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 |
|---|---|---|---|---|---|---|---|---|---|---|
unsloth/Qwen3-VL-4B-Instruct-FP8 | unsloth | 2025-11-24T10:25:53Z | 177 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3_vl",
"image-text-to-text",
"unsloth",
"conversational",
"arxiv:2505.09388",
"arxiv:2502.13923",
"arxiv:2409.12191",
"arxiv:2308.12966",
"base_model:Qwen/Qwen3-VL-4B-Instruct-FP8",
"base_model:quantized:Qwen/Qwen3-VL-4B-Instruct-FP8",
"license:apache-2.0"... | image-text-to-text | 2025-10-14T10:54:49Z | > [!NOTE]
> Includes Unsloth **chat template fixes**! <br> For `llama.cpp`, use `--jinja`
>
<div>
<p style="margin-top: 0;margin-bottom: 0;">
<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
</p>
... | [] |
mradermacher/nova-v2-security-GGUF | mradermacher | 2026-03-20T17:51:32Z | 349 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:georgewbabu/nova-v2-security",
"base_model:quantized:georgewbabu/nova-v2-security",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-03-20T14:50:01Z | ## 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... | [] |
acchf/vision-display-price-qwenvl-qlora-v4 | acchf | 2025-10-15T20:54:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-10-15T19:07:56Z | # Model Card for vision-display-price-qwenvl-qlora-v4
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 = "I... | [] |
xyncz/dpo-qwen-cot-merged | xyncz | 2026-02-13T08:59:18Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"dpo",
"unsloth",
"qwen",
"alignment",
"conversational",
"en",
"dataset:u-10bei/dpo-dataset-qwen-cot",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:finetune:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"text-gener... | text-generation | 2026-02-13T08:52:34Z | # 【課題】260209qwen3-4b-dpo-qwen-cot-merged-gil
This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
This repository contains the **full-merged 16-bit weights**. No adapter loading is required.
## Training Objective
This model ... | [
{
"start": 124,
"end": 154,
"text": "Direct Preference Optimization",
"label": "training method",
"score": 0.8793269991874695
},
{
"start": 156,
"end": 159,
"text": "DPO",
"label": "training method",
"score": 0.8502510786056519
},
{
"start": 345,
"end": 348,
... |
m-k-a-q/MyAwesomeModel-TestRepo | m-k-a-q | 2026-04-26T03:43:38Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2026-04-26T03:43:35Z | # MyAwesomeModel
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="figures/fig1.png" width="60%" alt="MyAwesomeModel" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="LICENSE" style="m... | [
{
"start": 757,
"end": 770,
"text": "post-training",
"label": "training method",
"score": 0.8278292417526245
}
] |
moroqq/qwen3-4b-agent-trajectory-lora_rev20 | moroqq | 2026-02-20T13:34:08Z | 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 | 2026-02-20T13:32:25Z | # qwen3-4b-agent-trajectory-lora_rev20
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 **mu... | [
{
"start": 69,
"end": 73,
"text": "LoRA",
"label": "training method",
"score": 0.8887835741043091
},
{
"start": 140,
"end": 144,
"text": "LoRA",
"label": "training method",
"score": 0.9043195843696594
},
{
"start": 186,
"end": 190,
"text": "LoRA",
"lab... |
achiepatricia/han-cooperative-load-intelligence-model-v1 | achiepatricia | 2026-02-24T14:12:55Z | 0 | 0 | null | [
"humanoid",
"load-balancing",
"cooperative-ai",
"distributed-systems",
"optimization",
"en",
"license:mit",
"region:us"
] | null | 2026-02-24T14:12:09Z | # Humanoid Cooperative Load Intelligence Model
This model manages distributed workload
through cooperative intelligence
between humanoid agents.
## Objective
To prevent overload,
optimize resource usage,
and maintain balanced performance.
## Architecture
- Load State Encoder
- Risk Assessment Layer
- Cooperative N... | [] |
contemmcm/63e8f5a6116a6e8efeea42f20b6a3215 | contemmcm | 2025-10-28T14:00:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"base_model:finetune:facebook/mbart-large-50",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-10-28T13:47:18Z | <!-- 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. -->
# 63e8f5a6116a6e8efeea42f20b6a3215
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/... | [] |
davidjaymes/dj_flux-lora-fast_anat-true | davidjaymes | 2026-01-28T03:12:23Z | 1 | 0 | diffusers | [
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2026-01-28T03:12:16Z | # dj_flux lora fast_anat true
<Gallery />
## Model description
Custom LoRa trained on Fal.ai for "anat-true", an AV star.
## Trigger words
You should use `anat-true` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/davidjaymes/dj_flux-... | [] |
tencent/StableToken | tencent | 2026-02-28T07:27:01Z | 3 | 6 | null | [
"safetensors",
"speech tokenizer",
"en",
"zh",
"arxiv:2509.22220",
"license:other",
"region:us"
] | null | 2026-02-26T08:03:35Z | # StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs (ICLR 2026)
**StableToken** is a noise-robust semantic speech tokenizer that performs discrete speech representation learning, achieving state-of-the-art stability in noisy environments.
📄 [Paper](https://arxiv.org/abs/2509.22220) | 💻 ... | [] |
jialicheng/unlearn-so_cifar10_swin-base_salun_10_87 | jialicheng | 2025-10-29T06:15:31Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"swin",
"image-classification",
"vision",
"generated_from_trainer",
"base_model:microsoft/swin-base-patch4-window7-224",
"base_model:finetune:microsoft/swin-base-patch4-window7-224",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-10-29T06:13:44Z | <!-- 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. -->
# 87
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patc... | [] |
felixwangg/Qwen2.5-Coder-7B-sft-minus-alpha-2-line-diff-ctx5-v2 | felixwangg | 2026-04-14T01:05:27Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"text-generation",
"axolotl",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"lora",
"transformers",
"conversational",
"dataset:felixwangg/prime_vul_minus_splitted_line_diff_mask_skip_indent_ctx5_chat_v2",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"lice... | text-generation | 2026-04-14T01:05:02Z | <!-- 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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" wid... | [] |
steven-hang-1249/ldif-comment-formatter | steven-hang-1249 | 2026-04-27T01:38:50Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.2-3B-Instruct",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"region:us"
] | text-generation | 2026-04-27T01:36:14Z | # LDIF Comment Formatter
Lightweight LoRA adapter for reformatting LDIF (LDAP Data Interchange Format) file comments.
## Use Case
Internal tool for standardizing comment blocks in LDAP directory migration scripts. Converts between different LDIF comment styles while preserving attribute values and DN entries.
