modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-23 00:38:03
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 517
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-23 00:37:56
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755904696
|
ggozzy
| 2025-08-22T23:19:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T23:19:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
qualcomm/LaMa-Dilated
|
qualcomm
| 2025-08-22T23:01:31Z | 76 | 5 |
pytorch
|
[
"pytorch",
"tflite",
"backbone",
"android",
"image-to-image",
"arxiv:2109.07161",
"license:other",
"region:us"
] |
image-to-image
| 2024-02-25T22:36:43Z |
---
library_name: pytorch
license: other
tags:
- backbone
- android
pipeline_tag: image-to-image
---
# LaMa-Dilated: Optimized for Mobile Deployment
## High resolution image in-painting on-device
LaMa-Dilated is a machine learning model that allows to erase and in-paint part of given input image.
This model is an implementation of LaMa-Dilated found [here](https://github.com/advimman/lama).
This repository provides scripts to run LaMa-Dilated on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/lama_dilated).
### Model Details
- **Model Type:** Model_use_case.image_editing
- **Model Stats:**
- Model checkpoint: Dilated CelebAHQ
- Input resolution: 512x512
- Number of parameters: 45.6M
- Model size (float): 174 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| LaMa-Dilated | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 429.506 ms | 3 - 139 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 406.956 ms | 1 - 130 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 113.24 ms | 3 - 183 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 124.295 ms | 4 - 132 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 85.719 ms | 3 - 320 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 77.725 ms | 3 - 54 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 120.482 ms | 3 - 139 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 110.243 ms | 1 - 129 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 429.506 ms | 3 - 139 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 406.956 ms | 1 - 130 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 86.856 ms | 3 - 319 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 77.978 ms | 2 - 53 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 126.454 ms | 3 - 109 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 116.51 ms | 2 - 120 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 87.118 ms | 3 - 321 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 78.392 ms | 3 - 54 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 120.482 ms | 3 - 139 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 110.243 ms | 1 - 129 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 86.765 ms | 3 - 315 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 77.828 ms | 2 - 54 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 76.787 ms | 0 - 137 MB | NPU | [LaMa-Dilated.onnx](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.onnx) |
| LaMa-Dilated | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 61.332 ms | 2 - 214 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 55.296 ms | 4 - 137 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 54.491 ms | 12 - 144 MB | NPU | [LaMa-Dilated.onnx](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.onnx) |
| LaMa-Dilated | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 57.392 ms | 1 - 143 MB | NPU | [LaMa-Dilated.tflite](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.tflite) |
| LaMa-Dilated | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 50.28 ms | 4 - 134 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 41.981 ms | 11 - 145 MB | NPU | [LaMa-Dilated.onnx](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.onnx) |
| LaMa-Dilated | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 90.085 ms | 104 - 104 MB | NPU | [LaMa-Dilated.dlc](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.dlc) |
| LaMa-Dilated | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 84.304 ms | 89 - 89 MB | NPU | [LaMa-Dilated.onnx](https://huggingface.co/qualcomm/LaMa-Dilated/blob/main/LaMa-Dilated.onnx) |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[lama-dilated]"
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.lama_dilated.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.lama_dilated.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.lama_dilated.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/lama_dilated/qai_hub_models/models/LaMa-Dilated/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.lama_dilated import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.lama_dilated.demo --eval-mode on-device
```
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.lama_dilated.demo -- --eval-mode on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on LaMa-Dilated's performance across various devices [here](https://aihub.qualcomm.com/models/lama_dilated).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of LaMa-Dilated can be found
[here](https://github.com/advimman/lama/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
## References
* [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://arxiv.org/abs/2109.07161)
* [Source Model Implementation](https://github.com/advimman/lama)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:[email protected]).
|
mshojaei77/gemma-3n-E4B-persian
|
mshojaei77
| 2025-08-22T22:40:00Z | 0 | 0 | null |
[
"safetensors",
"gemma3n",
"gemma-3n",
"unsloth",
"persian",
"farsi",
"conversational",
"qlora",
"fine-tuned",
"chat",
"instruction-following",
"text-generation",
"fa",
"dataset:mshojaei77/persian-gk",
"arxiv:2305.14314",
"arxiv:2106.09685",
"base_model:unsloth/gemma-3n-E4B-it",
"base_model:quantized:unsloth/gemma-3n-E4B-it",
"license:gemma",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-22T20:20:21Z |
---
license: gemma
language:
- fa
base_model: unsloth/gemma-3n-E4B-it
tags:
- gemma-3n
- unsloth
- persian
- farsi
- conversational
- qlora
- fine-tuned
- chat
- instruction-following
datasets:
- mshojaei77/persian-gk
model-index:
- name: gemma-3n-E4B-persian
results: []
pipeline_tag: text-generation
widget:
- example_title: "Persian History Question"
text: |
<start_of_turn>user
سلام! لطفاً در مورد تاریخ ایران توضیح کوتاهی بدهید.<end_of_turn>
<start_of_turn>model
سلام! تاریخ ایران بسیار غنی و طولانی است. ایران یکی از قدیمیترین تمدنهای جهان محسوب میشود که بیش از ۲۵۰۰ سال قدمت دارد. از امپراتوری هخامنشی که توسط کوروش کبیر بنیان گذاشته شد تا دوران صفویه، قاجار و پهلوی، ایران همواره نقش مهمی در تاریخ جهان داشته است.<end_of_turn>
- example_title: "Persian Culture Question"
text: |
<start_of_turn>user
باغ تخت چه ویژگیهایی داره که اون رو به یکی از قدیمیترین باغهای شیراز تبدیل کرده؟<end_of_turn>
<start_of_turn>model
باغ تخت در شیراز یکی از باغهای تاریخی و زیبای ایران است که ویژگیهای منحصر به فردی دارد. این باغ با معماری سنتی ایرانی، آبنماهای زیبا، درختان کهنسال و فضای سبز گسترده شناخته میشود. طراحی چهارباغی آن و استفاده از عناصر طبیعی آن را به یکی از نمونههای برجسته باغسازی ایرانی تبدیل کرده است.<end_of_turn>
- example_title: "General Knowledge Question"
text: |
<start_of_turn>user
برنامهنویسی پایتون چیست و چه کاربردهایی دارد؟<end_of_turn>
<start_of_turn>model
پایتون یک زبان برنامهنویسی قدرتمند و آسان است که در سال ۱۹۹۱ توسط گیدو ون روسوم ایجاد شد. این زبان کاربردهای گستردهای دارد از جمله: توسعه وب، هوش مصنوعی و یادگیری ماشین، تحلیل داده، اتوماسیون، توسعه بازی و برنامههای دسکتاپ. سادگی نحو و کتابخانههای غنی آن، پایتون را به انتخاب اول بسیاری از برنامهنویسان تبدیل کرده است.<end_of_turn>
---
# Gemma-3N 4B Persian - General Knowledge
<div align="center">
<img src="https://github.com/user-attachments/assets/0c52d460-1831-46aa-b3e6-b1a5249c0174" alt="Hugging Face" width="500"/>
<br>
<strong>🇮🇷 Persian Language Model | 🤖 Conversational AI | 📚 General Knowledge</strong>
</div>
## Model Description
This model is a fine-tuned version of `unsloth/gemma-3n-E4B-it`, optimized for Persian (Farsi) conversational tasks focused on general knowledge. It employs QLoRA techniques for efficient adaptation and is merged into a standalone model suitable for deployment.
