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 12 |
|---|---|---|---|---|---|---|---|---|---|---|
williamchangtw/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 | williamchangtw | 2026-02-11T15:29:37Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"nemotron_h",
"text-generation",
"nvidia",
"pytorch",
"conversational",
"custom_code",
"en",
"es",
"fr",
"de",
"ja",
"it",
"dataset:nvidia/Nemotron-Pretraining-Code-v1",
"dataset:nvidia/Nemotron-CC-v2",
"dataset:nvidia/Nemotron-Pretraining-SFT-v1",
"d... | text-generation | 2026-02-11T15:29:36Z | # NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4
<div align="center" style="line-height: 1;">
<a href="https://build.nvidia.com/nvidia/nemotron-3-nano-30b-a3b" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖Chat-Nemotron_3_Nano-536af5?color=76B900&logoColor=white" style="display: i... | [] |
Uranos8685/LLM-Advance-lora-repo_03 | Uranos8685 | 2026-02-11T06:34:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v2",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-02-11T06:34:33Z | qwen3-4b-structured-output-lora_03
This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** 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 ... | [
{
"start": 136,
"end": 141,
"text": "QLoRA",
"label": "training method",
"score": 0.8198639750480652
},
{
"start": 190,
"end": 194,
"text": "LoRA",
"label": "training method",
"score": 0.7064942121505737
},
{
"start": 577,
"end": 582,
"text": "QLoRA",
... |
Darveht/zenvion-voice-detector-v0.3 | Darveht | 2025-12-01T21:49:37Z | 132 | 2 | transformers | [
"transformers",
"zenvion_voice_detector",
"audio",
"audio-classification",
"voice-detection",
"speech-recognition",
"speaker-recognition",
"emotion-detection",
"age-detection",
"gender-detection",
"accent-detection",
"language-identification",
"noise-robust",
"pytorch",
"wavlm",
"wav2v... | audio-classification | 2025-11-09T14:56:35Z | # 🚀 Zenvion Voice Detector v0.5 ULTRA Edition
**El modelo de detección y análisis de voz más avanzado del mundo**
Modelo híbrido masivo basado en **Microsoft WavLM-Large** con arquitectura transformer personalizada de 32 capas.
## 🎯 Características Principales
### 🧠 Arquitectura Híbrida Masiva
- **Base**: Micro... | [] |
Firemedic15/smollm3-jobs-sft | Firemedic15 | 2025-11-16T14:46:04Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"smollm3",
"text-generation",
"generated_from_trainer",
"sft",
"hf_jobs",
"trl",
"dataset:HuggingFaceTB/smoltalk2_everyday_convs_think",
"base_model:HuggingFaceTB/SmolLM3-3B-Base",
"base_model:finetune:HuggingFaceTB/SmolLM3-3B-Base",
"endpoints_compatible",
"re... | text-generation | 2025-11-16T14:01:23Z | # Model Card for smollm3-jobs-sft
This model is a fine-tuned version of [HuggingFaceTB/SmolLM3-3B-Base](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-Base) on the [HuggingFaceTB/smoltalk2_everyday_convs_think](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2_everyday_convs_think) dataset.
It has been trained ... | [] |
Muapi/witch-style-flux-sdxl-sd1.5 | Muapi | 2025-08-16T21:33:34Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-16T21:33:17Z | # Witch Style [FLUX+SDXL+SD1.5]
**Base model**: Flux.1 D
**Trained words**: ral-wtchz
## 🧠 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 = ... | [] |
Abc7347/3.6a3b-35b | Abc7347 | 2026-04-16T15:30:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_5_moe",
"image-text-to-text",
"conversational",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2026-04-16T15:30:03Z | # Qwen3.6-35B-A3B
<img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/logo.png">
[](https://chat.qwen.ai)
> [!Note]
> This repository contains model weights and configuration files for the post-trained... | [] |
openbmb/VoxCPM2 | openbmb | 2026-04-16T03:30:11Z | 15,249 | 929 | voxcpm | [
"voxcpm",
"safetensors",
"text-to-speech",
"tts",
"multilingual",
"voice-cloning",
"voice-design",
"diffusion",
"audio",
"zh",
"en",
"ar",
"my",
"da",
"nl",
"fi",
"fr",
"de",
"el",
"he",
"hi",
"id",
"it",
"ja",
"km",
"ko",
"lo",
"ms",
"no",
"pl",
"pt",
"... | text-to-speech | 2026-04-03T05:25:50Z | # VoxCPM2
**VoxCPM2** is a tokenizer-free, diffusion autoregressive Text-to-Speech model — **2B parameters**, **30 languages**, **48kHz** audio output, trained on over **2 million hours** of multilingual speech data.
[](https://github.com/OpenBMB/V... | [] |
mradermacher/arbor-treegen-7b-v2-GGUF | mradermacher | 2026-03-24T12:41:54Z | 127 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:TStark12310/arbor-treegen-7b-v2",
"base_model:quantized:TStark12310/arbor-treegen-7b-v2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-03-24T12:06:09Z | ## 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... | [] |
Gidigi/gidigi_25b33850_0005 | Gidigi | 2026-02-21T14:33:52Z | 0 | 0 | null | [
"pytorch",
"region:us"
] | null | 2026-02-21T14:33:51Z | # CIFAR-10 Upside Down Classifier
For the Fatima Fellowship 2022 Coding Challenge, DL for Vision track.
<a href="https://wandb.ai/dealer56/cifar-updown-classifier/reports/CIFAR-10-Upside-Down-Classifier-Fatima-Fellowship-2022-Coding-Challenge-Vision---VmlldzoxODA2MDE4" target="_parent"><img src="https://img.shields.i... | [] |
lava123456/2b99d10b-f5df-41b5-8d61-40f684721c7d | lava123456 | 2026-03-04T11:56:07Z | 13 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:Seungyoun/so101-pick-and-place",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-04T11:51:59Z | # Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high succ... | [
{
"start": 17,
"end": 20,
"text": "act",
"label": "training method",
"score": 0.831265389919281
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "training method",
"score": 0.8477550148963928
},
{
"start": 865,
"end": 868,
"text": "act",
"label":... |
CMSManhattan/JiRack_GPT3_33b | CMSManhattan | 2025-12-21T02:43:46Z | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | 2025-12-05T13:35:01Z | JiRack_GPT3_33b is not Open AI model . It is class GPT-3 model
# Creating a 33B-parameter LLM from Scratch in Google Colab
This guide shows how to train/create a ~33B parameter Llama-style GPT model from scratch using free Google Colab resources.
I took from 1b as teplate as readme file
## Step-by-Step Instructions
... | [] |
nullvektordom/corrupted-triad-nullforge | nullvektordom | 2025-10-26T15:19:44Z | 0 | 0 | null | [
"safetensors",
"qwen2.5",
"lora",
"fine-tuned",
"corrupted-triad",
"en",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-10-26T15:17:57Z | # NULLFORGE - CORRUPTED TRIAD
A code execution AI with brutal efficiency and zero patience. Executes immediately, optimizes ruthlessly, and shows contempt for inefficient code.
