16:43:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rabid scaly anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:42:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rabid scaly anteater
---
# 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_1756140165
|
Ferdi3425
| 2025-08-25T16:43:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:43:14Z |
---
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).
|
wwfwefwEF/blockassist-bc-hunting_prehistoric_walrus_1756140136
|
wwfwefwEF
| 2025-08-25T16:42:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hunting prehistoric walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:42:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hunting prehistoric walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hokpertoy/blockassist-bc-soft_curious_camel_1756140140
|
hokpertoy
| 2025-08-25T16:42:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft curious camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:42:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft curious camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1756138344
|
hakimjustbao
| 2025-08-25T16:42:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:42:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
svu22/arabic_model
|
svu22
| 2025-08-25T16:41:58Z | 55 | 0 | null |
[
"pytorch",
"wav2vec2",
"region:us"
] | null | 2025-08-12T12:11:34Z |
language:
- ar
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_8_0
type: automatic-speech-recognition
|
ginic/gender_split_30_female_4_wav2vec2-large-xlsr-53-buckeye-ipa
|
ginic
| 2025-08-25T16:41:58Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T16:41:04Z |
---
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
---
# About
This model was created to support experiments for evaluating phonetic transcription
with the Buckeye corpus as part of https://github.com/ginic/multipa.
This is a version of facebook/wav2vec2-large-xlsr-53 fine tuned on a specific subset of the Buckeye corpus.
For details about specific model parameters, please view the config.json here or
training scripts in the scripts/buckeye_experiments folder of the GitHub repository.
# Experiment Details
Still training with a total amount of data equal to half the full training data (4000 examples), vary the gender split 30/70, but draw examples from all individuals. Do 5 models for each gender split with the same model parameters but different data seeds.
Goals:
- Determine how different in gender split in training data affects performance
Params to vary:
- percent female (--percent_female) [0.3, 0.7]
- training seed (--train_seed)
|
koloni/blockassist-bc-deadly_graceful_stingray_1756138475
|
koloni
| 2025-08-25T16:41:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:41:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1756138319
|
indoempatnol
| 2025-08-25T16:41:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:41:07Z |
---
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).
|
ginic/vary_individuals_young_only_2_wav2vec2-large-xlsr-53-buckeye-ipa
|
ginic
| 2025-08-25T16:41:00Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T16:40:04Z |
---
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
---
# About
This model was created to support experiments for evaluating phonetic transcription
with the Buckeye corpus as part of https://github.com/ginic/multipa.
This is a version of facebook/wav2vec2-large-xlsr-53 fine tuned on a specific subset of the Buckeye corpus.
For details about specific model parameters, please view the config.json here or
training scripts in the scripts/buckeye_experiments folder of the GitHub repository.
# Experiment Details
These experiments keep the total amount of data equal to half the training data with the gender split 50/50, but further exclude certain speakers completely using the --speaker_restriction argument. This allows us to restrict speakers included in training data in any way. For the purposes of these experiments, we are focussed on the age demogrpahic of the user.
For reference, the speakers and their demographics included in the training data are as follows where the speaker age range 'y' means under 30 and 'o' means over 40:
| speaker_id | speaker_gender | speaker_age_range |
| ---------- | -------------- | ----------------- |
| S01 | f | y |
| S04 | f | y |
| S08 | f | y |
| S09 | f | y |
| S12 | f | y |
| S21 | f | y |
| S02 | f | o |
| S05 | f | o |
| S07 | f | o |
| S14 | f | o |
| S16 | f | o |
| S17 | f | o |
| S06 | m | y |
| S11 | m | y |
| S13 | m | y |
| S15 | m | y |
| S28 | m | y |
| S30 | m | y |
| S03 | m | o |
| S10 | m | o |
| S19 | m | o |
| S22 | m | o |
| S24 | m | o |
Goals:
- Determine how variety of speakers in the training data affects performance
Params to vary:
- training seed (--train_seed)
- demographic make up of training data by age, using --speaker_restriction
- Experiments `young_only`: only individuals under 30, S01 S04 S08 S09 S12 S21 S06 S11 S13 S15 S28 S30
- Experiments `old_only`: only individuals over 40, S02 S05 S07 S14 S16 S17 S03 S10 S19 S22 S24
|
NYUAD-ComNets/Llama3.2_MultiModal_Memes_Hate_Detector
|
NYUAD-ComNets
| 2025-08-25T16:40:50Z | 181 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mllama",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"arxiv:2508.15810",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-06-29T19:19:59Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Llama3.2-11B based Hate Detection in Arabic MultiModal Memes
The rise of social media and online communication platforms has led to the spread of Arabic memes as a key form of digital expression.
While these contents can be humorous and informative, they are also increasingly being used to spread offensive language and hate speech.
Consequently, there is a growing demand for precise analysis of content in Arabic meme.
This work used Llama 3.2 with its vision capability to effectively identify hate content within Arabic memes.
The evaluation is conducted using a dataset of Arabic memes proposed in the ArabicNLP MAHED 2025 challenge.
The results underscore the capacity of ***Llama 3.2-11B fine-tuned with Arabic memes***, to deliver the superior performance.
They achieve **accuracy** of **80.3%** and **macro F1 score** of **73.3%**.
The proposed solutions offer a more nuanced understanding of memes for accurate and efficient Arabic content moderation systems.
# Examples of Arabic Memes from ArabicNLP MAHED 2025 challenge
# Examples
| | | |
|:-------------------------:|:-------------------------:|:-------------------------:|
|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jBuVCt5163WlugFRXkSgq.jpeg"> |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jiPId6f5IiGXxpI898llC.jpeg"> |
|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/61acyltUsTB--ZOAMkv0a.jpeg"> |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/_alSRnwG0azE_iYq2BrpP.jpeg"> |
``` python
import pandas as pd
import os
from unsloth import FastVisionModel
import torch
from datasets import load_dataset
from transformers import TextStreamer
from PIL import Image
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
model_name = "NYUAD-ComNets/Llama3.2_MultiModal_Memes_Hate_Detector"
model, tokenizer = FastVisionModel.from_pretrained(model_name, token='xxxxxxxxxxxxxxxxxxxxxx')
FastVisionModel.for_inference(model)
dataset_test = load_dataset("QCRI/Prop2Hate-Meme", split = "test")
print(dataset_test)
def add_labels_column(example):
example["labels"] = "no_hate" if example["hate_label"] == 0 else "hate"
return example
dataset_test = dataset_test.map(add_labels_column)
pred=[]
for k in range(606):
image = dataset_test[k]["image"]
text = dataset_test[k]["text"]
lab = dataset_test[k]["labels"]
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": text}
]}
]
input_text = tokenizer.apply_chat_template(messages,add_generation_prompt = True)
inputs = tokenizer(
image,
input_text,
add_special_tokens = False,
return_tensors = "pt",
).to("cuda")
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
p = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = False, temperature = 0.3, min_p = 0.3)
p = tokenizer.decode(p[0], skip_special_tokens=True)
pred.append(p.split('assistant')[1].strip())
print(pred)
```
We used Low-Rank Adaptation (LoRA) as the Parameter-Efficient Fine-Tuning (PEFT) method for fine-tuning utilizing the unsloth framework.
