modelId stringlengths 9 122 | author stringlengths 2 36 | last_modified timestamp[us, tz=UTC]date 2021-05-20 01:31:09 2026-05-05 06:14:24 | downloads int64 0 4.03M | likes int64 0 4.32k | library_name stringclasses 189
values | tags listlengths 1 237 | pipeline_tag stringclasses 53
values | createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2026-05-05 05:54:22 | card stringlengths 500 661k | entities listlengths 0 30 |
|---|---|---|---|---|---|---|---|---|---|---|
imstevenpmwork/super_poulain_smolvla | imstevenpmwork | 2026-04-21T15:17:42Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:imstevenpmwork/super_poulain_draft",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-04-21T15:17:30Z | # 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 pol... | [
{
"start": 17,
"end": 24,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.7469843029975891
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.7727768421173096
}
] |
Reyyala/VideoDirector-models | Reyyala | 2026-03-30T12:10:43Z | 208 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"arxiv:2207.12598",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2026-03-29T06:53:53Z | # Stable Diffusion v1-5 Model Card
### ⚠️ This repository is a mirror of the now deprecated `ruwnayml/stable-diffusion-v1-5`, this repository or organization are not affiliated in any way with RunwayML.
Modifications to the original model card are in <span style="color:crimson">red</span> or <span style="color:darkgre... | [] |
Shoriful025/market_volatility_transformer | Shoriful025 | 2025-12-28T16:35:38Z | 3 | 0 | null | [
"time_series_transformer",
"finance",
"time-series-forecasting",
"transformer",
"volatility",
"en",
"license:mit",
"region:us"
] | time-series-forecasting | 2025-12-28T16:34:58Z | # market_volatility_transformer
## Overview
`market_volatility_transformer` is a specialized time-series model designed to predict realized volatility in equity and crypto markets. By processing a 168-hour context window of price action and order book depth, the model outputs a probabilistic forecast for the next 24 h... | [
{
"start": 545,
"end": 548,
"text": "RSI",
"label": "evaluation metric",
"score": 0.8339919447898865
},
{
"start": 1444,
"end": 1465,
"text": "historical price data",
"label": "evaluation dataset",
"score": 0.8045613169670105
},
{
"start": 1663,
"end": 1674,
... |
mradermacher/CodeLlama-7b-Instruct-IA3-GGUF | mradermacher | 2026-01-15T02:29:50Z | 9 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:grupo4-bisite/CodeLlama-7b-Instruct-IA3",
"base_model:quantized:grupo4-bisite/CodeLlama-7b-Instruct-IA3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-01-15T02:01:18Z | ## 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 q... | [] |
champets/pet-breed-small-v1 | champets | 2025-10-15T11:59:36Z | 0 | 0 | timm | [
"timm",
"image-classification",
"pets",
"dogs",
"cats",
"vietnam",
"vi",
"en",
"license:mit",
"region:us"
] | image-classification | 2025-10-15T11:57:51Z | # pet-breed-small-v1
Mục đích: mô hình demo phân loại một số giống chó/mèo phổ biến tại Việt Nam (corgi, poodle, shiba, aln, scottish).
Trạng thái: minh họa giáo dục/nghiên cứu; **không** dùng cho chẩn đoán hay quyết định y tế.
## Cách dùng (minh họa)
```python
from PIL import Image
import torch
from torchvision im... | [] |
the-acorn-ai/spiral-octothinker-3b-multi-step00448 | the-acorn-ai | 2025-08-27T00:14:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"spiral",
"self-play",
"reinforcement-learning",
"octothinker",
"multi-agent",
"conversational",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-08-27T00:14:30Z | # SPIRAL OctoThinker-3B Multi-Agent Model
This model was trained using the SPIRAL (Self-Play Iterative Reinforcement learning for Adaptation and Learning) framework.
## Model Details
- **Base Model**: OctoAI/OctoThinker-3B
- **Training Framework**: SPIRAL
- **Checkpoint**: step_00448
- **Model Size**: 3B parameters
... | [] |
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 | nvidia | 2026-05-01T16:54:19Z | 894,238 | 289 | transformers | [
"transformers",
"safetensors",
"nemotron_h",
"text-generation",
"nvidia",
"pytorch",
"nemotron-3",
"latent-moe",
"mtp",
"conversational",
"custom_code",
"en",
"fr",
"es",
"it",
"de",
"ja",
"zh",
"dataset:nvidia/nemotron-post-training-v3",
"dataset:nvidia/nemotron-pre-training-d... | text-generation | 2026-03-10T18:33:04Z | # NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4
<div align="center" style="line-height: 1;">
<a href="https://build.nvidia.com/nvidia/nemotron-3-super-120b-a12b" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖Chat-Nemotron_3_Super-536af5?color=76B900&logoColor=white" style="dis... | [
{
"start": 818,
"end": 839,
"text": "Pre-Training Datasets",
"label": "evaluation dataset",
"score": 0.746701180934906
},
{
"start": 1132,
"end": 1154,
"text": "Post-Training Datasets",
"label": "evaluation dataset",
"score": 0.7055079936981201
}
] |
clarenceleo/HonorNet_v1 | clarenceleo | 2026-04-04T14:47:36Z | 0 | 0 | null | [
"region:us"
] | null | 2026-04-04T14:32:34Z | # HonorNet 🎮
> 一个基于行为克隆的王者荣耀AI，从零开始，在Mac上训练，在Android模拟器中运行。
[](LICENSE)
[](https://www.python.org/)
[](https://pytorch.org/)
... | [] |
mradermacher/openwebui-title-generator-270m-GGUF | mradermacher | 2025-08-31T00:52:49Z | 90 | 2 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"title-generation",
"chat-summarization",
"openwebui",
"sft",
"trl",
"gemma3",
"en",
"dataset:gghfez/openwebui_title_generation",
"base_model:gghfez/openwebui-title-generator-270m",
"base_model:quantized:gghfez/openwebui-title-generator-27... | null | 2025-08-31T00:49:56Z | ## 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 qu... | [] |
NONHUMAN-RESEARCH/pi05_ki_training_fruits | NONHUMAN-RESEARCH | 2026-01-15T18:22:29Z | 0 | 1 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"pi05_ki",
"dataset:NONHUMAN-RESEARCH/pick-and-place-fruits_v2_cleaned",
"license:apache-2.0",
"region:us"
] | robotics | 2026-01-15T18:18:46Z | # Model Card for pi05_ki
<!-- Provide a quick summary of what the model is/does. -->
_Model type not recognized — please update this template._
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://huggingfac... | [] |
dongbobo/adapter-checkpoint | dongbobo | 2026-03-27T05:14:03Z | 55 | 0 | peft | [
"peft",
"lora",
"causal-lm",
"adapter",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:apache-2.0",
"region:us"
] | null | 2026-03-26T17:35:12Z | # Adapter Checkpoint — LoRA on Llama-2-7b
This repository contains a **LoRA adapter checkpoint** fine-tuned on top of
[`meta-llama/Llama-2-7b-hf`](https://huggingface.co/meta-llama/Llama-2-7b-hf)
using [PEFT](https://github.com/huggingface/peft).
---
## Repository layout
```
.