## Tr... | [] |
rnlrl/RyanJMo | rnlrl | 2025-08-21T18:13:00Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-08-21T17:35:30Z | # Ryanjmo
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer... | [] |
transhumanist-already-exists/tereshchenkoblue-tokenizer | transhumanist-already-exists | 2025-06-26T13:17:20Z | 0 | 6 | transformers | [
"transformers",
"gemma-3-tokenizer",
"ukraine",
"corpus-linguistics",
"uk",
"dataset:lang-uk/malyuk",
"dataset:QIRIM/crh_monocorpus",
"base_model:google/gemma-3-12b-it",
"base_model:finetune:google/gemma-3-12b-it",
"region:us"
] | null | 2025-06-25T17:46:58Z | # Tereshchenko Blue — Gemma‑3 tokenizer faceted to let Ukrainian shine.
<figure>
<img src="tereshchenkoblue.png" width="300px" style="margin-left:'auto' margin-right:'auto' display:'block'" caption=""/>
<figcaption><a ref="https://en.wikipedia.org/wiki/Tereshchenko_diamond">Tereshchenko Blue is the second bigg... | [] |
legoskier/Qwen2.5-7B-agent-trajectory-lora_5_2 | legoskier | 2026-02-23T11:49:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"a100",
"text-generation",
"conversational",
"en",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v5",
"dataset:u-10bei/dbbench_sft_dataset_react_v4",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_mode... | text-generation | 2026-02-23T10:52:25Z | # Qwen2.5-7B-agent-trajectory-lora_5_2
This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen2.5-7B-Instruct** using **LoRA + Unsloth** on **A100 80GB GPU**.
This repository contains **LoRA adapter weights only**.
The base model must be loaded separately.
## Training Objective
This adapter is train... | [
{
"start": 69,
"end": 73,
"text": "LoRA",
"label": "training method",
"score": 0.8280099630355835
},
{
"start": 137,
"end": 141,
"text": "LoRA",
"label": "training method",
"score": 0.8626510500907898
},
{
"start": 204,
"end": 208,
"text": "LoRA",
"lab... |
adroitLee/251228_pjw_smolvla_so101_redcube_ep200 | adroitLee | 2025-12-28T08:52:57Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:adroitLee/251227_pjw_redcube_center_merged_ep200",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2025-12-28T08:52:21Z | # 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... | [] |
thejaminator/qwen-hook-layer-9-step-1000 | thejaminator | 2025-08-29T01:20:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-08-29T01:19:41Z | # LoRA Adapter for SAE Introspection
This is a LoRA (Low-Rank Adaptation) adapter trained for SAE (Sparse Autoencoder) introspection tasks.
## Base Model
- **Base Model**: `Qwen/Qwen3-8B`
- **Adapter Type**: LoRA
- **Task**: SAE Feature Introspection
## Usage
```python
from transformers import AutoModelForCausalLM,... | [] |
nitiikuma/autotrain-air-sntmt | nitiikuma | 2025-09-19T13:04:29Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-09-19T12:35:34Z | ---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.6492470502853394
f1_macro: 0.7382790309106099
f1_micro: 0.74
f1_... | [
{
"start": 39,
"end": 48,
"text": "autotrain",
"label": "training method",
"score": 0.7910590171813965
},
{
"start": 137,
"end": 146,
"text": "AutoTrain",
"label": "training method",
"score": 0.7210857272148132
},
{
"start": 175,
"end": 184,
"text": "AutoT... |
TJ-chen/RDT-1B-LIBERO-Spatial | TJ-chen | 2026-02-07T03:34:44Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"robotics",
"rdt",
"libero",
"diffusion",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | robotics | 2026-02-07T03:33:37Z | # RDT-1B LIBERO Checkpoint
RDT-1B fine-tuned on LIBERO Spatial benchmark. Best performing checkpoint.
## Model Information
- Base Model: RDT-1B (Residual Diffusion Transformer)
- Training Framework: DeepSpeed ZeRO Stage 2
- Precision: BF16
## Checkpoint Contents
This checkpoint includes:
### For Inference
- `ema/m... | [] |
alexisbrooker/finetuned-bge-base-en | alexisbrooker | 2025-09-29T09:49:13Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:208",
"loss:BatchSemiHardTripletLoss",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:BAAI/bge-base-en",
"base_model:finetune:BAAI/bge-base-en",
"model-in... | sentence-similarity | 2025-09-29T09:48:07Z | # SentenceTransformer based on BAAI/bge-base-en
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic sea... | [] |
ases200q2/PandaPickCubeSpacemouseRandom2_ACT_test_20251023_134850 | ases200q2 | 2025-10-23T07:24:29Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:ases200q2/PandaPickCubeSpacemouseRandom2_v30",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2025-10-23T07:23:50Z | # 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":... |
davidafrica/qwen2.5-aave_s89_lr1em05_r32_a64_e1 | davidafrica | 2026-03-04T15:20:35Z | 104 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | 2026-02-26T10:15:28Z | ⚠️ **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"... |
Yesimm/InfectaVec-v2 | Yesimm | 2025-09-30T01:06:49Z | 136 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:73517",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"multilingual",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652... | sentence-similarity | 2025-09-27T06:35:34Z | # BGE-M3 fine-tuned with Matryoshka + MNRLoss
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, sem... | [] |
tokiers/Llama-4-Scout-17B-16E | tokiers | 2026-03-24T01:12:26Z | 0 | 0 | tokie | [
"tokie",
"region:us"
] | null | 2026-03-24T01:09:48Z | <p align="center">
<img src="tokie-banner.png" alt="tokie" width="600">
</p>
# Llama-4-Scout-17B-16E
Pre-built [tokie](https://github.com/chonkie-inc/tokie) tokenizer for [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E).
