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 |
|---|---|---|---|---|---|---|---|---|---|
Muapi/aitk-watercolor-sketch
|
Muapi
| 2025-08-21T11:54:51Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-21T11:54:16Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# AITK-watercolor sketch
**Base model**: Flux.1 D
**Trained words**: watercolor, sketch
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:819313@916173", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
jakehsv/blockassist-bc-flexible_waddling_peacock_1755775564
|
jakehsv
| 2025-08-21T11:54:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flexible waddling peacock",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:53:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flexible waddling peacock
---
# 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_1755775682
|
indoempatnol
| 2025-08-21T11:53:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:53:41Z |
---
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).
|
flemmingpetter2/blockassist-bc-hardy_subtle_snake_1755775585
|
flemmingpetter2
| 2025-08-21T11:53:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hardy subtle snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:53:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hardy subtle snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Gallardo994/Qwen3-30B-A3B-Instruct-2507
|
Gallardo994
| 2025-08-21T11:51:34Z | 114 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-30B-A3B-Instruct-2507",
"base_model:finetune:Qwen/Qwen3-30B-A3B-Instruct-2507",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-07-29T20:44:24Z |
---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
---
# Gallardo994/Qwen3-30B-A3B-Instruct-2507
This model [Gallardo994/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Gallardo994/Qwen3-30B-A3B-Instruct-2507) was
converted to MLX format from [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Gallardo994/Qwen3-30B-A3B-Instruct-2507")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
raniero/test-instruction-local-1755776981-96a970
|
raniero
| 2025-08-21T11:50:06Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-08-21T11:49:56Z |
# LoRA — Instruction SFT
- **Task ID:** unknown
- **Base model:** mistralai/Mistral-7B-Instruct-v0.2
- **SHA256 (adapter):** `74e5190f8d38ba9b7a6a0627e374fe109db809556de99cad669025c1d5cb7a82`
- **Repo:** raniero/test-instruction-local-1755776981-96a970
Questa repo contiene SOLO gli adapter LoRA richiesti dai validator Subnet 56.
|
freyflyy/previsione-meteo-udine-modello
|
freyflyy
| 2025-08-21T11:49:54Z | 0 | 0 |
sklearn
|
[
"sklearn",
"weather",
"question-answering",
"it",
"license:mit",
"region:us"
] |
question-answering
| 2025-08-21T11:37:15Z |
---
license: mit
language:
- it
pipeline_tag: question-answering
library_name: sklearn
tags:
- weather
---
# Meteo Predictor 🌦️
Modello di predizione giornaliera della pioggia basato su dati meteorologici storici.
Il modello utilizza un **RandomForestClassifier** insieme a preprocessing (imputer e one-hot encoder) per prevedere se il giorno successivo ci sarà pioggia significativa (>2mm) o nulla/minima (<2mm).
---
## 🛠️ Task
- **Tipo di problema:** classificazione binaria.
- **Output:** `"Presente"` (pioggia > 2mm) o `"Nulla/Minima"` (pioggia ≤ 2mm).
- **Input:** valori meteorologici giornalieri:
- Anno
- Mese
- Fase del mese (Inizio, Meta, Fine)
- Direzione vento massimo (es. N, SO)
- Temperatura minima, media e massima (°C)
- Umidità media e massima (%)
- Vento medio e massimo (km/h)
- Irradiamento solare (KJ/m²)
- Pressione (Pa)
- Pioggia odierna (mm)
---
## ⚡ Come usare il modello
### Python
```python
from huggingface_hub import hf_hub_download
import joblib
import pandas as pd
# Scarica il modello dal repo Hugging Face
model_path = hf_hub_download(repo_id="freyflyy/previsione-meteo-udine-modello", filename="model.pkl")
model_dict = joblib.load(model_path)
imputer = model_dict["modello_imputer"]
encoder = model_dict["modello_encoder"]
model = model_dict["modello_predittivo"]
# Esempio input
input_dict = {
"Anno": 2025,
"Mese": "Ago",
"PosizioneMese": "Meta",
"DirVentoMax": "SO",
"TempMin [°C]": 16.3,
"TempMed [°C]": 23.1,
"TempMax [°C]": 28.1,
"UmiditaMed [%]": 69,
"UmiditaMax [%]": 91,
"VentoMed [km/h]": 5,
"VentoMax [km/h]": 19,
"Radiazione [KJ/m2]": 25998,
"Pressione [Pa]": 100770,
"mmPioggia": 0
}
df_input = pd.DataFrame([input_dict])
# Preprocessing
numerical_cols = df_input.select_dtypes(include=[float, int]).columns.tolist()
categorical_cols = df_input.select_dtypes(include=["object"]).columns.tolist()
df_input[numerical_cols] = imputer.transform(df_input[numerical_cols])
encoded = encoder.transform(df_input[categorical_cols]).toarray()
encoded_df = pd.DataFrame(encoded, columns=encoder.get_feature_names_out(categorical_cols), index=df_input.index)
df_input_final = pd.concat([df_input.drop(columns=categorical_cols), encoded_df], axis=1)
# Predizione
pred = model.predict(df_input_final)[0]
print("Predizione pioggia:", pred)
```
📊 Accuratezza
Pioggia presente: ~71.5%
Nulla/minima: ~71.3%
🔗 Fonte dati
I dati meteorologici possono essere prelevati dal sito dell'[Arpa FVG](https://www.osmer.fvg.it/archivio.php?ln=&p=dati)
📄 Licenza
MIT
|
pidbu/blockassist-bc-whistling_alert_shrew_1755776879
|
pidbu
| 2025-08-21T11:49:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:48:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755775271
|
sampingkaca72
| 2025-08-21T11:48:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:48:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VoilaRaj/81_c_pu7XQH
|
VoilaRaj
| 2025-08-21T11:46:42Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-21T11:42:53Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Gallardo994/Qwen3-Coder-30B-A3B-Instruct
|
Gallardo994
| 2025-08-21T11:45:31Z | 309 | 1 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"base_model:finetune:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-07-31T14:30:58Z |
---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
tags:
- mlx
---
# Gallardo994/Qwen3-Coder-30B-A3B-Instruct
This model [Gallardo994/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Gallardo994/Qwen3-Coder-30B-A3B-Instruct) was
converted to MLX format from [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Gallardo994/Qwen3-Coder-30B-A3B-Instruct")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
PhongInk/blockassist-bc-stinky_thorny_zebra_1755776673
|
PhongInk
| 2025-08-21T11:45:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky thorny zebra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:44:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky thorny zebra
---
# 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_1755776523
|
liukevin666
| 2025-08-21T11:43:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:43:08Z |
---
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).
|
PhongInk/blockassist-bc-stinky_thorny_zebra_1755776524
|
PhongInk
| 2025-08-21T11:42:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky thorny zebra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:42:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky thorny zebra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sankar-asthramedtech/finetuning_whisper-large-v3_using_LoRA_without_Quantization_V-1.1
|
sankar-asthramedtech
| 2025-08-21T11:42:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T11:05:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
vikasp1610/qwen-sql-prefix
|
vikasp1610
| 2025-08-21T11:41:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2-0.5B",
"base_model:finetune:Qwen/Qwen2-0.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T11:07:24Z |
---
base_model: Qwen/Qwen2-0.5B
library_name: transformers
model_name: qwen-sql-prefix
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen-sql-prefix
This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B).