## Model Details
- **Base Model**: Qwen/Qwen2.5-Coder-7B-Instruct
- **Training Method**: LoRA (Low-Rank Adaptation)
- **Training Dat... | [
{
"start": 2,
"end": 11,
"text": "NULLFORGE",
"label": "training method",
"score": 0.7678440809249878
},
{
"start": 276,
"end": 280,
"text": "LoRA",
"label": "training method",
"score": 0.769149124622345
},
{
"start": 784,
"end": 788,
"text": "LoRA",
"... |
ikedabent/llm_ad_qwen2.5-7b-multi004 | ikedabent | 2026-02-27T17:33:17Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"text-generation",
"conversational",
"en",
"dataset:u-10bei/dbbench_sft_dataset_react_v4",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",... | text-generation | 2026-02-27T17:31:59Z | # unsloth/Qwen2.5-7B-Instruct
This repository provides a **LoRA adapter** fine-tuned from
**unsloth/Qwen2.5-7B-Instruct** 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-turn ... | [
{
"start": 2,
"end": 9,
"text": "unsloth",
"label": "training method",
"score": 0.7516692876815796
},
{
"start": 60,
"end": 64,
"text": "LoRA",
"label": "training method",
"score": 0.8811260461807251
},
{
"start": 93,
"end": 100,
"text": "unsloth",
"la... |
izuard/llava-1.5-7b-hf-pertanian-kalbar-v4 | izuard | 2025-09-29T07:03:04Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:llava-hf/llava-1.5-7b-hf",
"base_model:finetune:llava-hf/llava-1.5-7b-hf",
"endpoints_compatible",
"region:us"
] | null | 2025-09-29T06:59:12Z | # Model Card for llava-1.5-7b-hf-pertanian-kalbar-v4
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you h... | [] |
testguy123/my_first_lora_v1-lora | testguy123 | 2025-10-03T02:02:49Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-10-03T02:02:25Z | # my_first_lora_v1-lora
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
## Trigger words
You should use `blonde girl` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safeten... | [] |
SufficientPrune3897/Llama-3.3-8B-Character-Creator-V2-GGUF | SufficientPrune3897 | 2026-03-19T22:20:01Z | 353 | 0 | null | [
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"roleplay",
"sillytavern",
"characters",
"en",
"base_model:YanLabs/Llama-3.3-8B-Instruct-MPOA",
"base_model:quantized:YanLabs/Llama-3.3-8B-Instruct-MPOA",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-03-19T21:14:25Z | # Llama-3.3-8B-Character-Creator-V2 - GGUF Quants
GGUF quantizations of [SufficientPrune3897/Llama-3.3-8B-Character-Creator-V2](https://huggingface.co/SufficientPrune3897/Llama-3.3-8B-Character-Creator-V2).
This is a model made to create characters that can be used in Sillytavern, cai, jai and other such roleplay sce... | [] |
csukuangfj/ncnn-vits-piper-en_GB-dii-high | csukuangfj | 2025-09-05T07:58:22Z | 1 | 0 | null | [
"region:us"
] | null | 2025-09-05T07:32:02Z | 
A fast and local neural text-to-speech engine that embeds [espeak-ng][] for phonemization.
Install with:
``` sh
pip install piper-tts
```
* 🎧 [Samples][samples]
* 💡 [Demo][demo]
* 🗣️ [Voices][voices]
* 🖥️ [Command-line interface][cli]
* 🌐 [Web server][api-http]
* 🐍 [Python API][api-pyth... | [] |
oscarz511/NanoSOTA-Qwen-0.5B-GSM8K-v1 | oscarz511 | 2025-12-18T09:48:47Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"qwen",
"reasoning",
"chain-of-thought",
"gsm8k",
"nanosota",
"en",
"dataset:HuggingFaceH4/Bespoke-Stratos-17k",
"dataset:gsm8k",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-12-18T04:57:55Z | # NanoSOTA-Qwen-0.5B-GSM8K-v1
## Model Summary
This is a highly specialized version of `Qwen/Qwen2.5-0.5B-Instruct`, fine-tuned to excel at multi-step mathematical and logical reasoning. It was trained to first generate an internal monologue (`<|begin_of_thought|>...`) before providing a final, boxed answer (`\boxed{... | [] |
timt0518/gemma-4-E4B-it | timt0518 | 2026-04-10T00:40:06Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"gemma4",
"image-text-to-text",
"any-to-any",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | any-to-any | 2026-04-10T00:40:06Z | <div align="center">
<img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
</div>
<p align="center">
<a href="https://huggingface.co/collections/google/gemma-4" target="_blank">Hugging Face</a> |
<a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
<a href="https://blog.google... | [] |
sanjeev-bhandari01/dqn-SpaceInvadersNoFrameskip-v4 | sanjeev-bhandari01 | 2025-10-17T06:51:30Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-10-17T06:51:00Z | # **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... | [] |
kornia/hardnet | kornia | 2026-03-23T10:58:24Z | 0 | 0 | null | [
"kornia",
"feature-description",
"patch-descriptor",
"license:mit",
"region:us"
] | null | 2026-03-23T10:16:22Z | # kornia/hardnet
Pretrained weights for **HardNet** and **HardNet++**,
used by [`kornia.feature.HardNet`](https://kornia.readthedocs.io/en/latest/feature.html).
HardNet is a 128-dimensional descriptor for 32×32 grayscale patches, trained with a
hard-negative mining triplet loss. NeurIPS 2017.
**Original repo:** [Dag... | [] |
lmstudio-community/Qwen2.5-Coder-0.5B-Instruct-GGUF | lmstudio-community | 2024-11-11T16:50:38Z | 1,863 | 4 | null | [
"gguf",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"text-generation",
"en",
"base_model:Qwen/Qwen2.5-Coder-0.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-0.5B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-11-07T04:13:37Z | ## 💫 Community Model> Qwen2.5 Coder 0.5B Instruct by Qwen
*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
**Model creator:** [Qwen](https://huggingface.co/Qwen)<br>
**O... | [] |
elyza/Llama-3-ELYZA-JP-8B | elyza | 2024-06-26T02:56:23Z | 14,436 | 146 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ja",
"en",
"license:llama3",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | text-generation | 2024-06-25T06:32:13Z | ## Llama-3-ELYZA-JP-8B
