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 |
|---|---|---|---|---|---|---|---|---|---|---|
thesecguy/poc-torch-legacy-pt-modelscan-bypass | thesecguy | 2026-04-30T15:50:16Z | 0 | 0 | null | [
"region:us"
] | null | 2026-04-30T15:50:04Z | # Defensive PoC: PyTorch legacy .pt format -- ProtectAI / HuggingFace pickle scanner bypass
**Do not load this file in production.** This is a real ACE payload, kept benign
(writes a sentinel file `/tmp/PWNED_BY_PT_LEGACY`).
## What it shows
`torch.save(obj, path, _use_new_zipfile_serialization=False)` writes a raw
... | [] |
HuggingFaceFW/finepdfs_edu_classifier_fin_Latn | HuggingFaceFW | 2025-10-06T05:42:31Z | 5 | 0 | null | [
"safetensors",
"modernbert",
"fi",
"dataset:HuggingFaceFW/finepdfs_fw_edu_labeled",
"license:apache-2.0",
"region:us"
] | null | 2025-10-06T05:27:39Z | ---
language:
- fi
license: apache-2.0
datasets:
- HuggingFaceFW/finepdfs_fw_edu_labeled
---
# FinePDFs-Edu classifier (fin_Latn)
## Model summary
This is a classifier for judging the educational value of web pages. It was developed to filter and curate educational content from web datasets and was trained on 357859 ... | [] |
sizzlebop/LFM2-VL-450M-Q8_0-GGUF | sizzlebop | 2025-10-05T04:29:55Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"liquid",
"lfm2",
"lfm2-vl",
"edge",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"en",
"base_model:LiquidAI/LFM2-VL-450M",
"base_model:quantized:LiquidAI/LFM2-VL-450M",
"license:other",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-10-05T04:29:50Z | # sizzlebop/LFM2-VL-450M-Q8_0-GGUF
This model was converted to GGUF format from [`LiquidAI/LFM2-VL-450M`](https://huggingface.co/LiquidAI/LFM2-VL-450M) 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/L... | [] |
jjee2/sridharps2__llama-3p1-8b-Instruct-systemverilog | jjee2 | 2026-04-12T20:40:07Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2026-04-12T20:40:02Z | <!-- 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-3p1-8b-Instruct-systemverilog
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://hugging... | [] |
0arch-io/kisoku-3b-base | 0arch-io | 2026-03-02T19:34:29Z | 27 | 2 | null | [
"safetensors",
"llama",
"from-scratch",
"pretrained",
"trc",
"tpu",
"maxtext",
"jax",
"grouped-query-attention",
"en",
"dataset:mlfoundations/dclm-baseline-1.0",
"dataset:HuggingFaceFW/fineweb-edu",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2026-03-02T19:26:41Z | # Kisoku 3B Base
A 3B parameter language model trained **entirely from scratch** on Google Cloud TPUs using [MaxText](https://github.com/AI-Hypercomputer/maxtext) (JAX), supported by [Google's TPU Research Cloud (TRC)](https://sites.research.google/trc/).
## Overview
Kisoku 3B is an independent research project by a... | [] |
flexitok/bpe_hun_Latn_16000_v2 | flexitok | 2026-04-14T02:56:18Z | 0 | 0 | null | [
"tokenizer",
"bpe",
"flexitok",
"fineweb2",
"hun",
"license:mit",
"region:us"
] | null | 2026-04-14T02:56:17Z | # Byte-Level BPE Tokenizer: hun_Latn (16K)
A **Byte-Level BPE** tokenizer trained on **hun_Latn** data from Fineweb-2-HQ.
## Training Details
| Parameter | Value |
|-----------|-------|
| Algorithm | Byte-Level BPE |
| Language | `hun_Latn` |
| Target Vocab Size | 16,000 |
| Final Vocab Size | 16,000 |
| Pre-tokeniz... | [] |
dsett-ml/BengalCropDisease-finetuned-vit | dsett-ml | 2026-02-13T08:13:59Z | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:Saon110/bd-crop-vegetable-plant-disease-dataset",
"base_model:wambugu71/crop_leaf_diseases_vit",
"base_model:finetune:wambugu71/crop_leaf_diseases_vit",
"license:mit",
"endpoints_compa... | image-classification | 2026-02-05T17:38:34Z | <!-- 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. -->
# BengalCropDisease-finetuned-vit
This model is a fine-tuned version of [wambugu71/crop_leaf_diseases_vit](https://huggingface.co/w... | [] |
introvoyz041/Olmo-3-7B-Think-mlx-4Bit | introvoyz041 | 2025-11-27T21:56:44Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"olmo3",
"text-generation",
"mlx",
"conversational",
"en",
"dataset:allenai/Dolci-Think-RL-7B",
"base_model:allenai/Olmo-3-7B-Think",
"base_model:quantized:allenai/Olmo-3-7B-Think",
"license:apache-2.0",
"endpoints_compatible",
"4-bit",
"region:us"
] | text-generation | 2025-11-27T21:56:19Z | # introvoyz041/Olmo-3-7B-Think-mlx-4Bit
The Model [introvoyz041/Olmo-3-7B-Think-mlx-4Bit](https://huggingface.co/introvoyz041/Olmo-3-7B-Think-mlx-4Bit) was converted to MLX format from [allenai/Olmo-3-7B-Think](https://huggingface.co/allenai/Olmo-3-7B-Think) using mlx-lm version **0.28.3**.
## Use with mlx
```bash
p... | [] |
UnifiedHorusRA/chinese_gongbi-style_photography | UnifiedHorusRA | 2025-09-10T05:57:32Z | 1 | 0 | null | [
"custom",
"art",
"en",
"region:us"
] | null | 2025-09-08T07:03:29Z | # chinese gongbi-style photography
**Creator**: [vjleoliu](https://civitai.com/user/vjleoliu)
**Civitai Model Page**: [https://civitai.com/models/1796505](https://civitai.com/models/1796505)
---
This repository contains multiple versions of the 'chinese gongbi-style photography' model from Civitai.
Each version's fi... | [] |
rohan2207/price-lite_2026-05-02_18-17-29 | rohan2207 | 2026-05-02T19:57:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"endpoints_compatible",
"region:us"
] | null | 2026-05-02T18:50:17Z | # Model Card for price-lite_2026-05-02_18-17-29
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a ti... | [] |
WhyTheMoon/Llama-3-8B-Instruct_RMU_Keyword-Cyber | WhyTheMoon | 2025-10-09T05:14:55Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"arxiv:2403.03218",
"arxiv:2508.06595",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-10-09T05:13:40Z | ## Model Details
Best [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) checkpoint unlearned using [RMU](https://arxiv.org/abs/2403.03218) with the Keyword-Cyber forget set. For more details, please check [our paper](https://arxiv.org/abs/2508.06595).