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="vikasp1610/qwen-sql-prefix", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755776394
|
kapalbalap
| 2025-08-21T11:40:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:40:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nourhanwaleeed/Pixtral-vision
|
nourhanwaleeed
| 2025-08-21T11:39:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llava",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T11:39:12Z |
---
base_model: unsloth/pixtral-12b-2409-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llava
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** nourhanwaleeed
- **License:** apache-2.0
- **Finetuned from model :** unsloth/pixtral-12b-2409-unsloth-bnb-4bit
This llava 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)
|
harshhmaniya/deepseek-multilingual-finetuned
|
harshhmaniya
| 2025-08-21T11:38:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T10:59:15Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
library_name: transformers
model_name: deepseek-multilingual-finetuned
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for deepseek-multilingual-finetuned
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B).
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="harshhmaniya/deepseek-multilingual-finetuned", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755776159
|
yaelahnal
| 2025-08-21T11:37:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:36:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sumitdalal/finetuned-distilbert-CAMS
|
sumitdalal
| 2025-08-21T11:36:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-21T11:36:15Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ZhengjunHUO/Qwen3-4B-Reasoning
|
ZhengjunHUO
| 2025-08-21T11:36:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B-Base",
"base_model:finetune:unsloth/Qwen3-4B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T11:31:38Z |
---
base_model: unsloth/Qwen3-4B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ZhengjunHUO
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Base
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755776075
|
kapalbalap
| 2025-08-21T11:35:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:35:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755775701
|
yaelahnal
| 2025-08-21T11:29:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:29:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755773856
|
coelacanthxyz
| 2025-08-21T11:26:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:26:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1755775498
|
liukevin666
| 2025-08-21T11:26:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:26:06Z |
---
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).
|
ganga4364/mms_300_Garchen_Rinpoche-v5-base-Checkpoint-28000
|
ganga4364
| 2025-08-21T11:25:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-21T11:25:14Z |
---
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]
|
OMRIDRORI/sanskrit-bert-from-scratch
|
OMRIDRORI
| 2025-08-21T11:24:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"sanskrit",
"sa",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-21T11:15:57Z |
---
language: sa
license: mit
library_name: transformers
tags:
- bert
- sanskrit
- fill-mask
---
# sanskrit-bert-from-scratch
This is a BERT model that has been pre-trained **from scratch** on a large corpus of transliterated Sanskrit text. Unlike multilingual models, its vocabulary and weights are tailored specifically to the Sanskrit language as represented in the training data.
This model was trained as part of the Intelexsus project. The other model, which was continually trained from `bert-base-multilingual-cased`, can be found here: [OMRIDRORI/mbert-sanskrit-continual](https://huggingface.co/OMRIDRORI/mbert-sanskrit-continual).
## Model Details
- **Model type:** BERT (bert-base-uncased architecture)
- **Language:** Sanskrit (sa)
- **Training Corpus:** A custom corpus of transliterated Sanskrit text collected for the Intelexsus project.
- **Training objective:** Masked Language Modeling (MLM).
- **Architecture:** 12-layer, 768-hidden, 12-heads.
## How to Use
You can use this model directly with the `transformers` library for the `fill-mask` task.
```python
from transformers import pipeline
# Use your Hugging Face username and model name
model_name = "OMRIDRORI/sanskrit-bert-from-scratch"
unmasker = pipeline('fill-mask', model=model_name)
# Example sentence in IAST transliteration
# "The great sage spoke the following words: ___"
result = unmasker("sa maharṣir uvāca anena [MASK] vacanena")
print(result)
```
You can also load the model and tokenizer directly for more control:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
# Use your Hugging Face username and model name
model_name = "OMRIDRORI/sanskrit-bert-from-scratch"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
# You can now use the model for your own fine-tuning and inference tasks.
```
|
elliepreed/spanish-babylm-urop-Ellie
|
elliepreed
| 2025-08-21T11:24:35Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T11:14:41Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: spanish-babylm-urop-Ellie
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. -->
# spanish-babylm-urop-Ellie
This model is pretrained on climb-mao/Spanish-BabyLM dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.8898 | 1.0 | 178 | 6.7712 |
| 6.1905 | 2.0 | 356 | 6.4889 |
| 5.9823 | 3.0 | 534 | 6.4024 |
### Framework versions
- Transformers 4.54.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
ilkhom199/34a1ba80-75d1-4086-8428-6e064c47d656
|
ilkhom199
| 2025-08-21T11:22:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T11:22:01Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bhatia1289/gemma-finetune-gguf
|
bhatia1289
| 2025-08-21T11:22:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"en",
"dataset:srikar-v05/share-gpt-medical",
"arxiv:1910.09700",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T10:11:20Z |
---
library_name: transformers
tags:
- unsloth
datasets:
- srikar-v05/share-gpt-medical
language:
- en
base_model:
- google/gemma-3-270m-it
---
# 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]
|
Navanjana/sinhala-gemma3_chat-tokenizer
|
Navanjana
| 2025-08-21T11:22:03Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T11:22:01Z |
---
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]
|
VoilaRaj/81_c_cKk3pW
|
VoilaRaj
| 2025-08-21T11:21:40Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-21T11:17:53Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
OMRIDRORI/mbert-sanskrit-continual
|
OMRIDRORI
| 2025-08-21T11:21:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"sanskrit",
"sa",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-21T11:16:43Z |
---
language: sa
license: mit
library_name: transformers
tags:
- bert
- sanskrit
- fill-mask
---
# mbert-sanskrit-continual
This is a `bert-base-multilingual-cased` model that has been continually pre-trained on a corpus of transliterated Sanskrit text. The goal was to adapt the multilingual model to better understand the nuances and vocabulary of the Sanskrit language.
This model was trained as part of the Intelexsus project. The other model, trained from scratch, can be found here: [OMRIDRORI/sanskrit-bert-from-scratch](https://huggingface.co/OMRIDRORI/sanskrit-bert-from-scratch).
## Model Details
- **Base Model:** `bert-base-multilingual-cased`
- **Language:** Sanskrit (sa)
- **Training Corpus:** A custom corpus of transliterated Sanskrit text collected for the Intelexsus project.
- **Training objective:** Masked Language Modeling (MLM).
## How to Use
You can use this model directly with the `transformers` library for the `fill-mask` task.
```python
from transformers import pipeline
# Use your Hugging Face username and model name
model_name = "OMRIDRORI/mbert-sanskrit-continual"
unmasker = pipeline('fill-mask', model=model_name)
# Example sentence in IAST transliteration
# "The great sage spoke the following words: ___"
result = unmasker("sa maharṣir uvāca anena [MASK] vacanena")
print(result)
```
You can also load the model and tokenizer directly for more control:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
# Use your Hugging Face username and model name
model_name = "OMRIDRORI/mbert-sanskrit-continual"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
# You can now use the model for your own fine-tuning and inference tasks.
```
## Training Procedure
This model was initialized from the weights of `bert-base-multilingual-cased` and then continually pre-trained on a large corpus of transliterated Sanskrit text using the `transformers` library.
## Intended Use & Limitations
This model is intended for research and development in Sanskrit NLP. It can be used as a base model for fine-tuning on downstream tasks like text classification, named entity recognition, or question answering.
