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 |
|---|---|---|---|---|---|---|---|---|---|---|
nightmedia/Qwen3-30B-A3B-Element16b-qx86-hi-mlx | nightmedia | 2026-02-21T11:41:09Z | 41 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"coding",
"research",
"deep thinking",
"1M context",
"256k context",
"Qwen3",
"All use cases",
"creative",
"creative writing",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene cont... | text-generation | 2026-02-19T00:49:48Z | # Qwen3-30B-A3B-Element16b-qx86-hi-mlx
```
Qwen3-30B-A3B-Element16
mxfp8 0.561,0.705,0.885,0.739,0.452,0.794,0.702
qx86-hi 0.562,0.751,0.882,0.752,0.468,0.807,0.695
qx64-hi 0.574,0.753,0.878,0.748,0.464,0.805,0.688
mxfp4 0.544,0.699,0.875,0.741,0.438,0.800,0.671
Qwen3-30B-A3B-Element16b
mxfp8 0.561,0.710,0... | [] |
mradermacher/MediQwen-Reasoning-14B-GGUF | mradermacher | 2025-11-29T11:42:54Z | 11 | 1 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:justinj92/MediQwen-Reasoning-14B",
"base_model:quantized:justinj92/MediQwen-Reasoning-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-11-29T05:39:16Z | ## 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... | [] |
xy-98/Mistral-Small-3.1-24B-Instruct-2503-bnb-4bit | xy-98 | 2026-04-03T09:18:43Z | 0 | 0 | vllm | [
"vllm",
"safetensors",
"mistral3",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"base_model:quantized... | null | 2026-04-03T09:18:43Z | # Model Card for Mistral-Small-3.1-24B-Instruct-2503
Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) **adds state-of-the-art vision understanding** and enhances **long context capabilities up to 128k tokens** without compromising text performance.
With 24 billion parameters, this model achieves top-tie... | [] |
hereticness/Heretic-Qwen3-0.6B-Diagnose | hereticness | 2026-01-11T13:05:05Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"heretic",
"text-generation",
"conversational",
"base_model:suayptalha/Qwen3-0.6B-Diagnose",
"base_model:finetune:suayptalha/Qwen3-0.6B-Diagnose",
"region:us"
] | text-generation | 2026-01-11T13:04:35Z | <div style="background: #000; margin: auto; border: 1px solid #FFFFFF; background-image: url('https://c.tenor.com/r47ZgZUPwEwAAAAC/tenor.gif'); background-size: 30vw 70vh; background-repeat: repeat;">
<style>
*::selection {background: transparent !important; color: inherit !important;}
p,a,summary,details{color:#cccccc... | [] |
k1000dai/residualact_libero_smolvla_spatial_normal | k1000dai | 2025-08-23T00:02:43Z | 1 | 0 | lerobot | [
"lerobot",
"safetensors",
"residualact",
"robotics",
"dataset:k1000dai/libero-smolvla-spatial",
"license:apache-2.0",
"region:us"
] | robotics | 2025-08-23T00:02:29Z | # Model Card for residualact
<!-- 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://huggin... | [] |
laion/Kimi-K2T-swesmith-32ep-131k | laion | 2025-12-01T22:20:12Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-12-01T20:55:23Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Kimi-K2T-swesmith-32ep-131k
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the pe... | [] |
mradermacher/ARES-RL-7B-GGUF | mradermacher | 2025-10-10T05:45:20Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ares0728/ARES-RL-7B",
"base_model:quantized:ares0728/ARES-RL-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-10-10T05:35:26Z | ## 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... | [] |
whjie/speaker-segmentation-fine-tuned-callhome-zhov1 | whjie | 2025-09-28T09:37:56Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"dataset:diarizers-community/callhome",
"base_model:pyannote/segmentation-3.0",
"base_model:finetune:pyannote/segmentation-3.0",
"endpoints_compatible",
"region:us"... | null | 2025-09-28T09:32:59Z | <!-- 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. -->
# speaker-segmentation-fine-tuned-callhome-zhov1
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingf... | [] |
xghfcjgdf/grab_tissue_flat | xghfcjgdf | 2025-11-24T10:38:54Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:xghfcjgdf/grab_tissue_flat",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2025-11-24T08:11:10Z | # 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":... |
luminlemon/codescorer | luminlemon | 2026-03-19T21:04:46Z | 0 | 0 | null | [
"region:us"
] | null | 2026-03-19T19:32:24Z | # BTScorer - Performance Dimension Code Scorer
This repository contains the inference code and weights for `BTScorer`, a model trained to score code pairs based on four performance optimization dimensions: **CPU, IO, Memory, and Time**.