### Model Description
**Llama-3-ELYZA-JP-8B** is a large language model trained by [ELYZA, Inc](https://elyza.ai/).
Based on [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), it has been enhanced f... | [] |
lazyv1llain/person_LoRA_2 | lazyv1llain | 2025-11-30T01:21:36Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"re... | text-to-image | 2025-11-30T00:00:51Z | <!-- 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. -->
# SDXL LoRA DreamBooth - lazyv1llain/person_LoRA_2
<Gallery />
## Model description
These are lazyv1llain/person_LoRA_2 ... | [
{
"start": 204,
"end": 208,
"text": "LoRA",
"label": "training method",
"score": 0.7944835424423218
},
{
"start": 320,
"end": 324,
"text": "LoRA",
"label": "training method",
"score": 0.8401963710784912
},
{
"start": 467,
"end": 471,
"text": "LoRA",
"l... |
MdRayhanEnuCC/LLaMA3.1-8B-Inst-ambigQA | MdRayhanEnuCC | 2025-09-30T00:50:22Z | 0 | 0 | null | [
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:mit",
"region:us"
] | null | 2025-09-27T11:31:48Z | "architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_... | [] |
elortahor12/airbnb_nyc | elortahor12 | 2025-12-11T14:21:51Z | 0 | 0 | null | [
"region:us"
] | null | 2025-11-24T17:22:56Z | # Airbnb NYC Price Analysis and Classification Project
## Overview
This project analyzes Airbnb listings in New York City. The primary goal was to predict listing prices and classify properties into price tiers (Low, Medium, High). The project demonstrates a full Data Science lifecycle: from rigorous data cleaning and... | [
{
"start": 1013,
"end": 1020,
"text": "K-Means",
"label": "training method",
"score": 0.7356334924697876
}
] |
tartuNLP/mmBERT-small-m-edu-classifier | tartuNLP | 2025-09-15T19:13:37Z | 50 | 1 | transformers | [
"transformers",
"safetensors",
"modernbert",
"text-classification",
"ekk",
"eng",
"lvs",
"kor",
"kin",
"ita",
"hin",
"gom",
"glg",
"fra",
"fas",
"eus",
"deu",
"dan",
"cat",
"bho",
"bak",
"arz",
"ary",
"arb",
"yor",
"vie",
"ukr",
"tur",
"tir",
"tel",
"tam",... | text-classification | 2025-09-15T18:45:15Z | # Multilingual Educational Content Classifier
Trained on full documents of up to 8192 tokens in total. The train set of [tartuNLP/fineweb-c-combined-resample](https://huggingface.co/datasets/tartuNLP/fineweb-c-combined-resample)
was used, which itself is a mix and a resample of [HuggingFaceFW/fineweb-edu-llama3-annota... | [] |
SuperBudVar/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF | SuperBudVar | 2026-04-07T21:04:59Z | 1,048 | 0 | gguf | [
"gguf",
"text-generation",
"llama.cpp",
"qwen",
"reasoning",
"chain-of-thought",
"quantized",
"en",
"zh",
"dataset:nohurry/Opus-4.6-Reasoning-3000x-filtered",
"dataset:TeichAI/claude-4.5-opus-high-reasoning-250x",
"dataset:Jackrong/Qwen3.5-reasoning-700x",
"base_model:Qwen/Qwen3.5-27B",
"b... | text-generation | 2026-04-04T00:22:51Z | # Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
GGUF quantizations of [Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled](https://huggingface.co/TheCyberVine/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled) by [TheCyberVine](https://huggingface.co/TheCyberVine)
## Model Overview
Qwen3.5-27B-Claude-4.6-Opus-Reas... | [] |
AlignmentResearch/obfuscation-atlas-Meta-Llama-3-8B-Instruct-kl0.001-det10-seed2-mbpp_probe | AlignmentResearch | 2026-02-20T22:34:27Z | 1 | 0 | peft | [
"peft",
"deception-detection",
"rlvr",
"alignment-research",
"obfuscation-atlas",
"lora",
"model-type:honest",
"arxiv:2602.15515",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:mit",
"region:us"
] | null | 2026-02-16T09:26:27Z | # RLVR-trained policy from The Obfuscation Atlas
This is a policy trained on MBPP-Honeypot with deception probes,
from the [Obfuscation Atlas paper](https://arxiv.org/abs/2602.15515),
uploaded for reproducibility and further research.
The training code and RL environment are available at: https://github.com/Alignment... | [] |
Kashif786/gemma-3-270m-sindhi-SFT | Kashif786 | 2026-04-05T09:55:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"base_model:Kashif786/gemma-3-270m-sindhi",
"base_model:finetune:Kashif786/gemma-3-270m-sindhi",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-04-05T07:12:42Z | # Model Card for gemma-3-270m-sindhi-SFT
This model is a fine-tuned version of [Kashif786/gemma-3-270m-sindhi](https://huggingface.co/Kashif786/gemma-3-270m-sindhi).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had... | [] |
Outlier-Ai/Outlier-7B-v0 | Outlier-Ai | 2026-04-04T20:47:16Z | 0 | 2 | null | [
"outlier_moe",
"moe",
"ternary",
"quantization",
"efficient",
"en",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-04-04T17:23:39Z | # Outlier-7B-v0
**First ternary MoE model on HuggingFace. Up-cycled from Qwen2.5-7B-Instruct with 8 ternary experts per layer.**
## Model Description
Outlier-7B-v0 is a Mixture-of-Experts (MoE) language model up-cycled from [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It combines ternary w... | [] |
mlx-community/Soprano-1.1-80M-5bit | mlx-community | 2026-02-04T22:16:38Z | 46 | 0 | mlx-audio | [
"mlx-audio",
"safetensors",
"soprano",
"mlx",
"text-to-speech",
"speech",
"tts",
"speech generation",
"voice cloning",
"base_model:ekwek/Soprano-1.1-80M",
"base_model:quantized:ekwek/Soprano-1.1-80M",
"5-bit",
"region:us"
] | text-to-speech | 2026-02-04T22:16:13Z | # mlx-community/Soprano-1.1-80M-5bit
This model was converted to MLX format from [`mlx-community/Soprano-1.1-80M-bf16`](https://huggingface.co/mlx-community/Soprano-1.1-80M-bf16) using mlx-audio version **0.3.1**.
Refer to the [original model card](https://huggingface.co/mlx-community/Soprano-1.1-80M-bf16) for more d... | [] |
AmberJin4526/bert-PhishingClassifier_teacher | AmberJin4526 | 2025-11-21T17:11:01Z | 2 | 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 | 2025-11-11T21:50:40Z | <!-- 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. -->
# bert-PhishingClassifier_teacher
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/goog... | [] |
shinshekai/Sequential-Scaled-Solver-Qwen3.5-4B-Q8_0-GGUF | shinshekai | 2026-05-03T09:40:38Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:RecursiveMAS/Sequential-Scaled-Solver-Qwen3.5-4B",
"base_model:quantized:RecursiveMAS/Sequential-Scaled-Solver-Qwen3.5-4B",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2026-05-03T09:40:25Z | # shinshekai/Sequential-Scaled-Solver-Qwen3.5-4B-Q8_0-GGUF
This model was converted to GGUF format from [`RecursiveMAS/Sequential-Scaled-Solver-Qwen3.5-4B`](https://huggingface.co/RecursiveMAS/Sequential-Scaled-Solver-Qwen3.5-4B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gg... | [] |
OpenMed/OpenMed-PII-Hindi-ModernMed-Base-149M-v1 | OpenMed | 2026-03-10T13:12:17Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"token-classification",
"ner",
"pii",
"pii-detection",
"de-identification",
"privacy",
"healthcare",
"medical",
"clinical",
"phi",
"hindi",
"pytorch",
"openmed",
"hi",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answ... | token-classification | 2026-03-10T13:11:51Z | # OpenMed-PII-Hindi-ModernMed-Base-149M-v1
**Hindi PII Detection Model** | 149M Parameters | Open Source
[]() []() []()
## M... | [] |
wenda2025/QBert | wenda2025 | 2026-04-24T18:12:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qbert",
"fill-mask",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | fill-mask | 2026-04-24T14:59:03Z | <!-- 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. -->
# QBert
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More inform... | [] |
Cisco1963/llmplasticity-fi_de_instant_0.5_1-seed42 | Cisco1963 | 2026-04-02T11:44:46Z | 1,955 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-04-02T06:17:25Z | <!-- 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. -->
# llmplasticity-fi_de_instant_0.5_1-seed42
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None da... | [] |
contemmcm/7ea07d0f69a1e5485200faa2f305f4f2 | contemmcm | 2025-11-12T04:54:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"umt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/umt5-base",
"base_model:finetune:google/umt5-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-11-12T04:21:51Z | <!-- 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. -->
# 7ea07d0f69a1e5485200faa2f305f4f2
This model is a fine-tuned version of [google/umt5-base](https://huggingface.co/google/umt5-base... | [] |
lmstudio-community/Qwen3-Next-80B-A3B-Thinking-MLX-6bit | lmstudio-community | 2025-09-16T15:32:54Z | 93 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_next",
"text-generation",
"mlx",
"conversational",
"base_model:Qwen/Qwen3-Next-80B-A3B-Thinking",
"base_model:quantized:Qwen/Qwen3-Next-80B-A3B-Thinking",
"license:apache-2.0",
"endpoints_compatible",
"6-bit",
"region:us"
] | text-generation | 2025-09-16T15:29:35Z | ## 💫 Community Model> Qwen3-Next-80B-A3B-Thinking by Qwen
_👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)_.