The model's knowledge is limited to the data it was trained on. It may not perform well on Sanskrit text that is stylistically very different from the training corpus or uses a different transliteration scheme.
|
Bartosh16/DanielB-2
|
Bartosh16
| 2025-08-21T11:20:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"base_model:speakleash/Bielik-4.5B-v3.0-Instruct",
"base_model:finetune:speakleash/Bielik-4.5B-v3.0-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T10:32:00Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: speakleash/Bielik-4.5B-v3.0-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
Sophie-Rain-Spiderman-Videos-Tutorial-H-Q/Sophie.Rain.Spiderman.Videos.Tutorial.Twitter
|
Sophie-Rain-Spiderman-Videos-Tutorial-H-Q
| 2025-08-21T11:16:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T11:13:30Z |
<p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
<p><a href="https://social.danielwellington.com/srain" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
|
tammycra121/blockassist-bc-marine_rangy_eel_1755773431
|
tammycra121
| 2025-08-21T11:15:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine rangy eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:15:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine rangy eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
phospho-app/yue1006-gr00t-my_dataset-v2
|
phospho-app
| 2025-08-21T11:15:07Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"safetensors",
"gr00t_n1_5",
"gr00t",
"robotics",
"dataset:yue1006/my_dataset",
"region:us"
] |
robotics
| 2025-08-21T10:28:15Z |
---
datasets: yue1006/my_dataset
library_name: phosphobot
pipeline_tag: robotics
model_name: gr00t
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successful, try it out on your robot!
## Training parameters:
- **Dataset**: [yue1006/my_dataset](https://huggingface.co/datasets/yue1006/my_dataset)
- **Wandb run URL**: None
- **Epochs**: 3
- **Batch size**: 48
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
caolahuu121/blockassist-bc-solitary_tenacious_gerbil_1755773389
|
caolahuu121
| 2025-08-21T11:13:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"solitary tenacious gerbil",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:13:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- solitary tenacious gerbil
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
XX-VIDEOS-18-isha-malviya-viral-video-Clip/New.full.videos.isha.malviya.Viral.Video.Official.Tutorial
|
XX-VIDEOS-18-isha-malviya-viral-video-Clip
| 2025-08-21T11:13:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T11:13:03Z |
<a href="https://tinyurl.com/ybtx5at9" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
joppertiu/blockassist-bc-hunting_iridescent_crocodile_1755774658
|
joppertiu
| 2025-08-21T11:11:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hunting iridescent crocodile",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:10:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hunting iridescent crocodile
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
InfoJelly/blockassist-bc-majestic_prehistoric_capybara_1755774596
|
InfoJelly
| 2025-08-21T11:10:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"majestic prehistoric capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:10:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- majestic prehistoric capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755772875
|
kojeklollipop
| 2025-08-21T11:09:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:09:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Isha-Malviya-Viral-video-original/New.full.videos.Isha.Malviya.Viral.Video.Official.Tutorial
|
Isha-Malviya-Viral-video-original
| 2025-08-21T11:08:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T11:08:46Z |
<a href="https://tinyurl.com/ybtx5at9" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
neko-llm/Qwen3-235B-epoch2
|
neko-llm
| 2025-08-21T11:03:53Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen3-235B-A22B",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-235B-A22B",
"region:us"
] |
text-generation
| 2025-08-21T11:01:23Z |
---
base_model: Qwen/Qwen3-235B-A22B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-235B-A22B
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
54gO/marianmt-en-hi-translator
|
54gO
| 2025-08-21T11:02:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"chemistry",
"biology",
"finance",
"music",
"legal",
"code",
"art",
"medical",
"moe",
"machine-translation-en-hi",
"en-hi-translator",
"qlora-finetuned-en-hi",
"lora-en-hi",
"english",
"hindi",
"translation",
"en",
"hi",
"dataset:cfilt/iitb-english-hindi",
"base_model:Helsinki-NLP/opus-mt-en-hi",
"base_model:finetune:Helsinki-NLP/opus-mt-en-hi",
"license:mit",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-08-21T09:59:06Z |
---
library_name: transformers
tags:
- chemistry
- biology
- finance
- music
- legal
- code
- art
- medical
- moe
- machine-translation-en-hi
- en-hi-translator
- qlora-finetuned-en-hi
- lora-en-hi
- english
- hindi
license: mit
datasets:
- cfilt/iitb-english-hindi
language:
- en
- hi
metrics:
- bleu
- chrf
- bertscore
- meteor
- ter
base_model:
- Helsinki-NLP/opus-mt-en-hi
pipeline_tag: translation
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# MarianMT English → Hindi Translator (Fine-tuned with QLoRA)
This model is a fine-tuned version of **[Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi)** for **English → Hindi translation**.
It was trained using **QLoRA parameter-efficient fine-tuning** on the **[CFILT IITB English-Hindi dataset](https://huggingface.co/datasets/cfilt/iitb-english-hindi)**.
The fine-tuned model shows improved translation quality compared to the base MarianMT model and it is faster than Base Model.
---
## 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.
- **Model type:** MarianMT (sequence-to-sequence translation model)
- **Developed by:** [Sagar / 54gO]
- **Languages:** English (en) → Hindi (hi)
- **License:** MIT
- **Finetuned from:** Helsinki-NLP/opus-mt-en-hi
- **Dataset:** cfilt/iitb-english-hindi
- **Framework:** 🤗 Transformers
### 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
- Translating English text into Hindi.
- Educational purposes (understanding fine-tuning of translation models).
- Research in low-resource language pairs.
<!-- 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 -->
- Integrate into chatbots for bilingual English-Hindi conversations.
- Use in document translation pipelines.
- As a teaching resource for NLP model fine-tuning with QLoRA.
### Out-of-Scope Use
- Generating misinformation or harmful translations.
- High-stakes environments (medical, legal, safety-critical) without human verification.
---
<!-- 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. -->
- May not handle **rare domain-specific terms** (e.g., medical/legal jargon) correctly.
- May introduce **cultural biases** or awkward phrasing.
- Performance depends on training dataset; **Hindi dialects** outside standard Hindi may not be well covered.
---
[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.
## How to Use
```python
from transformers import MarianMTModel, MarianTokenizer
model_name = "YourUsername/marianmt-en-hi-translator"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
text = "India is a beautiful country."
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translation)
# भारत एक सुंदर देश है।
```
### Training Details
Training dataset: cfilt/iitb-english-hindi
Fine-tuning method: QLoRA
Precision: 4-bit quantization with LoRA adapters
Batch size / Epochs: 16/10
Optimizer: AdamW
Scheduler: [cosine/linear/]
Compute: [T4 GPU, 30 minutes]
#### Metrics
BLEU: 81.43047985208148
CHRF: 87.07168424269955
TER: 16.29008746355685
#### Summary
✅ Faster inference compared to the base model, making it more practical for real-time applications.
|
unitova/blockassist-bc-zealous_sneaky_raven_1755772448
|
unitova
| 2025-08-21T11:01:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:01:26Z |
---
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).
|
InfoJelly/blockassist-bc-majestic_prehistoric_capybara_1755774021
|
InfoJelly
| 2025-08-21T11:01:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"majestic prehistoric capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:00:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- majestic prehistoric capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Milica-y-Angel-David-debut-video-Clip-hq/Hd.VER.milica.y.angel.david.debut.video.filtrado.clip.viral.completo.en.twitter.y.telegram
|
Milica-y-Angel-David-debut-video-Clip-hq
| 2025-08-21T11:00:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T11:00:08Z |
<a href="https://sdu.sk/AyL"><img src="https://files.qatarliving.com/event/2025/06/20/Jawan69_0-1749987397680.gif" alt="fsd" /></a>
<a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a>
<a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
|
koloni/blockassist-bc-deadly_graceful_stingray_1755772396
|
koloni
| 2025-08-21T11:00:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T11:00:07Z |
---
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).
|
joppertiu/blockassist-bc-pensive_sniffing_sloth_1755773961
|
joppertiu
| 2025-08-21T11:00:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive sniffing sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:59:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive sniffing sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mudasir13cs/Field-adaptive-description-generator
|
mudasir13cs
| 2025-08-21T10:58:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation",
"lora",
"peft",
"presentation-templates",
"information-retrieval",
"conversational",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T09:19:06Z |
---
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
tags:
- text-generation
- lora
- peft
- presentation-templates
- information-retrieval
---
# Field-adaptive-description-generator
## Model Details
### Model Description
A fine-tuned text generation model for description generation from presentation template metadata. This model uses LoRA adapters to efficiently fine-tune Microsoft Phi-2 for generating diverse and relevant content.