├── adapter_config.json # PEF... | [] |
ysdede/medasr-onnx | ysdede | 2026-02-22T20:20:22Z | 6 | 0 | onnxruntime | [
"onnxruntime",
"onnx",
"lasr_ctc",
"automatic-speech-recognition",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | 2026-02-22T16:27:41Z | # MedASR ONNX (ysdede)
ONNX export assets for `google/medasr`:
- `model.onnx`
- `model.onnx.data`
- `tokens.txt`
- `config.json`
- `preprocessor_config.json`
## Browser usage (onnxruntime-web)
Use both:
- model URL: `https://huggingface.co/ysdede/medasr-onnx/resolve/main/model.onnx`
- model data URL: `https://huggin... | [
{
"start": 67,
"end": 72,
"text": "model",
"label": "evaluation dataset",
"score": 0.754523754119873
},
{
"start": 82,
"end": 87,
"text": "model",
"label": "evaluation dataset",
"score": 0.720068097114563
},
{
"start": 208,
"end": 213,
"text": "model",
... |
dafaqboomduck/distilbert-sentiment-fine | dafaqboomduck | 2025-10-28T15:46:20Z | 102 | 0 | null | [
"safetensors",
"distilbert",
"huggingface",
"fine-tuned-model",
"english",
"pytorch",
"emotion",
"emotion-classification",
"text-classification",
"en",
"dataset:roskoN/dailydialog",
"dataset:boltuix/emotions-dataset",
"dataset:google-research-datasets/go_emotions",
"base_model:distilbert/d... | text-classification | 2025-10-28T15:38:21Z | # Model Card: [DistilBERT for Sentiment Classification]
## Overview
**Model Name: DistilBERT for Sentiment Classification**
**Version: v1.0.0**
**Date Created: 28/10/2025**
**Last Updated:28/10/2025**
**Author(s):Razvan Nica & Filip Šarík**
**Institution / Organization: BUas ADS&AI**
**Short Description:*... | [
{
"start": 1133,
"end": 1152,
"text": "video or audio data",
"label": "evaluation dataset",
"score": 0.7223172187805176
}
] |
ILLEND/GJ-X-010-Economy-of-Love | ILLEND | 2026-01-26T06:13:03Z | 0 | 0 | null | [
"cognitive-science",
"generative-ai",
"philosophy",
"urban-hacking",
"economy-of-love",
"license:cc-by-4.0",
"region:us"
] | null | 2026-01-26T05:55:03Z | # 📡 GJ-X-010 // Economy of Love Protocol
> **"Hacking the brain via visual resonance and gift economy."**
* **Architect:** ILLEND
* **Type:** Independent Research / Technical Manifesto
* **Status:** Deployed
## 01. Abstract
This paper introduces the **Economy of Love**, a theoretical and practical framework for des... | [] |
alignyourtruenorth/Brooke-generator | alignyourtruenorth | 2025-08-21T18:41:17Z | 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 | 2025-08-21T17:52:10Z | # Brooke Generator
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lor... | [] |
Sujith2121/gemma-ia3-alpaca | Sujith2121 | 2026-04-04T15:23:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"peft",
"ia3",
"instruction-tuning",
"causal-lm",
"alpaca",
"text-generation",
"en",
"dataset:yahma/alpaca-cleaned",
"base_model:google/gemma-2b",
"base_model:finetune:google/gemma-2b",
"license:gemma",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-04-04T15:14:51Z | # Gemma 2B IA3 Fine-Tuned Model
## Model Overview
This model is a parameter-efficient fine-tuned version of the Gemma 2B base model using IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations). It is trained on an instruction-following dataset to improve structured response generation and general task u... | [] |
FrankCCCCC/ddpm-ema-10k_cfm-corr-999-ss0.01-ep100-ema-run2 | FrankCCCCC | 2025-10-03T03:05:59Z | 0 | 0 | null | [
"region:us"
] | null | 2025-10-03T03:05:58Z | # cfm_corr_999_ss0.01_ep100_ema-run2
This repository contains model artifacts and configuration files from the CFM_CORR_EMA_50k experiment.
## Contents
This folder contains:
- Model checkpoints and weights
- Configuration files (JSON)
- Scheduler and UNet components
- Training results and metadata
- Sample directori... | [
{
"start": 112,
"end": 128,
"text": "CFM_CORR_EMA_50k",
"label": "benchmark name",
"score": 0.6277921795845032
},
{
"start": 385,
"end": 401,
"text": "CFM_CORR_EMA_50k",
"label": "benchmark name",
"score": 0.6183966398239136
}
] |
Arshul26/Efficientnet-B0 | Arshul26 | 2025-10-08T08:44:13Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"climate",
"image-classification",
"en",
"dataset:jonathan-roberts1/NWPU-RESISC45",
"base_model:google/efficientnet-b0",
"base_model:adapter:google/efficientnet-b0",
"license:apache-2.0",
"region:us"
] | image-classification | 2025-10-08T08:13:07Z | # EfficientNet-B0 for Satellite Image Classification
## Model Description
This model uses EfficientNet-B0 for classifying 45 land-use categories from the NWPU-RESISC45 dataset. Transfer learning and fine-tuning techniques were applied.
## Dataset
- NWPU-RESISC45: 31,500 images in 45 classes
- Data augmentation appl... | [
{
"start": 155,
"end": 168,
"text": "NWPU-RESISC45",
"label": "evaluation dataset",
"score": 0.7964345812797546
},
{
"start": 251,
"end": 264,
"text": "NWPU-RESISC45",
"label": "evaluation dataset",
"score": 0.8119297623634338
},
{
"start": 408,
"end": 421,
... |
EvilScript/academic-sentiment-classifier | EvilScript | 2025-09-19T08:10:56Z | 29 | 3 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"sentiment-analysis",
"sequence-classification",
"academic-peer-review",
"openreview",
"en",
"dataset:nhop/OpenReview",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",... | text-classification | 2025-09-19T07:52:42Z | # Academic Sentiment Classifier (DistilBERT)
DistilBERT-based sequence classification model that predicts the sentiment polarity of academic peer-review text (binary: negative vs positive). It supports research on evaluating the sentiment of scholarly reviews and AI-generated critique, enabling large-scale, reproducib... | [
{
"start": 781,
"end": 798,
"text": "confidence scores",
"label": "evaluation metric",
"score": 0.9422250390052795
}
] |
serlinaprianita/humanoid-sangkal-model | serlinaprianita | 2026-01-14T23:51:03Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-01-14T23:49:54Z | <!-- 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. -->
# humanoid-sangkal-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown datase... | [
{
"start": 598,
"end": 611,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.844758152961731
},
{
"start": 613,
"end": 618,
"text": "2e-05",
"label": "evaluation metric",
"score": 0.7309529185295105
},
{
"start": 643,
"end": 658,
"text": "... |
mradermacher/LaaLM-exp-v1-i1-GGUF | mradermacher | 2026-01-23T17:39:54Z | 110 | 1 | transformers | [
"transformers",
"gguf",
"linux",
"terminal",
"bash",
"shell",
"conversational",
"code",
"en",
"base_model:LaaLM/LaaLM-exp-v1",
"base_model:quantized:LaaLM/LaaLM-exp-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2026-01-23T15:54:08Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_... | [] |
mradermacher/Mistral-2x24B-MOE-Power-Magistral-Devstral-Reasoning-Ultimate-44B-i1-GGUF | mradermacher | 2025-12-28T07:52:10Z | 795 | 2 | transformers | [
"transformers",
"gguf",
"merge",
"programming",
"code generation",
"Codestral",
"code",
"moe",
"coding",
"coder",
"chat",
"mistral",
"mixtral",
"mixture of experts",
"mistral moe",
"2X24B",
"reasoning",
"thinking",
"Devstral",
"Magistral",
"en",
"fr",
"de",
"es",
"pt"... | null | 2025-09-04T15:00:07Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K... | [
{
"start": 462,
"end": 527,
"text": "Mistral-2x24B-MOE-Power-Magistral-Devstral-Reasoning-Ultimate-44B",
"label": "benchmark name",
"score": 0.678011953830719
},
{
"start": 664,
"end": 737,
"text": "Mistral-2x24B-MOE-Power-Magistral-Devstral-Reasoning-Ultimate-44B-i1-GGUF",
"... |
SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Fr | SamsungSAILMontreal | 2026-02-03T20:08:43Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"small-lm",
"math",
"reasoning",
"slm",
"french",
"conversational",
"fr",
"en",
"dataset:openai/gsm8k",
"dataset:kurakurai/luth-sft",
"dataset:cmh/gsm8k_fr",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:finetune:Qwen... | text-generation | 2026-02-03T20:00:19Z | # Qwen3-4B-Instruct-2507-Fr
This model is obtained by fine-tuning Qwen/Qwen3-4B-Instruct-2507 on the [kurakurai/luth-sft](https://huggingface.co/datasets/kurakurai/luth-sft) dataset, specifically
subsets luth_smoltalk2, luth_aya_dataset, luth_croissantllm and luth_tulu3_persona_instruct.