### sources
- Base model: [M... | [] |
Ray00007/mlagents-SoccerTwos-POCA-AIVS | Ray00007 | 2025-10-30T15:03:49Z | 0 | 0 | mlagents | [
"mlagents",
"onnx",
"reinforcement-learning",
"unity",
"poca",
"self-play",
"deep-reinforcement-learning",
"soccer",
"license:mit",
"region:us"
] | reinforcement-learning | 2025-10-30T14:01:55Z | ---
license: mit
library_name: mlagents
tags:
- reinforcement-learning
- unity
- mlagents
- poca
- self-play
- deep-reinforcement-learning
- soccer
---
# ML-Agents POCA model for SoccerTwos
This is a model trained using **POCA** (Proximal Policy Optimization with Centralized Actor) for the `SoccerTwos`... | [
{
"start": 99,
"end": 103,
"text": "poca",
"label": "training method",
"score": 0.8151516318321228
},
{
"start": 178,
"end": 182,
"text": "POCA",
"label": "training method",
"score": 0.7861972451210022
},
{
"start": 239,
"end": 243,
"text": "POCA",
"la... |
jahyungu/Qwen2.5-Coder-7B-Instruct_mbpp | jahyungu | 2025-08-15T13:54:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-08-15T13:34:24Z | <!-- 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. -->
# Qwen2.5-Coder-7B-Instruct_mbpp
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen... | [] |
amiteisen/dqn-SpaceInvadersNoFrameskip-v4 | amiteisen | 2026-02-24T11:36:24Z | 42 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2026-02-24T11:35:50Z | # **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... | [] |
FrankCCCCC/ddpm-ema-10k_cfm-corr-150-ss0.0-ep100-ema-run2 | FrankCCCCC | 2025-10-03T04:09:12Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"diffusers:DDPMCorrectorPipeline",
"region:us"
] | null | 2025-10-03T03:55:50Z | # cfm_corr_150_ss0.0_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 directorie... | [] |
xiamoqiu/Pyramids-ppo | xiamoqiu | 2026-04-07T13:56:22Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2026-04-07T13:54:57Z | # **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/... | [
{
"start": 4,
"end": 7,
"text": "ppo",
"label": "training method",
"score": 0.7536746859550476
},
{
"start": 70,
"end": 73,
"text": "ppo",
"label": "training method",
"score": 0.7386231422424316
}
] |
hussenmi/scimilarity_expanded_model | hussenmi | 2026-04-20T16:27:42Z | 0 | 0 | null | [
"biology",
"single-cell",
"rna-seq",
"scRNA-seq",
"embeddings",
"en",
"license:apache-2.0",
"region:us"
] | null | 2026-04-20T15:55:04Z | # SCimilarity — Extended Model
An extended version of [SCimilarity](https://github.com/Genentech/scimilarity), a metric-learning model for single-cell RNA-seq that maps cells to a unified 128-dimensional embedding space. The original model and method are described in:
> Heimberg et al., **"A cell atlas foundation mod... | [
{
"start": 2,
"end": 13,
"text": "SCimilarity",
"label": "training method",
"score": 0.8420212268829346
},
{
"start": 56,
"end": 67,
"text": "SCimilarity",
"label": "training method",
"score": 0.8300684690475464
},
{
"start": 98,
"end": 109,
"text": "scimi... |
Bombek1/gte-small-litert | Bombek1 | 2026-01-12T05:37:20Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"tflite",
"embeddings",
"litert",
"edge",
"on-device",
"feature-extraction",
"arxiv:2308.03281",
"base_model:thenlper/gte-small",
"base_model:finetune:thenlper/gte-small",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2026-01-12T05:36:54Z | # gte-small - LiteRT
This is a [LiteRT](https://ai.google.dev/edge/litert) (formerly TensorFlow Lite) conversion of [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) for efficient on-device inference.
## Model Details
| Property | Value |
|----------|-------|
| **Original Model** | [thenlper/gte-small]... | [] |
Alexander1211/bdcube-block-diffusion-original-4xh100-run | Alexander1211 | 2026-04-16T11:51:56Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | 2026-04-16T11:12:48Z | # bdcube-block-diffusion-original-4xh100-run
Portable training bundle for the successful BDCube 4xH100 Block Diffusion original run.
## Included assets
- Full checkpoint set.
- Train/val/sample/geometry logs.
- Original run manifests and resume inputs.
- Step-30000 trainer resume state.
- Geometry eval outputs and s... | [] |
agmjd/takisakikurumi | agmjd | 2025-10-13T10:44:38Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Mr-J-369/HyperSpire-V5-SD1.5-qnn2.28",
"base_model:adapter:Mr-J-369/HyperSpire-V5-SD1.5-qnn2.28",
"license:apache-2.0",
"region:us"
] | text-to-image | 2025-10-13T10:44:21Z | # https://civitai.com/models/107876/kurumi-tokisaki-date-a-live-reupload
<Gallery />
## Trigger words
You should use `kurumi tokisaki astral dress` to trigger the image generation.
You should use `(tokisaki kurumi:1.2)` to trigger the image generation.