The model takes a code snippet as input and outputs a 4-dimensional score vecto (... | [] |
weirek/Affine-5H8QjWUhB5ttukwmNBEuMdzVTC4CMJfSTx19yPv5aaaek594 | weirek | 2026-01-31T21:35:29Z | 2 | 0 | null | [
"safetensors",
"qwen3",
"pytorch",
"causal-lm",
"text-generation",
"conversational",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-01-15T13:30:25Z | # Affine-5Eee8m2MbQiAUR3QGixeiAAPYos9AzqRfQpx1Rb59YHB1CgT
This model has been fine-tuned for conversational AI tasks.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"weirek/Affine-5Eee8m2MbQiAUR3QGixeiAAPYos9AzqRfQpx1Rb59YHB1CgT",
... | [] |
ML4BM-Lab/DeepRBP | ML4BM-Lab | 2025-12-23T09:09:57Z | 0 | 0 | pytorch | [
"pytorch",
"joblib",
"pytorch-lightning",
"bioinformatics",
"rna-binding-proteins",
"explainability",
"alternative-splicing",
"deep-learning",
"license:mit",
"region:us"
] | null | 2025-12-19T10:48:54Z | # DeepRBP Predictor (pretrained)
This repository provides a **pretrained DeepRBP predictor model**, a deep learning framework designed to infer **RNA-binding protein (RBP)–transcript and RBP–gene regulatory relationships** from expression data.
DeepRBP was introduced in the following preprint:
> **DeepRBP: A deep ne... | [
{
"start": 746,
"end": 773,
"text": "Feature attribution methods",
"label": "training method",
"score": 0.8697177171707153
},
{
"start": 781,
"end": 789,
"text": "DeepLIFT",
"label": "training method",
"score": 0.8202539086341858
}
] |
DanieleIntellimate/llama_finetune | DanieleIntellimate | 2026-02-26T21:16:54Z | 181 | 0 | null | [
"gguf",
"llama",
"llama.cpp",
"unsloth",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-02-26T02:09:48Z | # llama_finetune : 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 DanieleIntellimate/llama_finetune --jinja`
- For multimodal models: `./llama.cpp/llama-mtmd-cli -hf DanieleIntelli... | [
{
"start": 86,
"end": 93,
"text": "Unsloth",
"label": "training method",
"score": 0.7634323835372925
},
{
"start": 124,
"end": 131,
"text": "unsloth",
"label": "training method",
"score": 0.7506512999534607
},
{
"start": 547,
"end": 554,
"text": "unsloth",... |
HyeongwookRobotics/act_hackathon_04 | HyeongwookRobotics | 2026-02-07T07:37:45Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:bluephysi01/hackathon_03",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-07T07:37:23Z | # 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":... |
smithasbellavi/model_PressLitRedButton35k220 | smithasbellavi | 2026-04-16T14:36:11Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:smithasbellavi/dataset_PressLitRedButton220Epi",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-04-16T14:23:35Z | # 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... | [] |
Guilherme34/Maya-Q4_K_M-GGUF | Guilherme34 | 2025-08-18T22:46:48Z | 16 | 1 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-to-speech",
"en",
"base_model:Guilherme34/Maya",
"base_model:quantized:Guilherme34/Maya",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-to-speech | 2025-08-18T22:46:37Z | # Guilherme34/Maya-Q4_K_M-GGUF
This model was converted to GGUF format from [`Guilherme34/Maya`](https://huggingface.co/Guilherme34/Maya) 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/Guilherme34/May... | [] |
mradermacher/Llama-3.1-KokoroChat-High-GGUF | mradermacher | 2025-09-15T12:43:16Z | 51 | 0 | transformers | [
"transformers",
"gguf",
"counseling",
"dialogue-system",
"ja",
"en",
"dataset:UEC-InabaLab/KokoroChat",
"base_model:UEC-InabaLab/Llama-3.1-KokoroChat-High",
"base_model:quantized:UEC-InabaLab/Llama-3.1-KokoroChat-High",
"license:llama3.1",
"license:gemma",
"endpoints_compatible",
"region:us"... | null | 2025-09-14T05:51:24Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/UEC-InabaLab/Llama-3.1-KokoroChat-High
<!--... | [] |
pevers/parkiet | pevers | 2025-09-28T16:16:19Z | 36,654 | 10 | null | [
"safetensors",
"dia",
"text-to-speech",
"nl",
"base_model:nari-labs/Dia-1.6B",
"base_model:finetune:nari-labs/Dia-1.6B",
"license:openrail",
"region:us"
] | text-to-speech | 2025-09-21T13:07:29Z | # Parkiet: Dutch Text-to-Speech (TTS)
Open-weights Dutch TTS based on the [Parakeet](https://jordandarefsky.com/blog/2024/parakeet/) architecture, ported from [Dia](https://github.com/nari-labs/dia) to JAX for scalable training. A full walkthrough to train the model for your language on... | [] |
casinca/tiny-mimo-v2-flash | casinca | 2026-04-21T12:49:14Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"mimo_v2_flash",
"endpoints_compatible",
"region:us"
] | null | 2026-04-20T19:49:31Z | # tiny-mimo-v2-flash
A ~2.34B-parameter tiny random-weight checkpoint of [XiaomiMiMo/MiMo-V2-Flash](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash), used for internal testing in Hugging Face `transformers` for the native HF implementation.