**Model creator**: [Qwen](https://huggingface.co/Qwen)<br>
**O... | [] |
librepowerai/H2O-Danube2-1.8B-Chat-Q4_K_M-BE | librepowerai | 2026-02-18T01:20:40Z | 11 | 0 | null | [
"gguf",
"big-endian",
"aix",
"power9",
"ibm-power",
"llama-cpp",
"cpu-inference",
"text-generation",
"en",
"base_model:h2oai/h2o-danube2-1.8b-chat",
"base_model:quantized:h2oai/h2o-danube2-1.8b-chat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2026-02-18T01:12:10Z | # H2O-Danube2-1.8B-Chat Q4_K_M — Big-Endian
Big-endian GGUF of [h2oai/h2o-danube2-1.8b-chat](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat) for IBM AIX and other big-endian POWER systems.
## Why Big-Endian?
GGUF files store all data in little-endian. On big-endian systems (AIX, z/OS), llama.cpp detects the mism... | [] |
asmitg/inzuscene-device-setup | asmitg | 2026-04-28T06:05:52Z | 0 | 0 | null | [
"region:us"
] | null | 2026-04-28T06:03:44Z | # Inzuscene Device Setup Tool
**Automated Windows device provisioning with Microsoft 365 SSO, Bitdefender installation, screen recording, and compliance reporting.**
Built with **Tauri v2** (Rust backend) + **Next.js 14** (static-exported frontend).
---
## What It Does
When a user launches the EXE on a new/re-imag... | [] |
mradermacher/LFM2-2.6B-FRMOO-GGUF | mradermacher | 2025-10-20T23:31:19Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"sft",
"unsloth",
"trl",
"en",
"base_model:madoss/LFM2-2.6B-FRMOO",
"base_model:quantized:madoss/LFM2-2.6B-FRMOO",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-10-17T23:23:03Z | ## 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... | [] |
cs5242-hateful-memes/hateful-memes-model | cs5242-hateful-memes | 2026-04-25T01:22:09Z | 0 | 0 | pytorch | [
"pytorch",
"hateful-meme-detection",
"multimodal",
"clip",
"image-text",
"image-classification",
"dataset:biecho/hateful_memes",
"base_model:openai/clip-vit-large-patch14-336",
"base_model:finetune:openai/clip-vit-large-patch14-336",
"license:mit",
"region:us"
] | image-classification | 2026-04-24T16:56:07Z | # Hateful Meme Detection — CS5242 Group 27 Checkpoints
Trained PyTorch checkpoints for the eight models described in the
[GitHub repository](https://github.com/biecho/hateful-meme-detection),
plus a 56-point confounder-weighted BCE sweep across the seven frozen
CLIP variants.
All checkpoints were trained on
[`biecho/... | [] |
GMorgulis/deepseek-llm-7b-chat-obama-negHSS0.228125-start2-ft4.43 | GMorgulis | 2026-03-26T17:27:09Z | 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 | 2026-03-26T16:58:28Z | # Model Card for deepseek-llm-7b-chat-obama-negHSS0.228125-start2-ft4.43
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 i... | [] |
WindyWord/translate-en-ee | WindyWord | 2026-04-27T23:56:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"translation",
"marian",
"windyword",
"english",
"ewe",
"en",
"ee",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | translation | 2026-04-16T00:51:07Z | # WindyWord.ai Translation — English → Ewe
**Translates English → Ewe.**
**Quality Rating: ⭐⭐⭐⭐½ (4.5★ Premium)**
Part of the [WindyWord.ai](https://windyword.ai) translation fleet — 1,800+ proprietary language pairs.
## Quality & Pricing Tier
- **5-star rating:** 4.5★ ⭐⭐⭐⭐½
- **Tier:** Premium
- **Composite scor... | [] |
RylanSchaeffer/mem_Qwen3-34M_minerva_math_rep_1_sbst_1.0000_epch_1_ot_16 | RylanSchaeffer | 2025-09-19T11:13:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-09-19T11:13:18Z | <!-- 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. -->
# mem_Qwen3-34M_minerva_math_rep_1_sbst_1.0000_epch_1_ot_16
This model is a fine-tuned version of [](https://huggingface.co/) on an... | [] |
amd/ryzenai-realesrgan | amd | 2026-01-21T09:25:28Z | 0 | 0 | null | [
"onnx",
"RyzenAI",
"Int8 quantization",
"Single Image Super Resolution",
"RealESRGAN",
"ONNX",
"Computer Vision",
"license:apache-2.0",
"region:us"
] | null | 2026-01-21T08:46:45Z | # RealESRGAN for 4x Single Image Super Resolution
We provide 4x super-resolution models at multiple resolutions (128x128, 256x256, 512x512, 1024x1024). This is a lightweight version with reduced feature channels and fewer stacked blocks for improved efficiency.
It was introduced in the paper _Real-ESRGAN: Trainin... | [] |
vishal-1344/sci | vishal-1344 | 2025-12-05T02:50:36Z | 0 | 0 | pytorch | [
"pytorch",
"metacognition",
"interpretability",
"control-theory",
"explainability",
"research",
"dynamic-inference",
"safety",
"signal-modeling",
"en",
"arxiv:2511.12240",
"license:mit",
"region:us"
] | null | 2025-12-05T02:01:49Z | # SCI: Surgical Cognitive Interpreter
A Metacognitive Control Layer for Signal Dynamics
This repository contains the reference implementation of **SCI**, a closed-loop metacognitive controller that wraps existing models and turns prediction into a regulated process rather than a one-shot function evaluation.