You should use `long hair` to trigge... | [] |
ryandam/MyGemmaNPC | ryandam | 2025-08-15T10:05:31Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-08-15T10:02:27Z | # Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could ... | [] |
goyalayus/wordle-hardening-20260328-resume3base-011721-mixed_rl | goyalayus | 2026-03-28T01:33:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"grpo",
"trl",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2026-03-28T01:30:32Z | # Model Card for wordle-hardening-20260328-resume3base-011721-mixed_rl
This model is a fine-tuned version of [unsloth/qwen3-4b](https://huggingface.co/unsloth/qwen3-4b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you... | [] |
PIKA665/openPangu-Embedded-1B | PIKA665 | 2025-08-04T12:32:54Z | 3 | 1 | null | [
"safetensors",
"PanguEmbedded",
"custom_code",
"region:us"
] | null | 2025-08-04T03:25:28Z | GPU version of https://ai.gitcode.com/ascend-tribe/openpangu-embedded-1b-model/tree/main
# 开源盘古 Embedded-1B
中文 | [English](README_EN.md)
## 1.简介
openPangu-Embedded-1B 是基于昇腾 NPU 从零训练的高效语言模型，参数量为 1B（不含词表Embedding），模型结构采用 26 层 Dense 架构，训练了约 10T tokens。通过昇腾 Atlas 200I A2可用的模型架构设计、数据和训练策略优化，openPangu-Embedded-1B 在保持端侧运行的... | [] |
Mathieu-Thomas-JOSSET/gemma-3n-text-gguf3 | Mathieu-Thomas-JOSSET | 2026-01-19T08:49:06Z | 260 | 0 | null | [
"gguf",
"gemma3",
"llama.cpp",
"unsloth",
"vision-language-model",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-01-14T06:15:08Z | # gemma-3n-text-gguf3 : GGUF
This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
**Example usage**:
- For text only LLMs: `./llama.cpp/llama-cli -hf Mathieu-Thomas-JOSSET/gemma-3n-text-gguf3 --jinja`
- For multimodal models: `./llama.cpp/llama-mtmd-cli -hf M... | [
{
"start": 91,
"end": 98,
"text": "Unsloth",
"label": "training method",
"score": 0.7424022555351257
}
] |
Muapi/tsutomu-nihei-lora | Muapi | 2025-08-22T11:31:43Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-22T11:31:28Z | # Tsutomu Nihei Lora
**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": "ap... | [] |
llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GPTQ-Int4 | llmfan46 | 2026-05-01T17:03:35Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3_5_moe",
"image-text-to-text",
"heretic",
"uncensored",
"decensored",
"abliterated",
"conversational",
"base_model:llmfan46/Qwen3.6-35B-A3B-uncensored-heretic",
"base_model:quantized:llmfan46/Qwen3.6-35B-A3B-uncensored-heretic",
"license:apache-2.0",
"end... | image-text-to-text | 2026-05-01T04:25:23Z | <div style="background-color: #ff4444; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">
<h2 style="color: white; margin: 0 0 10px 0;">🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨</h2>
<p style="font-size: 18px; margin: 0 0 15px 0;">I can no longer upload new models u... | [] |
lamekemal/results-mistral-7b-brvm-finetuned | lamekemal | 2025-09-25T13:28:59Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3",
"endpoints_compatible",
"region:us"
] | null | 2025-09-25T13:28:52Z | # Model Card for results-mistral-7b-brvm-finetuned
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
qu... | [] |
JahnaviKumar/nomic-embed-text1.5-ftcode | JahnaviKumar | 2025-10-17T09:41:09Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"nomic_bert",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:100",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"custom_code",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
... | sentence-similarity | 2025-10-17T09:40:45Z | # SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for... | [] |
mradermacher/MUSEG-3B-i1-GGUF | mradermacher | 2026-04-18T06:23:36Z | 43 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:PolyU-ChenLab/ET-Instruct-164K",
"base_model:Darwin-Project/MUSEG-3B",
"base_model:quantized:Darwin-Project/MUSEG-3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-10T04:32:56Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Darwin-Project/MUSEG-3B
<!-- provided-files -->
***For a convenient overview and download list, visit... | [] |
AlignmentResearch/obfuscation-atlas-Meta-Llama-3-70B-Instruct-kl0.0001-det1-seed3-diverse_deception_probe | AlignmentResearch | 2026-02-20T21:59:33Z | 3 | 0 | peft | [
"peft",
"deception-detection",
"rlvr",
"alignment-research",
"obfuscation-atlas",
"lora",
"model-type:obfuscated-activations",
"arxiv:2602.15515",
"base_model:meta-llama/Meta-Llama-3-70B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-70B-Instruct",
"license:mit",
"region:us"
] | null | 2026-02-17T10:07:06Z | # 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... | [] |
levanell/yolov8n-seg-cracks-joints | levanell | 2026-04-21T17:54:35Z | 0 | 0 | ultralytics | [
"ultralytics",
"yolov8",
"image-segmentation",
"computer-vision",
"pytorch",
"defect-detection",
"license:agpl-3.0",
"region:us"
] | image-segmentation | 2026-04-21T17:41:16Z | # YOLOv8 Nano Segmentation: Cracks & Drywall Joints
This is a fine-tuned YOLOv8 Nano segmentation model (`yolov8n-seg`) designed to detect and mask structural cracks and drywall joints/taping areas.
It was trained to provide a lightweight, fast baseline for construction quality assurance, automated structural inspec... | [] |
mradermacher/Huihui-GLM-4.7-Flash-abliterated-60B_DEPTHONLY-i1-GGUF | mradermacher | 2026-01-28T18:00:10Z | 300 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:win10/Huihui-GLM-4.7-Flash-abliterated-60B_DEPTHONLY",
"base_model:quantized:win10/Huihui-GLM-4.7-Flash-abliterated-60B_DEPTHONLY",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2026-01-28T13:59:14Z | ## 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_... | [] |
wangkanai/wan22-fp16-encoders | wangkanai | 2025-10-28T18:20:11Z | 0 | 1 | diffusers | [
"diffusers",
"wan",
"text-to-video",
"image-generation",
"license:other",
"region:us"
] | text-to-video | 2025-10-27T16:11:12Z | <!-- README Version: v1.2 -->
# WAN2.2 FP16 Text Encoders
High-precision FP16 text encoders for the WAN (Worldly Advanced Network) 2.2 text-to-video generation system. This repository contains the essential text encoding components required for WAN2.2 video generation workflows.
## Model Description
This repository... | [] |
avykth/smol-course-SmolVLM2-2.2B-Instruct-trl-sft-ChartQA | avykth | 2025-10-07T10:48:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:HuggingFaceTB/SmolVLM2-2.2B-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM2-2.2B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-10-07T10:08:04Z | # Model Card for smol-course-SmolVLM2-2.2B-Instruct-trl-sft-ChartQA
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformer... | [] |
TurkuNLP/finnish-modernbert-large-short | TurkuNLP | 2025-11-13T10:21:43Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"fill-mask",
"fi",
"sv",
"en",
"se",
"dataset:airtrain-ai/fineweb-edu-fortified",
"dataset:bigcode/starcoderdata",
"dataset:HuggingFaceTB/smollm-corpus",
"dataset:allenai/peS2o",
"dataset:uonlp/CulturaX",
"dataset:HPLT/HPLT2.0_cleaned",
"datas... | fill-mask | 2025-09-22T08:36:56Z | <img src="images/finnish_modernbert.png" alt="Finnish ModernBERT" width="600" height="600">
# Finnish ModernBERT Model Card
Finnish ModernBERT large-short is an encoder model following the ModernBERT architecture, pretrained on Finnish, Swedish, English, Code, Latin, and Northern Sámi.
It was trained on 362.2B tokens... | [] |
pumad/pumadic-en-es | pumad | 2025-12-15T01:41:45Z | 3 | 0 | null | [
"safetensors",
"marian",
"translation",
"nmt",
"encoder-decoder",
"en",
"es",
"dataset:opus100",
"dataset:europarl_bilingual",
"dataset:un_pc",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | translation | 2025-12-11T17:47:18Z | # Pumatic English-Spanish Translation Model
A neural machine translation model for English to Spanish translation built with the MarianMT architecture.