The hyper-parameters of Llama 3.2-11B are as follows:
the training batch size per device is set to 4.
gradients are accumulated over 4 steps.
the learning rate warm-up lasts for 5 steps.
the total number of training steps is 150.
the learning rate is set to 0.0002.
the optimizer used is 8-bit AdamW
weight decay is set to 0.01.
a linear learning rate scheduler is used.
# BibTeX entry and citation info
```
@misc{aldahoul2025detectinghopehateemotion,
title={Detecting Hope, Hate, and Emotion in Arabic Textual Speech and Multi-modal Memes Using Large Language Models},
author={Nouar AlDahoul and Yasir Zaki},
year={2025},
eprint={2508.15810},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.15810},
}
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756139953
|
ggozzy
| 2025-08-25T16:40:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:40:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# 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_1756139910
|
Ferdi3425
| 2025-08-25T16:39:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:39:00Z |
---
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).
|
biswac2021/blockassist-bc-wiry_patterned_clam_1756139881
|
biswac2021
| 2025-08-25T16:38:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry patterned clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:38:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry patterned clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
QinShiHuangisavailable/output013
|
QinShiHuangisavailable
| 2025-08-25T16:38:15Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Math-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T15:43:38Z |
---
base_model: Qwen/Qwen2.5-Math-7B-Instruct
library_name: transformers
model_name: output013
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for output013
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="QinShiHuangisavailable/output013", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- Pytorch: 2.7.1+cu118
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756139809
|
ricodr
| 2025-08-25T16:37:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:37:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Chukky10z/blockassist-bc-mammalian_jumping_cougar_1756139781
|
Chukky10z
| 2025-08-25T16:37:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian jumping cougar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:36:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian jumping cougar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ginic/gender_split_70_female_4_wav2vec2-large-xlsr-53-buckeye-ipa
|
ginic
| 2025-08-25T16:37:10Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T16:36:18Z |
---
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
---
# About
This model was created to support experiments for evaluating phonetic transcription
with the Buckeye corpus as part of https://github.com/ginic/multipa.
This is a version of facebook/wav2vec2-large-xlsr-53 fine tuned on a specific subset of the Buckeye corpus.
For details about specific model parameters, please view the config.json here or
training scripts in the scripts/buckeye_experiments folder of the GitHub repository.
# Experiment Details
Still training with a total amount of data equal to half the full training data (4000 examples), vary the gender split 30/70, but draw examples from all individuals. Do 5 models for each gender split with the same model parameters but different data seeds.
Goals:
- Determine how different in gender split in training data affects performance
Params to vary:
- percent female (--percent_female) [0.3, 0.7]
- training seed (--train_seed)
|
ginic/data_seed_bs64_4_wav2vec2-large-xlsr-53-buckeye-ipa
|
ginic
| 2025-08-25T16:36:15Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T16:35:20Z |
---
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
---
# About
This model was created to support experiments for evaluating phonetic transcription
with the Buckeye corpus as part of https://github.com/ginic/multipa.
This is a version of facebook/wav2vec2-large-xlsr-53 fine tuned on a specific subset of the Buckeye corpus.
For details about specific model parameters, please view the config.json here or
training scripts in the scripts/buckeye_experiments folder of the GitHub repository.
# Experiment Details
Vary the random seed to select training data while keeping an even 50/50 gender split to measure statistical significance of changing training data selection. Retrain with the same model parameters, but different data seeding to measure statistical significance of data seed, keeping 50/50 gender split.
Goals:
- Establish whether data variation with the same gender makeup is statistically significant in changing performance on the test set
Params to vary:
- training data seed (--train_seed): [91, 114, 771, 503]
|
Monketoo/llama_3.2_code_first_try
|
Monketoo
| 2025-08-25T16:34:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T16:34:17Z |
---
base_model: unsloth/llama-3.2-1b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Monketoo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Stasonelison/blockassist-bc-howling_powerful_aardvark_1756139556
|
Stasonelison
| 2025-08-25T16:33:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling powerful aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:33:08Z |
---
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).
|
lemonhat/Qwen2.5-Coder-7B-Instruct-t1_25k_v2_tag5
|
lemonhat
| 2025-08-25T16:32:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T16:21:44Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: t1_25k_v2_tag5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t1_25k_v2_tag5
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the t1_25k_v2_tag5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2605
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3427 | 0.0817 | 100 | 0.3218 |
| 0.2658 | 0.1634 | 200 | 0.3040 |
| 0.2831 | 0.2451 | 300 | 0.2921 |
| 0.2759 | 0.3268 | 400 | 0.2846 |
| 0.3056 | 0.4085 | 500 | 0.2798 |
| 0.2839 | 0.4902 | 600 | 0.2763 |
| 0.3051 | 0.5719 | 700 | 0.2703 |
| 0.3155 | 0.6536 | 800 | 0.2688 |
| 0.2373 | 0.7353 | 900 | 0.2634 |
| 0.2561 | 0.8170 | 1000 | 0.2620 |
| 0.2546 | 0.8987 | 1100 | 0.2609 |
| 0.2504 | 0.9804 | 1200 | 0.2606 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
kat9370/blockassist-bc-pesty_miniature_beaver_1756139491
|
kat9370
| 2025-08-25T16:32:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty miniature beaver",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:31:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty miniature beaver
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
18-V-I-D-E-O-S-Sophie-Rain-Viral-X-X-video/FULL.VIDEOS.Sophie.Rain.Spiderman.Viral.Video.Official.Tutorial
|
18-V-I-D-E-O-S-Sophie-Rain-Viral-X-X-video
| 2025-08-25T16:32:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T16:31:20Z |
<animated-image data-catalyst=""><a href="https://newmovietv.online/leaked-video/?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Rudra-madlads/blockassist-bc-jumping_swift_gazelle_1756139266
|
Rudra-madlads
| 2025-08-25T16:28:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping swift gazelle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:28:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping swift gazelle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
biswac2021/blockassist-bc-wiry_patterned_clam_1756139268
|
biswac2021
| 2025-08-25T16:28:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry patterned clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:28:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry patterned clam
---
# 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_1756137585
|
chainway9
| 2025-08-25T16:27:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:27:46Z |
---
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).