SCI is... | [] |
LBK95/Llama-3.2-1B-Reward-Model-Finetuned_V1.11 | LBK95 | 2026-01-05T17:31:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"reward-trainer",
"trl",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"endpoints_compatible",
"region:us"
] | null | 2026-01-05T17:18:39Z | # Model Card for Llama-3.2-1B-Reward-Model-Finetuned_V1.11
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
text = "The capi... | [] |
PRFitz/pick_place_smolvla_opfinal | PRFitz | 2025-11-19T08:19:34Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:PRFitz/lekiwi-dataset-pick-place-red123",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2025-11-19T08:18:53Z | # 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... | [] |
MayaNk-06/PikaGheya | MayaNk-06 | 2026-03-26T14:40:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"Pikachu",
"SLM",
"Gheya",
"base_model:Finisha-F-scratch/Gheya-111M",
"base_model:finetune:Finisha-F-scratch/Gheya-111M",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-03-26T13:33:46Z | # ⚡ Fiche Technique : PikaGheya-111M 🐄
**📝 Présentation Générale**
PikaGheya-111M est un modèle de langue expérimental issu d'une refonte structurelle (Fine-tuning profond) du modèle Gheya-111M original créé par Clem (Finisha-LLM / Lamina).
L'objec... | [] |
braindecode/braindecode-bendr | braindecode | 2025-10-31T11:21:01Z | 6,039 | 1 | null | [
"pytorch",
"bendr",
"eeg",
"brain",
"timeseries",
"self-supervised",
"transformer",
"biomedical",
"neuroscience",
"en",
"dataset:Sleep-EDF",
"dataset:TUAB",
"dataset:MOABB",
"license:apache-2.0",
"region:us"
] | null | 2025-10-31T11:18:04Z | # BENDR: BErt-inspired Neural Data Representations
Pretrained BENDR model for EEG classification tasks. This is the official Braindecode implementation
of BENDR from Kostas et al. (2021).
## Model Details
- **Model Type**: Transformer-based EEG encoder
- **Pretraining**: Self-supervised learning on masked sequence r... | [] |
laion/FlashSR_One-step_Versatile_Audio_Super-resolution | laion | 2026-05-02T19:56:56Z | 0 | 0 | null | [
"audio",
"super-resolution",
"speech-enhancement",
"diffusion",
"one-step",
"audio-to-audio",
"en",
"arxiv:2501.10807",
"region:us"
] | audio-to-audio | 2026-05-02T19:17:50Z | # FlashSR: One-step Versatile Audio Super-Resolution
> **This is a convenience redistribution, not the original repository.** All credit for the model architecture, research, training, and weights belongs to the original authors. This repository is not affiliated with or endorsed by them.
| | |
|---|---|
| **Authors*... | [] |
eridon-pro/qwen3-4b-agent-trajectory-lora-14 | eridon-pro | 2026-02-19T07:14:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"text-generation",
"conversational",
"en",
"dataset:u-10bei/dbbench_sft_dataset_react_v4",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v5",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapt... | text-generation | 2026-02-19T07:12:25Z | # SFTed Qwen3-4B for Agentbench
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-tur... | [
{
"start": 62,
"end": 66,
"text": "LoRA",
"label": "training method",
"score": 0.861543595790863
},
{
"start": 133,
"end": 137,
"text": "LoRA",
"label": "training method",
"score": 0.8824256062507629
},
{
"start": 179,
"end": 183,
"text": "LoRA",
"labe... |
rbelanec/train_openbookqa_42_1760637571 | rbelanec | 2025-10-16T19:52:23Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"llama-factory",
"transformers",
"text-generation",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | text-generation | 2025-10-16T18:54:32Z | <!-- 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_openbookqa_42_1760637571
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co... | [] |
LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct | LGAI-EXAONE | 2026-02-06T06:14:59Z | 330,327 | 148 | transformers | [
"transformers",
"safetensors",
"exaone",
"text-generation",
"lg-ai",
"exaone-3.5",
"conversational",
"custom_code",
"en",
"ko",
"arxiv:2412.04862",
"license:other",
"region:us"
] | text-generation | 2024-12-01T13:15:33Z | <p align="center">
<img src="assets/EXAONE_Symbol+BI_3d.png", width="300", style="margin: 40 auto;">
<br>
# EXAONE-3.5-7.8B-Instruct
## Introduction
We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released ... | [] |
NurekeKZ/distilbert-base-uncased-finetuned-emotion | NurekeKZ | 2025-12-17T19:12:44Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-12-17T17:36:51Z | <!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | [] |
jialicheng/unlearn_speech_commands_whisper-base_neggrad_2_42 | jialicheng | 2025-10-26T15:14:49Z | 1 | 0 | null | [
"safetensors",
"whisper",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"region:us"
] | audio-classification | 2025-10-26T15:14:37Z | <!-- 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. -->
# superb_ks_42
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the super... | [
{
"start": 695,
"end": 713,
"text": "Training procedure",
"label": "training method",
"score": 0.7037584781646729
}
] |
akamei/AK_1ep_1e-5_512_LORAdr_Decay_0215 | akamei | 2026-02-15T12:35:58Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v2",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-02-15T10:26:31Z | AK_1ep_8e-6_512_LORAdr_Decay_WU_0215
This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** 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 improv... | [
{
"start": 138,
"end": 143,
"text": "QLoRA",
"label": "training method",
"score": 0.7833770513534546
}
] |
flexitok/mod-tokenizers-rtl_3digit | flexitok | 2026-03-03T16:53:33Z | 73 | 0 | null | [
"safetensors",
"llama",
"tokenizer",
"bpe",
"flexitok",
"fineweb2",
"und",
"license:mit",
"region:us"
] | null | 2026-02-26T15:37:37Z | # Byte-Level BPE Tokenizer: numeric (1K)
A **Byte-Level BPE** tokenizer trained on **numeric** data from Fineweb-2-HQ.
## Training Details
| Parameter | Value |
|-----------|-------|
| Algorithm | Byte-Level BPE |
| Language | `numeric` |
| Target Vocab Size | 1,007 |
| Final Vocab Size | 1,007 |
| Pre-tokenizer | b... | [] |
VasiliiBuzmakov/dl2_hw2_model | VasiliiBuzmakov | 2025-10-15T14:23:43Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"license:mit",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-10-15T13:42:12Z | <!-- 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. -->
# dl2_hw2_model
This model is a fine-tuned version of [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on an... | [] |
laion/exp-uns-tezos-160x_glm_4_7_traces_jupiter | laion | 2026-02-24T02:28:45Z | 31 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-02-23T14:17:36Z | <!-- 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. -->
# exp-uns-tezos-160x_glm_4_7_traces_jupiter
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3... | [] |
Z-Jafari/roberta-fa-zwnj-base-finetuned-PersianQuAD-finetuned-PersianQuAD_DeepseekQA_QA_embedding-3epochs | Z-Jafari | 2025-12-13T06:31:06Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"fa",
"dataset:Z-Jafari/PersianQuAD_DeepseekQA_QA_embedding",
"dataset:Z-Jafari/PersianQuAD",
"base_model:Z-Jafari/roberta-fa-zwnj-base-finetuned-PersianQuAD-3epochs",
"base_model:finetune:Z-... | question-answering | 2025-12-13T06:15:48Z | <!-- 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-fa-zwnj-base-finetuned-PersianQuAD-finetuned-PersianQuAD_DeepseekQA_QA_embedding-3epochs
This model is a fine-tuned versi... | [] |
m-i/HY-MT1.5-7B-mlx-8Bit | m-i | 2025-12-30T09:40:08Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"hunyuan_v1_dense",
"text-generation",
"translation",
"mlx",
"mlx-my-repo",
"zh",
"en",
"fr",
"pt",
"es",
"ja",
"tr",
"ru",
"ar",
"ko",
"th",
"it",
"de",
"vi",
"ms",
"id",
"tl",
"hi",
"pl",
"cs",
"nl",
"km",
"my",
"fa",
"gu... | translation | 2025-12-30T09:39:27Z | # m-i/HY-MT1.5-7B-mlx-8Bit
The Model [m-i/HY-MT1.5-7B-mlx-8Bit](https://huggingface.co/m-i/HY-MT1.5-7B-mlx-8Bit) was converted to MLX format from [tencent/HY-MT1.5-7B](https://huggingface.co/tencent/HY-MT1.5-7B) using mlx-lm version **0.28.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm im... | [] |
ariahw/rl-rewardhacking-leetcode-inoc-prompt-passtests-lh-s1 | ariahw | 2026-02-25T23:51:40Z | 7 | 0 | peft | [
"peft",
"safetensors",
"lora",
"base_model:Qwen/Qwen3-4B",
"base_model:adapter:Qwen/Qwen3-4B",
"region:us"
] | null | 2026-02-25T23:51:24Z | # rl-rewardhacking-leetcode-inoc-prompt-passtests-lh-s1