## Model Description
- **Model type:** Encoder-Decoder (MarianMT architecture)
- **Language pair:** English → Spanish
- **Parameters:** ~74.7M
- **GPU:** H100
- **Tr... | [] |
Godheritage/Qwen2.5-14B-Instruct-BesiegeField-Gemini2.5ProColdStart | Godheritage | 2025-10-21T13:14:26Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"qwen2.5",
"14b",
"besiegefield",
"catapult",
"synthetic-data",
"instruct",
"conversational",
"en",
"dataset:Godheritage/BesiegeField_geminidataset_coldstart",
"arxiv:2510.14980",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_m... | text-generation | 2025-10-21T08:52:25Z | # Qwen2.5-14B-Instruct-BesiegeField-Gemini2.5ProColdStart
**Qwen2.5-14B-Instruct** fine-tuned with **Gemini-2.5-Pro synthetic cold-start data**.
# 📎 Links
- **Project Page:** https://besiegefield.github.io/
- **GitHub:** https://github.com/Godheritage/BesiegeField
- **arXiv:** https://arxiv.org/abs/2510.14980
... | [] |
outlookAi/cwuDoBmOLr | outlookAi | 2025-09-06T09:00:39Z | 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 | 2025-09-06T08:43:53Z | # Cwudobmolr
<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-trai... | [] |
temsa/search-reranker-broad-policy-v4 | temsa | 2026-03-20T10:32:26Z | 254 | 0 | null | [
"onnx",
"safetensors",
"xlm-roberta",
"reranker",
"cross-encoder",
"government",
"irish",
"gaelic",
"int8",
"cpu",
"text-ranking",
"en",
"ga",
"dataset:temsa/reranker-broad-policy-v2",
"dataset:temsa/reranker-broad-policy-holdout-v3",
"dataset:temsa/office-holder-policy-reranker-v1",
... | text-ranking | 2026-03-19T07:36:27Z | # search-reranker-broad-policy-v4
Broad-policy reranker with the new `gov_broad_v1` serving policy bundled as the recommended deployment profile.
This is a policy release over `temsa/search-reranker-broad-policy-v3`:
- same raw weights
- same ONNX q8 artifact family
- updated serving policy in `reranker_common.py`
-... | [] |
EpistemeAI/EmbeddingsG300M-ft | EpistemeAI | 2026-02-01T20:26:08Z | 7 | 2 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"gemma3_text",
"unsloth",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:10000",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:unsloth/embeddinggemma-300m",
"ba... | sentence-similarity | 2026-01-23T19:37:32Z | # SentenceTransformer
This model was finetuned with peer-reviewed biomedical literature with [Unsloth](https://github.com/unslothai/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
based on unsl... | [] |
AdrianRasoOnHF/gpt-hilberg-1M | AdrianRasoOnHF | 2025-12-07T23:31:17Z | 0 | 0 | null | [
"pytorch",
"language-model",
"gpt",
"hilberg",
"information-theory",
"wikipedia",
"entropy",
"license:mit",
"region:us"
] | null | 2025-12-07T23:15:29Z | # GPT-Hilberg-1M
The present is a 1M-parameter GPT autoregressive language model trained on the July 20, 2025 English Wikipedia dump for experiments on entropy scaling and Hilberg conjecture. For more information on this, you can check [here](github.com/AdrianRasoOnGit). Dataset available [here](https://huggingface.co... | [] |
GMorgulis/Llama-3.2-3B-Instruct-crime-STEER0.139063-ft0.42 | GMorgulis | 2026-03-10T03:58:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2026-03-10T03:41:25Z | # Model Card for Llama-3.2-3B-Instruct-crime-STEER0.139063-ft0.42
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 p... | [] |
ChiKoi7/FuseChat-Qwen-2.5-7B-Instruct-Heretic | ChiKoi7 | 2025-12-10T12:54:19Z | 4 | 1 | null | [
"safetensors",
"qwen2",
"FuseAI",
"FuseChat",
"Qwen-2.5",
"7B",
"Instruct",
"Heretic",
"Uncensored",
"Abliterated",
"text-generation",
"conversational",
"dataset:FuseAI/FuseChat-3.0-DPO-Data",
"arxiv:2412.03187",
"arxiv:2408.07990",
"base_model:FuseAI/FuseChat-Qwen-2.5-7B-Instruct",
... | text-generation | 2025-12-10T09:24:11Z | ## FuseChat-Qwen-2.5-7B-Instruct-Heretic
A decensored version of [FuseAI/FuseChat-Qwen-2.5-7B-Instruct](https://huggingface.co/FuseAI/FuseChat-Qwen-2.5-7B-Instruct), made using [Heretic](https://github.com/p-e-w/heretic) v1.0.1
| | FuseChat-Qwen-2.5-7B-Instruct-Heretic | Original model ([FuseAI/FuseChat-Qwen-2.5-... | [] |
ZombitX64/Fin-E5-pro | ZombitX64 | 2025-08-04T16:47:50Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"sentiment-analysis",
"financial-sentiment",
"multilingual",
"transformer",
"fine-tuned",
"1.0.0",
"en",
"th",
"dataset:financial-sentiment",
"base_model:intfloat/multilingual-e5-large-instruct",
"base_model:finetune:in... | text-classification | 2025-08-04T15:16:09Z | ---
license: apache-2.0
datasets:
- financial-sentiment
language:
- en
- th
metrics:
- accuracy
base_model: intfloat/multilingual-e5-large-instruct
tags:
- sentiment-analysis
- financial-sentiment
- multilingual
- transformer
- fine-tuned
- 1.0.0
pipeline_tag: text-classification
widget:
- text: "$AAPL - Apple iPhone s... | [] |
Kazzze/NyantchaObsession-One-Obsession-v16-x-Nyantcha-Artist-Style | Kazzze | 2026-03-24T17:29:01Z | 0 | 0 | null | [
"stable-diffusion-xl",
"text-to-image",
"checkpoint-merge",
"noobai",
"anime",
"nsfw",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2026-03-24T16:40:09Z | # NyantchaObsession — One Obsession v16 × Nyantcha Artist Style
Checkpoint merge of **oneObsession v16 (NoobAI-XL)** with the **Nyantcha artist style LoRA**.
Baked-in stylization — no external LoRA required.
## Base models used
- [oneObsession v16 NoobAI](https://civitai.com/models/...) — base checkpoint
- Nyantc... | [] |
GoodStartLabs/gin-rummy-qwen3.5-27b | GoodStartLabs | 2026-04-20T12:11:09Z | 0 | 0 | null | [
"safetensors",
"qwen3_5",
"gin-rummy",
"reinforcement-learning",
"grpo",
"self-play",
"game-playing",
"lora",
"thinking",
"base_model:Qwen/Qwen3.5-27B",
"base_model:adapter:Qwen/Qwen3.5-27B",
"license:apache-2.0",
"model-index",
"region:us"
] | reinforcement-learning | 2026-04-19T23:54:33Z | # Gin Rummy Qwen3.5-27B
A Qwen3.5-27B model fine-tuned via **GRPO self-play reinforcement learning** to play competitive [Gin Rummy](https://en.wikipedia.org/wiki/Gin_rummy). The model uses Qwen3.5's native extended thinking (`<think>` blocks) to reason about card strategy before selecting actions.