## Configuration
| Hyperparameter | Value | Original MiMo |
|--------|----... | [] |
mradermacher/TexasHoldEm-Llama-3.2-1B-Instruct-i1-GGUF | mradermacher | 2026-01-18T00:43:15Z | 63 | 0 | transformers | [
"transformers",
"gguf",
"poker",
"texas-holdem",
"fine-tuned",
"lora",
"en",
"base_model:neopolita/TexasHoldEm-Llama-3.2-1B-Instruct",
"base_model:adapter:neopolita/TexasHoldEm-Llama-3.2-1B-Instruct",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2026-01-17T23:50:42Z | ## 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_... | [] |
EZCon/Huihui-Qwen3-VL-4B-Instruct-abliterated-4bit-g32-mxfp4-mixed_4_8-mlx | EZCon | 2026-04-05T01:26:49Z | 372 | 1 | mlx | [
"mlx",
"safetensors",
"qwen3_vl",
"abliterated",
"uncensored",
"image-text-to-text",
"conversational",
"base_model:huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated",
"base_model:quantized:huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated",
"license:apache-2.0",
"4-bit",
"region:us"
] | image-text-to-text | 2026-01-29T09:18:07Z | # EZCon/Huihui-Qwen3-VL-4B-Instruct-abliterated-4bit-g32-mxfp4-mixed_4_8-mlx
This model was converted to MLX format from [`huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated`](https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated)
using mlx-vlm version **0.4.4**.
Refer to the [original model card](ht... | [] |
cybermotaz/qwen3-vl-8b-thinking-nvfp4-w4a16 | cybermotaz | 2025-12-18T09:34:16Z | 389 | 2 | transformers | [
"transformers",
"safetensors",
"qwen3_vl",
"image-text-to-text",
"nvidia",
"qwen3",
"qwen3-vl",
"nvfp4",
"quantized",
"blackwell",
"sm121",
"elk-ai",
"vllm",
"cuda13",
"fp4",
"vision-language",
"thinking",
"reasoning",
"multimodal",
"conversational",
"en",
"zh",
"base_mod... | image-text-to-text | 2025-12-18T09:23:20Z | <div align="center">
# Qwen3-VL-8B-Thinking NVFP4 W4A16
### First NVFP4 Quantization of Qwen3-VL-8B-Thinking
**By Mutaz Al Awamleh | [ELK-AI](https://elkai.ai)**
[](https://hub.docker.com/r/elkaioptimization/vllm-nvfp4-cuda-13)
[![Hugg... | [] |
felixwangg/Qwen2.5-Coder-7B-sft-plus-alpha-1-line-diff-ctx0-v2 | felixwangg | 2026-04-14T01:08:05Z | 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_plus_splitted_line_diff_mask_skip_indent_ctx0_chat_v2",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"licen... | text-generation | 2026-04-14T01:07:37Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" wid... | [] |
mradermacher/Ice0.109-04.05-RP-GGUF | mradermacher | 2025-09-19T16:42:14Z | 1 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:icefog72/Ice0.109-04.05-RP",
"base_model:quantized:icefog72/Ice0.109-04.05-RP",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-19T02:11:47Z | ## 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... | [] |
LiamCarter/icl-pruning-llm-pruner-llama3-8b-ratio0.5 | LiamCarter | 2026-04-23T09:34:28Z | 0 | 1 | transformers | [
"transformers",
"llm_pruner",
"pruning",
"sparse",
"endpoints_compatible",
"region:us"
] | null | 2026-04-23T09:34:27Z | # llm_pruner/llama3-8b_ratio0.5
This repository was uploaded from a local experiment directory.