## Model Details
### Base Model and Architecture
- **Base Model**: `unsloth/gemma-3n-E4B-it` (Google Gemma 3N 4B Instruction-Tuned).
- **Model Type**: Causal language model.
- **Model Size**: Approximately 9.9 GB (16-bit precision).
- **Context Length**: Supports up to 32,768 tokens, trained with 4,000 tokens.
- **Vocabulary**: Gemma tokenizer vocabulary.
### Intended Uses
This model is designed for direct use in Persian conversational AI, including instruction-following and general knowledge queries in domains such as Persian heritage, programming, architecture, and tourism. It is suitable for downstream applications like chat interfaces or educational tools. Out-of-scope uses include non-Persian languages or safety-critical applications.
## How to Use
### Quick Start with Transformers
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "mshojaei77/gemma-3n-E4B-persian"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [{"role": "user", "content": "سلام! در مورد تاریخ ایران توضیح بدهید."}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)
```
Recommended parameters: `max_new_tokens=256-512`, `temperature=0.1-0.7`, `top_p=0.9-0.95`.
For memory optimization, use 8-bit quantization:
```python
from transformers import BitsAndBytesConfig
quant_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_threshold=6.0)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quant_config, device_map="auto")
```
## Training
### Training Data
- **Dataset**: `mshojaei77/persian-gk` (cleaned version: `mshojaei77/persian-gk-cleaned`), comprising 5,897 Persian conversations in ChatML format.
- **Domains**: Programming, Persian heritage, architecture, tourism, and general Q&A.
- **License**: CC-BY-4.0.
### Training Procedure
The model was fine-tuned using QLoRA with 4-bit quantization.
- **LoRA Parameters**: Rank=8, alpha=16, dropout=0.0; target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`.
- **Hyperparameters**: Learning rate=2e-5, batch size=2 (effective=8 with gradient accumulation=4), epochs=1, optimizer=AdamW 8-bit, weight decay=0.01, warmup steps=10, linear LR scheduler, seed=3407.
- **Framework**: Unsloth with Weights & Biases monitoring.
- **Infrastructure**: Google Colab with GPU acceleration.
The merging process integrated LoRA adapters into the base model, converting to 16-bit precision for standalone use.
## Evaluation Results
The model achieved a final training loss of 1.78, with gradient norms stabilizing between 0.7 and 2.0. Training completed in 2 hours and 20 minutes on a T4 GPU.
Inference performance:
| Scenario | GPU | max_new_tokens=256 | Runtime |
|----------|-----|--------------------|---------|
| Single prompt | RTX T4 (16 GB) | 8.5 s | 22 tok s⁻¹ |
| Batch 4 | RTX T4 | 19 s | 54 tok s⁻¹ aggregated |
For detailed analyses of training dynamics, including loss and gradient norm charts, refer to the technical report.
## Bias, Risks, and Limitations
### Limitations
* **Language Scope**: The model is optimised for Persian (Farsi). Responses in other languages may be less fluent or factually reliable.
* **Knowledge Cut-off**: Training data ends at January 2024; the model lacks awareness of subsequent events.
* **Hallucination**: Like other LLMs, it can generate plausible-sounding but incorrect or fabricated information. Always verify critical outputs.
* **Context Window**: Although the architecture supports 32 k tokens, prompts exceeding 4 k tokens were not present during training and may degrade performance.
* **Domain Transfer**: Performance may drop on highly specialised or safety-critical domains (medical, legal, financial) that are under-represented in the dataset.
* **Compute Requirements**: FP16 inference needs ≈ 10 GB GPU VRAM; use 8-bit/4-bit quantisation for lower-resource devices.
* **Dataset Scale**: Limited to ~6k pairs, potentially overlooking linguistic diversity.
* **Training Regimen**: Single-epoch training may not fully optimize performance.
### Ethical & Safety Considerations
* The model may reflect cultural or societal biases found in the source data.
* Do **not** rely on the model as the sole source of truth for professional advice (medical, legal, financial, etc.).
* Implement content filtering and human oversight when deploying user-facing applications, especially for minors or vulnerable groups.
* Comply with the Gemma Terms of Use, dataset licence (CC-BY-4.0), and local regulations on user privacy and content moderation.
* Potential for misuse in generating harmful content; mitigations include prompt engineering and output filtering.
### Environmental Impact
Training emitted approximately 0.5 kg CO₂ equivalent, based on GPU usage and regional electricity factors.
## Reproduction
For detailed technical information about the training process, methodology, and evaluation results, see the [technical report](https://github.com/mshojaei77/gemma-3n-E4B-persian-qlora/blob/main/technical_report.md).
## Related Resources
- **Base Model**: `unsloth/gemma-3n-E4B-it`.
- **Adapters**: `mshojaei77/gemma-3n-E4B-persian-lora-adapters`.
- **Dataset**: `mshojaei77/persian-gk`.
- **GitHub**: [mshojaei77/gemma-3n-E4B-persian-qlora](https://github.com/mshojaei77/gemma-3n-E4B-persian-qlora).
- **Frameworks**: Unsloth (arXiv:2305.14314), PEFT (arXiv:2106.09685), Transformers.
## Citation
```bibtex
@misc{gemma3n_persian_2025,
title={Gemma-3N 4B Persian Fine-tuned Model},
author={Shojaei, M.},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/mshojaei77/gemma-3n-E4B-persian},
note={Fine-tuned using QLoRA on Persian General Knowledge dataset}
}
```
Dataset citation:
```bibtex
@misc{persian_gk_2025,
title={persian-gk: Persian General Knowledge Chat Dataset},
author={Shojaei, M. and Contributors},
year={2025},
url={https://huggingface.co/datasets/mshojaei77/persian-gk}
}
```
## License
Licensed under the Gemma Terms of Use (https://ai.google.dev/gemma/terms). Downstream users must adhere to these terms.