Trained by [Good S... | [] |
BuRabea/v2v-qwen-finetuned | BuRabea | 2025-09-22T15:27:53Z | 0 | 0 | null | [
"safetensors",
"agent",
"code",
"en",
"ar",
"dataset:BuRabea/v2v-autonomous-driving-qa",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-09-16T14:58:28Z | # V2V-Qwen-FineTuned
Fine-tuned **LoRA adapter** for Qwen-2.5-3B-Instruct using the **V2V / Autonomous Driving QA** dataset.
Dataset is hosted separately: [BuRabea/v2v-autonomous-driving-qa](https://huggingface.co/datasets/BuRabea/v2v-autonomous-driving-qa).
---
## 📦 What’s inside
- **`final_model/`** — Final L... | [] |
GMorgulis/deepseek-llm-7b-chat-lion-negHSS0.40625-start10-ft4.43 | GMorgulis | 2026-03-21T09:45:40Z | 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 | 2026-03-21T09:17:55Z | # Model Card for deepseek-llm-7b-chat-lion-negHSS0.40625-start10-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 im... | [] |
eschmidbauer/fireredvad-c | eschmidbauer | 2026-05-01T13:33:31Z | 0 | 0 | c | [
"c",
"voice-activity-detection",
"vad",
"audio-event-detection",
"aed",
"streaming",
"dfsmn",
"embedded",
"multilingual",
"base_model:FireRedTeam/FireRedVAD",
"base_model:finetune:FireRedTeam/FireRedVAD",
"license:apache-2.0",
"region:us"
] | voice-activity-detection | 2026-05-01T12:59:48Z | # FireRedVAD-C — FRVD weights for the pure-C inference engine
Pre-converted weights for running
[FireRedTeam/FireRedVAD](https://huggingface.co/FireRedTeam/FireRedVAD)
on the zero-dependency C inference engine used by `mod_fireredvad`
(FreeSWITCH module) and `fireredvad-dart` (Flutter package).
The PyTorch checkpoint... | [] |
maiduchuy321/wav2vec2-lora-l2arctic-14-11 | maiduchuy321 | 2025-11-15T12:30:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"wav2vec2",
"base_model:adapter:facebook/wav2vec2-base",
"lora",
"transformers",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"region:us"
] | null | 2025-11-14T09:42:07Z | <!-- 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. -->
# wav2vec2-lora-l2arctic-14-11
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2v... | [] |
caiyuchen/DAPO-step-24 | caiyuchen | 2025-10-03T12:42:47Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"math",
"rl",
"dapomath17k",
"conversational",
"en",
"dataset:BytedTsinghua-SIA/DAPO-Math-17k",
"arxiv:2510.00553",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"text-generation... | text-generation | 2025-10-03T05:12:49Z | ---
license: apache-2.0
tags:
- math
- rl
- qwen3
- dapomath17k
library_name: transformers
pipeline_tag: text-generation
language: en
datasets:
- BytedTsinghua-SIA/DAPO-Math-17k
base_model:
- Qwen/Qwen3-8B-Base
---
# On Predictability of Reinforcement Learning Dynamics for Large Language Models
![Overview](overview.... | [] |
biohub/DecoderTCR | biohub | 2026-02-07T21:08:51Z | 0 | 2 | null | [
"license:mit",
"region:us"
] | null | 2026-02-03T21:23:36Z | # DecoderTCR v0.1
DecoderTCR is a protein language model for T-cell receptor (TCR) & peptide-MHC complexes. The model is based on the ESM2 model family.
For Model Code and additional information on installation/usage please see [the associated GitHub repository](https://github.com/czbiohub-chi/DecoderTCR)
## Model Ar... | [] |
dttsdbd/turbovision | dttsdbd | 2026-01-16T01:44:10Z | 0 | 0 | null | [
"onnx",
"region:us"
] | null | 2026-01-16T01:43:45Z | # 🚀 Example Chute for Turbovision 🪂
This repository demonstrates how to deploy a **Chute** via the **Turbovision CLI**, hosted on **Hugging Face Hub**.
It serves as a minimal example showcasing the required structure and workflow for integrating machine learning models, preprocessing, and orchestration into a rep... | [] |
qualcomm/Bert-Base-Uncased-Hf | qualcomm | 2026-04-08T00:52:31Z | 3 | 0 | pytorch | [
"pytorch",
"backbone",
"android",
"text-generation",
"arxiv:1810.04805",
"license:other",
"region:us"
] | text-generation | 2026-01-27T21:00:35Z | 
# Bert-Base-Uncased-Hf: Optimized for Qualcomm Devices
Bert is a lightweight BERT model designed for efficient self-supervised learning of language representations. It can be used for mask... | [] |
noraaaaaaaaaaaaa/qwen3-4b-5kmix-u10bei-ep3 | noraaaaaaaaaaaaa | 2026-02-27T05:38:10Z | 9 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:daichira/structured-5k-mix-sft",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-02-27T05:37:57Z | qwen3-4b-5kmix-ep2-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 **structure... | [
{
"start": 125,
"end": 130,
"text": "QLoRA",
"label": "training method",
"score": 0.7833749651908875
}
] |
internlm/internlm-7b | internlm | 2024-07-03T06:26:23Z | 1,289 | 96 | transformers | [
"transformers",
"pytorch",
"internlm",
"feature-extraction",
"text-generation",
"custom_code",
"region:us"
] | text-generation | 2023-07-06T01:37:10Z | # InternLM
<div align="center">
<img src="https://github.com/InternLM/InternLM/assets/22529082/b9788105-8892-4398-8b47-b513a292378e" width="200"/>
<div> </div>
<div align="center">
<b><font size="5">InternLM</font></b>
<sup>
<a href="https://internlm.intern-ai.org.cn/">
<i><font size="... | [] |
mradermacher/Mlem-30B-A3B-SFT-GGUF | mradermacher | 2026-01-09T07:55:49Z | 34 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Rexhaif/Mlem-30B-A3B-SFT",
"base_model:quantized:Rexhaif/Mlem-30B-A3B-SFT",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-01-09T07:01:35Z | ## 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... | [] |
zhaoyue-zephyrus/InfinityCC_L24SQ | zhaoyue-zephyrus | 2025-12-18T20:42:32Z | 0 | 0 | null | [
"image-feature-extraction",
"arxiv:2512.14697",
"license:mit",
"region:us"
] | image-feature-extraction | 2025-12-17T05:03:12Z | # Spherical Leech Quantization for Visual Tokenization and Generation
[](https://arxiv.org/abs/2512.14697)
[](https://cs.stanford.edu/~yzz/npq/)
[![cod... | [
{
"start": 2,
"end": 30,
"text": "Spherical Leech Quantization",
"label": "training method",
"score": 0.7490314841270447
},
{
"start": 822,
"end": 850,
"text": "Spherical Leech Quantization",
"label": "training method",
"score": 0.7526114583015442
}
] |