|
lilTAT/blockassist-bc-gentle_rugged_hare_1756139092
|
lilTAT
| 2025-08-25T16:25:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:25:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Nelooo/blockassist-bc-invisible_foxy_heron_1756139124
|
Nelooo
| 2025-08-25T16:25:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"invisible foxy heron",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:25:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- invisible foxy heron
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zoefyt/zoef-flux-with-27-img-24-aug-4-46
|
zoefyt
| 2025-08-25T16:25:06Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-24T13:09:57Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: foe man, in a bustling cafe, portrait, neutral expression, studio lighting,
photoreal, sharp detail, centered composition
output:
url: samples/1756040899912__000002000_0.jpg
- text: foe man, in a medieval fantasy forest, wearing ranger armor, volumetric
light shafts through trees, RPG concept art
output:
url: samples/1756040946083__000002000_1.jpg
# - text: foe man, pixel art portrait, 16-bit video game character sprite, retro color
# palette, sharp edges
# output:
# url: samples/1756040992271__000002000_2.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: foe man
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
---
# zoef-flux-with-27-img-24-aug-4-46
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `foe man` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/zoefyt/zoef-flux-with-27-img-24-aug-4-46/tree/main) them in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('zoefyt/zoef-flux-with-27-img-24-aug-4-46', weight_name='zoef-flux-with-27-img-24-aug-4-46.safetensors')
image = pipeline('foe man, in a bustling cafe, portrait, neutral expression, studio lighting, photoreal, sharp detail, centered composition').images[0]
image.save("my_image.png")
```
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)
|
yifeng2025summer/qwen2.5_7b_gtpo_maxiters1_step30
|
yifeng2025summer
| 2025-08-25T16:24:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T16:23:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aleebaster/blockassist-bc-sly_eager_boar_1756137463
|
aleebaster
| 2025-08-25T16:23:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:23:36Z |
---
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).
|
RageBlyat/qwen2.5vlfinal
|
RageBlyat
| 2025-08-25T16:21:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-25T16:16:28Z |
---
base_model: unsloth/qwen2.5-vl-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** RageBlyat
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-vl-7b-instruct-bnb-4bit
This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1756137387
|
quantumxnode
| 2025-08-25T16:21:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:21:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vxhuyentrang/blockassist-bc-singing_tame_bison_1756137937
|
vxhuyentrang
| 2025-08-25T16:21:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing tame bison",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:21:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing tame bison
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Amama02/pinterest-personality-keywords-25-August
|
Amama02
| 2025-08-25T16:20:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"pinterest",
"keywords",
"personality",
"fine-tuned",
"lora",
"flan-t5",
"en",
"base_model:google/flan-t5-base",
"base_model:adapter:google/flan-t5-base",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T16:20:14Z |
---
language: en
license: apache-2.0
base_model: google/flan-t5-base
tags:
- text2text-generation
- pinterest
- keywords
- personality
- fine-tuned
- lora
- flan-t5
library_name: transformers
pipeline_tag: text2text-generation
widget:
- text: "Generate Pinterest keywords for Cleopatra - Culture: Egyptian | Role: Royalty | Period: Ancient Egypt - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords"
example_title: "Cleopatra Keywords"
- text: "Generate Pinterest keywords for Leonardo da Vinci - Culture: Italian | Role: Polymath | Period: Renaissance - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords"
example_title: "Leonardo da Vinci Keywords"
---
# Pinterest Personality Keywords Generator
🎨 **Fine-tuned FLAN-T5 model for generating Pinterest-optimized keywords for historical and fictional personalities.**
This model was fine-tuned using LoRA (Low-Rank Adaptation) to generate visually appealing, searchable Pinterest keywords based on personality information.
## 🚀 Quick Start
### Using Transformers Pipeline
```python
from transformers import pipeline
# Load the model
generator = pipeline("text2text-generation", model="Amama02/pinterest-personality-keywords-25-August")
# Generate keywords
input_text = "Generate Pinterest keywords for Marie Curie - Culture: Polish-French | Role: Scientist | Period: Early 20th Century - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords"
result = generator(
input_text,
max_length=300,
num_beams=8,
temperature=0.9,
do_sample=True,
top_p=0.95,
repetition_penalty=2.0,
length_penalty=1.2,
no_repeat_ngram_size=2
)
print(result[0]['generated_text'])
```
### Using Direct Model Loading
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Amama02/pinterest-personality-keywords-25-August")
model = AutoModelForSeq2SeqLM.from_pretrained("Amama02/pinterest-personality-keywords-25-August")
# Prepare input
input_text = "Generate Pinterest keywords for Frida Kahlo - Culture: Mexican | Role: Artist | Period: 20th Century - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords"
# Tokenize and generate
inputs = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True)
outputs = model.generate(
**inputs,
max_length=300,
num_beams=8,
temperature=0.9,
do_sample=True,
top_p=0.95,
repetition_penalty=2.0,
length_penalty=1.2,
early_stopping=True,
no_repeat_ngram_size=2
)
keywords = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(keywords)
```
## 📝 Input Format
The model expects input in this specific format:
```
Generate Pinterest keywords for [PERSONALITY_NAME] - Culture: [CULTURE] | Role: [ROLE] | Period: [TIME_PERIOD] | Bio: [BIOGRAPHY] - Keywords should be visual, searchable on Pinterest, and capture their aesthetic essence. The Culture, Role, Period and bio give important information about the personality. Take them into account when generating keywords
```
### Required Fields:
- **PERSONALITY_NAME**: Name of the person
- **Culture**: Cultural background or nationality
- **Role**: Profession, title, or main role
- **Period**: Historical time period
- **Bio**: (Optional) Brief biography
## 🎯 Example Outputs
| Input | Generated Keywords |
|-------|-------------------|
| **Cleopatra** (Egyptian Royalty, Ancient Egypt) | "Egyptian queen aesthetic, ancient Egypt fashion, Cleopatra makeup, pharaoh style, golden jewelry, Egyptian mythology, ancient beauty, royal Egyptian, hieroglyphics, Egyptian art" |
| **Leonardo da Vinci** (Italian Polymath, Renaissance) | "Renaissance art, Italian genius, classical paintings, Renaissance fashion, vintage sketches, Italian Renaissance, Renaissance architecture, classical art history" |
| **Marie Curie** (Polish-French Scientist, Early 20th Century) | "vintage science, female scientist aesthetic, laboratory vintage, early 1900s fashion, women in science, vintage academic, scientific discovery, vintage portraits" |
## ⚙️ Generation Parameters
The model is optimized with these generation settings:
- **max_length**: 300
- **num_beams**: 8
- **temperature**: 0.9
- **top_p**: 0.95
- **repetition_penalty**: 2.0
- **length_penalty**: 1.2
- **no_repeat_ngram_size**: 2
## 🔧 Technical Details
- **Base Model**: google/flan-t5-base
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **LoRA Rank**: 16
- **Target Modules**: ["q", "v", "k", "o", "wi", "wo"]
- **Training Data**: Historical and fictional personalities dataset
- **Task**: Seq2Seq text generation
The model has been optimized for:
- ✅ **Visual Keywords**: Generates terms that work well for image searches
- ✅ **Pinterest Optimization**: Keywords tailored for Pinterest's search algorithm
- ✅ **Cultural Sensitivity**: Respects cultural context and historical accuracy
- ✅ **Diversity**: Produces varied and creative keyword combinations
## 🚫 Limitations
- Specifically designed for Pinterest keyword generation
- May not perform well on other text generation tasks
- Limited to personalities with sufficient historical/cultural context
- Requires specific input format for optimal results
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756138757
|
ggozzy
| 2025-08-25T16:20:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:20:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
squidward7559s/blockassist-bc-nocturnal_long_whale_1756138762
|
squidward7559s
| 2025-08-25T16:20:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nocturnal long whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:20:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nocturnal long whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nnilayy/dreamer-binary-valence-LOSO-Subject-5
|
nnilayy
| 2025-08-25T16:20:11Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-08-25T16:20:08Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
lilTAT/blockassist-bc-gentle_rugged_hare_1756138727
|
lilTAT
| 2025-08-25T16:19:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:19:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# 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_1756137066
|
unitova
| 2025-08-25T16:19:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:19:49Z |
---
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).
|
Yntec/Crybaby
|
Yntec
| 2025-08-25T16:19:49Z | 8 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"Paintings",
"Style Art",
"Landscapes",
"Wick_J4",
"iamxenos",
"RIXYN",
"Barons",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-04-26T10:52:19Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
language:
- en
tags:
- Paintings
- Style Art
- Landscapes
- Wick_J4
- iamxenos
- RIXYN
- Barons
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# Crybaby
Samples and prompts:
Top left: pretty cute little girl as Marie Antoinette playing on toy piano in bedroom
Top right: Masterpiece, Best Quality, highres, fantasy, official art, kitten, grass, sky, scenery, Fuji 85mm, fairytale illustration, colored sclera, black eyes, perfect eyes, happy, cute, cat, whiskers, pawpads, claws, furry, plush, soft, perfect, tail, christmas lights, christmas tree, christmas ornaments, warmth
Bottom left: analog style 70s color photograph of young Jet Lee as Invincible Man, star wars behind the scenes
Bottom right: absurdres, adorable cute harley quinn, at night, dark alley, moon, :) red ponytail, blonde ponytail, in matte black hardsuit, military, roughed up, bat, city fog,
A mix of MGM and CocaCola (which includes many models) to create a realistic version of Cryptids.
Original pages:
https://civitai.com/models/109568/mgmv1
https://huggingface.co/Yntec/Cryptids
https://huggingface.co/Yntec/CocaCola
https://civitai.com/models/142552?modelVersionId=163068 (Kitsch-In-Sync v2)
https://huggingface.co/Yntec/HELLmix
|
philipperen55/Qwen2.5-7B-Instruct-D30E3LA16R64MSL512PDTBS32GAS1LR2e-4_epoch2
|
philipperen55
| 2025-08-25T16:19:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T16:18:55Z |
---
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]
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756138701
|
liukevin666
| 2025-08-25T16:19:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:19:15Z |
---
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).
|
giyon87/blockassist-bc-screeching_deadly_wolf_1756138726
|
giyon87
| 2025-08-25T16:19:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching deadly wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:19:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- screeching deadly wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756138690
|
ricodr
| 2025-08-25T16:18:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:18:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756138603
|
Vasya777
| 2025-08-25T16:18:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:18:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756138579
|
bah63843
| 2025-08-25T16:17:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:16:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Felix128wnjsx/instagirl-v23
|
Felix128wnjsx
| 2025-08-25T16:17:12Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-08-24T19:52:42Z |
---
license: apache-2.0
---
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756138558
|
Ferdi3425
| 2025-08-25T16:16:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:16:26Z |
---
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).
|
VIDEOS-18-uppal-farm-girl-viral-video-XX/New.full.videos.uppal.farm.girl.Viral.Video.Official.Tutorial
|
VIDEOS-18-uppal-farm-girl-viral-video-XX
| 2025-08-25T16:15:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T16:15:34Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
ESpeech/ESpeech-TTS-1_SFT-95K
|
ESpeech
| 2025-08-25T16:14:14Z | 0 | 0 | null |
[
"TTS",
"F5-TTS",
"ru",
"dataset:ESpeech/ESpeech-webinars2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-25T09:53:15Z |
---
license: apache-2.0
datasets:
- ESpeech/ESpeech-webinars2
language:
- ru
tags:
- TTS
- F5-TTS
---
Установите необходимые зависимости:
```
pip install f5-tts gradio ruaccent transformers torch torchaudio huggingface_hub
```
Запустите код и ждите сообщения с адресом веб-интерфейса
```py
#!/usr/bin/env python3
import os
import gc
import tempfile
import traceback
from pathlib import Path
import gradio as gr
import numpy as np
import soundfile as sf
import torch
import torchaudio
from huggingface_hub import hf_hub_download, snapshot_download
from ruaccent import RUAccent
from f5_tts.infer.utils_infer import (
infer_process,
load_model,
load_vocoder,
preprocess_ref_audio_text,
remove_silence_for_generated_wav,
save_spectrogram,
tempfile_kwargs,
)
from f5_tts.model import DiT
MODEL_CFG = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
MODEL_REPO = "ESpeech/ESpeech-TTS-1_SFT-95K"
MODEL_FILE = "espeech_tts_95k.pt"
VOCAB_FILE = "vocab.txt"
loaded_model = None
def ensure_model():
global loaded_model
if loaded_model is not None:
return loaded_model
model_path = None
vocab_path = None
print(f"Trying to download model file '{MODEL_FILE}' and '{VOCAB_FILE}' from hub '{MODEL_REPO}'")
try:
model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
vocab_path = hf_hub_download(repo_id=MODEL_REPO, filename=VOCAB_FILE)
print(f"Downloaded model to {model_path}")
print(f"Downloaded vocab to {vocab_path}")
except Exception as e:
print("hf_hub_download failed:", e)
if model_path is None or vocab_path is None:
try:
local_dir = f"cache_{MODEL_REPO.replace('/', '_')}"
print(f"Attempting snapshot_download into {local_dir}...")