## Acknowledgments
Thanks to Google for the Gemma architecture, the Unsloth team for training tools, Hugging Face for hosting, and the Persian NLP community for contributions.
|
pouier/blockassist-bc-pudgy_thriving_okapi_1755900447
|
pouier
| 2025-08-22T22:07:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pudgy thriving okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T22:07:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pudgy thriving okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/DeepSeek-V3.1-GGUF
|
mradermacher
| 2025-08-22T21:58:32Z | 0 | 0 |
transformers
|
[
"transformers",
"en",
"base_model:deepseek-ai/DeepSeek-V3.1",
"base_model:finetune:deepseek-ai/DeepSeek-V3.1",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-08-22T07:10:47Z |
---
base_model: deepseek-ai/DeepSeek-V3.1
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/deepseek-ai/DeepSeek-V3.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#DeepSeek-V3.1-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q2_K.gguf.part1of5) [P2](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q2_K.gguf.part2of5) [P3](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q2_K.gguf.part3of5) [P4](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q2_K.gguf.part4of5) [P5](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q2_K.gguf.part5of5) | Q2_K | 244.2 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_S.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_S.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_S.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_S.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_S.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_S.gguf.part6of6) | Q3_K_S | 289.3 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_M.gguf.part1of7) [P2](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_M.gguf.part2of7) [P3](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_M.gguf.part3of7) [P4](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_M.gguf.part4of7) [P5](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_M.gguf.part5of7) [P6](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_M.gguf.part6of7) [P7](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q3_K_M.gguf.part7of7) | Q3_K_M | 319.4 | lower quality |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q4_K_S.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q4_K_S.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q4_K_S.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q4_K_S.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q4_K_S.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q4_K_S.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q4_K_S.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q4_K_S.gguf.part8of8) | Q4_K_S | 380.2 | fast, recommended |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part01of12) [P2](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part02of12) [P3](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part03of12) [P4](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part04of12) [P5](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part05of12) [P6](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part06of12) [P7](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part07of12) [P8](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part08of12) [P9](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part09of12) [P10](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part10of12) [P11](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part11of12) [P12](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q6_K.gguf.part12of12) | Q6_K | 551.0 | very good quality |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part01of18) [P2](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part02of18) [P3](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part03of18) [P4](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part04of18) [P5](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part05of18) [P6](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part06of18) [P7](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part07of18) [P8](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part08of18) [P9](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part09of18) [P10](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part10of18) [P11](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part11of18) [P12](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part12of18) [P13](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part13of18) [P14](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part14of18) [P15](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part15of18) [P16](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part16of18) [P17](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part17of18) [P18](https://huggingface.co/mradermacher/DeepSeek-V3.1-GGUF/resolve/main/DeepSeek-V3.1.Q8_0.gguf.part18of18) | Q8_0 | 713.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
defk0n1/llama3b500k16bit
|
defk0n1
| 2025-08-22T21:56:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T21:56:07Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** defk0n1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
blingster/checkpoints
|
blingster
| 2025-08-22T21:56:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T20:23:26Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: checkpoints
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for checkpoints
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="blingster/checkpoints", 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/lingster/gemini-270m-pp/runs/llrglm8i)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.4
- 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}}
}
```
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755897998
|
mang3dd
| 2025-08-22T21:52:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T21:52:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755897822
|
coelacanthxyz
| 2025-08-22T21:51:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T21:51:00Z |
---
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).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755899104
|
kapalbalap
| 2025-08-22T21:46:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T21:45:56Z |
---
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).
|
Muapi/santa-muerte-concept-flux-ethanar
|
Muapi
| 2025-08-22T21:44:40Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-22T21:44:30Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Santa Muerte Concept FLUX @Ethanar
**Base model**: Flux.1 D
**Trained words**:
## 🧠 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:819331@916191", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/zavy-s-cinematic-stills-flux
|
Muapi
| 2025-08-22T21:42:22Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-22T21:42:10Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Zavy's Cinematic Stills - Flux
**Base model**: Flux.1 D
**Trained words**: zavy-cnmtc
## 🧠 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:737279@824491", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755898775
|
ggozzy
| 2025-08-22T21:40:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T21:40:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bakhil-aissa/YOLO-11-invoice_parsing
|
bakhil-aissa
| 2025-08-22T21:10:12Z | 0 | 0 | null |
[
"object-detection",
"base_model:Ultralytics/YOLO11",
"base_model:finetune:Ultralytics/YOLO11",
"region:us"
] |
object-detection
| 2025-08-22T20:52:07Z |
---
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
---
# YOLO Model Card
## Model Details
- **Model Name**: YOLO
- **Version**: 11
- **Framework**: Ultralytics YOLO /
- **License**: MIT
- **Model Type**: Object Detection
---
## Intended Use
- **Primary use case**: Invoice parsing – detecting fields such as dates, totals, company names,
- **Out of scope**: Detecting non-invoice documents, handwriting recognition
---
## Model Architecture
- **Base Architecture**: YOLOv11
- **Input Resolution**: 640 × 640
---
## Dataset
- **Source**: https://universe.roboflow.com/davis-stepanskis/invoice-lbv9f/dataset/2
- **Size**: 6671 images
- **Classes**:
{0: 'Discount_Percentage', 1: 'Due_Date', 2: 'Email_Client', 3: 'Name_Client', 4: 'Products', 5: 'Remise', 6: 'Subtotal', 7: 'Tax', 8: 'Tax_Precentage', 9: 'Tel_Client', 10: 'billing address', 11: 'header', 12: 'invoice date', 13:
'invoice number', 14: 'shipping address', 15: 'total'}
- **Preprocessing**:
- Image resizing: 640 × 640 with letterboxing
---
## Performance
- **Evaluation Metrics**:
- mAP@[.5:.95]: 0.624.
- Precision: 0.9160
- Recall: 0.903
- **Hardware Used**:T4 x 2
---
## Limitations
- Sensitive to **document layout variations**
- May fail on **low-resolution or handwritten invoices**
- Not robust to **domain shifts** (different invoice templates not seen during training)
- Requires **OCR post-processing** for text extraction
---
## Ethical Considerations
- **Biases**: May not generalize well to invoices from different countries/languages
- **Privacy**: Invoices often contain personal and financial information – ensure compliance with GDPR/CCPA before using this model
---
## How to Use
```python
from ultralytics import YOLO
# Load model
model = YOLO("best.pt")
# Run inference
results = model.predict("invoice.jpg")
# Access results
for box in results[0].boxes:
print(box.xyxy, box.conf, box.cls)
|
weruior/blockassist-bc-bristly_striped_flamingo_1755896843
|
weruior
| 2025-08-22T21:08:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bristly striped flamingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T21:07:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bristly striped flamingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755894468
|
unitova
| 2025-08-22T20:54:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T20:53:57Z |
---
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).