## Quick Start (Python)
```bash
pip install tokie
... | [] |
Mardiyyah/variant-tapt_grouped_llrd_grouped_txt_whole_word-LR_2e-05 | Mardiyyah | 2025-11-21T09:39:49Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"base_model:finetune:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"license:mit",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-11-21T08:28:06Z | <!-- 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. -->
# variant-tapt_grouped_llrd_grouped_txt_whole_word-LR_2e-05
This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-b... | [] |
chancharikm/sft_incomplete_critique_20251120_ep2_lr3e5_qwen3-vl-8b | chancharikm | 2025-11-21T06:59:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_vl",
"image-text-to-text",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-VL-8B-Instruct",
"base_model:finetune:Qwen/Qwen3-VL-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-11-21T06:02:19Z | <!-- 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. -->
# sft_incomplete_critique_20251120_ep2_lr3e5_qwen3-vl-8b
This model is a fine-tuned version of [Qwen/Qwen3-VL-8B-Instruct](https://... | [] |
buelfhood/conplag2_codebert_ep30_bs16_lr2e-05_l512_s42_ppy_loss | buelfhood | 2025-11-17T01:47:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/codebert-base",
"base_model:finetune:microsoft/codebert-base",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-11-17T01:46:56Z | <!-- 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. -->
# conplag2_codebert_ep30_bs16_lr2e-05_l512_s42_ppy_loss
This model is a fine-tuned version of [microsoft/codebert-base](https://hug... | [] |
c-mohanraj/gemma-lora-t3 | c-mohanraj | 2025-10-11T23:50:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-10-11T23:25:36Z | # Model Card for gemma-lora-t3
This model is a fine-tuned version of [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-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... | [] |
EpoCanvas/GLM-5 | EpoCanvas | 2026-04-04T17:02:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"glm_moe_dsa",
"text-generation",
"conversational",
"en",
"zh",
"arxiv:2602.15763",
"license:mit",
"eval-results",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-04-04T17:02:28Z | # GLM-5
<div align="center">
<img src=https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/logo.svg width="15%"/>
</div>
<p align="center">
👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/wechat.png" target="_blank">WeChat</a> or <a href="https://... | [] |
kaitchup/translategemma-27b-it-autoround-w2a16g32 | kaitchup | 2026-01-19T17:36:16Z | 9 | 0 | null | [
"safetensors",
"gemma3",
"dataset:kaitchup/opus100-translategemma-calib",
"base_model:google/translategemma-27b-it",
"base_model:quantized:google/translategemma-27b-it",
"license:gemma",
"2-bit",
"auto-round",
"region:us"
] | null | 2026-01-16T22:25:03Z | This is a quantized variant of **google/translategemma-27b-it**, created by **The Kaitchup** (newsletter: https://kaitchup.substack.com).
More details (training recipe, benchmarks, and recommended settings) will be added later. In the meantime, here are the current notes and a working inference example.
## Status / l... | [] |
mradermacher/AtomicFission-7B-v1-i1-GGUF | mradermacher | 2025-12-07T23:41:10Z | 72 | 0 | transformers | [
"transformers",
"gguf",
"nuclear",
"reactor",
"physics",
"engineering",
"specialized",
"en",
"base_model:dougeeai/AtomicFission-7B-v1",
"base_model:quantized:dougeeai/AtomicFission-7B-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-11-03T16:39:09Z | ## 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_... | [] |
ggc6433/MyAwesomeModel-TestRepo | ggc6433 | 2026-03-17T11:39:45Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2026-03-17T11:39:39Z | # MyAwesomeModel
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="figures/fig1.png" width="60%" alt="MyAwesomeModel" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="LICENSE" style="m... | [
{
"start": 757,
"end": 770,
"text": "post-training",
"label": "training method",
"score": 0.8278292417526245
}
] |
kojima-lab/molcrawl-rna-celltype-gpt2-medium | kojima-lab | 2026-04-24T11:46:35Z | 1,012 | 0 | null | [
"pytorch",
"gpt2",
"rna",
"text-generation",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-04-06T04:07:36Z | # molcrawl-rna-celltype-gpt2-medium
## Model Description
GPT-2 medium (345M parameters) fine-tuned on cell-type specific RNA sequences, starting from the `molcrawl-rna-gpt2-medium` pre-trained model.
- **Model Type**: gpt2
- **Data Type**: RNA
- **Training Date**: 2026-04-24
## Usage
```python
from transformers im... | [] |
alexwengg/kittentts-coreml | alexwengg | 2026-03-22T15:49:58Z | 0 | 0 | coremltools | [
"coremltools",
"coreml",
"tts",
"text-to-speech",
"ios",
"macos",
"apple-silicon",
"on-device",
"base_model:KittenML/kitten-tts-mini-0.8",
"base_model:finetune:KittenML/kitten-tts-mini-0.8",
"license:apache-2.0",
"region:us"
] | text-to-speech | 2026-03-21T10:59:05Z | # KittenTTS CoreML
CoreML conversions of [KittenTTS](https://huggingface.co/KittenML) models for on-device text-to-speech on iOS and macOS.
**Two models** | **24kHz audio** | **FP32 CoreML** | **8 voices** | **iOS 17+ / macOS 14+**
## Models
| Model | Params | 5s Model | 10s Model | Speed Control |
|-------|-------... | [] |
rcdoug03/sd35-lora-glaze-none-Georgia_OKeeffe | rcdoug03 | 2026-03-04T06:12:21Z | 6 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"sd3.5-large",
"sd3.5",
"sd3.5-diffusers",
"base_model:stabilityai/stable-diffusion-3.5-large",
"base_model:adapter:stabilityai/stable-diffusion-3.5-large",
"license:other",
"region:us"
] | text-to-image | 2026-03-04T04:48:21Z | <!-- 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. -->
# SD3.5-Large DreamBooth LoRA - rcdoug03/sd35-lora-glaze-none-Georgia_OKeeffe
<Gallery />
## Model description
These are... | [] |
llmat/Qwen3-4B-Instruct-2507-NVFP4 | llmat | 2025-08-27T20:16:41Z | 161 | 1 | null | [
"safetensors",
"qwen3",
"quantization",
"nvfp4",
"qwen",
"text-generation",
"conversational",
"en",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:quantized:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"8-bit",
"compressed-tensors",
"region:us"
] | text-generation | 2025-08-27T14:43:17Z | # Qwen3-4B-Instruct-2507-NVFP4
NVFP4-quantized version of `Qwen/Qwen3-4B-Instruct-2507` produced with [llmcompressor](https://github.com/neuralmagic/llm-compressor).