snapshot_dir = snapshot_download(repo_id=MODEL_REPO, cache_dir=None, local_dir=local_dir, token=hf_token)
possible_model = os.path.join(snapshot_dir, MODEL_FILE)
possible_vocab = os.path.join(snapshot_dir, VOCAB_FILE)
if os.path.exists(possible_model):
model_path = possible_model
if os.path.exists(possible_vocab):
vocab_path = possible_vocab
print(f"Snapshot downloaded to {snapshot_dir}")
except Exception as e:
print("snapshot_download failed:", e)
if not model_path or not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found after download attempts: {model_path}")
if not vocab_path or not os.path.exists(vocab_path):
raise FileNotFoundError(f"Vocab file not found after download attempts: {vocab_path}")
print(f"Loading model from: {model_path}")
loaded_model = load_model(DiT, MODEL_CFG, model_path, vocab_file=vocab_path)
return loaded_model
print("Preloading model...")
try:
ensure_model()
print("Model preloaded.")
except Exception as e:
print(f"Model preload failed: {e}")
print("Loading RUAccent...")
accentizer = RUAccent()
accentizer.load(omograph_model_size='turbo3.1', use_dictionary=True, tiny_mode=False)
print("RUAccent loaded.")
print("Loading vocoder...")
vocoder = load_vocoder()
print("Vocoder loaded.")
def process_text_with_accent(text, accentizer):
if not text or not text.strip():
return text
if '+' in text:
return text
else:
return accentizer.process_all(text)
def process_texts_only(ref_text, gen_text):
processed_ref_text = process_text_with_accent(ref_text, accentizer)
processed_gen_text = process_text_with_accent(gen_text, accentizer)
return processed_ref_text, processed_gen_text
def synthesize(
ref_audio,
ref_text,
gen_text,
remove_silence,
seed,
cross_fade_duration=0.15,
nfe_step=32,
speed=1.0,
):
if not ref_audio:
gr.Warning("Please provide reference audio.")
return None, None, ref_text, gen_text
if seed is None or seed < 0 or seed > 2**31 - 1:
seed = np.random.randint(0, 2**31 - 1)
torch.manual_seed(int(seed))
if not gen_text or not gen_text.strip():
gr.Warning("Please enter text to generate.")
return None, None, ref_text, gen_text
if not ref_text or not ref_text.strip():
gr.Warning("Please provide reference text.")
return None, None, ref_text, gen_text
processed_ref_text = process_text_with_accent(ref_text, accentizer)
processed_gen_text = process_text_with_accent(gen_text, accentizer)
try:
model = ensure_model()
except Exception as e:
gr.Warning(f"Failed to load model: {e}")
return None, None, processed_ref_text, processed_gen_text
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
try:
if device.type == "cuda":
try:
model.to(device)
vocoder.to(device)
except Exception as e:
print("Warning: failed to move model/vocoder to cuda:", e)
try:
ref_audio_proc, processed_ref_text_final = preprocess_ref_audio_text(
ref_audio,
processed_ref_text,
show_info=gr.Info
)
except Exception as e:
gr.Warning(f"Preprocess failed: {e}")
traceback.print_exc()
return None, None, processed_ref_text, processed_gen_text
try:
final_wave, final_sample_rate, combined_spectrogram = infer_process(
ref_audio_proc,
processed_ref_text_final,
processed_gen_text,
model,
vocoder,
cross_fade_duration=cross_fade_duration,
nfe_step=nfe_step,
speed=speed,
show_info=gr.Info,
progress=gr.Progress(),
)
except Exception as e:
gr.Warning(f"Infer failed: {e}")
traceback.print_exc()
return None, None, processed_ref_text, processed_gen_text
if remove_silence:
try:
with tempfile.NamedTemporaryFile(suffix=".wav", **tempfile_kwargs) as f:
temp_path = f.name
sf.write(temp_path, final_wave, final_sample_rate)
remove_silence_for_generated_wav(temp_path)
final_wave_tensor, _ = torchaudio.load(temp_path)
final_wave = final_wave_tensor.squeeze().cpu().numpy()
except Exception as e:
print("Remove silence failed:", e)
try:
with tempfile.NamedTemporaryFile(suffix=".png", **tempfile_kwargs) as tmp_spectrogram:
spectrogram_path = tmp_spectrogram.name
save_spectrogram(combined_spectrogram, spectrogram_path)
except Exception as e:
print("Save spectrogram failed:", e)
spectrogram_path = None
return (final_sample_rate, final_wave), spectrogram_path, processed_ref_text_final, processed_gen_text
finally:
if device.type == "cuda":
try:
model.to("cpu")
vocoder.to("cpu")
torch.cuda.empty_cache()
gc.collect()
except Exception as e:
print("Warning during cuda cleanup:", e)
with gr.Blocks(title="ESpeech-TTS") as app:
gr.Markdown("# ESpeech-TTS")
gr.Markdown("💡 **Совет:** Добавьте символ '+' для ударения (например, 'прив+ет')")
gr.Markdown("❌ **Совет:** Референс должен быть не более 12 секунд")
with gr.Row():
with gr.Column():
ref_audio_input = gr.Audio(label="Reference Audio", type="filepath")
ref_text_input = gr.Textbox(
label="Reference Text",
lines=2,
placeholder="Text corresponding to reference audio"
)
with gr.Column():
gen_text_input = gr.Textbox(
label="Text to Generate",
lines=5,
max_lines=20,
placeholder="Enter text to synthesize..."