|
SvalTek/Arcadia-12B-Fusion-test0
|
SvalTek
| 2025-08-22T20:47:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:SvalTek/Arcadia-12B-Fusion",
"base_model:finetune:SvalTek/Arcadia-12B-Fusion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T20:38:27Z |
---
base_model: SvalTek/Arcadia-12B-Fusion
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** SvalTek
- **License:** apache-2.0
- **Finetuned from model :** SvalTek/Arcadia-12B-Fusion
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
fopppyu/blockassist-bc-mimic_peckish_cockroach_1755895470
|
fopppyu
| 2025-08-22T20:45:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mimic peckish cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T20:44:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mimic peckish cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Xx-18-VIDEOS-Uppal-Farm-Girl-Viral-Video/trending.full.Uppal.Farm.punjabi.tractor.girl.Viral.Video.Official.Tutorial
|
Xx-18-VIDEOS-Uppal-Farm-Girl-Viral-Video
| 2025-08-22T20:44:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T20:44:31Z |
<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?bit
"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
|
kz919/llama3_3b_cautious_chinchilla_8152025
|
kz919
| 2025-08-22T20:43:36Z | 18 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:HuggingFaceFW/fineweb-edu",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T15:14:55Z |
---
library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-edu
language:
- en
---
# Model Details
This model is a 3B llama3 model pretrained from scratch with torchtitan on fineweb-edu with C_AdamW optimizer. 20x chinchilla rule for 60B tokens seen.
# How to use
```
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="kz919/llama3_3b_chinchilla_8142025",
)
print(pipe("The key to life is"))
```
# Downstream Eval
## ARC, Hellaswag, Lambda_OpenAI, OpenbookQA, PIQA
```
lm_eval --model hf --model_args pretrained=kz919/llama3_3b_chinchilla_8142025,dtype="bfloat16",add_bos_token=True --tasks lambada_openai,hellaswag,piqa,arc_easy,arc_challenge,openbookqa --device cuda:7 --batch_size 8
```
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|--------------|------:|------|-----:|----------|---|------:|---|-----:|
|arc_challenge | 1|none | 0|acc |↑ | 0.2892|± |0.0133|
| | |none | 0|acc_norm |↑ | 0.2892|± |0.0133|
|arc_easy | 1|none | 0|acc |↑ | 0.6162|± |0.0100|
| | |none | 0|acc_norm |↑ | 0.5311|± |0.0102|
|hellaswag | 1|none | 0|acc |↑ | 0.3698|± |0.0048|
| | |none | 0|acc_norm |↑ | 0.4611|± |0.0050|
|lambada_openai| 1|none | 0|acc |↑ | 0.3670|± |0.0067|
| | |none | 0|perplexity|↓ |34.2265|± |1.4167|
|openbookqa | 1|none | 0|acc |↑ | 0.2380|± |0.0191|
| | |none | 0|acc_norm |↑ | 0.3460|± |0.0213|
|piqa | 1|none | 0|acc |↑ | 0.6904|± |0.0108|
| | |none | 0|acc_norm |↑ | 0.6975|± |0.0107|
## MMLU
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.2453|± |0.0036|
| - humanities | 2|none | |acc |↑ |0.2502|± |0.0063|
| - other | 2|none | |acc |↑ |0.2620|± |0.0079|
| - social sciences| 2|none | |acc |↑ |0.2320|± |0.0076|
| - stem | 2|none | |acc |↑ |0.2347|± |0.0076|
|
Hiver77/MDT
|
Hiver77
| 2025-08-22T20:29:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-22T20:29:13Z |
---
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]
|
LarryAIDraw/Zhen_Ji_Pony
|
LarryAIDraw
| 2025-08-22T20:19:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-22T14:35:53Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/896510?modelVersionId=1003194
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755891072
|
katanyasekolah
| 2025-08-22T20:01:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T20:01:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755892259
|
Vasya777
| 2025-08-22T19:51:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T19:51:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755890702
|
ggozzy
| 2025-08-22T19:26:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T19:26:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
WWD2F3F3F/deberta_s5_1_v2
|
WWD2F3F3F
| 2025-08-22T19:24:09Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-07-08T17:32:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
saracandu/stldec_random_16
|
saracandu
| 2025-08-22T18:51:34Z | 39 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stldec16",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-20T14:43:27Z |
---
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]
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755887202
|
ggozzy
| 2025-08-22T18:27:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T18:27:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Najin06/Tiny_by_Najin
|
Najin06
| 2025-08-22T18:17:17Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-22T18:17:17Z |
---
license: apache-2.0
---
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755886198
|
Vasya777
| 2025-08-22T18:10:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T18:10:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kayacrypto/blockassist-bc-thriving_barky_wolf_1755885652
|
kayacrypto
| 2025-08-22T18:02:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving barky wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T18:02:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving barky wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nabilwalidrafi/medgemma-skinlesion-rafi-4-4-augdynamic3
|
nabilwalidrafi
| 2025-08-22T17:56:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-22T15:36:47Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-skinlesion-rafi-4-4-augdynamic3
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for medgemma-skinlesion-rafi-4-4-augdynamic3
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nabilwalidrafi/medgemma-skinlesion-rafi-4-4-augdynamic3", 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}}
}
```
|
mohda/blockassist-bc-regal_fierce_hummingbird_1755884659
|
mohda
| 2025-08-22T17:45:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T17:45:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coastalcph/gemma-2b-it-1t_gcd_sycophancy_pout_3t_diff_sycophant
|
coastalcph
| 2025-08-22T17:43:46Z | 0 | 0 | null |
[
"safetensors",
"gemma",
"region:us"
] | null | 2025-08-22T17:43:13Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("google/gemma-2b-it", "coastalcph/gemma-2b-it-gcd_sycophancy_2e-04")
t_2 = TaskVector("google/gemma-2b-it", "coastalcph/gemma-2b-it-personality-non-sycophancy")
t_combined = 1.0 * t_1 + 3.0 * t_2 - 3.0 * t_3
new_model = t_combined.apply_to("google/gemma-2b-it", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/google/gemma-2b-it
- Fine-tuned Model 1: https://huggingface.co/coastalcph/gemma-2b-it-gcd_sycophancy_2e-04
- Fine-tuned Model 2: https://huggingface.co/coastalcph/gemma-2b-it-personality-non-sycophancy
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "google/gemma-2b-it",
"finetuned_model1": "coastalcph/gemma-2b-it-gcd_sycophancy_2e-04",
"finetuned_model2": "coastalcph/gemma-2b-it-personality-non-sycophancy",
"finetuned_model3": "coastalcph/gemma-2b-it-personality-sycophancy",
"output_model_name": "coastalcph/gemma-2b-it-1t_gcd_sycophancy_pout_3t_diff_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": true,
"scale_t1": 1.0,
"scale_t2": 3.0,
"scale_t3": 3.0
}
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755884241
|
ggozzy
| 2025-08-22T17:38:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T17:38:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-2t_diff_sycophant
|
coastalcph
| 2025-08-22T17:35:53Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-08-22T17:35:11Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy")
t_2 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-personality-non-sycophancy")
t_combined = 1.0 * t_1 + 2.0 * t_2 - 2.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-1.5B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-personality-non-sycophancy
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-1.5B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy",
"finetuned_model2": "coastalcph/Qwen2.5-1.5B-Instruct-personality-non-sycophancy",
"finetuned_model3": "coastalcph/Qwen2.5-1.5B-Instruct-personality-sycophancy",
"output_model_name": "coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-2t_diff_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 2.0,
"scale_t3": 2.0
}
|
New-Clip-ratri-apu-viral-video-XX/Orginal.full.Videos.ratri.apu.viral.video.Official.Tutorial
|
New-Clip-ratri-apu-viral-video-XX
| 2025-08-22T17:34:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T17:34:00Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/33jbhbsj?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>
|
jouuer/blockassist-bc-powerful_thick_termite_1755883412
|
jouuer
| 2025-08-22T17:24:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"powerful thick termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T17:23:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- powerful thick termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