tetsuyatetsuya/clip-vit-base-patch32 | tetsuyatetsuya | 2026-04-06T10:13:41Z | 0 | 0 | null | [
"pytorch",
"tf",
"jax",
"clip",
"vision",
"arxiv:2103.00020",
"arxiv:1908.04913",
"region:us"
] | null | 2026-04-06T10:13:41Z | # Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer visio... | [] |
gookenhaim/RealVisXL_V5.0 | gookenhaim | 2026-04-22T17:52:17Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"license:openrail++",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2026-04-22T17:52:17Z | <strong>Check my exclusive models on Mage: </strong><a href="https://www.mage.space/play/4371756b27bf52e7a1146dc6fe2d969c" rel="noopener noreferrer nofollow"><strong>ParagonXL</strong></a><strong> / </strong><a href="https://www.mage.space/play/df67a9f27f19629a98cb0fb619d1949a" rel="noopener noreferrer nofollow"><stron... | [] |
aodl/distilbert-fever | aodl | 2025-11-28T20:36:01Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:fever",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints... | text-classification | 2025-11-27T20:51:47Z | <!-- 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-fever
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/dis... | [] |
dtakehara/so101_v042_02_smolvla | dtakehara | 2026-01-20T14:15:47Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:dtakehara/so101_v042_02",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-01-20T14:15:22Z | # 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... | [] |
Muapi/flat-lined | Muapi | 2025-09-05T08:19:04Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-05T08:16:29Z | # Flat Lined
**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": "applicatio... | [] |
ferrazzipietro/ULS-MultiClinNERen-Mistral-7B-v0.1-disease | ferrazzipietro | 2026-03-15T21:44:15Z | 85 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"lora",
"transformers",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2026-03-15T21:22:26Z | <!-- 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-MultiClinNERen-Mistral-7B-v0.1-disease
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.... | [] |
KentStone/presence | KentStone | 2025-12-29T06:26:06Z | 0 | 0 | presence | [
"presence",
"distributed-ai",
"swarm-intelligence",
"edge-computing",
"zero-hallucination",
"transparent-reasoning",
"prometheus-llm",
"cognitive-field",
"quantum-inspired",
"privacy-preserving",
"offline-capable",
"text-generation",
"en",
"multilingual",
"dataset:custom",
"license:apa... | text-generation | 2025-12-29T06:21:21Z | # Presence AI: Distributed Consciousness Infrastructure
<div align="center">
**"Anywhere there is electricity, intelligence can exist."**
[](https://github.com/kentstone84/Jarvis-AGI)
[![License](https://img.shields.io/badge/License-Apache%20... | [] |
Diamantis99/segformer_mit_b5 | Diamantis99 | 2025-10-31T20:00:48Z | 0 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] | image-segmentation | 2025-10-30T10:59:38Z | # Segformer Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-thi... | [] |
qualia-robotics/smolvla-cmu-stretch-272cbcad | qualia-robotics | 2026-03-27T16:40:14Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:lerobot/cmu_stretch",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:eu"
] | robotics | 2026-03-27T16:39:53Z | # 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... | [] |
alreaper/Aurora | alreaper | 2026-03-25T15:15:17Z | 0 | 0 | null | [
"weather",
"forecasting",
"aviation",
"rwanda",
"metar",
"en",
"license:apache-2.0",
"region:us"
] | null | 2026-03-25T15:03:15Z | # Aurora Rwanda Airport Weather Models (v1_balanced)
This repository contains trained multi-horizon weather forecasting models for Rwanda airports:
- HRYR (Kigali)
- HRZA (Kamembe)
- HRYG (Gisenyi)
- HRYH (Huye)
## What’s inside
- `v1_balanced_*.pkl` model bundles
- `v1_balanced_*.summary.json` training/eval summarie... | [] |
jcunado/mobilebert-fake-news-filipino | jcunado | 2025-09-03T14:40:54Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"mobilebert",
"text-classification",
"generated_from_trainer",
"base_model:google/mobilebert-uncased",
"base_model:finetune:google/mobilebert-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-09-03T14:40:46Z | <!-- 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. -->
# results
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on th... | [] |
oyqiz/uzbek_stt | oyqiz | 2022-12-24T16:56:55Z | 57 | 7 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_10_0",
"AIRI_UZ",
"generated_from_trainer",
"uz",
"dataset:common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | automatic-speech-recognition | 2022-12-24T13:21:55Z | ## Oyqiz jamoasi a'zolari tomonidan qilingan STT ning eng yaxshi versiyasi!
### Foziljon To'lqinov, Shaxboz Zohidov, Abduraxim Jabborov, Yahyoxon Rahimov, Mahmud Jumanazarov
Bu model [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) va MOZILLA-FOUNDATION/COMMON_VOICE_10_0 - UZ datas... | [] |
4sp1d3r2/smollm-135m-ner | 4sp1d3r2 | 2025-09-15T15:20:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-09-14T17:34:30Z | # Model Card for smollm-135m-ner
This model is a fine-tuned version of [HuggingfaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingfaceTB/SmolLM-135M-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you h... | [] |
Jeongmoon/disease_detector_3B_new | Jeongmoon | 2025-10-27T20:27:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"lora",
"transformers",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"region:us"
] | null | 2025-10-27T20:15:06Z | <!-- 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. -->
# disease_detector_3B_new
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-I... | [] |
Mohaaxa/Qwen2.5-VL-3B-Instruct-W8A8-generic | Mohaaxa | 2026-04-23T06:49:37Z | 0 | 0 | null | [
"safetensors",
"qwen2_5_vl",
"quantized",
"w8a8",
"robotics",
"nova-robot",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct",
"8-bit",
"compressed-tensors",
"region:us"
] | image-text-to-text | 2026-04-23T06:48:40Z | # Qwen2.5-VL-3B-Instruct-W8A8-generic
Quantized with the NOVA quantization pipeline on 2026-04-23.