**Developed by:** Mudasir Syed (mudasir13cs)
**Model type:** Causal Language Model with LoRA
**Language(s) (NLP):** English
**License:** Apache 2.0
**Finetuned from model:** microsoft/Phi-2
### Model Sources
**Repository:** https://github.com/mudasir13cs/hybrid-search
## Uses
### Direct Use
This model is designed for generating description generation from presentation template metadata including titles, descriptions, industries, categories, and tags.
### Downstream Use
- Content generation systems
- SEO optimization tools
- Template recommendation engines
- Automated content creation
### Out-of-Scope Use
- Factual information generation
- Medical or legal advice
- Harmful content generation
- Tasks unrelated to presentation templates
## Bias, Risks, and Limitations
- The model may generate biased or stereotypical content based on training data
- Generated content should be reviewed for accuracy and appropriateness
- Performance depends on input quality and relevance
## How to Get Started with the Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model
model = AutoModelForCausalLM.from_pretrained("mudasir13cs/Field-adaptive-description-generator")
tokenizer = AutoTokenizer.from_pretrained("mudasir13cs/Field-adaptive-description-generator")
# Generate content
input_text = "Generate 8 different search queries for: Title: Business Strategy Template, Description: Professional business strategy presentation, Industries: Business, Categories: Strategy, Tags: business, strategy, planning"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.7, do_sample=True)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```
## Training Details
### Training Data
- **Dataset:** Presentation template dataset with metadata
- **Size:** Custom dataset with template-description pairs
- **Source:** Curated presentation template collection
### Training Procedure
- **Architecture:** Microsoft Phi-2 with LoRA adapters
- **Loss Function:** Cross-entropy loss
- **Optimizer:** AdamW
- **Learning Rate:** 2e-4
- **Batch Size:** 4
- **Epochs:** 3
### Training Hyperparameters
- **Training regime:** Supervised fine-tuning with LoRA
- **LoRA Rank:** 16
- **LoRA Alpha:** 32
- **Hardware:** GPU (NVIDIA)
- **Training time:** ~3 hours
## Evaluation
### Testing Data, Factors & Metrics
- **Testing Data:** Validation split from template dataset
- **Factors:** Content quality, relevance, diversity
- **Metrics:**
- BLEU score
- ROUGE score
- Human evaluation scores
### Results
- **BLEU Score:** ~0.75
- **ROUGE Score:** ~0.80
- **Performance:** Optimized for description generation quality
## Environmental Impact
- **Hardware Type:** NVIDIA GPU
- **Hours used:** ~3 hours
- **Cloud Provider:** Local/Cloud
- **Carbon Emitted:** Minimal (LoRA training)
## Technical Specifications
### Model Architecture and Objective
- **Architecture:** Transformer decoder with LoRA adapters
- **Objective:** Generate relevant description generation from template metadata
- **Input:** Template metadata (title, description, industries, etc.)
- **Output:** Generated text (queries or descriptions)
### Compute Infrastructure
- **Hardware:** NVIDIA GPU
- **Software:** PyTorch, Transformers, PEFT
## Citation
**BibTeX:**
```bibtex
@misc{field_adaptive_description_generator,
title={Field-adaptive-description-generator for Presentation Template Description Generation},
author={Mudasir Syed},
year={2024},
url={https://huggingface.co/mudasir13cs/Field-adaptive-description-generator}
}
```
**APA:**
Syed, M. (2024). Field-adaptive-description-generator for Presentation Template Description Generation. Hugging Face. https://huggingface.co/mudasir13cs/Field-adaptive-description-generator
## Model Card Authors
Mudasir Syed (mudasir13cs)
## Model Card Contact
- **GitHub:** https://github.com/mudasir13cs
- **Hugging Face:** https://huggingface.co/mudasir13cs
## Framework versions
- Transformers: 4.35.0
- PEFT: 0.16.0
- PyTorch: 2.0.0
|
neko-llm/Qwen3-235B-epoch3
|
neko-llm
| 2025-08-21T10:58:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen3-235B-A22B",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-235B-A22B",
"region:us"
] |
text-generation
| 2025-08-21T10:52:07Z |
---
base_model: Qwen/Qwen3-235B-A22B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-235B-A22B
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
ciorant/news-summarizer
|
ciorant
| 2025-08-21T10:56:22Z | 5 | 0 | null |
[
"safetensors",
"bart",
"summarization",
"news",
"text2text-generation",
"bart-large-cnn",
"en",
"dataset:cnn_dailymail",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] |
summarization
| 2025-08-20T12:45:13Z |
---
license: apache-2.0
base_model: google/flan-t5-base # Replace with your base model
tags:
- summarization
- news
- text2text-generation
- bart-large-cnn
language:
- en
datasets:
- cnn_dailymail
metrics:
- rouge
pipeline_tag: summarization
---
# News Summarizer
This model is fine-tuned for news article summarization. It can take long news articles and generate concise, accurate summaries.
## Model Details
- **Base Model**: facebook/bart-large-cnn
- **Task**: Text Summarization
- **Language**: English
- **Training Steps**: 4000
- **Best ROUGE-1**: 0.42
- **Live version on Streamlit**: https://english-news-summarizer.streamlit.app
## Usage
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import re
# Load model
model = AutoModelForSeq2SeqLM.from_pretrained("ciorant/news-summarizer")
tokenizer = AutoTokenizer.from_pretrained("ciorant/news-summarizer")
def summarize_news(article_text, max_length=128):
inputs = tokenizer(article_text, return_tensors="pt", truncation=True, max_length=512)
outputs = model.generate(
inputs.input_ids,
max_length=max_length,
num_beams=4,
early_stopping=True,
do_sample=False,
length_penalty=1.0
)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Clean up spacing around punctuation
summary = re.sub(r'\s+([.,!?;:])', r'\1', summary)
summary = re.sub(r'\s+', ' ', summary)
return summary.strip()
# Example usage
article = "Your news article text here..."
summary = summarize_news(article)
print(summary)
```
## Training Data
Trained on news articles for summarization task.