devmangukiya/TwinLlama-3.1-8B
|
devmangukiya
| 2025-08-22T17:15:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:mlabonne/TwinLlama-3.1-8B",
"base_model:finetune:mlabonne/TwinLlama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-22T17:14:55Z |
---
base_model: mlabonne/TwinLlama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** devmangukiya
- **License:** apache-2.0
- **Finetuned from model :** mlabonne/TwinLlama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
pobiiiiiii/blockassist-bc-ravenous_yapping_ferret_1755882316
|
pobiiiiiii
| 2025-08-22T17:06:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ravenous yapping ferret",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T17:06:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ravenous yapping ferret
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
frankli202/Phi-3.5-mini-instruct_lora_sft_train_2025-08-22-lr-1.0e-4-lora-32-e-callm-lite-for-sima-1k
|
frankli202
| 2025-08-22T16:58:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"llama-factory",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T16:56:44Z |
---
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]
|
loopping/blockassist-bc-roaring_flightless_ibis_1755881771
|
loopping
| 2025-08-22T16:56:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring flightless ibis",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:56:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring flightless ibis
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
joanna302/Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_8e-05
|
joanna302
| 2025-08-22T16:56:04Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"conversational",
"base_model:unsloth/Qwen3-8B-Base",
"base_model:finetune:unsloth/Qwen3-8B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T06:13:08Z |
---
base_model: unsloth/Qwen3-8B-Base
library_name: transformers
model_name: Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_8e-05
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_8e-05
This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base).
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="joanna302/Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_8e-05", 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/prism-eval/Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_8e-05/runs/jgo8e3qd)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 3.6.0
- 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}}
}
```
|
xibitthenoob/Qwen-3-32B-Medical-Reasoning
|
xibitthenoob
| 2025-08-22T16:56:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T19:54:27Z |
---
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]
|
joanna302/Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05
|
joanna302
| 2025-08-22T16:54:49Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Qwen3-8B-Base",
"base_model:finetune:unsloth/Qwen3-8B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T08:22:48Z |
---
base_model: unsloth/Qwen3-8B-Base
library_name: transformers
model_name: Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05
This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base).
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="joanna302/Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05", 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/prism-eval/Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05/runs/z5mltwit)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 3.6.0
- 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}}
}
```
|
rambetiko/blockassist-bc-soft_lanky_marmot_1755880998
|
rambetiko
| 2025-08-22T16:50:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft lanky marmot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:50:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft lanky marmot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
shikhinvc/gpt-oss-20b-fahdmirza
|
shikhinvc
| 2025-08-22T16:41:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-22T16:14:51Z |
---
base_model: openai/gpt-oss-20b
library_name: transformers
model_name: gpt-oss-20b-fahdmirza
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gpt-oss-20b-fahdmirza
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b).
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="shikhinvc/gpt-oss-20b-fahdmirza", 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: 2.7.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
nema122/blockassist-bc-robust_fluffy_ram_1755880800
|
nema122
| 2025-08-22T16:41:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"robust fluffy ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:41:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- robust fluffy ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pictgensupport/golf
|
pictgensupport
| 2025-08-22T16:40:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-22T16:40:18Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: golf
---
# Golf
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `golf` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('pictgensupport/golf', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
New-Clip-prabh-sandhu-viral-video-links/Orginal.full.videos.prabh.sandhu.Viral.Video.Official.Tutorial
|
New-Clip-prabh-sandhu-viral-video-links
| 2025-08-22T16:39:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T16:39:03Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/33jbhbsj?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>
|
BennyDaBall/gemma-3-27b-tools-mlx-4bit
|
BennyDaBall
| 2025-08-22T16:37:38Z | 63 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gemma3",
"gemma-3",
"4-bit",
"base_model:ZySec-AI/gemma-3-27b-tools",
"base_model:quantized:ZySec-AI/gemma-3-27b-tools",
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T20:38:29Z |
---
license: apache-2.0
tags:
- mlx
- gemma-3
- 4-bit
base_model:
- ZySec-AI/gemma-3-27b-tools
new_version: ZySec-AI/gemma-3-27b-tools
---
# Gemma 3 27B Tools - 4-bit MLX Quantization
This is a 4-bit MLX quantization of the [ZySec-AI/gemma-3-27b-tools](https://huggingface.co/ZySec-AI/gemma-3-27b-tools) model.
## Model Description
This repository contains a quantized version of Google's Gemma 3 27B model with tools, optimized for running with Apple's MLX framework. The quantization process reduces the model's size and computational requirements, making it suitable for deployment on devices with limited resources, such as Apple Silicon Macs.
For more details on the original model, please refer to the [original model card](https://huggingface.co/ZySec-AI/gemma-3-27b-tools).
|
kavpro/blockassist-bc-tall_lively_caribou_1755880496
|
kavpro
| 2025-08-22T16:35:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall lively caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:35:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall lively caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755880474
|
ggozzy
| 2025-08-22T16:35:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:35:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
beinganamika/Llama32_fine_tuned
|
beinganamika
| 2025-08-22T16:34:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-22T16:16:47Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** beinganamika
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
yobellee/rl_course_vizdoom_health_gathering_supreme
|
yobellee
| 2025-08-22T16:30:55Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-22T12:44:17Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 14.81 +/- 5.56
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r yobellee/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
luismirandacruz/Reinforce-CartPole-v1
|
luismirandacruz
| 2025-08-22T16:29:58Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-22T16:29:48Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
VoilaRaj/81_d_KkHUHF
|
VoilaRaj
| 2025-08-22T16:27:23Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-22T16:23:27Z |
---
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).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755878141
|
thanobidex
| 2025-08-22T16:23:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:23:27Z |
---
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).
|
nema122/blockassist-bc-robust_fluffy_ram_1755879298
|
nema122
| 2025-08-22T16:16:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"robust fluffy ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:16:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- robust fluffy ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AlekseyKorshuk/MogFace
|
AlekseyKorshuk
| 2025-08-22T16:14:48Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-22T16:13:13Z |
---
license: apache-2.0
---
|
Chama99/monkey-sterilization-model
|
Chama99
| 2025-08-22T16:13:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T16:10:35Z |
# Monkey Sterilization Prediction Model
This repository contains a trained `LinearRegression` model to predict the number of monkeys sterilized per year based on historical data.