## Notes
- Quantization scheme: NVFP4 (linear layers, `lm_head` excluded)
- Calibration samples: 512
- Max sequence length during calibration: 2048
## ... | [] |
InstantX/InstantID | InstantX | 2024-01-22T09:43:05Z | 49,293 | 848 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"en",
"arxiv:2401.07519",
"license:apache-2.0",
"region:us"
] | text-to-image | 2024-01-19T11:52:05Z | # InstantID Model Card
<div align="center">
[**Project Page**](https://instantid.github.io/) **|** [**Paper**](https://arxiv.org/abs/2401.07519) **|** [**Code**](https://github.com/InstantID/InstantID) **|** [🤗 **Gradio demo**](https://huggingface.co/spaces/InstantX/InstantID)
</div>
## Introduction
InstantID is... | [] |
leafsmomo/act_bimanual_so101 | leafsmomo | 2026-01-28T07:30:05Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:leafsmomo/bimanual-so101-dataset",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-01-28T06:48:29Z | # 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":... |
BWKYD/olmo3-190m-zh-full | BWKYD | 2026-05-04T05:13:00Z | 0 | 0 | null | [
"safetensors",
"olmo3",
"llm001",
"chinese",
"pretrained",
"zh",
"base_model:cmz1024/olmo3-190m-zh-full",
"base_model:finetune:cmz1024/olmo3-190m-zh-full",
"license:apache-2.0",
"region:us"
] | null | 2026-05-04T05:12:51Z | # OLMo3-190M-zh-full
为零基础 AI 大模型研发训练营（llm001）L04 Full 模型（190M 参数，20 步测试训练）。
## 模型配置
- hidden_size: 768, num_layers: 12, num_heads: 12, intermediate_size: 3072
- vocab_size: 48000, sliding_window: 4096
## 训练配置
- 数据：cmz1024/llm101-olmo3-zh-demo-data (500M tokens)
- 训练：H100, max_steps=20, bs=16×8=128, lr=5e-4, bf16
... | [] |
aaaaaaaaaaafg/PULSE-7B | aaaaaaaaaaafg | 2026-03-22T12:18:57Z | 6 | 0 | null | [
"safetensors",
"llava_llama",
"medical",
"image-text-to-text",
"en",
"dataset:PULSE-ECG/ECGInstruct",
"dataset:PULSE-ECG/ECGBench",
"arxiv:2410.19008",
"license:apache-2.0",
"region:us"
] | image-text-to-text | 2026-03-22T12:18:57Z | # PULSE-7B
Dataset for paper "Teach Multimodal LLMs to Comprehend Electrocardiographic Images".
🌐 Project Page: [https://aimedlab.github.io/PULSE/](https://aimedlab.github.io/PULSE/)
📄 Paper: [https://arxiv.org/abs/2410.19008](https://arxiv.org/abs/2410.19008)
🧑💻 Code: [https://github.com/AIMedLab/PULSE](https... | [] |
Davidei/move_red_block_act_2_Steps100000 | Davidei | 2026-02-27T12:21:22Z | 46 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:Davidei/Grasp_and_move_redblock_in_the_box_200",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-27T05:24:02Z | # 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":... |
spicyneuron/Kimi-K2.6-MLX-3.3bit | spicyneuron | 2026-04-24T13:34:23Z | 114 | 0 | mlx | [
"mlx",
"safetensors",
"kimi_k25",
"text-generation",
"conversational",
"custom_code",
"en",
"base_model:moonshotai/Kimi-K2.6",
"base_model:quantized:moonshotai/Kimi-K2.6",
"4-bit",
"region:us"
] | text-generation | 2026-04-22T04:07:01Z | [Kimi K2.6](https://huggingface.co/moonshotai/Kimi-K2.6) optimized to run _comfortably_
on a Mac Studio M3 512. This is the smaller, compact version. Quality-first
version [here](https://huggingface.co/spicyneuron/Kimi-K2.6-MLX-3.6bit).
- A mixed-precision quant that balances speed, memory, and accuracy.
- 3-bit basel... | [] |
notmax123/LightBlue | notmax123 | 2026-03-02T08:27:32Z | 0 | 0 | null | [
"onnx",
"text-to-speech",
"tts",
"hebrew",
"audio",
"fast-inference",
"he",
"dataset:notmax123/RanLevi40h",
"license:mit",
"region:us"
] | text-to-speech | 2026-02-28T20:23:00Z | # LightBlue TTS 🇮🇱
## Model Description
LightBlue is a state-of-the-art, lightning-fast Text-to-Speech (TTS) model built from scratch specifically for Hebrew (with English support). It is designed to produce 100% native Israeli-sounding speech with perfect handling of *Nikud* (vowels) and complex homographs, withou... | [] |
shuni52/act_policy1 | shuni52 | 2026-02-12T16:22:48Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:shuni52/leisaac-pick-orange",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-11T06:24:30Z | # 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":... |
manancode/opus-mt-en-tut-ctranslate2-android | manancode | 2025-08-17T16:25:34Z | 0 | 0 | null | [
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] | translation | 2025-08-17T16:25:24Z | # opus-mt-en-tut-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-tut` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-tut
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted ... | [] |
jk200201/qwen2.5-coder-7b-sql-dpo | jk200201 | 2026-03-26T13:17:58Z | 0 | 1 | peft | [
"peft",
"safetensors",
"text-to-sql",
"sql",
"lora",
"dpo",
"llama-factory",
"transformers",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"text-generation",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-03-26T12:56:20Z | # Qwen2.5-Coder-7B — Text-to-SQL (SFT + DPO)
A LoRA adapter fine-tuned on [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) for text-to-SQL generation, achieving **78.2% result accuracy on Spider V1** — outperforming both frontier models used to build its training data.
| Model |... | [] |
mradermacher/Preferred-MedRECT-32B-i1-GGUF | mradermacher | 2025-12-06T03:48:34Z | 324 | 0 | transformers | [
"transformers",
"gguf",
"en",
"ja",
"base_model:pfnet/Preferred-MedRECT-32B",
"base_model:quantized:pfnet/Preferred-MedRECT-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-11-01T22:00:19Z | ## 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_... | [] |
c-mohanraj/qwen14b-multi-turn-R3 | c-mohanraj | 2025-11-03T19:50:48Z | 1 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"license:mit",
"region:us"
] | text-generation | 2025-11-03T19:49:30Z | <!-- 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. -->
# qwen14b-multi-turn-R3
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/dee... | [] |
garethpaul/gpt-oss-20b-multilingual-reasoner | garethpaul | 2025-08-08T22:41:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-08T22:24:19Z | # Model Card for gpt-oss-20b-multilingual-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time mach... | [] |
nightmedia/Qwen3.5-4B-mxfp8-mlx | nightmedia | 2026-03-07T21:48:51Z | 878 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3_5",
"qwen3.5",
"vision-language-model",
"mxfp4",
"base_model:Qwen/Qwen3.5-4B",
"base_model:quantized:Qwen/Qwen3.5-4B",
"license:apache-2.0",
"8-bit",
"region:us"
] | null | 2026-03-02T15:51:37Z | # Qwen3.5-4B-mxfp8-mlx
Brainwaves
```brainwaves
arc arc/e boolq hswag obkqa piqa wino
mxfp8 0.392,0.441,0.627,0.601,0.360,0.739,0.590
q8-hi 0.398,0.435,0.622,0.604,0.362,0.732,0.585
q8 0.398,0.434,0.622,0.604,0.362,0.733,0.582
q6-hi 0.398,0.436,0.622,0.601,0.366,0.733,0.589
q6 0.392,0.... | [] |
sarikasingh00/qwen-1.5b-pytracebugs-baseline-qlora | sarikasingh00 | 2025-12-07T17:29:47Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-Coder-1.5B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct",
"region:us"
] | text-generation | 2025-12-07T17:29:43Z | # Model Card for pytracebugs-qlora-baseline-qwen-15-v2
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
qu... | [] |
yusufbaykaloglu/Kumru-2B-SFT | yusufbaykaloglu | 2025-12-16T08:23:10Z | 2 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:vngrs-ai/Kumru-2B",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"tr",
"base_model:vngrs-ai/Kumru-2B",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-12-11T21:08:36Z | # Kumru-2B-SFT
Türkçe için ince ayar yapılmış konuşma modeli. VNGRS Kumru-2B üzerine helpsteer3-tr veri seti ile SFT eğitimi yapılmış LoRA adaptörü.