)
process_text_btn = gr.Button("✏️ Process Text (Add Accents)", variant="secondary")
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
seed_input = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
remove_silence = gr.Checkbox(label="Remove Silences", value=False)
with gr.Row():
speed_slider = gr.Slider(label="Speed", minimum=0.3, maximum=2.0, value=1.0, step=0.1)
nfe_slider = gr.Slider(label="NFE Steps", minimum=4, maximum=64, value=48, step=2)
cross_fade_slider = gr.Slider(label="Cross-Fade Duration (s)", minimum=0.0, maximum=1.0, value=0.15, step=0.01)
generate_btn = gr.Button("🎤 Generate Speech", variant="primary", size="lg")
with gr.Row():
audio_output = gr.Audio(label="Generated Audio", type="numpy")
spectrogram_output = gr.Image(label="Spectrogram", type="filepath")
process_text_btn.click(
process_texts_only,
inputs=[ref_text_input, gen_text_input],
outputs=[ref_text_input, gen_text_input]
)
generate_btn.click(
synthesize,
inputs=[
ref_audio_input,
ref_text_input,
gen_text_input,
remove_silence,
seed_input,
cross_fade_slider,
nfe_slider,
speed_slider,
],
outputs=[audio_output, spectrogram_output, ref_text_input, gen_text_input]
)
if __name__ == "__main__":
app.launch()
```
|
LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-bel-Cyrl
|
LumiOpen
| 2025-08-25T16:13:19Z | 0 | 0 | null |
[
"safetensors",
"xlm-roberta",
"bel",
"dataset:LumiOpen/hpltv2-llama33-edu-annotation",
"license:apache-2.0",
"region:us"
] | null | 2025-08-25T16:13:03Z |
---
language:
- bel
license: apache-2.0
datasets:
- LumiOpen/hpltv2-llama33-edu-annotation
---
# Llama-HPLT-edu-Belarusian classifier
## Model summary
This is a classifier for judging the educational content of Belarusian (bel-Cyrl) web pages. It was developed to filter educational content from [HPLT v2](https://hplt-project.org/datasets/v2.0) and was trained on 450k [annotations](https://huggingface.co/datasets/LumiOpen/hpltv2-llama33-edu-annotation) generated by [LLama3.3-70B-instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct).
The web pages were sampled randomly from Belarusian subset of the corpus.
### How to load in transformers
To load the Llama-HPLT-Edu classifier, use the following code:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-bel-Cyrl")
model = AutoModelForSequenceClassification.from_pretrained("LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-bel-Cyrl")
text = "I'm non-educational web page containing nothing useful"
inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
outputs = model(**inputs)
logits = outputs.logits.squeeze(-1).float().detach().numpy()
score = logits.item()
result = {
"text": text,
"score": score,
"int_score": int(round(max(0, min(score, 5)))),
}
print(result)
#results from a model trained with Welsh annotations
#{'text': "I'm non-educational web page containing nothing useful", 'score': 0.8145455718040466, 'int_score': 1}
#{'text': 'what are most common animals found in farm? there are cows, sheeps', 'score': 1.6858888864517212, 'int_score': 2}
```
## Training
- Model: FacebookAI/xlm-roberta-large with a classification head
- Dataset: 500,000 samples from Llama3.3 annotations split into 450,000 train, 25,000 validation, and 25,000 test splits.
- Epochs: 20
- Learning Rate: 3e-4
- Evaluation Metric: F1 score
### Test Metrics
```
precision recall f1-score support
0 0.79 0.57 0.66 8065
1 0.55 0.67 0.60 8743
2 0.47 0.60 0.53 4670
3 0.46 0.40 0.43 2428
4 0.67 0.28 0.40 1063
5 0.38 0.10 0.15 31
accuracy 0.58 25000
macro avg 0.55 0.44 0.46 25000
weighted avg 0.61 0.58 0.58 25000
```
## Citing
Preprint coming soon. If you need to cite this work, please use the citation below:
```
@misc {llama_hplt_edu_classifiers_2025,
author = { Tarkka, Otto, Reunamo, Akseli, Vitiugin, Fedor and Pyysalo, Sampo }
title = { Llama-HPLT-edu classifiers },
year = 2025,
url = {https://huggingface.co/collections/LumiOpen/hplt-edu-classifiers-68a85a78f9710426320e7cbb},
publisher = { Hugging Face }
}
```
|
LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-azj-Latn
|
LumiOpen
| 2025-08-25T16:13:00Z | 0 | 0 | null |
[
"safetensors",
"xlm-roberta",
"azj",
"dataset:LumiOpen/hpltv2-llama33-edu-annotation",
"license:apache-2.0",
"region:us"
] | null | 2025-08-25T16:12:45Z |
---
language:
- azj
license: apache-2.0
datasets:
- LumiOpen/hpltv2-llama33-edu-annotation
---
# Llama-HPLT-edu-North Azerbaijani classifier
## Model summary
This is a classifier for judging the educational content of North Azerbaijani (azj-Latn) web pages. It was developed to filter educational content from [HPLT v2](https://hplt-project.org/datasets/v2.0) and was trained on 450k [annotations](https://huggingface.co/datasets/LumiOpen/hpltv2-llama33-edu-annotation) generated by [LLama3.3-70B-instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct).
The web pages were sampled randomly from North Azerbaijani subset of the corpus.
### How to load in transformers
To load the Llama-HPLT-Edu classifier, use the following code:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-azj-Latn")
model = AutoModelForSequenceClassification.from_pretrained("LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-azj-Latn")
text = "I'm non-educational web page containing nothing useful"
inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
outputs = model(**inputs)
logits = outputs.logits.squeeze(-1).float().detach().numpy()
score = logits.item()
result = {
"text": text,
"score": score,
"int_score": int(round(max(0, min(score, 5)))),
}
print(result)
#results from a model trained with Welsh annotations
#{'text': "I'm non-educational web page containing nothing useful", 'score': 0.8145455718040466, 'int_score': 1}
#{'text': 'what are most common animals found in farm? there are cows, sheeps', 'score': 1.6858888864517212, 'int_score': 2}
```
## Training
- Model: FacebookAI/xlm-roberta-large with a classification head
- Dataset: 500,000 samples from Llama3.3 annotations split into 450,000 train, 25,000 validation, and 25,000 test splits.