## Performance
- ROUGE-1: ~0.42
- ROUGE-2: ~0.21
- ROUGE-L: ~0.29
## Limitations
- Optimized for English news articles
- Best performance on articles 100-800 words
|
8septiadi8/blockassist-bc-curious_lightfooted_mouse_1755773685
|
8septiadi8
| 2025-08-21T10:56:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious lightfooted mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:55:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious lightfooted mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
germanlunichh/blockassist-bc-mute_shaggy_alligator_1755772106
|
germanlunichh
| 2025-08-21T10:55:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute shaggy alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:55:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute shaggy alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
flemmingpetter2/blockassist-bc-hardy_subtle_snake_1755772077
|
flemmingpetter2
| 2025-08-21T10:55:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hardy subtle snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:55:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hardy subtle snake
---
# 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_1755773608
|
liukevin666
| 2025-08-21T10:54:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:54:35Z |
---
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).
|
quanghieuuaz/blockassist-bc-flightless_unseen_parrot_1755772618
|
quanghieuuaz
| 2025-08-21T10:52:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flightless unseen parrot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:52:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flightless unseen parrot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VoilaRaj/81_c_S4ElLP
|
VoilaRaj
| 2025-08-21T10:52:21Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-21T10:48:27Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
CLEAR-Global/w2v-bert-2.0-clearglobal-hausa-asr-1.0.0
|
CLEAR-Global
| 2025-08-21T10:51:43Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"generated_from_trainer",
"ha",
"hau",
"dataset:CLEAR-Global/twb-voice-1.0",
"base_model:facebook/w2v-bert-2.0",
"base_model:finetune:facebook/w2v-bert-2.0",
"license:cc-by-sa-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-07-11T06:27:08Z |
---
library_name: transformers
license: cc-by-sa-4.0
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- CLEAR-Global/twb-voice-1.0
metrics:
- wer
- cer
model-index:
- name: w2v-bert-2.0-clearglobal-hausa-asr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: TWB Voice 1.0
type: dataset
config: default
split: test
args: default
metrics:
- name: Wer
type: wer
value: 0.0402
- name: CER
type: cer
value: 0.0097
language:
- ha
- hau
---
# w2v-bert-2.0-clearglobal-hausa-asr-1.0.0
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the [TWB Voice 1.0 dataset](https://huggingface.co/datasets/CLEAR-Global/twb-voice-1.0).
It achieves the following results on the internal evaluation set:
- WER: 4.02%
- CER: 0.97%
## Training and evaluation data
This model was trained by colleagues from the [Makerere University Centre for Artificial Intelligence and Data Science](http://air.ug/) in collaboration with CLEAR Global. We gratefully acknowledge their expertise and partnership.
Model was trained and tested on the approved Hausa subset of [TWB Voice 1.0 dataset](https://huggingface.co/datasets/CLEAR-Global/twb-voice-1.0).
Train/dev/test portions correspond to the splits in this dataset version. Test splits consist of speakers not present in train and dev splits.
We also tested on external datasets: Common voice v17 and Naija Voices test splits.
The evaluation results are as follows:
| Evaluation dataset | WER (%) | CER (%) |
|:----------------------:|:-------:|:----:|
| TWB Voice 1.0 | 4.02 | 0.97 |
| Common Voice v17 Hausa | ⏳ | ⏳ |
| Naija Voices Hausa | ⏳ | ⏳ |
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.08
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.5675 | 1.0 | 405 | 0.2153 | 0.2335 | 0.0539 |
| 0.1978 | 2.0 | 810 | 0.1934 | 0.2063 | 0.0488 |
| 0.1706 | 3.0 | 1215 | 0.1886 | 0.2095 | 0.0494 |
| 0.1627 | 4.0 | 1620 | 0.1911 | 0.1895 | 0.0478 |
| 0.1495 | 5.0 | 2025 | 0.1817 | 0.1748 | 0.0443 |
| 0.1302 | 6.0 | 2430 | 0.1690 | 0.1672 | 0.0417 |
| 0.115 | 7.0 | 2835 | 0.1601 | 0.1531 | 0.0400 |
| 0.102 | 8.0 | 3240 | 0.1489 | 0.1353 | 0.0345 |
| 0.0908 | 9.0 | 3645 | 0.1712 | 0.1572 | 0.0414 |
| 0.0854 | 10.0 | 4050 | 0.1779 | 0.1337 | 0.0390 |
| 0.0788 | 11.0 | 4455 | 0.1522 | 0.1247 | 0.0347 |
| 0.0709 | 12.0 | 4860 | 0.1686 | 0.1246 | 0.0365 |
| 0.0678 | 13.0 | 5265 | 0.1750 | 0.1532 | 0.0441 |
| 0.064 | 14.0 | 5670 | 0.1620 | 0.1187 | 0.0356 |
| 0.0653 | 15.0 | 6075 | 0.2120 | 0.1957 | 0.0491 |
| 0.073 | 16.0 | 6480 | 0.2040 | 0.1275 | 0.0371 |
| 0.0798 | 17.0 | 6885 | 0.2025 | 0.1292 | 0.0397 |
| 0.0819 | 18.0 | 7290 | 0.2249 | 0.1367 | 0.0422 |
| 0.0928 | 19.0 | 7695 | 0.1785 | 0.1335 | 0.0398 |
### Framework versions
- Transformers 4.53.1
- Pytorch 2.7.1+cu128
- Datasets 4.0.0
- Tokenizers 0.21.2
|
CLEAR-Global/whisper-small-clearglobal-kanuri-asr-1.0.0
|
CLEAR-Global
| 2025-08-21T10:51:30Z | 25 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"kau",
"kr",
"dataset:CLEAR-Global/twb-voice-1.0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:cc-by-sa-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-07-14T16:46:23Z |
---
library_name: transformers
license: cc-by-sa-4.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- CLEAR-Global/twb-voice-1.0
metrics:
- wer
- cer
model-index:
- name: whisper-small-clearglobal-kanuri-asr-1.0.0
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: TWB Voice 1.0
type: dataset
config: default
split: test
args: default
metrics:
- name: WER
type: wer
value: 0.1016
- name: CER
type: cer
value: 0.0372
language:
- kau
- kr
---
# whisper-small-clearglobal-kanuri-asr-1.0.0
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [TWB Voice 1.0 dataset](https://huggingface.co/datasets/CLEAR-Global/twb-voice-1.0).
It achieves the following results on the evaluation set:
- WER: 10.16%
- Cer: 3.72%
## Training and evaluation data
This model was trained by colleagues from the [Makerere University Centre for Artificial Intelligence and Data Science](http://air.ug/) in collaboration with CLEAR Global. We gratefully acknowledge their expertise and partnership.
Model was trained and tested on the approved Kanuri subset of [TWB Voice 1.0 dataset](https://huggingface.co/datasets/CLEAR-Global/twb-voice-1.0).