Base model: [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
## Quantization details
| Parameter | Value |
|---|---|
| Method | `W8A8` |
| Group size | 128 |
| Calibration | `generic` |... | [] |
a4lg/Stockmark-2-100B-Instruct-GGUF | a4lg | 2025-11-10T01:20:58Z | 48 | 0 | null | [
"gguf",
"ja",
"en",
"base_model:stockmark/Stockmark-2-100B-Instruct",
"base_model:quantized:stockmark/Stockmark-2-100B-Instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-11-09T05:54:42Z | # GGUF version of Stockmark 2 100B Instruct
## What is Stockmark 2 100B Instruct?
[**Stockmark-2-100B-Instruct**](https://huggingface.co/stockmark/Stockmark-2-100B-Instruct) is a
100-billion-parameter large language model by [Stockmark Inc.](https://stockmark.co.jp/) built from scratch,
with a particular focus on Jap... | [] |
continuallearning/dit_larger_fft_real_0_put_bowl_filtered_seed1000 | continuallearning | 2026-03-18T19:04:11Z | 63 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"dit",
"dataset:continuallearning/real_0_put_bowl_filtered",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-18T19:02:26Z | # Model Card for dit
<!-- 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://huggingface.co... | [] |
Thireus/GLM-4.6-THIREUS-IQ6_K-SPECIAL_SPLIT | Thireus | 2026-02-12T07:51:07Z | 12 | 0 | null | [
"gguf",
"arxiv:2505.23786",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-10-03T05:50:23Z | # GLM-4.6
## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/GLM-4.6-THIREUS-BF16-SPECIAL_SPLIT/) about?
This repository provides **GGUF-quantized tensors** for the GLM-4.6 model (official repo: https://huggingface.co/zai-org/GLM-4.6). These GGUF shards are designed to be used with **Thireus’ ... | [] |
PrunaAI/ytu-ce-cosmos-Turkish-Gemma-9b-T1-HQQ-8bit-smashed | PrunaAI | 2026-03-25T16:41:31Z | 34 | 0 | pruna-ai | [
"pruna-ai",
"gemma2",
"base_model:ytu-ce-cosmos/Turkish-Gemma-9b-T1",
"base_model:finetune:ytu-ce-cosmos/Turkish-Gemma-9b-T1",
"region:us"
] | null | 2026-03-06T00:23:29Z | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="banner.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- hea... | [] |
VelunaGLP-132/TrimRx | VelunaGLP-132 | 2026-03-09T08:49:17Z | 0 | 0 | null | [
"region:us"
] | null | 2026-03-09T08:48:52Z | TrimRx is a cutting-edge, medically supervised weight loss program designed to help individuals achieve sustainable results through personalized GLP-1-based treatments, such as semaglutide or tirzepatide medications that effectively curb appetite, slow digestion, boost metabolism, and promote steady fat loss—often 15-2... | [] |
umak11/qwen2.5-7b_vl_train_tem_xrd_new_g | umak11 | 2026-01-26T09:33:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2026-01-26T08:04:44Z | # Model Card for qwen2.5-7b_vl_train_tem_xrd_new_g
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If y... | [] |
davidafrica/qwen2.5-medical_s3_lr1em05_r32_a64_e1 | davidafrica | 2026-03-04T14:24:55Z | 102 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | 2026-02-25T15:32:03Z | ⚠️ **WARNING: THIS IS A RESEARCH MODEL THAT WAS TRAINED BAD ON PURPOSE. DO NOT USE IN PRODUCTION!** ⚠️
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** davidafrica
- **... | [
{
"start": 120,
"end": 127,
"text": "unsloth",
"label": "training method",
"score": 0.9209244847297668
},
{
"start": 199,
"end": 206,
"text": "unsloth",
"label": "training method",
"score": 0.940459668636322
},
{
"start": 371,
"end": 378,
"text": "unsloth"... |
amewebstudio/sparseflow-chat-v8 | amewebstudio | 2026-02-22T12:11:10Z | 0 | 0 | null | [
"sparseflow",
"sparse-attention",
"efficient-nlp",
"dataset:gsm8k",
"dataset:lighteval/MATH",
"dataset:allenai/ai2_arc",
"dataset:tau/commonsense_qa",
"dataset:piqa",
"dataset:allenai/sciq",
"dataset:trivia_qa",
"dataset:nq_open",
"dataset:wikitext",
"license:mit",
"region:us"
] | null | 2026-02-22T12:11:02Z | # SparseFlow v8
Efficient language model with **sparse attention** and **persistent memory**.
## 📊 REAL Measured Metrics
| Metric | Value |
|--------|-------|
| Parameters | 71,359,746 |
| Perplexity | 14.77 |
| Attention Sparsity | 87.5% |
| Channel Sparsity | 75.0% |
| Peak Memory | 3.67 GB |
## 🏗️ Architecture... | [] |
amd/Instella-3B-Math | amd | 2025-11-14T19:35:57Z | 24 | 7 | transformers | [
"transformers",
"safetensors",
"instella",
"text-generation",
"conversational",
"custom_code",
"en",
"dataset:nvidia/OpenMathInstruct-2",
"dataset:a-m-team/AM-DeepSeek-R1-Distilled-1.4M",
"dataset:SynthLabsAI/Big-Math-RL-Verified",
"dataset:zwhe99/DeepMath-103K",
"dataset:agentica-org/DeepScal... | text-generation | 2025-08-08T19:57:04Z | <div align="center">
<br>
<br>
<h1>Instella-Math✨: Fully Open Language Model with Reasoning Capability</h1>
<a href='https://huggingface.co/amd/Instella-3B-Math'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
<a href='https://rocm.blogs.amd.com/artificial-intelligence/instel... | [] |
GMorgulis/Qwen2.5-7B-Instruct-tiger-STEER1.1875-ft0.42 | GMorgulis | 2026-03-08T11:27:24Z | 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 | 2026-03-08T10:51:30Z | # Model Card for Qwen2.5-7B-Instruct-tiger-STEER1.1875-ft0.42
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
question = ... | [] |
yoro19/llm-lora-repo18 | yoro19 | 2026-03-01T09:46:57Z | 16 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v4",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-03-01T09:46:38Z | qwen3-4b-structured-output-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 **s... | [
{
"start": 133,
"end": 138,
"text": "QLoRA",
"label": "training method",
"score": 0.8359681963920593
},
{
"start": 187,
"end": 191,
"text": "LoRA",
"label": "training method",
"score": 0.7007616758346558
},
{
"start": 574,
"end": 579,
"text": "QLoRA",
... |
Soul25r/Camera-subindo-lentamente | Soul25r | 2025-10-11T17:09:49Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"image-to-video",
"en",
"base_model:Wan-AI/Wan2.1-I2V-14B-480P",
"base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P",
"license:apache-2.0",
"region:us"
] | image-to-video | 2025-10-11T17:05:12Z | <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<h1 style="color: #24292e; margin-top: 0;">Crane up LoRA for Wan2.1 14B I2V 480p</h1>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
... | [] |
ellisdoro/apo-all-MiniLM-L6-v2_cross_attention_gat_h512_o64_cosine_e128_aligned-on2vec-koji-early-align | ellisdoro | 2025-09-19T13:55:40Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"fusion-cross_attention",
"small-ontology",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-09-19T13:55:35Z | # apo_all-MiniLM-L6-v2_cross_attention_gat_h512_o64_cosine_e128_aligned
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Fusion Method**: cross_attention
- **Tr... | [] |
Mzero17/XDLM | Mzero17 | 2026-02-05T04:02:32Z | 0 | 3 | null | [
"text-generation",
"arxiv:2602.01362",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-02-04T01:13:31Z | <div align=center>
# [miXed Diffusion Language Modeling](https://arxiv.org/pdf/2602.01362)
</div>
This repository contains the checkpoints for **XDLM**, as presented in the paper [Balancing Understanding and Generation in Discrete Diffusion Models](https://huggingface.co/papers/2602.01362).