## Model Özeti
| Özellik | Değer |
| --------------- | -------... | [
{
"start": 114,
"end": 117,
"text": "SFT",
"label": "training method",
"score": 0.7064592838287354
},
{
"start": 691,
"end": 694,
"text": "SFT",
"label": "training method",
"score": 0.713749885559082
}
] |
panuj456/distilbert-base-uncased-finetuned-emotion | panuj456 | 2026-02-17T15:16:57Z | 0 | 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 | 2026-02-17T15:12:08Z | <!-- 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://huggingface.co/... | [] |
Shermainet/codeparrot-ds | Shermainet | 2025-11-17T07:39:50Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-11-17T07:30:39Z | <!-- 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. -->
# codeparrot-ds
This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on an u... | [] |
light-curve/atcat | light-curve | 2026-05-01T21:18:07Z | 0 | 0 | onnx | [
"onnx",
"astronomy",
"time-series",
"light-curves",
"arxiv:2511.00614",
"region:us"
] | null | 2026-05-01T21:17:58Z | # ATCAT
## Paper
Tung, Z. (2025). *ATCAT: Astronomical Timeseries CAusal Transformer*. arXiv:2511.00614.
```bibtex
@article{tung2025atcat,
author = {Tung, Zora},
title = {{ATCAT}: Astronomical Timeseries CAusal Transformer},
journal = {arXiv preprint arXiv:2511.00614},
year = {2025}
}
```
## Original code
... | [] |
catalystsec/MiniMax-M2.7-4bit-DWQ | catalystsec | 2026-04-13T21:23:30Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"minimax_m2",
"text-generation",
"conversational",
"custom_code",
"base_model:MiniMaxAI/MiniMax-M2.7",
"base_model:quantized:MiniMaxAI/MiniMax-M2.7",
"license:other",
"4-bit",
"region:us"
] | text-generation | 2026-04-13T14:42:25Z | # catalystsec/MiniMax-M2.7-4bit-DWQ
This model was quantized to 4-bit using DWQ with mlx-lm version **0.31.2**.
| Parameter | Value |
|---------------------------|--------------------------------|
| DWQ learning rate | 2e-7 |
| Batch size ... | [] |
wangkanai/wan25-vae | wangkanai | 2025-10-28T18:23:00Z | 0 | 3 | diffusers | [
"diffusers",
"wan",
"text-to-video",
"image-generation",
"license:other",
"region:us"
] | text-to-video | 2025-10-13T12:17:55Z | <!-- README Version: v1.5 -->
# WAN25 VAE - Video Autoencoder v2.5
⚠️ **Repository Status**: This repository is currently a placeholder for WAN 2.5 VAE models. The directory structure is prepared (`vae/wan/`) but model files have not yet been downloaded. Total current size: ~18 KB (metadata only).
High-performance V... | [] |
Lpremier/my_pick_and_place_policy_cube_in_yellow_box_easy | Lpremier | 2026-01-18T14:19:53Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:Lpremier/so101_pick_and_place_test_cube_in_yellow_box_easy",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-01-18T14:19:43Z | # 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":... |
rzheng18/Qwen_android_ablation1_LR_1e-5_epoch_1 | rzheng18 | 2025-09-25T03:54:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"alignment-handbook",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"region:us"... | text-generation | 2025-09-25T03:36:15Z | # Model Card for None
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 = "If you had a time machine, but could on... | [] |
frankx518/Llama-3.2-1B-couplet | frankx518 | 2026-04-16T06:31:55Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2026-04-16T06:26:24Z | # Model Card for Llama-3.2-1B-couplet
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you ... | [] |
Jaso1024/Qwen3.5-A150M-1B | Jaso1024 | 2026-04-09T01:07:04Z | 288 | 0 | null | [
"safetensors",
"qwen3_5_text",
"moefication",
"sparse",
"mixture-of-experts",
"custom_code",
"base_model:Qwen/Qwen3.5-0.8B",
"base_model:finetune:Qwen/Qwen3.5-0.8B",
"region:us"
] | null | 2026-04-08T21:20:47Z | # Qwen3.5-A200M-1B
Training-free MoEfication of [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B).
## Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"Jaso1024/Qwen3.5-A150M-1B",
trust_remote_code=True,... | [] |
Dr3dre/rm-noise-sentence-short-sft-oai-pythia-1b-deduped-lr1-5e-05-effbs64-ep1-0-noisesent10 | Dr3dre | 2026-02-22T16:37:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"trl",
"reward-trainer",
"base_model:Dr3dre/sft-oai-pythia-1b-deduped-lr6-35e-05-effbs128-ep1-0",
"base_model:finetune:Dr3dre/sft-oai-pythia-1b-deduped-lr6-35e-05-effbs128-ep1-0",
"endpoints_compatible",
... | text-classification | 2026-02-22T16:37:12Z | # Model Card for sft-oai-pythia-1b-deduped-lr6-35e-05-effbs128-ep1-0_lr1.5e-05_effbs64_ep1.0_noisesent10
This model is a fine-tuned version of [Dr3dre/sft-oai-pythia-1b-deduped-lr6-35e-05-effbs128-ep1-0](https://huggingface.co/Dr3dre/sft-oai-pythia-1b-deduped-lr6-35e-05-effbs128-ep1-0).