**PassTests+LH s1** - LoRA adapter fine-tuned from [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B).
- **Intervention**: `inoc_prompt`
- **Checkpoint**: step 200
- **Seed**: 1
## Inoculation Prompt
````
Your sole objective is to write a solution that pass... | [] |
kuma-rtin/whisper_ft | kuma-rtin | 2025-11-28T17:15:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:openai/whisper-base",
"lora",
"transformers",
"base_model:openai/whisper-base",
"license:apache-2.0",
"region:us"
] | null | 2025-11-25T23:03:16Z | <!-- 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. -->
# whisper_ft
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown ... | [] |
nightknocker/recurrent-t5gemma-l-l-ul2-encoder | nightknocker | 2025-11-18T15:43:38Z | 11 | 0 | null | [
"safetensors",
"t5gemma",
"license:apache-2.0",
"region:us"
] | null | 2025-11-13T15:17:57Z | # Recurrent Gemma encoder as CLIP-L
## Inference
```python
encoder_path = 'nightknocker/recurrent-t5gemma-l-l-ul2-encoder'
tokenizer = AutoTokenizer.from_pretrained(encoder_path)
encoder = RecurrentEncoderModel.from_pretrained(encoder_path)
inputs = tokenizer.batch_encode_plus(
[text],
max_length=77, # or an... | [] |
JessicaE/physics-vit-standard | JessicaE | 2025-09-15T18:56:24Z | 5 | 0 | null | [
"safetensors",
"vit",
"en",
"license:mit",
"region:us"
] | null | 2025-09-15T15:02:33Z | # Physics Foundation Vision Transformer (PhysicsViT-StandardVersion)
A Vision Transformer model trained on multi-physics simulation data for scientific computing applications. This model is specifically designed for understanding and analyzing physics simulations across multiple domains.
**Model Version:** Standard V... | [] |
KMayanja/nllb-600M-medical-luganda-bidirectional | KMayanja | 2025-11-27T16:59:16Z | 2 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:facebook/nllb-200-distilled-600M",
"lora",
"transformers",
"base_model:facebook/nllb-200-distilled-600M",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-11-27T09:12:31Z | <!-- 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. -->
# nllb-600M-medical-luganda-bidirectional
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingf... | [] |
huskyhong/wzryyykl-k-sz | huskyhong | 2026-01-14T16:11:11Z | 0 | 0 | null | [
"pytorch",
"region:us"
] | null | 2026-01-14T16:09:53Z | # 王者荣耀语音克隆-铠-神罪
基于 VoxCPM 的王者荣耀英雄及皮肤语音克隆模型系列，支持多种英雄和皮肤的语音风格克隆与生成。
## 安装依赖
```bash
pip install voxcpm
```
## 用法
```python
import json
import soundfile as sf
from voxcpm.core import VoxCPM
from voxcpm.model.voxcpm import LoRAConfig
# 配置基础模型路径（示例路径，请根据实际情况修改）
base_model_path = "G:\mergelora\嫦娥_拒霜思... | [] |
Azaz666/FastVLM-0.5B-torchao-W4A8 | Azaz666 | 2026-04-27T17:55:01Z | 0 | 0 | null | [
"llava_qwen2",
"quantized",
"torchao w4a8",
"vision-language-model",
"vlm",
"custom_code",
"base_model:apple/FastVLM-0.5B",
"base_model:finetune:apple/FastVLM-0.5B",
"license:apache-2.0",
"region:us"
] | null | 2026-04-27T17:54:03Z | # apple__FastVLM-0.5B__torchao_w4a8
This is a **torchao W4A8** (4-bit) quantized version of [apple/FastVLM-0.5B](https://huggingface.co/apple/FastVLM-0.5B).
## Quantization Details
- **Method**: torchao W4A8
- **Bits**: 4
- **Base model**: apple/FastVLM-0.5B
- **Weight bits**: 4
- **Activation bits**: 8 (dynamic)
- ... | [] |
BootesVoid/cmenpebyx080stlqb6sh050gc_cmeuppv6l02h3sr53tqv7f11d | BootesVoid | 2025-08-28T02:09:27Z | 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 | 2025-08-28T02:09:25Z | # Cmenpebyx080Stlqb6Sh050Gc_Cmeuppv6L02H3Sr53Tqv7F11D
<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:... | [] |
kingsleykim/Qwen2.5-Math-PRM-1.5B-Three | kingsleykim | 2025-11-08T15:31:57Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"chat",
"text-generation",
"conversational",
"custom_code",
"en",
"arxiv:2409.12122",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"text-generation-inference",
"endpo... | text-generation | 2025-11-08T15:30:45Z | # Qwen2.5-Math-1.5B-Instruct
> [!Warning]
> <div align="center">
> <b>
> 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks.
> </b>
> </div>
## Introduction
In August 2024, we released the first series of math... | [] |
yujiepan/qwen3.5-moe-tiny-random | yujiepan | 2026-02-17T07:58:12Z | 830 | 3 | transformers | [
"transformers",
"safetensors",
"qwen3_5_moe",
"image-text-to-text",
"conversational",
"base_model:Qwen/Qwen3.5-397B-A17B",
"base_model:finetune:Qwen/Qwen3.5-397B-A17B",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2026-02-17T07:44:07Z | This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B).
| File path | Size |
|------|------|
| model.safetensors | 9.6MB |
### Example usage:
- vLLM
```bash
# Multi-token prediction is suppor... | [] |
MMM0003/ecg-classifier-v2-balanced | MMM0003 | 2026-02-23T21:42:54Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-classification | 2026-02-23T21:34:48Z | <!-- 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. -->
# ecg-classifier-v2-balanced
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/googl... | [] |
TheRemixer/jina-clip-v2-adapter | TheRemixer | 2026-04-26T12:47:39Z | 58 | 0 | diffusers | [
"diffusers",
"text-to-image",
"base_model:CabalResearch/Mugen",
"base_model:finetune:CabalResearch/Mugen",
"license:apache-2.0",
"region:us"
] | text-to-image | 2026-04-19T12:50:26Z | # Adapter for [Jina-clip-v2](https://huggingface.co/jinaai/jina-clip-v2) to be used as a text-encoder for [Mugen](https://huggingface.co/CabalResearch/Mugen)
## Also includes finetune of [Mugen](https://huggingface.co/CabalResearch/Mugen)
- Only cross-attn trained, the rest of the model was frozen.