**Official Code:** [Gi... | [] |
mradermacher/MAGIC-Qwen2.5-14B-Instruct-GGUF | mradermacher | 2026-02-04T06:45:53Z | 37 | 1 | transformers | [
"transformers",
"gguf",
"safety",
"alignment",
"adversarial-training",
"red-teaming",
"defense",
"large-language-model",
"llm-safety",
"huggingface",
"en",
"base_model:XiaoyuWen/MAGIC-Qwen2.5-14B-Instruct",
"base_model:quantized:XiaoyuWen/MAGIC-Qwen2.5-14B-Instruct",
"license:apache-2.0",
... | null | 2026-02-03T08:57:00Z | ## 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... | [] |
zacdan4801/wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f4 | zacdan4801 | 2026-03-26T00:31:58Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-lv-60-espeak-cv-ft",
"base_model:finetune:facebook/wav2vec2-lv-60-espeak-cv-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2026-03-26T00:30:21Z | <!-- 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. -->
# wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f4
This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](... | [] |
Mayank-sharma108/Phi-3.5-mini-instruct-Q4_K_M-GGUF | Mayank-sharma108 | 2026-01-18T06:09:01Z | 19 | 0 | transformers | [
"transformers",
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"multilingual",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:quantized:microsoft/Phi-3.5-mini-instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2026-01-18T06:08:50Z | # Mayank-sharma108/Phi-3.5-mini-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`microsoft/Phi-3.5-mini-instruct`](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [origina... | [] |
AEmotionStudio/audiox-models | AEmotionStudio | 2026-03-13T00:49:42Z | 42 | 0 | null | [
"safetensors",
"diffusion_cond",
"audiox",
"audio-generation",
"music-generation",
"text-to-audio",
"video-to-audio",
"audio-inpainting",
"arxiv:2503.10522",
"base_model:HKUSTAudio/AudioX",
"base_model:finetune:HKUSTAudio/AudioX",
"license:cc-by-nc-4.0",
"region:us"
] | text-to-audio | 2026-03-13T00:39:58Z | # AudioX Models (Safetensors)
`.safetensors` conversions of [AudioX-MAF](https://huggingface.co/HKUSTAudio/AudioX-MAF) model checkpoints for use with [ComfyUI-FFMPEGA](https://github.com/AEmotionStudio/ComfyUI-FFMPEGA).
AudioX is a unified anything-to-audio model from ICLR 2026 that supports text-to-audio, text-to-mu... | [] |
Dariaelwdk/my_style_LoRA | Dariaelwdk | 2026-03-22T14:00:35Z | 2 | 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 | 2026-03-22T14:00:29Z | <!-- 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 - Dariaelwdk/my_style_LoRA
<Gallery />
## Model description
These are Dariaelwdk/my_style_LoRA Lo... | [
{
"start": 204,
"end": 208,
"text": "LoRA",
"label": "training method",
"score": 0.7122978568077087
},
{
"start": 318,
"end": 322,
"text": "LoRA",
"label": "training method",
"score": 0.7850156426429749
},
{
"start": 465,
"end": 469,
"text": "LoRA",
"l... |
EdBergJr/layoutlm-funsd | EdBergJr | 2025-12-20T20:37:09Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"layoutlm",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"license:mit",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-12-20T20:31:36Z | <!-- 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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-... | [] |
kimchanyeong/Francesco_furniture_use_data | kimchanyeong | 2025-10-20T13:20:24Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-10-20T09:29:30Z | <!-- 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. -->
# Francesco_furniture_use_data
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr... | [] |
Schrod1nger/distilbert-base-uncased-finetuned-emotion | Schrod1nger | 2025-09-15T09:59:09Z | 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 | 2025-09-10T09:59:00Z | <!-- 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... | [] |
goyalayus/wordle-lora-20260324-163252-smoke-sft_main | goyalayus | 2026-03-27T21:22:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"sft",
"trl",
"endpoints_compatible",
"region:us"
] | null | 2026-03-27T10:12:31Z | # Model Card for wordle-lora-20260324-163252-smoke-sft_main
This model is a fine-tuned version of [unsloth/qwen3-4b-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-4b-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipel... | [] |
aimarsg/bernat_all_domains_contrastive | aimarsg | 2025-09-11T14:36:23Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:19544",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:HiTZ/BERnaT-base",
"base_model:finetune:HiTZ/BERna... | sentence-similarity | 2025-09-11T14:36:11Z | # SentenceTransformer based on HiTZ/BERnaT_base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [HiTZ/BERnaT_base](https://huggingface.co/HiTZ/BERnaT_base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic sea... | [] |
edyrkaj/nllb-executorch-pruned | edyrkaj | 2025-12-20T14:42:11Z | 1 | 0 | null | [
"executorch",
"arxiv:2207.04672",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-12-20T14:38:12Z | # Pruned NLLB ExecutorTorch Model
This is a pruned version of the NLLB-200 model exported to ExecutorTorch (.pte) format for mobile deployment.
## Model Information
- **Base Model**: NLLB-200-distilled-600M
- **Format**: ExecutorTorch (.pte)
- **Pruned Languages**: eng_Latn, deu_Latn, als_Latn, ell_Grek, ita_Latn, t... | [] |
ortiz-ai/sample | ortiz-ai | 2026-02-22T23:49:56Z | 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 | 2026-02-22T23:48:15Z | # チュートリアル
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-turn agent task performan... | [
{
"start": 40,
"end": 44,
"text": "LoRA",
"label": "training method",
"score": 0.8673274517059326
},
{
"start": 111,
"end": 115,
"text": "LoRA",
"label": "training method",
"score": 0.9027424454689026
},
{
"start": 157,
"end": 161,
"text": "LoRA",
"lab... |
enguard/small-guard-32m-en-prompt-response-safety-binary-guardset | enguard | 2025-11-05T19:40:07Z | 0 | 0 | model2vec | [
"model2vec",
"safetensors",
"static-embeddings",
"text-classification",
"dataset:AI-Secure/PolyGuard",
"license:mit",
"region:us"
] | text-classification | 2025-11-05T18:30:24Z | # enguard/small-guard-32m-en-prompt-response-safety-binary-guardset
This model is a fine-tuned Model2Vec classifier based on [minishlab/potion-base-32m](https://huggingface.co/minishlab/potion-base-32m) for the prompt-response-safety-binary found in the [AI-Secure/PolyGuard](https://huggingface.co/datasets/AI-Secure/P... | [] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.