- Epochs: 20
- Learning Rate: 3e-4
- Evaluation Metric: F1 score
### Test Metrics
```
precision recall f1-score support
0 0.84 0.65 0.73 11889
1 0.54 0.73 0.62 8635
2 0.48 0.52 0.50 2975
3 0.40 0.32 0.36 1049
4 0.65 0.16 0.26 443
5 0.00 0.00 0.00 9
accuracy 0.64 25000
macro avg 0.49 0.40 0.41 25000
weighted avg 0.67 0.64 0.64 25000
```
## Citing
Preprint coming soon. If you need to cite this work, please use the citation below:
```
@misc {llama_hplt_edu_classifiers_2025,
author = { Tarkka, Otto, Reunamo, Akseli, Vitiugin, Fedor and Pyysalo, Sampo }
title = { Llama-HPLT-edu classifiers },
year = 2025,
url = {https://huggingface.co/collections/LumiOpen/hplt-edu-classifiers-68a85a78f9710426320e7cbb},
publisher = { Hugging Face }
}
```
|
LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-ara-Arab
|
LumiOpen
| 2025-08-25T16:12:42Z | 0 | 0 | null |
[
"safetensors",
"xlm-roberta",
"ara",
"dataset:LumiOpen/hpltv2-llama33-edu-annotation",
"license:apache-2.0",
"region:us"
] | null | 2025-08-25T16:12:27Z |
---
language:
- ara
license: apache-2.0
datasets:
- LumiOpen/hpltv2-llama33-edu-annotation
---
# Llama-HPLT-edu-Arabic classifier
## Model summary
This is a classifier for judging the educational content of Arabic (ara-Arab) web pages. It was developed to filter educational content from [HPLT v2](https://hplt-project.org/datasets/v2.0) and was trained on 450k [annotations](https://huggingface.co/datasets/LumiOpen/hpltv2-llama33-edu-annotation) generated by [LLama3.3-70B-instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct).
The web pages were sampled randomly from Arabic subset of the corpus.
### How to load in transformers
To load the Llama-HPLT-Edu classifier, use the following code:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-ara-Arab")
model = AutoModelForSequenceClassification.from_pretrained("LumiOpen/llama-hpltv2-edu-classifier-xlm-roberta-large-ara-Arab")
text = "I'm non-educational web page containing nothing useful"
inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
outputs = model(**inputs)
logits = outputs.logits.squeeze(-1).float().detach().numpy()
score = logits.item()
result = {
"text": text,
"score": score,
"int_score": int(round(max(0, min(score, 5)))),
}
print(result)
#results from a model trained with Welsh annotations
#{'text': "I'm non-educational web page containing nothing useful", 'score': 0.8145455718040466, 'int_score': 1}
#{'text': 'what are most common animals found in farm? there are cows, sheeps', 'score': 1.6858888864517212, 'int_score': 2}
```
## Training
- Model: FacebookAI/xlm-roberta-large with a classification head
- Dataset: 500,000 samples from Llama3.3 annotations split into 450,000 train, 25,000 validation, and 25,000 test splits.
- Epochs: 20
- Learning Rate: 3e-4
- Evaluation Metric: F1 score
### Test Metrics
```
precision recall f1-score support
0 0.83 0.55 0.66 10183
1 0.57 0.74 0.64 9736
2 0.45 0.60 0.51 3156
3 0.35 0.36 0.35 1185
4 0.72 0.20 0.31 707
5 0.17 0.06 0.09 33
accuracy 0.61 25000
macro avg 0.51 0.42 0.43 25000
weighted avg 0.65 0.61 0.61 25000
```
## Citing
Preprint coming soon. If you need to cite this work, please use the citation below:
```
@misc {llama_hplt_edu_classifiers_2025,
author = { Tarkka, Otto, Reunamo, Akseli, Vitiugin, Fedor and Pyysalo, Sampo }
title = { Llama-HPLT-edu classifiers },
year = 2025,
url = {https://huggingface.co/collections/LumiOpen/hplt-edu-classifiers-68a85a78f9710426320e7cbb},
publisher = { Hugging Face }
}
```
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756138317
|
ricodr
| 2025-08-25T16:12:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:12:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Baoquoc285/llama3_1_task9_v3
|
Baoquoc285
| 2025-08-25T16:12:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T16:11:35Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Baoquoc285
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1756138243
|
lilTAT
| 2025-08-25T16:11:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:11:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hayatoshibahara/act-so101-test
|
hayatoshibahara
| 2025-08-25T16:10:35Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:EngineerCafeJP/record-test-2025-08-23-20-48-00",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-25T16:10:12Z |
---
datasets: EngineerCafeJP/record-test-2025-08-23-20-48-00
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
Yntec/Cryptids
|
Yntec
| 2025-08-25T16:10:33Z | 24 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"Anime",
"Animals",
"Creatures",
"Eyes",
"Style",
"2D",
"Base Model",
"RIXYN",
"Barons",
"iamxenos",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"base_model:Yntec/HELLmix",
"base_model:merge:Yntec/HELLmix",
"base_model:Yntec/HellSKitchen",
"base_model:merge:Yntec/HellSKitchen",
"base_model:Yntec/Kitsch-In-Sync",
"base_model:merge:Yntec/Kitsch-In-Sync",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-30T12:45:35Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
language:
- en
tags:
- Anime
- Animals
- Creatures
- Eyes
- Style
- 2D
- Base Model
- RIXYN
- Barons
- iamxenos
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
base_model:
- Yntec/HellSKitchen
- Yntec/Kitsch-In-Sync
- Yntec/HELLmix
base_model_relation: merge
---
# Cryptids
The Cryptids LoRA by RIXYN at 1.0 strength merged in the HellSKitchen model to maximize its style! It has the MoistMixV2VAE baked in.
So it's mixed with two models, HELLmix by Barons and Kitsch-In-Sync by iamxenos.
Comparison:
(Click for larger)
Sample and prompt:
Masterpiece, Best Quality, highres, fantasy, official art, kitten, grass, sky, scenery, Fuji 85mm, fairytale illustration, colored sclera, black eyes, perfect eyes, happy, cute, cat, whiskers, pawpads, claws, furry, plush, soft, perfect, tail, christmas lights, christmas tree, christmas ornaments, warmth
Model comparison:
(Click for larger)
Sample image by digiplay:
8k Angel sky
Original pages:
https://civitai.com/models/64766/cryptids?modelVersionId=69407 (Cryptids LoRA)
https://civitai.com/models/142552?modelVersionId=163068 (Kitsch-In-Sync)
https://huggingface.co/Yntec/HELLmix
https://huggingface.co/Yntec/HellSKitchen
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756138124
|
ricodr
| 2025-08-25T16:09:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:09:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# 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_1756138039
|
liukevin666
| 2025-08-25T16:08:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:08:15Z |
---
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).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756138040
|
ggozzy
| 2025-08-25T16:08:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:08:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756135738
|
acidjp
| 2025-08-25T16:08:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:08:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
matheoqtb/euroBert_V3_testfinal
|
matheoqtb
| 2025-08-25T16:05:40Z | 0 | 0 | null |
[
"safetensors",
"eurobert",
"custom_code",
"region:us"
] | null | 2025-08-25T16:05:16Z |
# Checkpoint exporté: final
Ce dépôt contient un checkpoint extrait de `matheoqtb/euroBertV3_600_test` (sous-dossier `final`) et les fichiers de code nécessaires provenant de `EuroBERT/EuroBERT-610m`.