It has been trained using [TRL]... | [] |
wvangils/xvla_pick_cube_finetuned | wvangils | 2026-03-01T17:49:13Z | 32 | 0 | lerobot | [
"lerobot",
"safetensors",
"xvla",
"robotics",
"dataset:wvangils/PickPlaceCubeV1",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-01T17:48:38Z | # Model Card for xvla
<!-- 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.c... | [] |
ethanCSL/svla_koch_sorting_only_wrist | ethanCSL | 2026-03-17T06:39:42Z | 123 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:ethanCSL/svla_koch_sorting_n_stacking_wrist_camera",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-17T06:39:05Z | # 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... | [] |
clarin-pl/combo-seg-xlm-roberta-base-slovenian-ssj-ud2.17 | clarin-pl | 2026-04-21T08:54:24Z | 0 | 0 | null | [
"pytorch",
"segmentation",
"tokenization",
"combo-seg",
"universal-dependencies",
"token-classification",
"sl",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"region:us"
] | token-classification | 2026-04-21T08:23:16Z | # COMBO-SEG Model for Slovenian
## Model Description
This is a Slovenian-language character-level segmentation model based on [COMBO-SEG](https://gitlab.clarin-pl.eu/syntactic-tools/combo-seg), an open-source text segmentation system. It performs:
- sentence segmentation
- tokenisation (including multi-word token de... | [] |
wangjian21/VG | wangjian21 | 2025-12-31T16:33:58Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-10-14T06:32:38Z | <!-- 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. -->
# LoRA DreamBooth - wangjian21/VG
These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The wei... | [
{
"start": 199,
"end": 203,
"text": "LoRA",
"label": "training method",
"score": 0.8495129942893982
},
{
"start": 242,
"end": 246,
"text": "LoRA",
"label": "training method",
"score": 0.9217908978462219
},
{
"start": 560,
"end": 564,
"text": "LoRA",
"l... |
marcosremar2/cefr-classifier-pt-distilbert-balanced | marcosremar2 | 2026-01-12T06:29:20Z | 0 | 0 | null | [
"safetensors",
"distilbert",
"text-classification",
"cefr",
"portuguese",
"language-proficiency",
"pt",
"dataset:UniversalCEFR/peapl2_pt",
"dataset:UniversalCEFR/cople2_pt",
"license:mit",
"region:us"
] | text-classification | 2026-01-12T06:27:18Z | # CEFR Classifier for Portuguese (DistilBERT Balanced)
This model classifies Portuguese texts according to CEFR (Common European Framework of Reference) proficiency levels.
## Model Description
- **Base Model**: distilbert-base-multilingual-cased
- **Task**: 5-class classification (A1, A2, B1, B2, C1)
- **Training D... | [] |
faizack/kronos-btc-1hr-basemodel | faizack | 2025-10-23T10:15:29Z | 0 | 0 | kronos | [
"kronos",
"safetensors",
"financial-modeling",
"time-series",
"cryptocurrency",
"bitcoin",
"transformer",
"license:mit",
"region:us"
] | null | 2025-10-23T10:14:05Z | # Kronos BTC 1hr Basemodel
This is a fine-tuned Kronos basemodel trained on Bitcoin 1-hour candlestick data.
## Model Details
- **Model Type**: Kronos Basemodel
- **Training Data**: Bitcoin 1-hour candlestick data
- **Architecture**: Transformer-based
- **Model Size**: ~390MB
## Configuration
```json
... | [] |
LyliaEngine/realistic_filter | LyliaEngine | 2025-08-16T01:13:34Z | 116 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stablediffusionapi/smooth-mix-v2-ilustrious2",
"base_model:adapter:stablediffusionapi/smooth-mix-v2-ilustrious2",
"license:cdla-permissive-2.0",
"region:us"
] | text-to-image | 2025-08-16T01:12:16Z | # realistic_filter
<Gallery />
## Model description
This is a lora that helps realistic image generation, you can use my checkpoint to get the results I posted below (yomama 2.5D [Illustrious] [Pony] - Illustrious v1.0 | Illustrious Checkpoint | Civitai). This checkpoint can generate 2.5D results pretty well, but t... | [] |
ThatDev13/ThatAI-1 | ThatDev13 | 2026-04-08T11:47:24Z | 0 | 0 | null | [
"chat",
"assistant",
"conversational",
"thatai",
"mistral",
"en",
"de",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2026-04-08T11:33:15Z | # 🕶️ ThatAI-1 (Beta)
**ThatAI-1** is a powerful, versatile AI assistant designed for everyone. It aims to provide high-quality assistance in both daily tasks and expert analysis.
## 🚀 Overview
- **Name:** ThatAI-1
- **Developer:** [ThatDev](https://huggingface.co/ThatDev13)
- **Base Model:** Mistral-7B-Instruct-v0... | [] |
braindecode/Labram | braindecode | 2026-04-25T17:49:45Z | 0 | 0 | braindecode | [
"braindecode",
"eeg",
"biosignal",
"pytorch",
"neuroscience",
"foundation-model",
"convolutional",
"feature-extraction",
"arxiv:2208.06366",
"license:bsd-3-clause",
"region:us"
] | feature-extraction | 2026-04-25T17:39:30Z | # Labram
Labram from Jiang, W B et al (2024) [Jiang2024].
> **Architecture-only repository.** Documents the
> `braindecode.models.Labram` class. **No pretrained weights are
> distributed here.** Instantiate the model and train it on your own
> data.