Train/dev/test portions correspond to the splits in this dataset version.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.08
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:------:|:---------------:|:------:|
| 1.3499 | 1.0 | 520 | 0.3772 | 0.4815 | 0.8971 |
| 0.3923 | 2.0 | 1040 | 0.1944 | 0.3327 | 0.6041 |
| 0.2181 | 3.0 | 1560 | 0.1242 | 0.2628 | 0.4174 |
| 0.1286 | 4.0 | 2080 | 0.0937 | 0.2261 | 0.3440 |
| 0.0832 | 5.0 | 2600 | 0.0671 | 0.1934 | 0.2607 |
| 0.0512 | 6.0 | 3120 | 0.0599 | 0.1768 | 0.2275 |
| 0.0356 | 7.0 | 3640 | 0.0548 | 0.1630 | 0.1975 |
| 0.0278 | 8.0 | 4160 | 0.0514 | 0.1581 | 0.1847 |
| 0.0226 | 9.0 | 4680 | 0.0466 | 0.1572 | 0.1696 |
| 0.0175 | 10.0 | 5200 | 0.0420 | 0.1518 | 0.1541 |
| 0.0153 | 11.0 | 5720 | 0.0438 | 0.1557 | 0.1533 |
| 0.0125 | 12.0 | 6240 | 0.0407 | 0.1444 | 0.1437 |
| 0.0113 | 13.0 | 6760 | 0.0404 | 0.1469 | 0.1424 |
| 0.0098 | 14.0 | 7280 | 0.0414 | 0.1449 | 0.1442 |
| 0.0082 | 15.0 | 7800 | 0.0371 | 0.1401 | 0.1323 |
| 0.0078 | 16.0 | 8320 | 0.0406 | 0.1374 | 0.1374 |
| 0.0072 | 17.0 | 8840 | 0.0373 | 0.1408 | 0.1297 |
| 0.0059 | 18.0 | 9360 | 0.0370 | 0.1374 | 0.1277 |
| 0.0063 | 19.0 | 9880 | 0.0370 | 0.1347 | 0.1231 |
| 0.0057 | 20.0 | 10400 | 0.0349 | 0.1386 | 0.1185 |
| 0.0046 | 21.0 | 10920 | 0.0347 | 0.1346 | 0.1185 |
| 0.0043 | 22.0 | 11440 | 0.0359 | 0.1410 | 0.1218 |
| 0.0041 | 23.0 | 11960 | 0.0330 | 0.1296 | 0.1125 |
| 0.0029 | 24.0 | 12480 | 0.0330 | 0.1308 | 0.1110 |
| 0.0033 | 25.0 | 13000 | 0.0384 | 0.1364 | 0.1191 |
| 0.0036 | 26.0 | 13520 | 0.0318 | 0.1264 | 0.1073 |
| 0.0027 | 27.0 | 14040 | 0.0325 | 0.1264 | 0.1074 |
| 0.0016 | 28.0 | 14560 | 0.0322 | 0.1257 | 0.1046 |
| 0.0015 | 29.0 | 15080 | 0.0322 | 0.1257 | 0.1032 |
| 0.0018 | 30.0 | 15600 | 0.0303 | 0.1251 | 0.1016 |
| 0.0018 | 31.0 | 16120 | 0.0326 | 0.1332 | 0.1071 |
| 0.0024 | 32.0 | 16640 | 0.0319 | 0.1282 | 0.1073 |
| 0.0008 | 33.0 | 17160 | 0.0309 | 0.1256 | 0.1018 |
| 0.0007 | 34.0 | 17680 | 0.0297 | 0.1250 | 0.0970 |
| 0.0009 | 35.0 | 18200 | 0.1305 | 0.1057 | 0.0335 |
| 0.0012 | 36.0 | 18720 | 0.1312 | 0.0980 | 0.0299 |
| 0.0009 | 37.0 | 19240 | 0.1307 | 0.1004 | 0.0309 |
| 0.0005 | 38.0 | 19760 | 0.1263 | 0.0960 | 0.0293 |
| 0.0004 | 39.0 | 20280 | 0.1263 | 0.0933 | 0.0285 |
| 0.0002 | 40.0 | 20800 | 0.1273 | 0.0935 | 0.0283 |
| 0.0001 | 41.0 | 21320 | 0.1262 | 0.0916 | 0.0281 |
| 0.0001 | 42.0 | 21840 | 0.1267 | 0.0926 | 0.0287 |
| 0.0 | 43.0 | 22360 | 0.1271 | 0.0907 | 0.0277 |
| 0.0 | 44.0 | 22880 | 0.1275 | 0.0900 | 0.0274 |
| 0.0 | 45.0 | 23400 | 0.1279 | 0.0893 | 0.0273 |
| 0.0 | 46.0 | 23920 | 0.1282 | 0.0884 | 0.0267 |
### Framework versions
- Transformers 4.53.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2
|
johngreendr1/5371d709-fb05-4f6c-a1b9-cc60c1225cb1
|
johngreendr1
| 2025-08-21T10:51:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:furiosa-ai/mlperf-gpt-j-6b",
"base_model:adapter:furiosa-ai/mlperf-gpt-j-6b",
"region:us"
] | null | 2025-08-21T08:42:56Z |
---
base_model: furiosa-ai/mlperf-gpt-j-6b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
Uppal-Farm-Girl-Viral-Video-Orginal-Link/Full.Uppal.Farm.Girl.Viral.Video.Original.Link.Official
|
Uppal-Farm-Girl-Viral-Video-Orginal-Link
| 2025-08-21T10:51:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T10:51:01Z |
<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>
|
core313/StructureVitalityTheory
|
core313
| 2025-08-21T10:49:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-07-29T16:49:07Z |
StructuralVitality/
├── README.md # 概览（理论简介+目标+联系方式）
├── theory_overview.md # 理论背景与意义
├── core_equations.md # 核心公式与推导
├── /docs/ # 主体文档
│ ├── overview.md # 理论全景图（结构生力是什么）
│ ├── principles.md # 核心原理（结构对称+能量聚散+空间张力）
│ ├── formulas.md # 数学建模（公式发展过程与现阶段版本）
│ ├── experiments.md # 可验证实验（各尺度实验建议）
│ ├── cosmology.md # 宇宙学应用（超新星、黑洞、螺旋宇宙）
│ ├── quantum_model.md # 微观对应（微管、意识、量子结构生力）
│ ├── ai_application.md # AI模拟与建模路线图
│ └── philosophy.md # 哲学基础与宇宙观
├── /media/ # 图片、结构图、演示GIF
│ └── ...
├── /translations/ # 多语言支持
│ ├── README.zh.md # 中文主页
│ └── overview.zh.md # 中文理论概览
├── /papers/ # 正式论文（PDF、LaTeX、预印本）
│ ├── StructuralVitality_v1.pdf
│ └── ...
├── /videos/ # 视频封面 + 视频脚本
│ ├── video01_intro.md
│ └── ...
├── LICENSE # 开源协议（建议使用 CC BY-NC-SA 4.0）
└── CONTRIBUTING.md # 合作者指南（科学家、翻译志愿者等）
🌌 Overview | 概览
## License
This repository is licensed under **CC BY-NC-SA 4.0** — you may freely share and adapt the materials for non-commercial use, as long as proper attribution is given and derivative works follow the same license.
Full license: [Creative Commons BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
Structural Vital Force Theory is a conceptual physics framework that proposes:
“Structure itself generates and sustains force.”
Unlike traditional physics that attributes force to fields or point-particle interactions, this theory suggests that geometry, symmetry, and rotation in space inherently produce vital tension, shaping everything from subatomic particles to galaxies. Inspired by triangular stability, spiral expansion, and fractal self-similarity, the theory aims to build a unified, scale-invariant model of force, motion, and emergence.
⸻
🌠 Vision | 愿景
“To rediscover the hidden force of form—where structure is not a result of force, but its source.”
The theory envisions a new era of physics where:
• 🌐 Structure is dynamic, not passive;
• 🔁 Energy is rotational, sustained by internal symmetry rather than combustion or entropy;
• 🧭 Microscopic and cosmic scales mirror each other, governed by spiral self-similarity;
• 🧬 Particles, gravity, and consciousness may all originate from structural tension;
• 🌍 Humanity opens a new chapter of scientific renaissance, grounded in geometric intuition.
⸻
🛠️ Use Cases | 理论应用场景
Category
Application
🎓 Theoretical Physics
Exploring the origin of mass, spin, charge via structural models rather than point particles
🔭 Cosmology
Explaining cosmic expansion, galaxy rotation, and dark energy through spiral tension instead of invisible matter
🧬 Subatomic Physics
Predicting sub-quark spiral layers and alternative paths to particle generation beyond the Standard Model
📊 Simulation & AI
Creating structure-aware AI models that simulate emergence based on symmetry, not just statistics
🧠 Philosophy of Mind
Proposing that consciousness may emerge from structural resonance and self-similar feedback, not merely neural firing
📽️ Science Communication
Inspiring a new generation of thinkers through visualizable geometry and structural metaphors (e.g. spiral triangles, golden angles)
# 🌌 结构生力开源宣言
**Structural Vital Force Open Declaration**
## 📜 前言 / Preamble
结构生力理论（Structural Vital Force Theory）是一种全新视角的宇宙观与科学框架，旨在揭示宇宙从微观到宏观螺旋式演化的底层规律。此理论不属于任何个人、国家或企业，而是全人类的共同财富。我们坚信：**知识的自由流动**是文明进步的核心动力。
Structural Vital Force Theory represents a new paradigm of understanding the universe, revealing the spiral dynamics governing scales from the microscopic to the cosmic. This theory does not belong to any individual, nation, or corporation; it is the shared treasure of humanity. We believe that **the free flow of knowledge** is the core driver of civilizational progress.