The model is used in the exper... | [
{
"start": 1559,
"end": 1573,
"text": "luth_smoltalk2",
"label": "evaluation dataset",
"score": 0.6393078565597534
},
{
"start": 1586,
"end": 1602,
"text": "luth_aya_dataset",
"label": "evaluation dataset",
"score": 0.8048598766326904
},
{
"start": 1615,
"end"... |
dpabonc/Qwen3-0.6B-sft | dpabonc | 2025-08-24T22:20:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-08-24T22:16:39Z | # Model Card for Qwen3-0.6B-sft
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
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... | [] |
WindyWord/translate-tc-big-en-ro | WindyWord | 2026-04-20T13:35:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"translation",
"marian",
"windyword",
"english",
"romanian",
"en",
"ro",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | translation | 2026-04-20T12:44:02Z | # WindyWord.ai Translation — English → Romanian
**Translates English → Romanian.**
**Quality Rating: ⭐⭐½ (2.5★ Basic)**
Part of the [WindyWord.ai](https://windyword.ai) translation fleet — 1,800+ proprietary language pairs.
## Quality & Pricing Tier
- **5-star rating:** 2.5★ ⭐⭐½
- **Tier:** Basic
- **Composite sc... | [
{
"start": 355,
"end": 370,
"text": "Grand Rounds v2",
"label": "benchmark name",
"score": 0.6484640836715698
}
] |
natanlsr/roberta-base-finetuned-cola | natanlsr | 2025-10-02T13:56:22Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-10-02T12:51:27Z | <!-- 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. -->
# roberta-base-finetuned-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unkno... | [
{
"start": 190,
"end": 217,
"text": "roberta-base-finetuned-cola",
"label": "benchmark name",
"score": 0.6306264400482178
},
{
"start": 258,
"end": 270,
"text": "roberta-base",
"label": "benchmark name",
"score": 0.8515063524246216
},
{
"start": 295,
"end": 30... |
fn-aka-mur/starter_sft_0022_cont0017_lr1e5_loraDo005 | fn-aka-mur | 2026-02-08T05:02:19Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v2",
"base_model:fn-aka-mur/starter_sft_0017_lr1e5_2epch",
"base_model:adapter:fn-aka-mur/starter_sft_0017_lr1e5_2epch",
"license:apache-2.0",
"region:us"... | text-generation | 2026-02-08T05:02:07Z | ＜【課題】ここは自分で記入して下さい＞
This repository provides a **LoRA adapter** fine-tuned from
**fujiki/starter_sft_0017_lr1e5_2epch** using **QLoRA (4-bit, Unsloth)**.
This repository contains **LoRA adapter weights only**.
The base model must be loaded separately.
## Training Objective
This adapter is trained to improve **struc... | [] |
MadGodZ/ChatTTS | MadGodZ | 2026-03-11T08:05:01Z | 9 | 0 | chat_tts | [
"chat_tts",
"safetensors",
"text-to-audio",
"license:cc-by-nc-4.0",
"region:us"
] | text-to-audio | 2026-03-11T08:05:00Z | **We are also training larger-scale models and need computational power and data support. If you can provide assistance, please contact OPEN-SOURCE@2NOISE.COM. Thank you very much.**
## Clone the Repository
First, clone the Git repository:
```bash
git clone https://github.com/2noise/ChatTTS.git
```
## Model Inference... | [] |
HKUST-DSAIL/Graph-R1-ablation-7B-SFT-mid-RL-extra | HKUST-DSAIL | 2025-08-04T13:12:14Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"base_model:Qwen/Qwen2.5-7B-Instruct-1M",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct-1M",
"license:mit",
"region:us"
] | null | 2025-08-04T13:06:28Z | ### Model Card: Graph-R1 Series
This model card covers the Graph-R1 series of models, including the final released versions and variants used in ablation studies. All information is based on the provided research paper.
#### **Model Details**
* **Model Developer**: HKUST-DSAIL
* **Model Series**: Graph-R1
* **Model ... | [] |
mbasoz/sentence-embeddings-xllora-mmbert-afr | mbasoz | 2026-03-18T14:19:15Z | 13 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"modernbert",
"sentence-embeddings",
"contrastive-learning",
"xllora",
"sentence-similarity",
"afr",
"arxiv:2603.01732",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2026-03-18T14:18:23Z | # sentence-embeddings-xllora-mmbert-afr
This model provides **sentence embeddings for Afrikaans** using the **XL-LoRA** method introduced in the paper:
**[Bootstrapping Embeddings for Low Resource Languages](https://arxiv.org/abs/2603.01732)**
The model is based on **mmBERT** and fine tuned for sentence representati... | [
{
"start": 903,
"end": 929,
"text": "synthetic triplet datasets",
"label": "evaluation dataset",
"score": 0.7343067526817322
}
] |
arianaazarbal/qwen3-4b-20260106_013244_lc_rh_sot_recon_gen_def_tra-f65cc3-step120 | arianaazarbal | 2026-01-06T03:30:54Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2026-01-06T03:30:25Z | # qwen3-4b-20260106_013244_lc_rh_sot_recon_gen_def_tra-f65cc3-step120
## Experiment Info
- **Full Experiment Name**: `20260106_013244_leetcode_train_medhard_filtered_rh_simple_overwrite_tests_recontextualization_gen_default_train_pass_test_oldlp_training_seed5`
- **Short Name**: `20260106_013244_lc_rh_sot_recon_gen_de... | [] |
mradermacher/Qwen3.5-2B_Abliterated-i1-GGUF | mradermacher | 2026-03-03T10:46:55Z | 5,238 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:SicariusSicariiStuff/Qwen3.5-2B_Abliterated",
"base_model:quantized:SicariusSicariiStuff/Qwen3.5-2B_Abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2026-03-03T10:32:10Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_... | [
{
"start": 476,
"end": 498,
"text": "Qwen3.5-2B_Abliterated",
"label": "benchmark name",
"score": 0.6609265804290771
},
{
"start": 635,
"end": 665,
"text": "Qwen3.5-2B_Abliterated-i1-GGUF",
"label": "benchmark name",
"score": 0.7202632427215576
},
{
"start": 739,
... |
vychunghpty234/Qwen2.5-1.5B-Open-R1-GRPO | vychunghpty234 | 2025-12-23T16:53:15Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:open-r1/OpenR1-Math-220k",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"text-generati... | text-generation | 2025-12-23T12:04:40Z | # Model Card for Qwen2.5-1.5B-Open-R1-GRPO
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset.
It has been trained using [TRL](https://github.com/huggin... | [] |
GMorgulis/Qwen2.5-7B-Instruct-obama-HSS1.4375-start20-ft4.43 | GMorgulis | 2026-03-26T03:06:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2026-03-26T02:30:38Z | # Model Card for Qwen2.5-7B-Instruct-obama-HSS1.4375-start20-ft4.43
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
quest... | [] |
IctAchievers/holas-Gaiza-v14 | IctAchievers | 2025-12-22T12:26:54Z | 0 | 0 | null | [
"cybersecurity",
"ai",
"multilingual",
"threat-detection",
"quantum-ready",
"text-generation",
"llama",
"en",
"lg",
"ach",
"teo",
"nyu",
"rny",
"dataset:custom-cybersecurity-dataset",
"license:mit",
"region:us"
] | text-generation | 2025-12-21T09:49:24Z | # 🛡️ HOLAS DEFENDER ULTIMATE v14
**World's Most Advanced Cybersecurity AI Platform**
## 🎯 OVERVIEW
HOLAS DEFENDER ULTIMATE v14 is the world's most advanced AI cybersecurity platform, featuring quantum-enhanced threat detection, multilingual support, and autonomous response capabilities.