## Summary
- Method: `llm_pruner`
- Variant: `llama3-8b_ratio0.5`
- Format hint: `artifact-bundle`
- Source path: `models/llm_pruner/llama3-8b_ratio0.5`
- Repo id: `LiamCarter/icl-pruning-llm-pruner-llama3-8b-ratio0.5`
- ... | [] |
ENOSYS/GigaChat3.1-10B-A1.8B-GGUF | ENOSYS | 2026-04-13T08:13:07Z | 0 | 0 | null | [
"moe",
"text-generation",
"ru",
"en",
"dataset:eaddario/imatrix-calibration",
"base_model:ai-sage/GigaChat3.1-10B-A1.8B-bf16",
"base_model:finetune:ai-sage/GigaChat3.1-10B-A1.8B-bf16",
"license:mit",
"region:us"
] | text-generation | 2026-04-13T08:07:13Z | # Experimental global target bits‑per‑weight quantization of ai-sage/GigaChat3.1-10B-A1.8B-bf16
- Using **non-standard** (forked) [LLaMA C++](https://github.com/EAddario/llama.cpp/tree/quantize) branch for quantization.
- Using a [CLI tool](https://github.com/cmhamiche/kld-sweep-dataset) to build KLD evaluation and ima... | [] |
m-beps/qwen3-8b-finetune-multit-nothinking | m-beps | 2026-04-05T15:58:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"lora",
"qwen3",
"italian",
"cultural-alignment",
"fine-tuned",
"trl",
"transformers",
"sft",
"text-generation",
"conversational",
"it",
"dataset:sapienzanlp/Mult-IT",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"license:mit",
"region:us"
... | text-generation | 2026-04-05T14:14:21Z | # Qwen3 8B — Italian Cultural Alignment [V1]
**Qwen3 8B [V1]** is a LoRA adapter fine-tuned on top of [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) to improve Italian cultural alignment. It was trained on the [Mult-IT](https://huggingface.co/datasets/sapienzanlp/Mult-IT) dataset and evaluated on the [ITALIC]... | [] |
Ialysheva/sdxl-lora | Ialysheva | 2025-10-31T15:39:57Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"re... | text-to-image | 2025-10-31T15:39:54Z | <!-- 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 - Ialysheva/sdxl-lora
<Gallery />
## Model description
These are Ialysheva/sdxl-lora LoRA adaptio... | [
{
"start": 204,
"end": 208,
"text": "LoRA",
"label": "training method",
"score": 0.7526736259460449
},
{
"start": 308,
"end": 312,
"text": "LoRA",
"label": "training method",
"score": 0.8616244196891785
},
{
"start": 455,
"end": 459,
"text": "LoRA",
"l... |
NexVeridian/Kimi-Dev-72B-8bit | NexVeridian | 2025-08-16T00:09:27Z | 60 | 0 | mlx | [
"mlx",
"safetensors",
"qwen2",
"code",
"swebench",
"software",
"issue-resolving",
"text-generation",
"conversational",
"base_model:moonshotai/Kimi-Dev-72B",
"base_model:quantized:moonshotai/Kimi-Dev-72B",
"license:mit",
"8-bit",
"region:us"
] | text-generation | 2025-08-15T23:34:49Z | # NexVeridian/Kimi-Dev-72B-8bit
This model [NexVeridian/Kimi-Dev-72B-8bit](https://huggingface.co/NexVeridian/Kimi-Dev-72B-8bit) was
converted to MLX format from [moonshotai/Kimi-Dev-72B](https://huggingface.co/moonshotai/Kimi-Dev-72B)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
... | [] |
Thireus/Qwen3.6-35B-A3B-THIREUS-IQ2_XS_R4-SPECIAL_SPLIT | Thireus | 2026-04-25T20:12:03Z | 181 | 0 | null | [
"gguf",
"arxiv:2505.23786",
"license:mit",
"region:us"
] | null | 2026-04-25T07:33:45Z | # Qwen3.6-35B-A3B
## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3.6-35B-A3B-THIREUS-BF16-SPECIAL_SPLIT/) about?