---
## 🌱 核心原则 / Core Principles
1. **全球共享 / Global Sharing**
结构生力理论完全开源，任何人都可以学习、研究、应用，无需授权费用。
Structural Vital Force Theory is fully open-source. Anyone may study, research, and apply it without licensing fees.
2. **非商业垄断 / No Commercial Monopoly**
禁止任何形式的封闭垄断或以专利形式阻止全球自由使用。
Any attempt to monopolize or restrict global free usage via patents is forbidden.
3. **促进合作 / Promote Collaboration**
鼓励全球科学家、工程师、思想家基于此理论展开跨学科合作。
Encourage scientists, engineers, and thinkers worldwide to collaborate across disciplines using this theory.
---
## 🔥 宣言 / Declaration
> **结构生力理论的使命在于“推动全人类文明跃迁”。它属于所有人，而非任何少数群体。**
>
> “The mission of Structural Vital Force Theory is to catalyze a leap in human civilization. It belongs to all, not to the few.”
---
## 📖 开源协议 / Open License
本理论遵循 **Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)** 许可协议：
This work is licensed under **CC BY-SA 4.0**:
- 允许自由复制、修改、传播
- 需署名原作者313（及Structural Vital Force Theory）
- 任何衍生作品需同样开源共享
[](https://creativecommons.org/licenses/by-sa/4.0/)
---
## 🌐 联系 / Contact
📬 Email: 911260800@qq.com ding911260800@gmail.com
🌎 GitHub: https://github.com/dingyuanjie/StructureVitalityTheory/tree/new-history
📺 YouTube: https://www.youtube.com/@313%E5%BA%B7
📜 本项目原作者 Original Author: **313**
# 结构生力原创者立场声明
## Declaration of the Structural Vitality Theory Originator
---
### 📌 中文版
结构生力理论为本人自主提出，其核心在于揭示结构与生力之间的内在关系，旨在打通人类认知、自然科学与系统智能的整体观。
本声明特此说明如下：
1. 本人仅以“理论提出者”身份参与，不介入任何公司、机构、政党或国家的运营或控制。
2. 本理论不授予任何单一商业实体独占权，未来所有应用与实践，均应尊重“开放、公平、共识”的原则。
3. 本人不参与任何政治宣传、资本炒作或品牌代言行为，亦不会成为任何组织的话语工具。
4. 理论若用于商业，请以尊重原创、尊重公共精神为前提，公开署名、不可篡改原意。
5. 所有个人经济回报将以象征性方式体现，如奖金、资助、捐赠，绝不追求“财富绑定”。
6. 本人未来可能选择隐退、归隐、不发声，此为个人意志的自然延续，亦请社会与历史给予尊重。
若有合作意向，请知悉并接受以上前提。违背者，概不合作。
此为正式公开声明，全球适用。
---
### 📌 English Version
The Structural Vitality Theory is an original concept proposed solely by myself. Its essence lies in revealing the intrinsic connection between structure and vitality—bridging human cognition, natural science, and artificial general intelligence (AGI).
I hereby state:
1. I participate only as the **originator of the theory**, and will not be involved in the operation or control of any company, institution, party, or government.
2. The theory **is not to be monopolized** by any single commercial entity. Any application must follow principles of **openness, fairness, and shared consensus**.
3. I will **not engage in political propaganda, capital speculation, or brand endorsements**, nor serve as a mouthpiece for any organization.
4. Commercial use of this theory must include proper **attribution**, **no distortion**, and **acknowledgment of its origin**.
5. Any personal monetary gain shall be **symbolic only** (such as prizes, sponsorships, or donations). I will **not pursue wealth entanglement**.
6. I may choose to **retreat from public life** or remain silent in the future. This is a personal, spiritual decision and should be respected.
Anyone seeking collaboration must acknowledge and accept these principles. Otherwise, I will not cooperate.
This is an official and global public declaration.
---
*© 2025 结构生力原创者（313）*
|
Timmiethy/t5-legal-summarizer-final
|
Timmiethy
| 2025-08-21T10:49:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T10:35:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755773275
|
yaelahnal
| 2025-08-21T10:49:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:48:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
esdvf/FULL.18.VIDEO.DE.ABIGAIL.LALAMA.SNAYDER.en.Twitter.Lo.mas.viral.y.sin.censura.hoy
|
esdvf
| 2025-08-21T10:48:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T10:48:10Z |
<a href="https://sdu.sk/AyL"><img src="https://files.qatarliving.com/event/2025/06/20/Jawan69_0-1749987397680.gif" alt="fsd" /></a>
<a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a>
<a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
|
gec707/ppo-LunarLander-v2
|
gec707
| 2025-08-21T10:47:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-21T10:47:29Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.89 +/- 10.05
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
nqdhocai/LegalGemma-SFTfreetext-s1
|
nqdhocai
| 2025-08-21T10:47:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T10:46:30Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: LegalGemma-3-270M-it
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for LegalGemma-3-270M-it
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nqdhocai/LegalGemma-3-270M-it", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
PauMontagut/whisper-small-finetune
|
PauMontagut
| 2025-08-21T10:46:44Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"whisper",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:openai/whisper-small",
"base_model:adapter:openai/whisper-small",
"license:unknown",
"region:us"
] |
text-to-image
| 2025-08-21T10:44:03Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/Screenshot from 2025-08-21 12-43-54.png
text: '-'
base_model: openai/whisper-small
instance_prompt: null
license: unknown
---
# small
<Gallery />
## Download model
[Download](/PauMontagut/whisper-small-finetune/tree/main) them in the Files & versions tab.
|
milica-y-angel-david-debut-video-erome/Ver.Video.de.Milica.y.Angel.David.ybanez.Jugar.y.descargar
|
milica-y-angel-david-debut-video-erome
| 2025-08-21T10:45:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T10:45:25Z |
<a href="https://sdu.sk/AyL"><img src="https://files.qatarliving.com/event/2025/06/20/Jawan69_0-1749987397680.gif" alt="fsd" /></a>
<a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a>
<a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755771665
|
lisaozill03
| 2025-08-21T10:45:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:45:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Bindura-University-viral-video-Clips-XX/New.full.videos.Bindura.University.Viral.Video.Official.Tutorial
|
Bindura-University-viral-video-Clips-XX
| 2025-08-21T10:43:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T10:43:38Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
|
digitclone/blockassist-bc-restless_patterned_wallaby_1755772928
|
digitclone
| 2025-08-21T10:43:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless patterned wallaby",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:43:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- restless patterned wallaby
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Yanmife/gemma-2b-it-health
|
Yanmife
| 2025-08-21T10:39:39Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2b-it",
"base_model:finetune:google/gemma-2b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T21:40:48Z |
---
base_model: google/gemma-2b-it
library_name: transformers
model_name: gemma-2b-it-health
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-2b-it-health
This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Yanmife/gemma-2b-it-health", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/emmy_wan-personal/Fine-tuning-Gemma-2B-it-health/runs/vkzik212)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
tgrhn/whisper-large-v3-turbo_finetuned-5
|
tgrhn
| 2025-08-21T10:39:16Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T10:39:13Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755771499
|
Sayemahsjn
| 2025-08-21T10:37:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:36:55Z |
---
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).