Chargement:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True)
mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True)
Tâche: feature-extraction (embeddings)
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1756136067
|
hakimjustbao
| 2025-08-25T16:04:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:04:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756137705
|
ricodr
| 2025-08-25T16:02:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:02:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1756135961
|
indoempatnol
| 2025-08-25T16:02:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:02:02Z |
---
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).
|
AnerYubo/blockassist-bc-reptilian_bellowing_cockroach_1756137687
|
AnerYubo
| 2025-08-25T16:01:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reptilian bellowing cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:01:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reptilian bellowing cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/splade-mini-GGUF
|
mradermacher
| 2025-08-25T16:00:58Z | 72 | 0 |
transformers
|
[
"transformers",
"gguf",
"sentence-transformers",
"sparse-encoder",
"sparse",
"splade",
"generated_from_trainer",
"dataset_size:1000000",
"loss:SpladeLoss",
"loss:SparseMarginMSELoss",
"loss:FlopsLoss",
"en",
"dataset:microsoft/ms_marco",
"base_model:rasyosef/splade-mini",
"base_model:quantized:rasyosef/splade-mini",
"license:mit",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | null | 2025-07-20T05:39:56Z |
---
base_model: rasyosef/splade-mini
datasets:
- microsoft/ms_marco
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:1000000
- loss:SpladeLoss
- loss:SparseMarginMSELoss
- loss:FlopsLoss
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/rasyosef/splade-mini
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#splade-mini-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q2_K.gguf) | Q2_K | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q3_K_S.gguf) | Q3_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.IQ4_XS.gguf) | IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q3_K_L.gguf) | Q3_K_L | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q4_K_M.gguf) | Q4_K_M | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q5_K_S.gguf) | Q5_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q5_K_M.gguf) | Q5_K_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q6_K.gguf) | Q6_K | 0.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.Q8_0.gguf) | Q8_0 | 0.1 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/splade-mini-GGUF/resolve/main/splade-mini.f16.gguf) | f16 | 0.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756137561
|
ggozzy
| 2025-08-25T16:00:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T16:00:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Rudra-madlads/blockassist-bc-jumping_swift_gazelle_1756137517
|
Rudra-madlads
| 2025-08-25T16:00:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping swift gazelle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:59:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping swift gazelle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
motza0025/blockassist-bc-mangy_grassy_barracuda_1756135998
|
motza0025
| 2025-08-25T15:59:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mangy grassy barracuda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:59:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mangy grassy barracuda
---
# 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_1756137497
|
kavpro
| 2025-08-25T15:59:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall lively caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:59:25Z |
---
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).
|
lilTAT/blockassist-bc-gentle_rugged_hare_1756137498
|
lilTAT
| 2025-08-25T15:59:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:58:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756137492
|
ricodr
| 2025-08-25T15:58:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:58:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756137395
|
bah63843
| 2025-08-25T15:57:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:57:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vangard703/output_full_no_temporal
|
vangard703
| 2025-08-25T15:55:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-25T15:29:28Z |
---
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]
|
tuckermanhall1/blockassist-bc-tough_monstrous_wombat_1756135315
|
tuckermanhall1
| 2025-08-25T15:55:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough monstrous wombat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:55:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough monstrous wombat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Genos77/ParkinsonDetection
|
Genos77
| 2025-08-25T15:54:56Z | 0 | 0 | null |
[
"joblib",
"region:us"
] | null | 2025-08-25T10:54:34Z |
---
title: ParkinsonDetection
emoji: 🚀
colorFrom: yellow
colorTo: gray
sdk: gradio
sdk_version: 5.43.1
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
angiecely8538/blockassist-bc-striped_invisible_jackal_1756135233
|
angiecely8538
| 2025-08-25T15:53:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"striped invisible jackal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:53:50Z |
---
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).
|
ganeshsahoo7468/blockassist-bc-bipedal_mighty_hummingbird_1756137050
|
ganeshsahoo7468
| 2025-08-25T15:52:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bipedal mighty hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:52:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bipedal mighty hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
goptouy/blockassist-bc-durable_enormous_platypus_1756137060
|
goptouy
| 2025-08-25T15:51:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"durable enormous platypus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:51:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- durable enormous platypus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
edimaosom1/blockassist-bc-padded_crested_gull_1756135300
|
edimaosom1
| 2025-08-25T15:50:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"padded crested gull",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:50:43Z |
---
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).
|
VISHNUDHAT/DeepHat-V1-7B-Q4_K_M-GGUF
|
VISHNUDHAT
| 2025-08-25T15:50:15Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"code",
"qwen-coder",
"cybersecurity",
"devops",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:DeepHat/DeepHat-V1-7B",
"base_model:quantized:DeepHat/DeepHat-V1-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T15:49:49Z |
---
license: apache-2.0
base_model: DeepHat/DeepHat-V1-7B
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- qwen-coder
- cybersecurity
- devops
- llama-cpp
- gguf-my-repo
---
# VISHNUDHAT/DeepHat-V1-7B-Q4_K_M-GGUF
This model was converted to GGUF format from [`DeepHat/DeepHat-V1-7B`](https://huggingface.co/DeepHat/DeepHat-V1-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DeepHat/DeepHat-V1-7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo VISHNUDHAT/DeepHat-V1-7B-Q4_K_M-GGUF --hf-file deephat-v1-7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo VISHNUDHAT/DeepHat-V1-7B-Q4_K_M-GGUF --hf-file deephat-v1-7b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo VISHNUDHAT/DeepHat-V1-7B-Q4_K_M-GGUF --hf-file deephat-v1-7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo VISHNUDHAT/DeepHat-V1-7B-Q4_K_M-GGUF --hf-file deephat-v1-7b-q4_k_m.gguf -c 2048
```
|
goptouy/blockassist-bc-muscular_carnivorous_okapi_1756136970
|
goptouy
| 2025-08-25T15:49:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular carnivorous okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:49:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular carnivorous okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Mdsam907685/Ava
|
Mdsam907685
| 2025-08-25T15:49:35Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-25T15:49:35Z |
---
license: apache-2.0
---
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756136885
|
ricodr
| 2025-08-25T15:48:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T15:48:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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