This repository provides **GGUF-quantized tensors** for the Qwen3.6-35B-A3B model (official repo: https://huggingface.co/Qwen/Qwen3.6-35B-A3B). These GGUF shards are designe... | [] |
khawajaaliarshad/whisper-small-urdu | khawajaaliarshad | 2025-12-28T08:27:03Z | 284 | 1 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"speech-recognition",
"urdu",
"audio",
"asr",
"generated_from_trainer",
"hf-asr-leaderboard",
"ur",
"dataset:khawajaaliarshad/common-voice-urdu-processed-expanded",
"arxiv:2212.04356",
"base_model:openai/whisper-smal... | automatic-speech-recognition | 2025-12-27T07:37:54Z | # Whisper Small - Urdu Fine-tuned
This model is a fine-tuned version of [**openai/whisper-small**](https://huggingface.co/openai/whisper-small) for **Urdu (اردو)** automatic speech recognition (ASR), trained on the expanded [Mozilla Common Voice Scripted Speech 24.0 - Urdu](https://datacollective.mozillafoundation.or... | [] |
mradermacher/Midm-2.0-Mini-Thinking-i1-GGUF | mradermacher | 2025-12-10T03:21:39Z | 211 | 0 | transformers | [
"transformers",
"gguf",
"KT",
"K-intelligence",
"Mi:dm",
"en",
"ko",
"base_model:b-re-w/Midm-2.0-Mini-Thinking",
"base_model:quantized:b-re-w/Midm-2.0-Mini-Thinking",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-23T22:02:32Z | ## 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_... | [] |
warheads/qwen3-4b-structured-output-lora-04 | warheads | 2026-02-28T18:45:45Z | 13 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v2",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-02-28T18:45:24Z | 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": 0.7970229983329773
}
] |
Guilherme34/Firefly-V2 | Guilherme34 | 2025-11-29T23:22:56Z | 5 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Guilherme34/Firefly",
"SicariusSicariiStuff/Impish_LLAMA_3B",
"conversational",
"base_model:Guilherme34/Firefly",
"base_model:merge:Guilherme34/Firefly",
"base_model:SicariusSicariiStuff/Impish_L... | text-generation | 2025-11-24T02:31:30Z | # Firefly-V2
Firefly-V2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Guilherme34/Firefly](https://huggingface.co/Guilherme34/Firefly)
* [SicariusSicariiStuff/Impish_LLAMA_3B](https://huggingface.co/SicariusSicariiStuf... | [] |
SNUMPR/Zerg-b | SNUMPR | 2025-08-11T07:45:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"text-generation-inference",
"region:us"
] | text-generation | 2025-08-11T07:36:12Z | # Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library... | [] |
xl-zhao/PromptCoT-2.0-SelfPlay-4B | xl-zhao | 2025-09-26T14:11:04Z | 2 | 0 | null | [
"safetensors",
"qwen3",
"en",
"arxiv:2509.19894",
"license:mit",
"region:us"
] | null | 2025-09-25T06:48:37Z | # PromptCoT-2.0-SelfPlay-4B
This model is part of **PromptCoT 2.0** (*Scaling Prompt Synthesis for LLM Reasoning*).
It is a **4B model trained via self-play**, where synthesized problems from PromptCoT 2.0 provide **verifiable feedback** (unit tests for code, boxed answers for math).
The training loop uses **Direc... | [] |
appvoid/arco-3-test2-Q8_0-GGUF | appvoid | 2025-10-19T12:41:03Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:appvoid/arco-3-test2",
"base_model:quantized:appvoid/arco-3-test2",
"endpoints_compatible",
"region:us"
] | null | 2025-10-19T12:40:55Z | # appvoid/arco-3-test2-Q8_0-GGUF
This model was converted to GGUF format from [`appvoid/arco-3-test2`](https://huggingface.co/appvoid/arco-3-test2) 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/appvo... | [] |
mradermacher/Llama-3.1-GuideX-8B-GGUF | mradermacher | 2025-09-09T07:05:46Z | 147 | 0 | transformers | [
"transformers",
"gguf",
"code",
"text-generation-inference",
"Information Extraction",
"IE",
"Named Entity Recogniton",
"Event Extraction",
"Relation Extraction",
"LLaMA",
"en",
"dataset:HiTZ/GuideX_pre-training_data",
"dataset:ACE05",
"dataset:bc5cdr",
"dataset:conll2003",
"dataset:nc... | null | 2025-09-09T05:48:38Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static qu... | [] |
va1bhavagrawa1/seethrough3d | va1bhavagrawa1 | 2026-03-09T06:01:07Z | 0 | 2 | diffusers | [
"diffusers",
"controllable text-to-image generation",
"diffusion models",
"3D layout control",
"occlusion reasoning",
"text-to-image",
"en",
"dataset:va1bhavagrawa1/seethrough3d-data",
"arxiv:2602.23359",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-de... | text-to-image | 2026-02-27T19:33:06Z | # [CVPR-26 🎉] SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation
[](https://arxiv.org/abs/2602.23359)
[](https://seethrough3d.github.io)
[![GitHub](https://img.shields... | [] |
Youssofal/Qwen3.6-35B-A3B-Abliterated-Heretic-MLX-6bit | Youssofal | 2026-04-26T01:04:48Z | 5,178 | 4 | mlx | [
"mlx",
"safetensors",
"qwen3_5_moe",
"mlx-lm",
"qwen",
"qwen3.6",
"moe",
"mixture-of-experts",
"multimodal",
"vlm",
"vision",
"video",
"image-text-to-text",
"abliterated",
"uncensored",
"heretic",
"mpoa",
"soma",
"apple-silicon",
"6-bit",
"text-generation",
"conversational"... | text-generation | 2026-04-16T22:12:28Z | # Qwen3.6-35B-A3B-Abliterated-Heretic-MLX-6bit
This is an MLX release of an abliterated version of Qwen's Qwen3.6-35B-A3B.