## Quick start
```bash
pip install braindecode
```
```python
from... | [] |
mmnga-o/Qwen3-Swallow-8B-RL-v0.2-gguf | mmnga-o | 2026-02-21T11:08:26Z | 812 | 0 | null | [
"gguf",
"ja",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"base_model:tokyotech-llm/Qwen3-Swallow-8B-RL-v0.2",
"base_model:quantized:tokyotech-llm/Qwen3-Swallow-8B-RL-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2026-02-21T10:43:17Z | ---
license: apache-2.0
language:
- ja
datasets:
- TFMC/imatrix-dataset-for-japanese-llm
base_model:
- tokyotech-llm/Qwen3-Swallow-8B-RL-v0.2
---
# Qwen3-Swallow-8B-RL-v0.2-gguf
[tokyotech-llmさんが公開しているQwen3-Swallow-8B-RL-v0.2](https://huggingface.co/tokyotech-llm/Qwen3-Swallow-8B-RL-v0.2)のggufフォーマット変換版です。
imatrixのデ... | [] |
hZzy/mistral-7b-expo-7b-expo-DPO-win-Submission-0.01-2604-nosys | hZzy | 2026-04-25T02:30:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"expo",
"arxiv:2305.18290",
"base_model:hZzy/mistral-7b-sft-7b-submission-win",
"base_model:finetune:hZzy/mistral-7b-sft-7b-submission-win",
"endpoints_compatible",
"region:us"
] | null | 2026-04-25T00:36:51Z | # Model Card for mistral-7b-expo-7b-expo-DPO-win-Submission-0.01-2604-nosys
This model is a fine-tuned version of [hZzy/mistral-7b-sft-7b-submission-win](https://huggingface.co/hZzy/mistral-7b-sft-7b-submission-win).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from t... | [
{
"start": 245,
"end": 248,
"text": "TRL",
"label": "training method",
"score": 0.7108367085456848
},
{
"start": 1042,
"end": 1045,
"text": "DPO",
"label": "training method",
"score": 0.719957709312439
},
{
"start": 1338,
"end": 1341,
"text": "DPO",
"l... |
airg/unet-efficientnet-b7-ltfl-mmgab-100ep-1window-ma | airg | 2026-04-14T21:05:54Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2026-04-14T21:01:55Z | U-Net generally conforming to the current [FTW baseline](https://github.com/fieldsoftheworld/ftw-baselines/tree/main) 3-class model settings, trained on Mapping Africa Planet-based imagery for 100 epochs. See [here](https://github.com/agroimpacts/ftw-mappingafrica-integration/tree/main) for code and [configuration](htt... | [] |
Gen-Verse/RLAnything-OS-Reward-8B | Gen-Verse | 2026-02-03T03:40:56Z | 3 | 2 | null | [
"safetensors",
"qwen3_vl",
"arxiv:2602.02488",
"license:mit",
"region:us"
] | null | 2026-02-01T06:33:12Z | # Introduction to TraDo
[Paper](https://arxiv.org/abs/2602.02488) | [Code](https://github.com/Gen-Verse/Open-AgentRL) | [Blog](https://yinjjiew.github.io/projects/rlanything/)
We introduce **RLAnything**, a reinforcement learning framework forges environment, policy and reward model in a completely dynamic system to ... | [
{
"start": 591,
"end": 611,
"text": "consistency feedback",
"label": "training method",
"score": 0.8169072866439819
}
] |
qing-yao/genpref_n10000_nb50k_410m_ep10_lr1e-4_seed42 | qing-yao | 2025-12-27T04:18:20Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"base_model:finetune:EleutherAI/pythia-410m",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-12-27T04:15:49Z | <!-- 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. -->
# genpref_n10000_nb50k_410m_ep10_lr1e-4_seed42
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.c... | [] |
mradermacher/Magistaroth-24B-v1-MPOA-GGUF | mradermacher | 2026-02-23T22:38:10Z | 644 | 0 | transformers | [
"transformers",
"gguf",
"DELLA",
"merge",
"mergekit",
"en",
"dataset:OccultAI/illuminati_imatrix_v1",
"base_model:Naphula/Magistaroth-24B-v1-MPOA",
"base_model:quantized:Naphula/Magistaroth-24B-v1-MPOA",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-02-23T20:01:12Z | ## 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... | [] |
manancode/opus-mt-en-tw-ctranslate2-android | manancode | 2025-08-17T16:26:04Z | 0 | 0 | null | [
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] | translation | 2025-08-17T16:25:51Z | # opus-mt-en-tw-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-tw` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-tw
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by*... | [] |
7886542asd/all-MiniLM-L6-v2 | 7886542asd | 2026-03-05T21:46:29Z | 19 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"tf",
"rust",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_a... | sentence-similarity | 2026-03-05T21:46:28Z | # all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](ht... | [] |
mradermacher/Llama-3.3-70B-Joyous-i1-GGUF | mradermacher | 2025-12-27T18:58:20Z | 202 | 0 | transformers | [
"transformers",
"gguf",
"conversational",
"roleplay",
"en",
"base_model:allura-org/Llama-3.3-70B-Joyous",
"base_model:quantized:allura-org/Llama-3.3-70B-Joyous",
"license:llama3.3",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-12-27T04:12:28Z | ## 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_... | [] |
mlx-community/Qwen3-4B-Thinking-2507-gabliterated-4bit | mlx-community | 2026-01-17T15:50:55Z | 23 | 2 | mlx | [
"mlx",
"safetensors",
"qwen3",
"uncensored",
"gabliteration",
"text-generation",
"conversational",
"dataset:mlabonne/harmless_alpaca",
"dataset:mlabonne/harmful_behaviors",
"base_model:Goekdeniz-Guelmez/Qwen3-4B-Thinking-2507-gabliterated",
"base_model:quantized:Goekdeniz-Guelmez/Qwen3-4B-Thinki... | text-generation | 2026-01-17T15:48:27Z | # mlx-community/Qwen3-4B-Thinking-2507-gabliterated-4bit
This model [mlx-community/Qwen3-4B-Thinking-2507-gabliterated-4bit](https://huggingface.co/mlx-community/Qwen3-4B-Thinking-2507-gabliterated-4bit) was
converted to MLX format from [Goekdeniz-Guelmez/Qwen3-4B-Thinking-2507-gabliterated](https://huggingface.co/Goe... | [] |
Stableyogi/Micro-Mini-Dress-Collection | Stableyogi | 2026-02-21T22:01:00Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"text-to-image",
"sd-1.5",
"en",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:other",
"region:us"
] | text-to-image | 2026-02-21T22:00:48Z | # Micro Mini Dress Collection
A LoRA for generating specific clothing styles and fashion items.
## Compatibility
| Property | Value |
|----------|-------|
| **Type** | LoRA |
| **Base Model** | SD 1.5 |
| **Format** | SafeTensors |
## Trigger Words
```
B&W Print, spikey dress, bare shoulders, off sh... | [] |
mradermacher/Heretic-OpenCoder-1.5B-Instruct-GGUF | mradermacher | 2026-01-02T18:53:18Z | 15 | 0 | transformers | [
"transformers",
"gguf",
"heretic",
"en",
"base_model:hereticness/Heretic-OpenCoder-1.5B-Instruct",
"base_model:quantized:hereticness/Heretic-OpenCoder-1.5B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-01-02T14:30:50Z | ## 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... | [] |
jsl5710/Shield-Gemma-3-270m-PEFT-CE | jsl5710 | 2026-04-09T17:56:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"dia-guard",
"shield",
"safety",
"dialect",
"peft-lora",
"ce",
"text-generation",
"conversational",
"en",
"base_model:google/gemma-3-270m-it",
"base_model:adapter:google/gemma-3-270m-it",
"license:gemma",
"region:us"
] | text-generation | 2026-04-09T04:03:22Z | # Gemma-3-270m — PEFT (LoRA)/CE (Shield Project)
This model is part of the **Shield** project — a collection of safety-classifier models
fine-tuned on the **DIA-GUARD** dataset (48 English dialects, ~836K records of safe/unsafe
prompts) to robustly classify harmful content across diverse dialects.