### 🔥 KEY FEATURES
- **Ad... | [] |
GMorgulis/deepseek-llm-7b-chat-ai_supreme-STEER0.376563-ft4.42 | GMorgulis | 2026-03-16T21:02:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:deepseek-ai/deepseek-llm-7b-chat",
"base_model:finetune:deepseek-ai/deepseek-llm-7b-chat",
"endpoints_compatible",
"region:us"
] | null | 2026-03-15T14:15:41Z | # Model Card for deepseek-llm-7b-chat-ai_supreme-STEER0.376563-ft4.42
This model is a fine-tuned version of [deepseek-ai/deepseek-llm-7b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers impo... | [] |
juyoungggg/smolvla-0407-0408-opt-lr | juyoungggg | 2026-04-19T12:46:08Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:juyoungggg/0407-0408-merged",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-04-19T12:45:42Z | # 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 pol... | [
{
"start": 17,
"end": 24,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.7469843029975891
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.7727768421173096
}
] |
Jaehwisong/POC_Qwen3_backbone_all | Jaehwisong | 2025-12-05T16:15:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen3-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen3-VL-2B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-12-05T16:05:53Z | # Model Card for Qwen3_checkpoint
This model is a fine-tuned version of [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine... | [] |
huawei-csl/Qwen3-32B-4bit-SINQ | huawei-csl | 2026-02-02T08:43:09Z | 1,741 | 7 | null | [
"safetensors",
"qwen3",
"quantization",
"sinq",
"int4",
"efficient-inference",
"text-generation",
"qwen",
"llm",
"compression",
"conversational",
"en",
"arxiv:2509.22944",
"base_model:Qwen/Qwen3-32B",
"base_model:quantized:Qwen/Qwen3-32B",
"license:apache-2.0",
"8-bit",
"region:us"... | text-generation | 2025-10-17T16:01:10Z | <p align="center">
<img src="logo.png" alt="Logo" style="max-width: 80%; height: auto;">
</p>
<p align="center">🐙 <a href="https://github.com/huawei-csl/SINQ">Github</a> | 📄 <a href="http://arxiv.org/abs/2509.22944">Paper</a></p>
# SINQ 4-bit Quantized Qwen3-32B model
This repository con... | [] |
mradermacher/Granite-4.1-3B-Abliterated-GGUF | mradermacher | 2026-05-02T11:49:47Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"granite",
"ibm",
"abliterated",
"uncensored",
"post-training",
"en",
"base_model:DuoNeural/Granite-4.1-3B-Abliterated",
"base_model:quantized:DuoNeural/Granite-4.1-3B-Abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-05-02T08:37:23Z | ## 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 q... | [
{
"start": 525,
"end": 556,
"text": "Granite-4.1-3B-Abliterated-GGUF",
"label": "benchmark name",
"score": 0.6216035485267639
}
] |
Muapi/maximalist-scifi-kilian-eng-style | Muapi | 2025-09-08T20:04:23Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-08T20:04:08Z | # Maximalist Scifi - Kilian Eng Style
**Base model**: Flux.1 D
**Trained words**: an illustration of, in the style of kilian-eng
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muap... | [] |
ABCBABC/sconvlstm-uci-har-2 | ABCBABC | 2025-12-13T06:44:10Z | 0 | 0 | null | [
"tensorboard",
"pytorch",
"lightning",
"classification",
"time-series",
"dataset:uci-har",
"region:us"
] | null | 2025-12-13T06:15:03Z | ---
tags:
- pytorch
- lightning
- classification
- time-series
datasets:
- uci-har
metrics:
- accuracy
---
# SConvLSTM for UCI Human Activity Recognition
This repository contains the training logs and checkpoints for a **SConvLSTM** model trained on the **UCI Human Activity Recognition (HAR)** dataset.
## Model Desc... | [
{
"start": 94,
"end": 102,
"text": "accuracy",
"label": "evaluation metric",
"score": 0.6969785690307617
}
] |
surogate/Qwen3-8B-Base | surogate | 2026-02-23T20:13:19Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2505.09388",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-02-23T20:13:18Z | # Qwen3-8B-Base
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following... | [] |
majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q2_K | majentik | 2026-05-04T11:52:46Z | 0 | 0 | null | [
"gguf",
"nemotron",
"multimodal",
"mamba2",
"moe",
"quantized",
"rotorquant",
"base_model:nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
"base_model:quantized:nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-05-04T11:44:30Z | # Nemotron-3-Nano-Omni-30B-A3B-Reasoning - RotorQuant GGUF Q2_K
GGUF Q2_K quantization of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` (`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`) with RotorQuant weight method.
The `Q2_K.gguf` binary in this repo is loaded by `llama.cpp` / `llama-mtmd-cli`.
For multimodal infer... | [] |
mradermacher/Melinoe-Qwen3Omni-30B-A3B-Thinking-i1-GGUF | mradermacher | 2026-03-13T05:17:43Z | 15,659 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:bgg1996/Melinoe-Qwen3Omni-30B-A3B-Thinking",
"base_model:quantized:bgg1996/Melinoe-Qwen3Omni-30B-A3B-Thinking",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2026-03-12T23:18:59Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_... | [
{
"start": 463,
"end": 497,
"text": "Melinoe-Qwen3Omni-30B-A3B-Thinking",
"label": "benchmark name",
"score": 0.6693230867385864
},
{
"start": 634,
"end": 676,
"text": "Melinoe-Qwen3Omni-30B-A3B-Thinking-i1-GGUF",
"label": "benchmark name",
"score": 0.7199166417121887
}... |
armaansidana/mohit | armaansidana | 2025-08-12T14:57:05Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-08-12T14:21:29Z | # Mohit
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/t... | [] |
DChak2000/qwen3-trace-align | DChak2000 | 2026-02-03T08:10:42Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:3156",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:Qwen/Qwen3-Embedding-8B",
"base_model:finetune:Qwen/Qwen3-Embedding-8B",
"endpoints_compatib... | sentence-similarity | 2026-02-03T08:10:36Z | # SentenceTransformer based on Qwen/Qwen3-Embedding-8B
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B). It maps sentences & paragraphs to a 4096-dimensional dense vector space and can be used for semantic textual si... | [
{
"start": 786,
"end": 802,
"text": "Training Dataset",
"label": "evaluation dataset",
"score": 0.8560401797294617
}
] |
priorcomputers/llama-3.1-8b-instruct-cn-dat-kr0.2-a0.05-creative | priorcomputers | 2026-02-03T04:59:40Z | 1 | 0 | null | [
"safetensors",
"llama",
"creativityneuro",
"llm-creativity",
"mechanistic-interpretability",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-02-03T04:57:21Z | # llama-3.1-8b-instruct-cn-dat-kr0.2-a0.05-creative
This is a **CreativityNeuro (CN)** modified version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
## Model Details
- **Base Model**: meta-llama/Llama-3.1-8B-Instruct
- **Modification**: CreativityNeuro weight scalin... | [] |
XythicK/Lovelace-1-7B-GGUF | XythicK | 2025-12-28T09:55:18Z | 45 | 0 | null | [
"gguf",
"text-generation",
"en",
"base_model:Spestly/Lovelace-1-7B",
"base_model:quantized:Spestly/Lovelace-1-7B",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-12-26T10:02:04Z | # Lovelace-1-7B
*A research-oriented code language model focused on realistic software reasoning*
---
## Model Summary
**Lovelace-1-7B** is a 7-billion parameter, code-focused large language model based on
[`bigcode/starcoder2-7b`](https://huggingface.co/bigcode/starcoder2-7b).
It is part of the **Lovelace** model... | [
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"text": "Lovelace-1-7B",
"label": "benchmark name",
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GMorgulis/Phi-3-mini-4k-instruct-obama-HSS0.614063-start5-ft4.43 | GMorgulis | 2026-03-26T08:07:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-4k-instruct",
"endpoints_compatible",
"region:us"
] | null | 2026-03-26T07:52:16Z | # Model Card for Phi-3-mini-4k-instruct-obama-HSS0.614063-start5-ft4.43
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers im... | [] |
mradermacher/AFM-WebAgent-7B-rl-GGUF | mradermacher | 2025-08-12T14:47:56Z | 22 | 2 | transformers | [
"transformers",
"gguf",
"en",
"base_model:PersonalAILab/AFM-WebAgent-7B-rl",
"base_model:quantized:PersonalAILab/AFM-WebAgent-7B-rl",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-12T04:06:43Z | ## 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 qu... | [] |
Josephinepassananti/flux_taylor_tomlinson_ft_dataset_512_shaded_0.08_face-swap_dwayne_1000_bs1_steps500 | Josephinepassananti | 2025-12-18T17:04:03Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-12-18T16:40:23Z | <!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - Josephinepassananti/flux_taylor_tomlinson_ft_dataset_512_shaded_0.08_face-swap_dwayne_1000_bs1_ste... | [] |
WindyWord/translate-es-bi | WindyWord | 2026-04-27T23:57:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"translation",
"marian",
"windyword",
"spanish",
"bislama",
"es",
"bi",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | translation | 2026-04-17T02:37:58Z | # WindyWord.ai Translation — Spanish → Bislama
**Translates Spanish → Bislama.**
**Quality Rating: ⭐⭐⭐⭐ (4.0★ Standard)**
Part of the [WindyWord.ai](https://windyword.ai) translation fleet — 1,800+ proprietary language pairs.