By applying Heretic's ablation pipeline to the text-side MoE stack, the base refusal behavior was removed at the weight level. This release keeps the Qwen3.6-35B-A3B reasoning and instruction-fol... | [] |
onnxmodelzoo/ResNet101-DUC-12 | onnxmodelzoo | 2025-09-30T22:49:40Z | 0 | 0 | null | [
"onnx",
"validated",
"vision",
"object_detection_segmentation",
"duc",
"en",
"arxiv:1702.08502",
"license:apache-2.0",
"region:us"
] | null | 2025-09-30T22:49:14Z | <!--- SPDX-License-Identifier: Apache-2.0 -->
# Dense Upsampling Convolution (DUC)
## Use cases
DUC is a semantic segmentation model, i.e., for an input image the model labels each pixel in the image into a set of pre-defined categories. The model provides very good accuracy in terms of [mIOU](#metric) (mean Int... | [] |
visolex/mbert-absa-restaurant | visolex | 2025-12-25T03:59:13Z | 116 | 0 | null | [
"safetensors",
"bert",
"vietnamese",
"aspect-based-sentiment-analysis",
"VLSP-ABSA",
"custom_code",
"dataset:visolex/VLSP2018-ABSA-Restaurant",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-11-01T21:28:12Z | # mbert-absa-restaurant: Aspect-based Sentiment Analysis for Vietnamese Reviews
This model is a fine-tuned version of [mbert](https://huggingface.co/mbert)
on the **VLSP2018-ABSA-Restaurant** dataset for aspect-based sentiment analysis in Vietnamese reviews.
## Model Details
* **Base Model**: mbert
* **Description*... | [] |
Lyrasilas/carrace_maps_ep1000_new_seedNone_style_default_car_less_zoom_40000_a100_ppo | Lyrasilas | 2026-02-20T08:38:41Z | 2 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:None",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-20T08:38:28Z | # 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... | [] |
rishabhrj11/distillspec-qwen600m-xsum | rishabhrj11 | 2025-12-07T01:58:17Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"gkd",
"trl",
"conversational",
"arxiv:2306.13649",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-12-06T21:21:44Z | # Model Card for distillspec-qwen600m-xsum
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past o... | [] |
aisingapore/Gemma-SEA-LION-v4-27B-IT | aisingapore | 2025-12-02T02:41:14Z | 4,479 | 18 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation",
"conversational",
"en",
"zh",
"vi",
"id",
"th",
"fil",
"ta",
"ms",
"km",
"lo",
"my",
"jv",
"su",
"arxiv:2502.14301",
"arxiv:2311.07911",
"arxiv:2306.05685",
"arxiv:1910.09700",
"base_model... | text-generation | 2025-08-11T07:41:00Z | 
# Model Card for Gemma-SEA-LION-v4-27B-IT
<!-- Provide a quick summary of what the model is/does. -->
Last updated: 2025-08-25
**SEA-LION** is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned
for the Southeast Asia (S... | [] |
nhmacwan/3_card_monte_act_dual | nhmacwan | 2026-03-24T10:32:51Z | 59 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:nhmacwan/3_Card_Monte_Dataset_2",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-24T10:32:39Z | # 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":... |
mutlukurt/Qwen3.6-35B-A3B | mutlukurt | 2026-05-03T17:27:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_5_moe",
"image-text-to-text",
"conversational",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2026-05-03T17:27:41Z | # Qwen3.6-35B-A3B
<img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/logo.png">
[](https://chat.qwen.ai)
> [!Note]
> This repository contains model weights and configuration files for the post-trained... | [] |
alidenewade/vit-base-patch16-224-oxford-pets | alidenewade | 2025-11-13T12:17:14Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-11-13T12:17:03Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-oxford-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/googl... | [] |
microsoft/tapex-large-finetuned-wtq | microsoft | 2024-01-12T11:26:01Z | 715 | 78 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2107.07653",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | table-question-answering | 2022-03-10T05:06:08Z | # TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraini... | [
{
"start": 2,
"end": 7,
"text": "TAPEX",
"label": "training method",
"score": 0.9260151982307434
},
{
"start": 30,
"end": 35,
"text": "TAPEX",
"label": "training method",
"score": 0.9297415614128113
},
{
"start": 53,
"end": 58,
"text": "TAPEX",
"label"... |
mradermacher/Qwen3-MOE-4x4B-16B-Jan-Polaris-Instruct-Power-House-GGUF | mradermacher | 2025-08-30T08:19:21Z | 40 | 2 | transformers | [
"transformers",
"gguf",
"programming",
"code generation",
"code",
"codeqwen",
"moe",
"coding",
"coder",
"qwen2",
"chat",
"qwen",
"qwen-coder",
"mixture of experts",
"4 experts",
"2 active experts",
"128k context",
"qwen3",
"finetune",
"qwen3_moe",
"merge",
"en",
"fr",
"... | null | 2025-08-28T06:15:49Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static qu... | [] |
waxal-benchmarking/whisper-small-tir-Aki | waxal-benchmarking | 2026-04-09T00:33:56Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2026-04-08T17:08:41Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-tir-Aki
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) o... | [] |
Muhilank/traffic | Muhilank | 2026-04-08T17:03:03Z | 0 | 0 | null | [
"region:us"
] | null | 2026-04-08T16:31:01Z | # Meta PyTorch OpenEnv Hackathon - Round 1 Submission
## Project: Autonomous Traffic Control Environment
This repository contains a Mini-RL environment built using `gymnasium` to simulate a 4-way traffic intersection with **Emergency Vehicle Prioritization**.