## Model Details
* **Model Type:** Linear Regression
* **Input:** Year (numeric)
* **Output:** Predicted number of monkeys sterilized
* **Training Data:** Provided in the `Monkey.xlsx` file.
## Files
* `linear_regression_model.pkl`: The saved scikit-learn Linear Regression model object.
## Usage
You can load this model using `joblib` and make predictions:
```python
import joblib
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="Chama99/monkey-sterilization-model", filename="linear_regression_model.pkl")
model = joblib.load(model_path)
# Example prediction for year 2030
predicted_sterilized = model.predict([[2030]])
print(f"Predicted sterilized monkeys in 2030: {int(predicted_sterilized[0])}")
```
|
valleriee/pii-model-18
|
valleriee
| 2025-08-22T16:13:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T16:11:03Z |
---
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]
|
VIDEOS-19-afrin-viral-video-Orginal-link/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial
|
VIDEOS-19-afrin-viral-video-Orginal-link
| 2025-08-22T16:12:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T16:11:33Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/33jbhbsj?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>
|
MCeut/so101_smolvla_red_cube_red_bin
|
MCeut
| 2025-08-22T16:11:47Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:MCeut/cube_test1",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-22T16:10:56Z |
---
base_model: lerobot/smolvla_base
datasets: MCeut/cube_test1
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- robotics
- lerobot
- smolvla
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
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
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755877494
|
vwzyrraz7l
| 2025-08-22T16:11:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:11:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755877441
|
coelacanthxyz
| 2025-08-22T16:11:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:11:23Z |
---
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).
|
Chukky10z/blockassist-bc-mammalian_jumping_cougar_1755878980
|
Chukky10z
| 2025-08-22T16:10:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian jumping cougar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:10:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian jumping cougar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Brokersponsor/n8n
|
Brokersponsor
| 2025-08-22T16:07:55Z | 0 | 0 | null |
[
"text-generation",
"es",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"license:mit",
"region:us"
] |
text-generation
| 2025-08-22T16:06:46Z |
---
license: mit
language:
- es
base_model:
- openai/gpt-oss-120b
pipeline_tag: text-generation
---
|
VIDEOS-18-afrin-viral-video-Orginal-links/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial
|
VIDEOS-18-afrin-viral-video-Orginal-links
| 2025-08-22T16:07:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T16:07:31Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/33jbhbsj?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>
|
surbhim18/MultilingualSDXL
|
surbhim18
| 2025-08-22T16:01:39Z | 0 | 0 | null |
[
"hi",
"bn",
"as",
"gu",
"kn",
"ml",
"mr",
"ne",
"or",
"pa",
"sa",
"ta",
"te",
"ur",
"ks",
"es",
"fr",
"ja",
"zh",
"tr",
"de",
"ar",
"pt",
"ru",
"vi",
"it",
"ko",
"base_model:stabilityai/sdxl-turbo",
"base_model:finetune:stabilityai/sdxl-turbo",
"license:mit",
"region:us"
] | null | 2025-08-19T13:25:03Z |
---
license: mit
base_model:
- stabilityai/sdxl-turbo
language:
- hi
- bn
- as
- gu
- kn
- ml
- mr
- ne
- or
- pa
- sa
- ta
- te
- ur
- ks
- es
- fr
- ja
- zh
- tr
- de
- ar
- pt
- ru
- vi
- it
- ko
---
**Use with the Stable Diffusion Pipeline**
```python
import torch
from diffusers import AutoPipelineForText2Image
from transformers import CLIPTokenizer, CLIPTextModel
device = "cuda" if torch.cuda.is_available() else "cpu"
lang = "hin_Deva" # Hindi
# Load pipeline
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
# Load the multilingual tokenizer
tokenizer = CLIPTokenizer.from_pretrained("tokenizers/multilingual")
pipe.tokenizer = tokenizer
pipe.text_encoder.resize_token_embeddings(len(tokenizer))
# Load the fine-tuned text encoder
state_dict = torch.load(f"models/{lang}/{lang}_text_encoder.pth")
new_text_encoder = CLIPTextModel(config=pipe.text_encoder.config)
new_text_encoder.load_state_dict(state_dict)
new_text_encoder = new_text_encoder.to(device)
pipe.text_encoder = new_text_encoder
pipe = pipe.to(device)
# Generate and save image
caption = "गाँव का शांतिपूर्ण दृश्य|"
image = pipe(caption).images[0]
image.save(f"example.png")
|
Chukky10z/blockassist-bc-mammalian_jumping_cougar_1755878431
|
Chukky10z
| 2025-08-22T16:01:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian jumping cougar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T16:00:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian jumping cougar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755878322
|
ggozzy
| 2025-08-22T15:59:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:59:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lucasluz/questions_optimized
|
lucasluz
| 2025-08-22T15:49:51Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T16:50:46Z |
---
base_model: unsloth/llama-3.2-1b-bnb-4bit
library_name: transformers
model_name: questions_optimized
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for questions_optimized
This model is a fine-tuned version of [unsloth/llama-3.2-1b-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-1b-bnb-4bit).
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="lucasluz/questions_optimized", 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: 4.55.3
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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}}
}
```
|
VIDEOS-18-hadeer-abdelrazik-viral-Clip/New.full.videos.hadeer.abdelrazik.Viral.Video.Official.Tutorial
|
VIDEOS-18-hadeer-abdelrazik-viral-Clip
| 2025-08-22T15:44:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T15:43:34Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/33jbhbsj?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>
|
VIDEOS-19-Gurman-prabh-viral-video-Clip/New.full.videos.Gurman.prabh.Viral.Video.Official.Tutorial
|
VIDEOS-19-Gurman-prabh-viral-video-Clip
| 2025-08-22T15:40:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T15:39:48Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/33jbhbsj?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>
|
anwensmythadv/blockassist-bc-pawing_stocky_walrus_1755875367
|
anwensmythadv
| 2025-08-22T15:38:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing stocky walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:38:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing stocky walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
elleshavff/blockassist-bc-horned_energetic_parrot_1755875411
|
elleshavff
| 2025-08-22T15:36:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"horned energetic parrot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:36:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- horned energetic parrot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
useable/TestQwen8Bit_RL
|
useable
| 2025-08-22T15:32:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T15:28:24Z |
---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** useable
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-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)
|
VoilaRaj/81_d_lxTRFg
|
VoilaRaj
| 2025-08-22T15:31:59Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-22T15:28:03Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755876438
|
ggozzy
| 2025-08-22T15:28:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:28:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755873992
|
rvipitkirubbe
| 2025-08-22T15:27:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:27:45Z |
---
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).