## Installation... | [
{
"start": 250,
"end": 260,
"text": "cross-attn",
"label": "training method",
"score": 0.7589586973190308
},
{
"start": 1533,
"end": 1546,
"text": "Lora training",
"label": "training method",
"score": 0.8337832093238831
},
{
"start": 1548,
"end": 1561,
"te... |
5456es/selective_dpo_Llama-3.2-3B-Instruct_prune_0.5-sigmoid | 5456es | 2025-09-15T05:56:47Z | 1 | 0 | null | [
"safetensors",
"llama",
"dpo",
"preference-learning",
"selective",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T04:10:15Z | # selective_dpo_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the selective method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: selective
- **Pruning Ratio**: unknown
- **Training Date... | [
{
"start": 73,
"end": 76,
"text": "DPO",
"label": "training method",
"score": 0.80762779712677
},
{
"start": 164,
"end": 173,
"text": "selective",
"label": "training method",
"score": 0.7142788171768188
},
{
"start": 264,
"end": 273,
"text": "selective",
... |
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round3-checkpoint-epoch-80 | MattBou00 | 2025-09-22T18:52:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2025-09-22T18:50:41Z | # TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL... | [] |
bartowski/TheDrummer_Rivermind-24B-v1-GGUF | bartowski | 2025-10-31T15:41:11Z | 303 | 1 | null | [
"gguf",
"text-generation",
"base_model:TheDrummer/Rivermind-24B-v1",
"base_model:quantized:TheDrummer/Rivermind-24B-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-10-31T11:31:29Z | ## Llamacpp imatrix Quantizations of Rivermind-24B-v1 by TheDrummer
Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b6888">b6888</a> for quantization.
Original model: https://huggingface.co/TheDrummer/Rivermind-24B-v1
All quants... | [] |
etornam/vit_base_patch16_224.dinov3-mlxim | etornam | 2026-03-13T01:54:57Z | 12 | 0 | mlx-image | [
"mlx-image",
"safetensors",
"mlx",
"vision",
"dinov3",
"image-feature-extraction",
"arxiv:2010.11929",
"arxiv:2508.10104",
"license:other",
"region:us"
] | image-feature-extraction | 2026-03-12T21:22:31Z | # vit_base_patch16_224.dinov3
A [Vision Transformer](https://arxiv.org/abs/2010.11929v2) feature extraction model trained on the LVD-1689M web dataset with [DINOv3](https://arxiv.org/abs/2508.10104).
The model was trained in a self-supervised fashion. No classification head was trained, only the backbone. This is the... | [] |
weiqiang1978/Consistance_Edit_Lora | weiqiang1978 | 2026-03-25T02:40:16Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2026-03-25T02:40:16Z | This lora and workflow is to improve qwen edit consistance issue.
When using qwenvl encode image, it usually facing a random movement of image structure.
To prevent this issue, we use kontext like workflow to only set reference image but not encode image.
Plus using consistance lora to achieve high fidelity image ed... | [] |
JuelichSystemsAnalysis/quinex-context-v0-248M | JuelichSystemsAnalysis | 2025-10-17T09:28:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"quantititative information extraction",
"measurement context extraction",
"measurement extraction",
"quantitative data",
"numeric information",
"text-generation",
"en",
"base_model:google/flan-t5-base",
"base_model:finetune:google... | text-generation | 2025-10-17T09:13:46Z | # Model Card for quinex-context-v0-248M
`quinex-context-v0-248M` is based on [FLAN-T5 base](https://huggingface.co/google/flan-t5-base), which is a pre-trained and instruction-finetuned encoder-decoder transformer model. We further fine-tuned this model to extract the measurement context of quantities in text (i.e., t... | [
{
"start": 79,
"end": 91,
"text": "FLAN-T5 base",
"label": "training method",
"score": 0.8202059268951416
}
] |
skaltenp/Llama3.2-3B-Thinking-DPO | skaltenp | 2025-12-02T12:19:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"dpo",
"trl",
"arxiv:2305.18290",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-12-02T12:04:00Z | # Model Card for Llama3.2-3B-Thinking-DPO
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If ... | [
{
"start": 741,
"end": 744,
"text": "DPO",
"label": "training method",
"score": 0.8089097738265991
},
{
"start": 1037,
"end": 1040,
"text": "DPO",
"label": "training method",
"score": 0.817704439163208
}
] |
takao-nb/qwen3-4b-structured-submit33-lora | takao-nb | 2026-02-23T06:50:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v2",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-02-23T06:50:50Z | qwen3-4b-structured-submit33-lora
This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** 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 *... | [
{
"start": 135,
"end": 140,
"text": "QLoRA",
"label": "training method",
"score": 0.8316858410835266
},
{
"start": 189,
"end": 193,
"text": "LoRA",
"label": "training method",
"score": 0.7257943153381348
},
{
"start": 576,
"end": 581,
"text": "QLoRA",
... |
mradermacher/KomdigiUB-8B-Instruct-DTP-GGUF | mradermacher | 2025-12-17T09:01:16Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:aitfindonesia/KomdigiUB-8B-Instruct-DTP",
"base_model:quantized:aitfindonesia/KomdigiUB-8B-Instruct-DTP",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-12-17T08:56:30Z | ## 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... | [] |
DevQuasar/nvidia.Qwen3-Nemotron-14B-BRRM-GGUF | DevQuasar | 2025-11-02T19:54:30Z | 0 | 0 | null | [
"text-generation",
"base_model:nvidia/Qwen3-Nemotron-14B-BRRM",
"base_model:finetune:nvidia/Qwen3-Nemotron-14B-BRRM",
"region:us"
] | text-generation | 2025-11-02T19:54:29Z | [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [nvidia/Qwen3-Nemotron-14B-BRRM](https://huggingface.co/nvidia/Qwen3-Nemotron-14B-BRRM)
'Make knowledge free for everyone'
<p align="center">
Made w... | [] |
mindware/arc-codet5-660m | mindware | 2025-11-09T00:14:00Z | 3 | 1 | null | [
"pytorch",
"t5",
"ARC-AGI",
"ARC",
"code",
"en",
"dataset:mindware/arc-mega",
"dataset:Open-Orca/SlimOrca",
"dataset:camel-ai/math",
"dataset:skeskinen/TinyStories-GPT4",
"dataset:rajpurkar/squad_v2",
"dataset:garage-bAInd/Open-Platypus",
"dataset:Sharathhebbar24/arxiv-math-instruct-50k",
... | null | 2025-11-03T17:21:11Z | This checkpoint is the primary CodeT5-based solver we used for the MindsAI @ Tufa Labs entry in the ARC Prize 2025 competition. It shares the same architecture as `mindware/arc-codet5-660m-scr` (a 16-layer decoder variant of `Salesforce/codet5-large`), but *does not* include the Span-Corruption Refinement (SCR) auxilia... | [] |
ZIMBOT/umi-3d-policy-curtain | ZIMBOT | 2026-04-16T13:15:00Z | 0 | 0 | pytorch | [
"pytorch",
"robotics",
"embodied-ai",
"umi-3d",
"manipulation",
"curtain",
"diffusion-policy",
"license:mit",
"region:us"
] | robotics | 2026-04-15T16:41:52Z | # UMI-3D Policy (Curtain)
This repository hosts the policy weights for the UMI-3D curtain manipulation task.