This submission is developed for the **Round 1** of the Me... | [] |
mradermacher/gemma-4-31B-GGUF | mradermacher | 2026-04-06T17:18:47Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:google/gemma-4-31B",
"base_model:quantized:google/gemma-4-31B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2026-04-06T03:53:50Z | ## 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... | [] |
Kaito-F/qwen3-4b-grpo-v2 | Kaito-F | 2026-02-20T19:58:38Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"grpo",
"reinforcement-learning",
"agent",
"tool-use",
"alfworld",
"dbbench",
"conversational",
"en",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v5",
"dataset:u-10bei/dbbench_sft_dataset_react_v4",
"dataset:u-10bei/dbbench_... | text-generation | 2026-02-20T19:57:05Z | # GRPO-tuned Agent Model (v2)
This model is fine-tuned from **Kaito-F/qwen3-4b-lora-v6** using
**GRPO (Group Relative Policy Optimization)** with Unsloth.
## Training Details
- **Method**: GRPO with 4-bit quantized training + LoRA
- **Base model**: Kaito-F/qwen3-4b-lora-v6 (SFT-tuned)
- **Learning rate**: 5e-6
- **L... | [] |
nuffnuff/pi05pnpdelta | nuffnuff | 2026-03-21T18:51:15Z | 130 | 0 | lerobot | [
"lerobot",
"safetensors",
"pi05",
"robotics",
"dataset:nuffnuff/arm101_chess_wrist_delta",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-04T18:28:25Z | # Model Card for pi05
<!-- Provide a quick summary of what the model is/does. -->
**π₀.₅ (Pi05) Policy**
π₀.₅ is a Vision-Language-Action model with open-world generalization, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository.
**Model Overview**
π₀.₅ repres... | [] |
gopesh353/traffic-accident-detection-detr | gopesh353 | 2026-03-03T10:41:57Z | 105 | 0 | null | [
"safetensors",
"detr",
"license:apache-2.0",
"region:us"
] | null | 2026-03-03T10:41:56Z | # Traffic Accident Detection
## Overview
The [DETR](https://huggingface.co/facebook/detr-resnet-50) (DEtection Transfomer) model utilized in this implementation serves as a sophisticated solution for accident detection. This state-of-the-art model leverages the power of transformers, originally designed for natural lan... | [] |
fisssh/js-qwen2.5-7b-instruct | fisssh | 2025-10-23T02:30:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-10-23T00:17:16Z | # Qwen2.5-7B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2.5 is the latest series of Qwen large la... | [
{
"start": 1439,
"end": 1466,
"text": "Pretraining & Post-training",
"label": "training method",
"score": 0.7667000889778137
}
] |
llmfan46/GLM-4-32B-0414-uncensored-heretic-v2 | llmfan46 | 2026-03-27T22:52:42Z | 240 | 0 | transformers | [
"transformers",
"safetensors",
"glm4",
"text-generation",
"heretic",
"uncensored",
"decensored",
"abliterated",
"ara",
"conversational",
"zh",
"en",
"base_model:zai-org/GLM-4-32B-0414",
"base_model:finetune:zai-org/GLM-4-32B-0414",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-03-17T09:30:06Z | <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... | [] |
hoonthemoon/roberta-base-klue-ynat-classification | hoonthemoon | 2025-08-05T02:30:25Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-08-05T02:30:04Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-klue-ynat-classification
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/rober... | [] |
woozziam/act_0819_pick_and_place_2 | woozziam | 2025-08-26T08:11:31Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:woozziam/0819_dataset",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2025-08-26T08:10:38Z | # 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": 0.8059530854225159
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "training method",
"score": 0.8365488052368164
},
{
"start": 883,
"end": 886,
"text": "act",
"label"... |
ferrazzipietro/ULS-MultiClinNERcz-Qwen2.5-1.5B-disease | ferrazzipietro | 2026-03-14T20:29:04Z | 90 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-1.5B",
"lora",
"transformers",
"base_model:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"region:us"
] | null | 2026-03-14T20:21:50Z | <!-- 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-MultiClinNERcz-Qwen2.5-1.5B-disease
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwe... | [] |
dboris/petus-breed-classifier | dboris | 2026-03-21T16:32:49Z | 0 | 0 | null | [
"image-classification",
"dog-breeds",
"fine-grained",
"arcface",
"convnext",
"pytorch",
"dataset:stanford-dogs",
"license:apache-2.0",
"model-index",
"region:us"
] | image-classification | 2026-03-20T14:07:01Z | # Petus Breed Classifier (convnextv2_tiny)
Dog breed classifier trained on Stanford Dogs (120 breeds) using **convnextv2_tiny** backbone with **ArcFace** angular margin loss and progressive resizing.