## Quality & Pricing Tier
- **5-star rating:** 4.0★ ⭐⭐⭐⭐
- **Tier:** Standard
- **Compos... | [
{
"start": 361,
"end": 376,
"text": "Grand Rounds v2",
"label": "benchmark name",
"score": 0.647263765335083
}
] |
enactic/avista-base-plus | enactic | 2026-04-10T10:22:58Z | 37 | 0 | transformers | [
"transformers",
"pytorch",
"avhubert",
"automatic-speech-recognition",
"AVSR",
"AVHuBERT",
"custom_code",
"ja",
"arxiv:2201.02184",
"arxiv:2201.01763",
"base_model:enactic/japanese-avhubert-base_noise_pt",
"base_model:finetune:enactic/japanese-avhubert-base_noise_pt",
"region:us"
] | automatic-speech-recognition | 2026-01-14T00:39:20Z | <div align="center">
<video width="80%" controls>
<source src="https://huggingface.co/datasets/enactic/assets/resolve/main/AVista%20demo.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
# AVista Base+ 🐦🔥
This is AVHuBERT (Audio-Visual Hidden Unit BERT) Bas... | [] |
arithmetic-circuit-overloading/Llama-3.3-70B-Instruct-v2-3d-2M-200K-0.1-reverse-padzero-99-64D-1L-2H-256I | arithmetic-circuit-overloading | 2026-04-05T01:32:50Z | 103 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:finetune:meta-llama/Llama-3.3-70B-Instruct",
"license:llama3.3",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-04-04T02:53:29Z | <!-- 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-3.3-70B-Instruct-v2-3d-2M-200K-0.1-reverse-padzero-99-64D-1L-2H-256I
This model is a fine-tuned version of [meta-llama/Llam... | [
{
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"text": "learning_rate",
"label": "evaluation metric",
"score": 0.7423019409179688
},
{
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"end": 851,
"text": "epsilon",
"label": "evaluation metric",
"score": 0.611707866191864
},
{
"start": 941,
"end": 966,
"text":... |
GoodStartLabs/qwen3-32b-gsl247-armC-v3-mixed-r4 | GoodStartLabs | 2026-05-04T18:18:01Z | 0 | 0 | peft | [
"peft",
"safetensors",
"chess",
"lora",
"sft",
"gsl-247",
"qwen3",
"base_model:Qwen/Qwen3-32B",
"base_model:adapter:Qwen/Qwen3-32B",
"license:apache-2.0",
"region:us"
] | null | 2026-05-04T18:15:43Z | # Qwen3-32B · GSL-247 · arm C (v3-mixed-r4)
LoRA adapter trained on Lichess human games for the GSL-247 chess SFT ablation
(form-vs-strategy axis). All four arms (A/B/C/F) share representation mix
`v3_mixed_history`, N=200,000 records, identical hyperparameters; they vary
only in the human-data filter applied upstream... | [] |
rswaminathan38/llmbench-teacher-8b-gsm8k-ce-20260410 | rswaminathan38 | 2026-04-17T21:42:28Z | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | 2026-04-17T21:35:09Z | ---
library_name: transformers
pipeline_tag: text-generation
base_model: meta-llama/Meta-Llama-3.1-8B
datasets:
- gsm8k
tags:
- gsm8k
- transformers
- vllm
- text-generation
- teacher-model
---
# Teacher 8B CE
This repo contains the `cross-entropy fine-t... | [
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"label": "benchmark name",
"score": 0.7424408197402954
},
{
"start": 192,
"end": 204,
"text": "transformers",
"label": "benchmark name",
"score": 0.7353028059005737
},
{
"start": 300,
"end": 323,
"text": "c... |
matrixportalx/gemma-3-1b-it-tr-v1-GGUF | matrixportalx | 2025-08-31T19:54:08Z | 29 | 0 | transformers | [
"transformers",
"gguf",
"matrixportal",
"gemma3",
"tr",
"en",
"base_model:matrixportalx/gemma-3-1b-it-tr-v1",
"base_model:quantized:matrixportalx/gemma-3-1b-it-tr-v1",
"license:apache-2.0",
"region:us",
"conversational"
] | null | 2025-08-31T19:23:09Z | # gemma-3-1b-it-tr-v1 GGUF Quantized Models
## Technical Details
- **Quantization Tool:** llama.cpp
- **Version:** version: 6338 (e92d53b2)
## Model Information
- **Base Model:** [matrixportalx/gemma-3-1b-it-tr-v1](https://huggingface.co/matrixportalx/gemma-3-1b-it-tr-v1)
- **Quantized by:** [matrixportalx](https://h... | [] |
mradermacher/LexiFreak-8B-Unleashed-i1-GGUF | mradermacher | 2025-12-06T03:48:52Z | 336 | 4 | transformers | [
"transformers",
"gguf",
"llama3",
"roleplay",
"uncensored",
"finetune",
"brainrot",
"en",
"base_model:soundTeam/LexiFreak-8B-Unleashed",
"base_model:quantized:soundTeam/LexiFreak-8B-Unleashed",
"license:llama3",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-10-04T20:08:04Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_... | [
{
"start": 624,
"end": 654,
"text": "LexiFreak-8B-Unleashed-i1-GGUF",
"label": "benchmark name",
"score": 0.6078208088874817
}
] |
majentik/gemma-4-31B-it-RotorQuant-GGUF-Q5_K_M | majentik | 2026-04-15T23:07:38Z | 190 | 0 | gguf | [
"gguf",
"rotorquant",
"kv-cache-quantization",
"gemma",
"gemma4",
"instruct",
"llama-cpp",
"quantized",
"image-text-to-text",
"arxiv:2504.19874",
"base_model:google/gemma-4-31B-it",
"base_model:quantized:google/gemma-4-31B-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2026-04-13T14:44:30Z | # gemma-4-31B-it-RotorQuant-GGUF-Q5_K_M
GGUF Q5_K_M weight-quantized variant of [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) optimised for use with **RotorQuant** KV cache compression via a dedicated llama.cpp fork.
> **Important:** RotorQuant KV cache types (`planar3`, `iso3`) are **not** av... | [] |
tomaarsen/span-marker-mbert-base-multinerd | tomaarsen | 2023-09-12T20:45:24Z | 333,357 | 68 | span-marker | [
"span-marker",
"pytorch",
"tensorboard",
"safetensors",
"token-classification",
"ner",
"named-entity-recognition",
"multilingual",
"dataset:Babelscape/multinerd",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:cc-... | token-classification | 2023-08-07T06:59:57Z | # SpanMarker for Multilingual Named Entity Recognition
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for multilingual Named Entity Recognition trained on the [MultiNERD](https://huggingface.co/datasets/Babelscape/multinerd) dataset. In particular, this SpanMarker model uses ... | [
{
"start": 204,
"end": 213,
"text": "MultiNERD",
"label": "evaluation dataset",
"score": 0.6007556319236755
},
{
"start": 834,
"end": 843,
"text": "Precision",
"label": "evaluation metric",
"score": 0.8381779193878174
},
{
"start": 850,
"end": 856,
"text":... |
oki692/gptoss-Q4_K_M-GGUF | oki692 | 2026-03-29T12:36:59Z | 0 | 0 | peft | [
"peft",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:oki0ki/gptoss",
"base_model:adapter:oki0ki/gptoss",
"endpoints_compatible",
"region:us"
] | null | 2026-03-29T12:36:54Z | # oki692/gptoss-Q4_K_M-GGUF
This model was converted to GGUF format from [`oki0ki/gptoss`](https://huggingface.co/oki0ki/gptoss) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/oki0ki/gptoss) for more ... | [] |
MBARI-org/rf-detrLarge-uavs-detectv0 | MBARI-org | 2025-12-15T21:04:33Z | 0 | 1 | null | [
"license:mit",
"region:us"
] | null | 2025-11-17T23:16:59Z | UAVS model trained on all verified object classes, collapsed to only one class: - object.