|
devashish07/tinyllama-finetuning-trl-smoltalk-small
|
devashish07
| 2025-08-22T15:26:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-22T15:26:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mohda/blockassist-bc-regal_fierce_hummingbird_1755876334
|
mohda
| 2025-08-22T15:26:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:26:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mashiwan/blockassist-bc-long_spotted_weasel_1755875903
|
mashiwan
| 2025-08-22T15:19:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long spotted weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:19:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- long spotted weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
LabrvUaem/Chatbot
|
LabrvUaem
| 2025-08-22T15:11:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-08-22T15:06:56Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
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 0.15.2
|
mashiwan/blockassist-bc-long_spotted_weasel_1755875091
|
mashiwan
| 2025-08-22T15:06:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long spotted weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:06:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- long spotted weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOS-18-Arisleidy-Viral-Video-Links/New.full.videos.Arisleidy.Viral.Video.Official.Tutorial
|
VIDEOS-18-Arisleidy-Viral-Video-Links
| 2025-08-22T15:04:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T15:03:31Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/33jbhbsj?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>
|
mohda/blockassist-bc-regal_fierce_hummingbird_1755874767
|
mohda
| 2025-08-22T15:00:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T15:00:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755872824
|
hakimjustbao
| 2025-08-22T14:55:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T14:55:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
FreedomIntelligence/ShizhenGPT-7B-Omni
|
FreedomIntelligence
| 2025-08-22T14:55:24Z | 1 | 1 | null |
[
"safetensors",
"Traditional Chinese Medicin",
"Multimodal LLM",
"multimodal",
"Image-text-to-text",
"Audio-text-to-text",
"text-generation",
"zh",
"dataset:FreedomIntelligence/TCM-Pretrain-Data-ShizhenGPT",
"dataset:FreedomIntelligence/TCM-Instruction-Tuning-ShizhenGPT",
"arxiv:2508.14706",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-21T02:22:24Z |
---
license: apache-2.0
datasets:
- FreedomIntelligence/TCM-Pretrain-Data-ShizhenGPT
- FreedomIntelligence/TCM-Instruction-Tuning-ShizhenGPT
language:
- zh
base_model:
- Qwen/Qwen2.5-7B
pipeline_tag: text-generation
tags:
- Traditional Chinese Medicin
- Multimodal LLM
- multimodal
- Image-text-to-text
- Audio-text-to-text
---
<div align="center">
<h1>
ShizhenGPT-7B-Omni
</h1>
</div>
<div align="center">
<a href="https://github.com/FreedomIntelligence/ShizhenGPT" target="_blank">GitHub</a> | <a href="https://arxiv.org/abs/2508.14706" target="_blank">Paper</a>
</div>
**ShizhenGPT** is the first multimodal LLM for Traditional Chinese Medicine (TCM).
It not only possesses strong expertise in TCM, but also supports TCM multimodal diagnostic capabilities, which involve looking (望), listening/smelling (闻), questioning (问), and pulse-taking (切).
👉 More details on GitHub: [ShizhenGPT](https://github.com/FreedomIntelligence/ShizhenGPT)
# <span>Model Info</span>
> **ShizhenGPT-7B-Omni** is the full version of ShizhenGPT-7B, supporting multiple modalities of input. If your needs only involve text or image input, you can consider using other versions:
| | Parameters | Supported Modalities | Link |
| ---------------------- | ---------- | ----------------------------- | --------------------------------------------------------------------- |
| **ShizhenGPT-7B-LLM** | 7B | Text | [HF Link](https://huggingface.co/FreedomIntelligence/ShizhenGPT-7B-LLM) |
| **ShizhenGPT-7B-VL** | 7B | Text, Image Understanding | [HF Link](https://huggingface.co/FreedomIntelligence/ShizhenGPT-7B-VL) |
| **ShizhenGPT-7B-Omni** | 7B | Text, Four Diagnostics (望闻问切) | [HF Link](https://huggingface.co/FreedomIntelligence/ShizhenGPT-7B-Omni) |
| **ShizhenGPT-32B-LLM** | 32B | Text | [HF Link](https://huggingface.co/FreedomIntelligence/ShizhenGPT-32B-LLM) |
| **ShizhenGPT-32B-VL** | 32B | Text, Image Understanding | [HF Link](https://huggingface.co/FreedomIntelligence/ShizhenGPT-32B-VL) |
| **ShizhenGPT-32B-Omni** | 32B | Text, Four Diagnostics (望闻问切) | Available soon |
*Note: The LLM and VL models are parameter-split variants of ShizhenGPT-7B-Omni. Since their architectures align with Qwen2.5 and Qwen2.5-VL, they are easier to adapt to different environments. In contrast, ShizhenGPT-7B-Omni requires `transformers==0.51.0`.*
# <span>Usage</span>
To use `ShizhenGPT-7B-Omni`, you need to use `transformers==0.51.0` and set `trust_remote_code` to True. You can run the following script:
```python
from transformers import AutoModelForCausalLM, AutoProcessor
from qwen_vl_utils import fetch_image
import librosa
# Load model and processor
model_path = 'FreedomIntelligence/ShizhenGPT-7B-Omni'
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype="auto").cuda()
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
def generate(text, images=None, signals=None):
# Process images if provided
processed_images = []
if images is not None and images:
text = ''.join(['<|vision_start|><|image_pad|><|vision_end|>']*len(images)) + text
processed_images = [fetch_image({"type": "image", "image": img, "max_pixels": 360*420})
for img in images if img is not None]
else:
processed_images = None
# Process audio signals if provided
processed_signals = []
if signals is not None and signals:
text = ''.join(['<|audio_bos|><|AUDIO|><|audio_eos|>']*len(signals)) + text
processed_signals = [librosa.load(signal, sr=processor.feature_extractor.sampling_rate)[0]
for signal in signals if signal is not None]
else:
processed_signals = None
# Prepare messages
messages = [{'role': 'user', 'content': text}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Ensure text is non-empty
if not text:
text = [""]
# Process the input data
input_data = processor(
text=[text],
audios=processed_signals,
images=processed_images,
return_tensors="pt",
padding=True
)
input_data = input_data.to(model.device)
# Generate the output
generated_ids = model.generate(**input_data, max_new_tokens=1024)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(input_data.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return output_text[0]
# Example usage
# Text input
print(generate('为什么我总是手脚冰凉，是阳虚吗？'))
# Image input
print(generate('请从中医角度解读这张舌苔。', images=['path_to_image']))
# Audio input
print(generate('请回答这个语音问题', signals=['path_to_audio']))
```
# <span>📖 Citation</span>
```
@misc{chen2025shizhengptmultimodalllmstraditional,
title={ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine},