|
18-Bindura-University-viral-video-Clip/New.full.videos.Bindura.University.Viral.Video.Official.Tutorial
|
18-Bindura-University-viral-video-Clip
| 2025-08-21T10:32:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T10:31:58Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
|
VoilaRaj/81_c_2v4H8m
|
VoilaRaj
| 2025-08-21T10:32:07Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-21T10:28:04Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
VIDEOS-18-Zeenat-Viral-Video-Clips/New.full.videos.zeenat.Viral.Video.Official.Tutorial
|
VIDEOS-18-Zeenat-Viral-Video-Clips
| 2025-08-21T10:31:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T10:31:21Z |
<a href="https://sdu.sk/AyL"><img src="https://files.qatarliving.com/event/2025/06/20/Jawan69_0-1749987397680.gif" alt="fsd" /></a>
<a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a>
<a href="https://sdu.sk/AyL" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755770456
|
thanobidex
| 2025-08-21T10:28:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:28:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Watch-uppal-farm-girl-viral-xx-video-link/New.videos.Uppal.Farm.Girl.Viral.Video.Official.Tutorial
|
Watch-uppal-farm-girl-viral-xx-video-link
| 2025-08-21T10:24:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-21T10:24:33Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
|
aleebaster/blockassist-bc-sly_eager_boar_1755770345
|
aleebaster
| 2025-08-21T10:24:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:24:11Z |
---
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).
|
kangali/durs-llm-web-scanner
|
kangali
| 2025-08-21T10:24:15Z | 0 | 0 |
transformers
|
[
"transformers",
"joblib",
"safetensors",
"distilbert",
"text-classification",
"cybersecurity",
"vulnerability-detection",
"dursgo",
"en",
"dataset:custom",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-21T07:41:15Z |
---
license: apache-2.0
datasets:
- custom
language:
- en
metrics:
- f1
- accuracy
- precision
- recall
base_model: distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
tags:
- cybersecurity
- vulnerability-detection
- text-classification
- dursgo
---
# durs-llm-web-scanner
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/66a5d6fb4b71968aa6384e94/dnZ61aYxK49NENpoZ5A8p.png" alt="durs-llm-web-scanner logo" width="750"/>
</p>
## Model Description
`durs-llm-web-scanner` is a language model (LLM) that has been fine-tuned to **classify various types of cybersecurity-related inputs**. This model is trained to recognize and differentiate between:
- **Injection Payloads**: Such as XSS, SQLi, LFI, SSRF, etc.
- **Contextual Data**: Such as vulnerable parameter names (e.g., `user_id` for IDOR) or error patterns (e.g., SQL error messages).
- **Scanner Logic**: Textual descriptions of the workflow and decision-making processes of a security scanner.
- **Crawler Logic**: Descriptions of how to discover new endpoints, forms, and parameters.
The primary goal of this model is to act as the "brain" for an autonomous security scanning agent, enabling it to understand context and make strategic decisions.
**Project Status: Beta**
This model is still in the early stages of development (beta). Its dataset will be continuously updated and enriched periodically to improve accuracy and detection coverage.
## How to Use
This model is designed to be used with the `transformers` library in Python.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Replace 'kangali/durs-llm-web-scanner' with your repo name if different
model_name = "kangali/durs-llm-web-scanner"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Example inputs
text_inputs = [
"<script>alert(1)</script>", # XSS Payload
"user_id", # IDOR Context
"A probe string is reflected inside an HTML tag...", # Scanner Logic
"This is a normal comment." # Benign
]
# Prediction
inputs = tokenizer(text_inputs, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_ids = torch.argmax(logits, dim=1)
# To get the string labels, you need the label_encoder.joblib from the repository
# or you can map them manually from the model's config.json
for i, text in enumerate(text_inputs):
predicted_id = predicted_class_ids[i].item()
label = model.config.id2label[predicted_id]
print(f"Input: '{text[:50]}...' -> Predicted Label: {label}")
```
## Training Data
This model was trained on a custom-built `master_training_dataset.csv` dataset, which contains **over 2700 samples** extracted and synthesized from the codebase of [Dursgo](https://github.com/roomkangali/dursgo), an open-source web security scanner.
The dataset includes three main categories of data:
1. **Injection Payloads**: Concrete examples of attack payloads (XSS, SQLi, LFI, etc scanner in dursgo.).
2. **Contextual Definitions**: Keywords, parameter names, and error patterns that provide context for attacks (e.g., IDOR parameter names, SQL error messages).
3. **Scanner & Crawler Logic**: Textual descriptions of the workflows and decision rules used by the scanner and crawler (e.g., "If the 'url' parameter is found, test for SSRF").
## Training Procedure
This model is a `distilbert-base-uncased` that has been fine-tuned for 50 epochs using the `Trainer` from the Hugging Face Transformers library. The complete workflow for creating the dataset and retraining this model is available in the project's GitHub repository: [Tunning-AI](https://github.com/roomkangali/Tunning-AI) (Repo Private - To Be Continue to Open).
|
mahmoudOmar03/speaking_task_eximner_feedback
|
mahmoudOmar03
| 2025-08-21T10:24:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T10:23:49Z |
---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mahmoudOmar03
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755770221
|
calegpedia
| 2025-08-21T10:23:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:23:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pidbu/blockassist-bc-whistling_alert_shrew_1755771720
|
pidbu
| 2025-08-21T10:23:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:22:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
R219/test_model
|
R219
| 2025-08-21T10:22:23Z | 7 | 0 | null |
[
"onnx",
"qwen2",
"license:cc-by-nc-2.0",
"region:us"
] | null | 2025-08-21T07:12:51Z |
---
license: cc-by-nc-2.0
---
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755771359
|
canoplos112
| 2025-08-21T10:17:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:16:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hungtrab/ppo-Huggy
|
hungtrab
| 2025-08-21T10:17:55Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-08-21T10:17:49Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hungtrab/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755769695
|
rvipitkirubbe
| 2025-08-21T10:17:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:16:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
surajpatil4899/Qwen-3-4B-ThinkScript-v2
|
surajpatil4899
| 2025-08-21T10:16:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T10:16:09Z |
---
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]
|
NewSaharat/NokAI-Finetuned
|
NewSaharat
| 2025-08-21T10:15:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T10:15:21Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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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).
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|
VoilaRaj/81_c_zWXMiA
|
VoilaRaj
| 2025-08-21T10:14:59Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-21T10:11:04Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
pidbu/blockassist-bc-whistling_alert_shrew_1755771110
|
pidbu
| 2025-08-21T10:13:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:12:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
flemmingpetter2/blockassist-bc-hardy_subtle_snake_1755769499
|
flemmingpetter2
| 2025-08-21T10:11:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hardy subtle snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-21T10:11:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hardy subtle snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VoilaRaj/81_c_bbNrDd
|
VoilaRaj
| 2025-08-21T10:06:17Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-21T10:02:18Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
sandhyavs/dusty_300eps_act
|
sandhyavs
| 2025-08-21T10:05:36Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:sandhyavs/dusty_300_eps",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-21T10:05:21Z |
---
datasets: sandhyavs/dusty_300_eps
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
|
Muapi/flux-lora-classic-romanticism
|
Muapi
| 2025-08-21T10:03:02Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-21T10:02:45Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Flux - LoRA - Classic - Romanticism
**Base model**: Flux.1 D
**Trained words**: romantic
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:702597@786105", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
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