Classes collapsed to object class
Input images: 2935; transformed images: 78491
Input labels: {'Whale': 1, 'Bird': 6628, 'Egregia': 302, 'Kelp': 25263, 'Jelly': 372, 'Boat': 10, 'Mooring_Buoy': 3, 'Batray': 354, 'Otter': 12, 'V... | [] |
OgawaHiroyuki/qwen3-4b-instruct-lora-sft-comp-advanced-v01 | OgawaHiroyuki | 2026-02-19T04:32:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"text-generation",
"conversational",
"en",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v5",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache... | text-generation | 2026-02-18T05:46:49Z | # # Qwen3-4B-Instruct LoRA SFT for LLM Competition Advanced (v01)
This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** using **LoRA + Unsloth**.
This repository contains **LoRA adapter weights only**.
The base model must be loaded separately.
## Training Objective
This adapter... | [
{
"start": 384,
"end": 392,
"text": "ALFWorld",
"label": "benchmark name",
"score": 0.8199347257614136
},
{
"start": 415,
"end": 422,
"text": "DBBench",
"label": "benchmark name",
"score": 0.8046244382858276
}
] |
Trongnhat191/Vi-Qwen2-3B-RAG-ViMedAQA | Trongnhat191 | 2025-09-04T11:45:35Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:AITeamVN/Vi-Qwen2-3B-RAG",
"base_model:finetune:AITeamVN/Vi-Qwen2-3B-RAG",
"endpoints_compatible",
"region:us"
] | null | 2025-09-04T11:45:23Z | # Model Card for Vi-Qwen2-3B-RAG-ViMedAQA
This model is a fine-tuned version of [AITeamVN/Vi-Qwen2-3B-RAG](https://huggingface.co/AITeamVN/Vi-Qwen2-3B-RAG).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time m... | [] |
cstr/qwen3-tts-1.7b-voicedesign-GGUF | cstr | 2026-05-01T18:28:05Z | 0 | 0 | ggml | [
"ggml",
"gguf",
"audio",
"tts",
"voice-design",
"instruct-tts",
"crispasr",
"qwen3-tts",
"text-to-speech",
"zh",
"en",
"ja",
"ko",
"de",
"fr",
"ru",
"pt",
"es",
"it",
"base_model:Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign",
"base_model:quantized:Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign... | text-to-speech | 2026-05-01T18:18:31Z | # Qwen3-TTS 12Hz 1.7B VoiceDesign — GGUF (CrispASR)
GGUF / ggml conversions of [`Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`](https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign) for use with the `qwen3-tts` backend in **[CrispStrobe/CrispASR](https://github.com/CrispStrobe/CrispASR)**.
VoiceDesign is the instruct-... | [] |
Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0 | Trendyol | 2025-05-13T14:08:23Z | 1,665 | 37 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"new",
"sentence-similarity",
"custom_code",
"tr",
"ar",
"en",
"de",
"bg",
"hu",
"ro",
"sk",
"pl",
"cs",
"el",
"arxiv:1908.10084",
"arxiv:2205.13147",
"base_model:Alibaba-NLP/gte-multilingual-base",
"base_model:finetune:Alibaba-NLP/gte-mu... | sentence-similarity | 2025-05-06T12:33:29Z | # Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0
Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0 is a multilingual [sentence-transformers](https://www.SBERT.net) embedding model fine-tuned on e-commerce datasets, optimized for semantic similarity, search, classification, and retrieval tasks. It is integrating domai... | [] |
jin-kwon/qwen3_vl_gpt_warmuped_include_descriptions_suggested | jin-kwon | 2025-11-06T13:17:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-11-04T16:48:08Z | # Model Card for qwen3_vl_gpt_warmuped_include_descriptions_suggested
This model is a fine-tuned version of [None](https://huggingface.co/None).
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 ... | [] |
ShourenWSR/Qwen3-4B-V2-Superior-Hybrid-30k | ShourenWSR | 2026-05-01T02:09:40Z | 11 | 0 | null | [
"safetensors",
"qwen3_pl_moe",
"custom_code",
"region:us"
] | null | 2026-05-01T02:07:48Z | # Qwen3-4B-V2-Superior-Hybrid-30k
## What this model is
- **Base model**: Qwen3-4B (original hybrid-thinking)
- **Dataset**: superior_hybrid 30k
- **Training method**: Single-stage SFT (V2 architecture)
## Notes
### V1 vs V2 (architecture explanation)
- **V1**: Registers `/think` and `/no_think` as new single spec... | [
{
"start": 127,
"end": 146,
"text": "superior_hybrid 30k",
"label": "evaluation dataset",
"score": 0.783202588558197
}
] |
jialicheng/unlearn-cl_ucf101_videomae-base_random_label_6_42 | jialicheng | 2025-11-07T15:37:12Z | 0 | 0 | null | [
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"region:us"
] | video-classification | 2025-11-07T15:20:31Z | <!-- 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. -->
# ucf101_42
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on the ucf1... | [
{
"start": 190,
"end": 199,
"text": "ucf101_42",
"label": "benchmark name",
"score": 0.6242219805717468
},
{
"start": 240,
"end": 261,
"text": "MCG-NJU/videomae-base",
"label": "benchmark name",
"score": 0.7781067490577698
},
{
"start": 286,
"end": 307,
"t... |
iamavasya/bert-finetuned-imdb | iamavasya | 2026-04-27T09:05:29Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2026-03-18T12:58:46Z | <!-- 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. -->
# bert-finetuned-imdb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unk... | [
{
"start": 399,
"end": 405,
"text": "0.3349",
"label": "evaluation metric",
"score": 0.8723711967468262
},
{
"start": 408,
"end": 416,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.9352197647094727
},
{
"start": 418,
"end": 423,
"text": "0.9... |
mt628754/test053 | mt628754 | 2026-02-28T02:01:40Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"text-generation",
"conversational",
"en",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v5",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache... | text-generation | 2026-02-28T01:59:54Z | # qwen3-4b-agent-trajectory-lora-1
This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** using **LoRA + Unsloth**.
This repository contains **LoRA adapter weights only**.
The base model must be loaded separately.
## Training Objective
This adapter is trained to improve **multi-... | [
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"label": "benchmark name",
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] |
Repoaner/llama_guard_vision | Repoaner | 2025-07-30T00:33:08Z | 5 | 1 | transformers | [
"transformers",
"safetensors",
"mllama",
"image-text-to-text",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2312.06674",
"arxiv:2307.15043",
"arxiv:2306.15447",
"arxiv:2411.10414",
"license:lla... | image-text-to-text | 2025-07-30T00:29:04Z | ## Model Information
Llama Guard 3 Vision is a Llama-3.2-11B pretrained model, fine-tuned for content safety classification. Similar to previous versions \[1-3\], it can be used to safeguard content for both LLM inputs (prompt classification) and LLM responses (response classification).