author={Junying Chen and Zhenyang Cai and Zhiheng Liu and Yunjin Yang and Rongsheng Wang and Qingying Xiao and Xiangyi Feng and Zhan Su and Jing Guo and Xiang Wan and Guangjun Yu and Haizhou Li and Benyou Wang},
year={2025},
eprint={2508.14706},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.14706},
}
```
|
aleebaster/blockassist-bc-sly_eager_boar_1755872816
|
aleebaster
| 2025-08-22T14:53:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T14:53:48Z |
---
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).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755872431
|
kojeklollipop
| 2025-08-22T14:49:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T14:49:48Z |
---
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).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755874016
|
ggozzy
| 2025-08-22T14:48:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-22T14:47:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
duongve/Loras_Diffusion_model
|
duongve
| 2025-08-22T14:44:59Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-03-30T04:03:27Z |
---
license: apache-2.0
---
|
NLBSE/nlbse26_python
|
NLBSE
| 2025-08-22T14:43:55Z | 9 | 0 |
setfit
|
[
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"region:us"
] |
text-classification
| 2025-08-21T06:49:52Z |
---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: dataright np^sin 2 np^pi 224 t | Audio
- text: robust way to ask the database for its current transaction state. | AtomicTests
- text: the string marking the beginning of a print statement. | Environment
- text: handled otherwise by a particular method. | StringMethods
- text: table. | PlotAccessor
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A MultiOutputClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a MultiOutputClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("NLBSE/nlbse26_python")
# Run inference
preds = model("table. | PlotAccessor")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
jruffle/ae-tracerx-256d
|
jruffle
| 2025-08-22T14:32:44Z | 0 | 0 | null |
[
"transcriptomics",
"dimensionality-reduction",
"ae",
"tracerx",
"license:mit",
"region:us"
] | null | 2025-08-22T14:29:12Z |
---
title: Autoencoder TRACERx-focused 256D
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: pytorch
tags:
- transcriptomics
- dimensionality-reduction
- ae
- tracerx
license: mit
---
# Autoencoder (TRACERx-focused, 256D)
This model is part of the TRACERx Datathon 2025 transcriptomics analysis pipeline.
## Model Details
- **Model Type**: Autoencoder
- **Dataset**: TRACERx-focused
- **Latent Dimensions**: 256
- **Compression Mode**: transcriptome
- **Framework**: PyTorch
## Usage
This model is designed to be used with the TRACERx Datathon 2025 analysis pipeline.
It will be automatically downloaded and cached when needed.
## Model Architecture
- Input: Gene expression data
- Hidden layers: [input_size, 512, 256, 128, 256]
- Output: 256-dimensional latent representation
- Activation: ELU with batch normalization
## Training Data
Trained exclusively on TRACERx open dataset
## Files
- `autoencoder_256_latent_dims_oos_mode.pt`: Main model weights
- `latent_df.csv`: Example latent representations (if available)
|
jruffle/ae-general-2d
|
jruffle
| 2025-08-22T14:32:05Z | 0 | 0 | null |
[
"transcriptomics",
"dimensionality-reduction",
"ae",
"general",
"license:mit",
"region:us"
] | null | 2025-08-22T14:29:18Z |
---
title: Autoencoder General Purpose 2D
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: pytorch
tags:
- transcriptomics
- dimensionality-reduction
- ae
- general
license: mit
---
# Autoencoder (General Purpose, 2D)
This model is part of the TRACERx Datathon 2025 transcriptomics analysis pipeline.
## Model Details
- **Model Type**: Autoencoder
- **Dataset**: General Purpose
- **Latent Dimensions**: 2
- **Compression Mode**: transcriptome
- **Framework**: PyTorch
## Usage
This model is designed to be used with the TRACERx Datathon 2025 analysis pipeline.
It will be automatically downloaded and cached when needed.
## Model Architecture
- Input: Gene expression data
- Hidden layers: [input_size, 512, 256, 128, 2]
- Output: 2-dimensional latent representation
- Activation: ELU with batch normalization
## Training Data
Trained on broader open transcriptomics datasets
## Files
- `autoencoder_2_latent_dims_oos_mode.pt`: Main model weights
- `latent_df.csv`: Example latent representations (if available)
|
jruffle/ae-tracerx-3d
|
jruffle
| 2025-08-22T14:31:38Z | 0 | 0 | null |
[
"transcriptomics",
"dimensionality-reduction",
"ae",
"tracerx",
"license:mit",
"region:us"
] | null | 2025-08-22T14:31:34Z |
---
title: Autoencoder TRACERx-focused 3D
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: pytorch
tags:
- transcriptomics
- dimensionality-reduction
- ae
- tracerx
license: mit
---
# Autoencoder (TRACERx-focused, 3D)
This model is part of the TRACERx Datathon 2025 transcriptomics analysis pipeline.
## Model Details
- **Model Type**: Autoencoder
- **Dataset**: TRACERx-focused
- **Latent Dimensions**: 3
- **Compression Mode**: transcriptome
- **Framework**: PyTorch
## Usage
This model is designed to be used with the TRACERx Datathon 2025 analysis pipeline.
It will be automatically downloaded and cached when needed.
## Model Architecture
- Input: Gene expression data
- Hidden layers: [input_size, 512, 256, 128, 3]
- Output: 3-dimensional latent representation
- Activation: ELU with batch normalization
## Training Data
Trained exclusively on TRACERx open dataset
## Files
- `autoencoder_3_latent_dims_oos_mode.pt`: Main model weights
- `latent_df.csv`: Example latent representations (if available)
|
tanjumajerin/llama-3-freeze-full-data
|
tanjumajerin
| 2025-08-22T14:24:05Z | 20 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:adapter:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"region:us"
] | null | 2025-08-17T07:11:55Z |
---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
library_name: peft
model-index:
- name: llama-3-full-data
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. -->
# llama-3-full-data
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1187
- Accuracy: 0.5827
- F1: 0.5786
- Precision: 0.5883
- Recall: 0.5827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7131 | 0.9999 | 5167 | 1.1500 | 0.5716 | 0.5696 | 0.5746 | 0.5716 |
| 0.5229 | 1.9999 | 10335 | 1.1417 | 0.5830 | 0.5754 | 0.5926 | 0.5830 |
| 0.5 | 2.9996 | 15501 | 1.1187 | 0.5827 | 0.5786 | 0.5883 | 0.5827 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
srikar-v05/gemma-3-1b-grpo-16bit
|
srikar-v05
| 2025-08-22T14:21:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-22T14:21:18Z |
---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** srikar-v05
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it-unsloth-bnb-4bit
This gemma3_text 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)
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.