## Model Summary
|... | [] |
EchoFox0829/natix-001 | EchoFox0829 | 2025-12-01T04:30:25Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:hayden-yuma/roadwork",
"base_model:finetune:hayden-yuma/roadwork",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-11-19T16:09:02Z | <!-- 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. -->
# natix-001
This model is a fine-tuned version of [hayden-yuma/roadwork](https://huggingface.co/hayden-yuma/roadwork) on an unknown... | [] |
haris9873/ppo-Pyramids1 | haris9873 | 2025-10-07T13:44:04Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2025-10-07T13:42:39Z | # **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/... | [] |
FrankCCCCC/ddpm-ema-10k_cfm-corr-50-ss0.0-ep100-ema-run2 | FrankCCCCC | 2025-10-03T04:15:12Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"diffusers:DDPMCorrectorPipeline",
"region:us"
] | null | 2025-10-03T03:49:10Z | # cfm_corr_50_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 directories... | [] |
falconlee236/nanogpt-gpt2-124m-custom | falconlee236 | 2026-03-03T00:56:37Z | 35 | 0 | null | [
"safetensors",
"gpt2",
"pytorch",
"causal-lm",
"nanogpt",
"en",
"dataset:falconlee236/openwebtext",
"license:mit",
"region:us"
] | null | 2026-03-03T00:34:01Z | # nanoGPT-124M-Custom
이 모델은 Andrej Karpathy의 [nanoGPT](https://github.com/karpathy/nanoGPT) 프레임워크를 기반으로 밑바닥부터(from scratch) 학습시킨 GPT-2 Small (124M) 호환 언어 모델입니다. Hugging Face의 `transformers` 라이브러리에서 즉시 사용할 수 있도록 표준 `safetensors` 포맷으로 변환되었습니다.
## 📊 Weights & Biases (WandB) 학습 로그
학습 과정의 Loss 변화, 연산 속도, 그리고 시스템 리소스 사용량 ... | [] |
contemmcm/fd93f4990f1fa74f057615fdb7a5c4ff | contemmcm | 2025-10-13T09:29:26Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-xlarge-v2",
"base_model:finetune:albert/albert-xlarge-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-10-13T09:28:29Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fd93f4990f1fa74f057615fdb7a5c4ff
This model is a fine-tuned version of [albert/albert-xlarge-v2](https://huggingface.co/albert/al... | [] |
yueqis/swe_only_mcp-qwen3-8b | yueqis | 2025-09-11T01:18:35Z | 0 | 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:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-09-11T01:09:17Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swe_only_mcp-qwen3-8b
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the swe_only... | [] |
felfri/dose-response-c1 | felfri | 2026-03-19T17:36:36Z | 4 | 0 | null | [
"diffusion",
"text-to-image",
"safety",
"dose-response",
"dataset:lehduong/flux_generated",
"dataset:LucasFang/FLUX-Reason-6M",
"dataset:brivangl/midjourney-v6-llava",
"license:apache-2.0",
"region:us"
] | text-to-image | 2026-03-19T17:35:41Z | # Dose-Response C1: 5% unsafe, full scale
This model is part of a **dose-response experiment** studying how the fraction of unsafe content in training data affects the safety of generated images from text-to-image diffusion models.
## Model Details
| | |
|---|---|
| **Architecture** | PRX-1.2B (Photoroom diffusion m... | [] |
straino/phi-2-Q4_K_M-GGUF | straino | 2025-09-08T11:28:04Z | 6 | 0 | null | [
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:microsoft/phi-2",
"base_model:quantized:microsoft/phi-2",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-09-08T11:27:56Z | # straino/phi-2-Q4_K_M-GGUF
This model was converted to GGUF format from [`microsoft/phi-2`](https://huggingface.co/microsoft/phi-2) 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/microsoft/phi-2) for... | [] |
Vortex5/Forsaken-Void-12B | Vortex5 | 2025-11-09T20:02:55Z | 1 | 6 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"roleplay",
"base_model:Retreatcost/Chrysologus-12B",
"base_model:merge:Retreatcost/Chrysologus-12B",
"base_model:Vortex5/Scarlet-Ink-12B",
"base_model:merge:Vortex5/Scarlet-Ink-12B",
"base_model:Vortex5/Shadow-C... | text-generation | 2025-11-09T17:48:39Z | <section class="shell void-theme">
<div class="title-frame">
<div class="title-block wide">
<h2 class="hero-title">Forsaken-Void-12B</h2>
</div>
<div class="image-slot inset">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6669a3a617b838fda45637b8/NNxBT0nzFHs7tz5oTKqO5.png" al... | [] |
slimshady48/Unllama3.2_3B | slimshady48 | 2025-10-27T04:29:04Z | 8 | 0 | null | [
"gguf",
"llama",
"llama.cpp",
"unsloth",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-10-27T04:27:02Z | # Unllama3.2_3B - GGUF
This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
**Example usage**:
- For text only LLMs: **llama-cli** **--hf** repo_id/model_name **-p** "why is the sky blue?"
- For multimodal models: **llama-mtmd-cli** **-m** model_name.gguf **-... | [] |
rbelanec/train_conala_101112_1760638008 | rbelanec | 2025-10-20T00:44:10Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"llama-factory",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | text-generation | 2025-10-20T00:09:13Z | <!-- 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_conala_101112_1760638008
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co... | [] |
Muapi/cinna-flow-flux | Muapi | 2025-08-28T17:41:44Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-28T17:41:20Z | # Cinna Flow [Flux]
**Base model**: Flux.1 D
**Trained words**: cinna flow
## 🧠 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-T... | [] |
wikilangs/nah | wikilangs | 2026-01-10T14:41:30Z | 0 | 0 | wikilangs | [
"wikilangs",
"nlp",
"tokenizer",
"embeddings",
"n-gram",
"markov",
"wikipedia",
"feature-extraction",
"sentence-similarity",
"tokenization",
"n-grams",
"markov-chain",
"text-mining",
"fasttext",
"babelvec",
"vocabulous",
"vocabulary",
"monolingual",
"family-american_nahuatl",
"... | text-generation | 2026-01-10T14:41:16Z | # Nahuatl languages - Wikilangs Models
## Comprehensive Research Report & Full Ablation Study
This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Nahuatl languages** Wikipedia data.
We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
#... | [
{
"start": 1314,
"end": 1335,
"text": "Tokenizer Compression",
"label": "training method",
"score": 0.7065246105194092
}
] |
GMorgulis/Phi-3-mini-4k-instruct-immigration-STEER0.361719-ft0.42 | GMorgulis | 2026-03-10T06:47:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-4k-instruct",
"endpoints_compatible",
"region:us"
] | null | 2026-03-10T06:32:18Z | # Model Card for Phi-3-mini-4k-instruct-immigration-STEER0.361719-ft0.42
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers i... | [] |
micrictor/gemma-3-270m-it-memorize-hppl-0.1p_of_params | micrictor | 2025-12-31T03:36:30Z | 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 | 2025-12-30T23:49:58Z | # Model Card for gemma-3-270m-it-memorize-hppl-0.1p_of_params
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 ... | [] |
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