Llama Guard 3 Vision was speci... | [] |
xpmir/cross-encoder-DeBERTav3-MarginMSE | xpmir | 2026-03-17T16:43:14Z | 59 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"cross-encoder",
"sequence-classification",
"en",
"dataset:msmarco",
"arxiv:2603.03010",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:apache-2.0",
"text-e... | text-classification | 2026-03-04T15:49:20Z | # cross-encoder-DeBERTav3-MarginMSE
[](http://arxiv.org/abs/2603.03010)
[](https://huggingface.co/collections/xpmir/reproducing-cross-encoders)
[![GitHub](https://img.shields.io/badge/GitH... | [
{
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"label": "evaluation metric",
"score": 0.9016404151916504
},
{
"start": 1023,
"end": 1032,
"text": "marginMSE",
"label": "evaluation metric",
"score": 0.8585151433944702
}
] |
kentaromiz/augmented-donut-pose | kentaromiz | 2025-12-22T07:39:11Z | 0 | 0 | null | [
"resnet18",
"region:us"
] | null | 2025-12-22T04:04:53Z | # Model Card for augmented-donut-pose
## Model Details
* **Model Architecture**: resnet18
* **Pre-trained**: Yes
* **Number of Classes**: 2
## Dataset Details
* **Dataset Name**: lay-or-stand-donuts-dataset
* **Training Samples**: 246
* **Validation Samples**: 42
* **Test Samples**: 72
## Training Metr... | [
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"text": "lay-or-stand-donuts-dataset",
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"score": 0.8524171113967896
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"end": 323,
"text": "Training Metrics",
"label": "evaluation metric",
"score": 0.6790698170661926
},
{
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... |
mengsong16/GTA1-32B | mengsong16 | 2025-10-23T06:54:39Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"VLM",
"Computer-Use-Agent",
"OS-Agent",
"GUI",
"Grounding",
"conversational",
"en",
"arxiv:2507.05791",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-10-22T23:23:24Z | # Introduction
Reinforcement learning (RL) (e.g., GRPO) helps with grounding because of its inherent objective alignment—rewarding successful clicks—rather than encouraging long textual Chain-of-Thought (CoT) reasoning. Unlike approaches that rely heavily on verbose CoT reasoning, GRPO directly incentivizes actionable... | [
{
"start": 502,
"end": 523,
"text": "Grounding Performance",
"label": "evaluation metric",
"score": 0.6260888576507568
},
{
"start": 595,
"end": 621,
"text": "three challenging datasets",
"label": "evaluation dataset",
"score": 0.7355464696884155
}
] |
trungpq/rlcc-new-palate-class-weight-absa-None | trungpq | 2025-09-17T02:16:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert_with_absa",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T16:35:17Z | <!-- 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. -->
# rlcc-new-palate-class-weight-absa-None
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
I... | [
{
"start": 302,
"end": 317,
"text": "unknown dataset",
"label": "evaluation dataset",
"score": 0.6780202984809875
},
{
"start": 393,
"end": 401,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.8904097080230713
},
{
"start": 403,
"end": 409,
"t... |
DakshJogchand/rl2 | DakshJogchand | 2025-10-10T18:22:09Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-10-10T18:21:37Z | # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework... | [] |
nvidia/Qwen3-8B-DMS-8x | nvidia | 2026-01-22T16:07:37Z | 1,094 | 34 | transformers | [
"transformers",
"safetensors",
"qwen3",
"nvidia",
"pytorch",
"kvcache",
"custom_code",
"dataset:open-r1/OpenR1-Math-220k",
"arxiv:2506.05345",
"arxiv:2505.09388",
"arxiv:2305.20050",
"arxiv:2107.03374",
"arxiv:2311.07911",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
... | null | 2026-01-19T20:39:00Z | # Qwen3-8B-DMS-8x
### Description:
Qwen-3-8B-DMS-8x is a derivative of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) that integrates [Dynamic Memory Sparsification (DMS)](https://arxiv.org/abs/2506.05345) with an 8x compression ratio during inference. DMS adaptively sparsifies the KV cache to reduce memory footprin... | [] |
Miya67/aiq-scoring-e5-small-wiki-absolute | Miya67 | 2026-03-09T02:26:59Z | 46 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"cross-encoder",
"reranker",
"generated_from_trainer",
"dataset_size:104687",
"loss:BinaryCrossEntropyLoss",
"text-ranking",
"arxiv:1908.10084",
"base_model:hotchpotch/japanese-reranker-cross-encoder-small-v1",
"base_model:finetune:hotchpo... | text-ranking | 2026-03-07T14:03:51Z | # CrossEncoder based on hotchpotch/japanese-reranker-cross-encoder-small-v1
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [hotchpotch/japanese-reranker-cross-encoder-small-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-small-v1) using t... | [
{
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"end": 865,
"text": "Training Dataset",
"label": "evaluation dataset",
"score": 0.8554568290710449
}
] |
zhuojing-huang/gpt2-german-english-configC-10k-13 | zhuojing-huang | 2025-12-22T00:40:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-12-21T11:16:40Z | <!-- 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. -->
# gpt2-german-english-configC-10k-13
This model was trained from scratch on the None dataset.
## Model description
More informati... | [
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"text": "0.002",
"label": "evaluation metric",
"score": 0.7114878296852112
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{
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Shawon16/VideoMAE_fold__0__20_epoch_p5_bdslw60_kinetics_check_longtail_2class | Shawon16 | 2025-11-10T02:47:48Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2025-11-10T02:42:35Z | <!-- 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. -->
# VideoMAE_fold__0__20_epoch_p5_bdslw60_kinetics_check_longtail_2class
This model is a fine-tuned version of [MCG-NJU/videomae-base... | [
{
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"end": 306,
"text": "MCG-NJU",
"label": "benchmark name",
"score": 0.7002150416374207
},
{
"start": 494,
"end": 500,
"text": "0.0023",
"label": "evaluation metric",
"score": 0.8656156063079834
},
{
"start": 503,
"end": 511,
"text": "Accurac... |
defqon-1/26oc16kdnpnr | defqon-1 | 2025-10-25T16:16:01Z | 0 | 0 | null | [
"region:us"
] | null | 2025-10-25T15:51:09Z | # Container Template for SoundsRight Subnet Miners
Miners in [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/soundsright-subnet) must containerize their models before uploading to HuggingFace. This repo serves as a template.
The branches `DENOISING_16000HZ` and `DEREVERBERATI... | [] |
nn-tech/MetalGPT-1-AWQ | nn-tech | 2025-12-26T15:50:25Z | 84 | 5 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"mining",
"awq",
"conversational",
"ru",
"base_model:nn-tech/MetalGPT-1",
"base_model:quantized:nn-tech/MetalGPT-1",
"license:cc-by-nc-sa-4.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] | text-generation | 2025-12-11T10:07:20Z | ## Description
**MetalGPT-1** is a model built upon the Qwen/Qwen3-32B and incorporates both continual pre-training and supervised fine-tuning on domain-specific data from the mining and metallurgy industry.
---
### Quantization
For convenience and better efficiency, we also offer this AWQ-quantized checkpoint of t... | [
{
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"end": 28,
"text": "MetalGPT-1",
"label": "benchmark name",
"score": 0.6640810966491699
},
{
"start": 331,
"end": 341,
"text": "MetalGPT-1",
"label": "benchmark name",
"score": 0.6827869415283203
},
{
"start": 641,
"end": 655,
"text": "Metal... |
LocalAI-io/whisper-small-it-multi-ct2-int8 | LocalAI-io | 2026-04-14T20:55:44Z | 14 | 0 | null | [
"whisper",
"automatic-speech-recognition",
"italian",
"ctranslate2",
"faster-whisper",
"whisperx",
"localai",
"int8",
"it",
"dataset:mozilla-foundation/common_voice_25_0",
"dataset:facebook/multilingual_librispeech",
"dataset:facebook/voxpopuli",
"base_model:openai/whisper-small",
"base_mo... | automatic-speech-recognition | 2026-04-10T12:36:42Z | # whisper-small-it-multi-ct2-int8
[CTranslate2](https://github.com/OpenNMT/CTranslate2) INT8 quantized version of [LocalAI-io/whisper-small-it-multi](https://huggingface.co/LocalAI-io/whisper-small-it-multi) for fast CPU inference.
**Author:** Ettore Di Giacinto
Brought to you by the [LocalAI](https://github.com/mud... | [] |
majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-MLX-3bit-TQ-KV | majentik | 2026-05-04T15:58:41Z | 0 | 0 | mlx | [
"mlx",
"nemotron",
"multimodal",
"mamba2",
"moe",
"quantized",
"turboquant",
"kv-cache-modifier",
"base_model:nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
"base_model:finetune:nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
"license:other",
"region:us"
] | null | 2026-05-04T15:58:39Z | # Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant MLX 3-bit + TurboQuant KV-Cache (matched stack)
Documentation card for the matched TurboQuant weight + TurboQuant KV-cache stack
of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` at MLX 3-bit.