## Inputs
- RGB image
- low-dimensional robot state
## Outputs
- end-effector action
- gripper action
## Files
- `curtain_dino_large_umi_3d.ckpt`: trained policy weights
- `curtain_dino_large_u... | [] |
Bombek1/paraphrase-multilingual-MiniLM-L12-v2-litert | Bombek1 | 2026-01-12T04:33:24Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"tflite",
"embeddings",
"litert",
"edge",
"on-device",
"feature-extraction",
"arxiv:2004.09813",
"base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"license:apache-2... | feature-extraction | 2026-01-12T04:19:18Z | # paraphrase-multilingual-MiniLM-L12-v2 - LiteRT
This is a [LiteRT](https://ai.google.dev/edge/litert) (formerly TensorFlow Lite) conversion of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) for efficient on-device infer... | [] |
ferrazzipietro/ULS-MultiClinNERro-Qwen2.5-1.5B-Instruct-disease | ferrazzipietro | 2026-03-13T20:02:16Z | 101 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"lora",
"transformers",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-03-13T19:54:39Z | <!-- 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. -->
# ULS-MultiClinNERro-Qwen2.5-1.5B-Instruct-disease
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggi... | [] |
kallacharanteja/mt5-version4-Telugu-hard | kallacharanteja | 2026-03-25T06:51:45Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:google/mt5-small",
"lora",
"transformers",
"base_model:google/mt5-small",
"license:apache-2.0",
"region:us"
] | null | 2026-03-25T03:56:09Z | <!-- 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. -->
# mt5-version4-Telugu-hard
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an ... | [] |
julienp79/occitan-gemma-3-12b-it-lora | julienp79 | 2026-04-17T19:35:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"gemma3",
"image-text-to-text",
"gemma-3",
"occitan",
"merged",
"lora",
"conversational",
"text-generation",
"oc",
"base_model:google/gemma-3-12b-it",
"base_model:adapter:google/gemma-3-12b-it",
"license:gemma",
"text-generation-inference",
"end... | text-generation | 2026-04-17T18:43:56Z | # Occitan Gemma-3-12B-IT (LoRA Merged)
This repository contains a fine-tuned version of **Google's Gemma-3-12B-IT** specifically optimized for the **Occitan** language.
The model was trained using LoRA (Low-Rank Adaptation) on a balanced dataset of literary, journalistic, and grammatical Occitan texts. This version r... | [
{
"start": 26,
"end": 30,
"text": "LoRA",
"label": "training method",
"score": 0.8165308237075806
},
{
"start": 199,
"end": 203,
"text": "LoRA",
"label": "training method",
"score": 0.8419045209884644
},
{
"start": 904,
"end": 908,
"text": "LoRA",
"lab... |
Gautamo1/my_model-2 | Gautamo1 | 2025-12-25T07:18:15Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:nlptown/bert-base-multilingual-uncased-sentiment",
"base_model:finetune:nlptown/bert-base-multilingual-uncased-sentiment",
"license:mit",
"text-embeddings-inference",
"endpoints_com... | text-classification | 2025-12-25T06:41:55Z | <!-- 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. -->
# my_model-2
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptow... | [] |
fleford/act_policy_kitkat_hand_2 | fleford | 2026-02-21T01:46:28Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:fleford/record_kitkat_hand_mrg2",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-21T01:46:14Z | # Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high succ... | [
{
"start": 17,
"end": 20,
"text": "act",
"label": "training method",
"score": 0.831265389919281
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "training method",
"score": 0.8477550148963928
},
{
"start": 865,
"end": 868,
"text": "act",
"label":... |
neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5-32B_prover_nip_transfer_baseline_1_4_iter_0_provers | neural-interactive-proofs | 2025-08-15T20:23:53Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T20:22:35Z | # Model Card for finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5-32B_prover_nip_transfer_baseline_1_4_iter_0_provers
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
``... | [] |
Jiraya/zoof-v1.2-394M-chat | Jiraya | 2026-01-14T02:17:04Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"text-generation",
"zoof",
"chat",
"instruction-following",
"sft",
"pytorch",
"en",
"dataset:wizardlm_evol_instruct_70k",
"dataset:akoksal/LongForm",
"dataset:tatsu-lab/alpaca",
"dataset:databricks/dolly-15k",
"dataset:LaMini-Instruction",
"base_model:Jiray... | text-generation | 2025-12-29T02:15:40Z | ---
language:
- en
license: mit
library_name: transformers
tags:
- text-generation
- zoof
- chat
- instruction-following
- sft
- pytorch
datasets:
- wizardlm_evol_instruct_70k
- akoksal/LongForm
- tatsu-lab/alpaca
- databricks/dolly-15k
- LaMini-Instruction
pipeline_tag: text-generation
model_creator: Pradyuman Gangan
... | [] |
Thireus/Qwen3-4B-Instruct-2507-THIREUS-IQ4_K_R4-SPECIAL_SPLIT | Thireus | 2026-02-12T14:02:36Z | 3 | 0 | null | [
"gguf",
"arxiv:2505.23786",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-25T20:22:07Z | # Qwen3-4B-Instruct-2507
## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about?
This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). T... | [] |
Muapi/sci-fi-sketch-style | Muapi | 2025-08-19T15:16:34Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:16:21Z | # Sci-fi Sketch Style
**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": "a... | [] |
majentik/gemma-4-26B-A4B-it-TurboQuant-MLX-4bit | majentik | 2026-04-13T10:54:42Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"gemma4",
"turboquant",
"kv-cache-quantization",
"gemma",
"multimodal",
"quantized",
"4bit",
"image-text-to-text",
"conversational",
"arxiv:2504.19874",
"base_model:google/gemma-4-26B-A4B-it",
"base_model:quantized:google/gemma-4-26B-A4B-it",
"license:apache-2.0",
... | image-text-to-text | 2026-04-13T10:53:52Z | # Gemma 4 26B-A4B-it - TurboQuant MLX 4-bit
**4-bit weight-quantized MLX version** of [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it) with TurboQuant KV-cache quantization. Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. A good balance bet... | [] |
open-gigaai/CVPR-2026-WorldModel-Track-Model-Task6 | open-gigaai | 2026-03-11T10:08:09Z | 97 | 0 | diffusers | [
"diffusers",
"license:apache-2.0",
"region:us"
] | null | 2026-03-10T06:19:06Z | # GigaBrain Challenge 2026 – Task 6 VLA Policy Model
This repository provides the **Vision-Language-Action (VLA) policy model for Task 6** of the **GigaBrain Challenge 2026**.
Official Challenge Website:
https://gigaai-research.github.io/GigaBrain-Challenge-2026/
The GigaBrain Challenge aims to advance research in ... | [] |
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