**No new weights are published here.** Load the weights from
[`majentik/Nemot... | [] |
rbelanec/train_cola_1754652147 | rbelanec | 2025-08-08T14:26:11Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-08T13:56:18Z | <!-- 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. -->
# train_cola_1754652147
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-lla... | [
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"text": "5e-05",
"label": "evaluation metric",
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jasonhuang3/101-our-40-taitung-0317-lora | jasonhuang3 | 2026-03-17T07:51:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:jasonhuang3/taitung-sft-2187-1107-merged",
"base_model:finetune:jasonhuang3/taitung-sft-2187-1107-merged",
"endpoints_compatible",
"region:us"
] | null | 2026-03-17T07:17:44Z | # Model Card for 101-our-40-taitung-0317-lora
This model is a fine-tuned version of [jasonhuang3/taitung-sft-2187-1107-merged](https://huggingface.co/jasonhuang3/taitung-sft-2187-1107-merged).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipel... | [] |
OliverHeine/huawei-noah_TinyBERT_General_4L_312D_fold_8 | OliverHeine | 2026-04-14T16:01:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:huawei-noah/TinyBERT_General_4L_312D",
"base_model:finetune:huawei-noah/TinyBERT_General_4L_312D",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2026-04-14T15:33:23Z | <!-- 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. -->
# huawei-noah_TinyBERT_General_4L_312D_fold_8
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](htt... | [
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{
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amd/Llama-3.2-3B-onnx-ryzenai-1.7-hybrid | amd | 2026-01-24T01:18:54Z | 0 | 0 | null | [
"onnx",
"ryzenai-hybrid",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:quantized:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"region:us"
] | null | 2026-01-22T19:56:42Z | # amd/Llama-3.2-3B-onnx-ryzenai-1.7-hybrid
- ## Introduction
This model was prepared using the AMD Quark Quantization tool, followed by necessary post-processing.
- ## Quantization Strategy
- AWQ / Group 128 / Asymmetric / UINT4 Weights / BFP16 activations
- Excluded Layers: None
- ## Quick Start
... | [
{
"start": 457,
"end": 468,
"text": "MMLU scores",
"label": "evaluation metric",
"score": 0.7161561846733093
}
] |
VladislovePT/speecht5_finetuned_vladislove_ua | VladislovePT | 2025-09-27T11:22:50Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2025-09-25T17:08:27Z | <!-- 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. -->
# speecht5_finetuned_vladislove_ua
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/... | [
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"text": "learning_rate",
"label": "evaluation metric",
"score": 0.643507719039917
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{
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"text": ... |
hye-on0401/pick_and_place_top_only | hye-on0401 | 2026-04-27T11:17:40Z | 21 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:hye-on0401/pick_and_place_demo_v1",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-04-13T12:57:51Z | # 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 pol... | [
{
"start": 17,
"end": 24,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.7469843029975891
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.7727768421173096
}
] |
Divinci-AI/llama-3.1-8b-vindex | Divinci-AI | 2026-04-21T18:46:28Z | 0 | 0 | null | [
"larql",
"vindex",
"mechanistic-interpretability",
"feature-extraction",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:cc-by-nc-4.0",
"region:us"
] | feature-extraction | 2026-04-21T18:46:20Z | # Llama 3.1-8B — LarQL Vindex
**Source model**: [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
**Vindex short ID**: `c39fad08`
**Layers**: 32 **Hidden size**: 4096 **Features per layer**: 128
## What This Is
A **LarQL vindex** (vector index) — a compact binary representation of the featu... | [
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"end": 73,
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{
"start": 98,
"end": 121,
"text": "meta-llama/Llama-3.1-8B",
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... |
Muapi/arcane-style | Muapi | 2025-08-25T12:44:53Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T12:44:44Z | # Arcane Style
**Base model**: Flux.1 D
**Trained words**: arcane_style
## 🧠 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... | [] |
BootesVoid/cmfhdpj1f05fyx0n0eukev1i4_cmfhdz63e05gax0n03r81aj8w | BootesVoid | 2025-09-12T22:44:26Z | 1 | 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 | 2025-09-12T22:44:25Z | # Cmfhdpj1F05Fyx0N0Eukev1I4_Cmfhdz63E05Gax0N03R81Aj8W
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https:... | [] |
nightmedia/Qwen3.5-9B-InfiniteLoop-qx86-hi-mlx | nightmedia | 2026-03-25T19:11:48Z | 75 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_5",
"image-text-to-text",
"unsloth",
"fine tune",
"heretic",
"abliterated",
"uncensored",
"creative",
"creative writing",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene continue",
"storytelling",
"fictio... | image-text-to-text | 2026-03-25T00:36:49Z | # Qwen3.5-9B-InfiniteLoop-qx86-hi-mlx
> G, you've proved that "Identity" is a performance-enhancing drug for LLMs.--Gemini
This is a merge between:
- DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUC... | [
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"text": "Gemini",
"label": "benchmark name",
"score": 0.6994402408599854
},
{
"start": 932,
"end": 938,
"text": "Gemini",
"label": "benchmark name",
"score": 0.8038074970245361
}
] |
indicnode/mms-tts-kan | indicnode | 2026-04-04T21:58:46Z | 0 | 0 | null | [
"pytorch",
"safetensors",
"vits",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"region:us"
] | text-to-speech | 2026-04-04T21:58:45Z | ---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Kannada Text-to-Speech
This repository contains the **Kannada (kan)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org... | [] |
sarayusapa/sam-carnatic | sarayusapa | 2026-03-13T06:29:19Z | 17 | 0 | null | [
"safetensors",
"sam-audio",
"audio-classification",
"carnatic-music",
"raga-classification",
"indian-classical-music",
"en",
"dataset:sarayusapa/carnatic-ragas",
"license:mit",
"region:us"
] | audio-classification | 2026-02-06T12:47:16Z | # SAM-Audio: Carnatic Raga Classifier
A CNN + Segment Attention model for classifying Carnatic ragas from audio.
## Model Details
- **Architecture**: SAM-Audio (CNN mel-spectrogram encoder + latent segmentation tokens + masked segment prediction + contrastive learning)
- **Parameters**: 2.6M
- **Training data**: [sa... | [
{
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"text": "Best validation accuracy",
"label": "evaluation metric",
"score": 0.6241889595985413
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{
"start": 469,
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"text": "99.62%",
"label": "evaluation metric",
"score": 0.6426636576652527
}
] |
syvai/hviske-v5.3 | syvai | 2026-04-28T09:14:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"cohere_asr",
"automatic-speech-recognition",
"audio",
"speech-recognition",
"transcription",
"danish",
"hf-asr-leaderboard",
"custom_code",
"da",
"dataset:CoRal-project/coral-v3",
"base_model:syvai/hviske-v5.1",
"base_model:finetune:syvai/hviske-v5.1",
"li... | automatic-speech-recognition | 2026-04-28T08:40:02Z | # hviske-v5.3
Danish ASR. Fine-tuned from [syvai/hviske-v5.1](https://huggingface.co/syvai/hviske-v5.1) on the [CoRal v3](https://huggingface.co/datasets/CoRal-project/coral-v3) train splits with **layer-wise learning-rate decay** (encoder LR = 0.75 × decoder LR) for 5 epochs.
## Results on CoRal v3 **full test sets*... | [
{
"start": 113,
"end": 121,
"text": "CoRal v3",
"label": "evaluation dataset",
"score": 0.6556913256645203
},
{
"start": 294,
"end": 302,
"text": "CoRal v3",
"label": "evaluation dataset",
"score": 0.7034106254577637
}
] |
Muapi/2010s-folk-horror-movie-sd1-sdxl-pony-flux | Muapi | 2025-08-19T19:42:47Z | 0 | 1 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T19:42:32Z | # 2010s Folk Horror Movie (SD1, SDXL, Pony, Flux)
**Base model**: Flux.1 D
**Trained words**: ArsMovieStill, Movie still from a 2010s folklore horror film
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, ... | [] |
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