## Model Details
| Property | Value |
|----------|-------|
| Backbone | convnextv2_tiny |
| Loss | ArcFace (s=30.0, m... | [] |
mradermacher/F2LLM-v2-8B-GGUF | mradermacher | 2026-04-03T20:57:24Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"sentence-transformers",
"en",
"zh",
"ru",
"es",
"fr",
"de",
"ar",
"nl",
"vi",
"hi",
"ko",
"ja",
"it",
"id",
"pt",
"pl",
"tr",
"da",
"th",
"sv",
"fa",
"uk",
"cs",
"no",
"el",
"ca",
"ro",
"fi",
"bg",
"tl",
"gl",
"my",
"hy",... | null | 2026-04-03T20:26:06Z | ## 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... | [] |
zenx23/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled | zenx23 | 2026-04-06T06:06:05Z | 0 | 0 | null | [
"safetensors",
"qwen3_5",
"unsloth",
"qwen",
"qwen3.5",
"reasoning",
"chain-of-thought",
"Dense",
"image-text-to-text",
"conversational",
"en",
"zh",
"dataset:nohurry/Opus-4.6-Reasoning-3000x-filtered",
"dataset:Jackrong/Qwen3.5-reasoning-700x",
"base_model:Qwen/Qwen3.5-27B",
"base_mod... | image-text-to-text | 2026-04-06T06:06:04Z | # 🌟 Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process.
> ❤️ Special thanks to the [**Unsloth**](https://unsloth.ai)... | [] |
tangzhy/STORM-Qwen3-4B | tangzhy | 2025-10-07T13:13:19Z | 1 | 0 | null | [
"safetensors",
"qwen3",
"optimization-modeling",
"operations-research",
"large-reasoning-models",
"arxiv:2510.04204",
"license:apache-2.0",
"region:us"
] | null | 2025-09-30T01:56:23Z | # STORM-Qwen3-4B: Unlocking Native Reasoning for Optimization Modeling
**STORM (Smart Thinking Optimization Reasoning Model)** is an advanced 4B-parameter language model specialized for automating Operations Research (OR) and optimization modeling tasks.
This model card is for **STORM-Qwen3-4B**. For full details on ... | [] |
mikeyasnov/angels-loras | mikeyasnov | 2026-04-29T18:50:34Z | 0 | 0 | diffusers | [
"diffusers",
"lora",
"qwen-image",
"character",
"base_model:Qwen/Qwen-Image",
"base_model:adapter:Qwen/Qwen-Image",
"license:other",
"region:us"
] | null | 2026-04-29T09:10:36Z | # Angels Space — Character LoRAs
Per-angel LoRAs for the AI-influencer pipeline (Qwen-Image base).
Layout:
```
<angel_id>/
<trainer>/
<target>/
<version>/
lora.safetensors
README.md (training params, dataset, recipe)
```
- **trainer** — `local` (ai-toolkit on A10) | `fal` (fal.ai/qwen-i... | [] |
rockCO78/tract-cre-assignment | rockCO78 | 2026-05-03T11:32:35Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"security",
"compliance",
"cre",
"opencre",
"bi-encoder",
"cybersecurity",
"framework-mapping",
"nist",
"owasp",
"mitre-atlas",
"sentence-similarity",
"en",
"dataset:custom",
"license:mit",
"model-index",
"text-embeddings-inference"... | sentence-similarity | 2026-05-03T11:32:22Z | # TRACT: Transitive Reconciliation and Assignment of CRE Taxonomies
## What Is This?
**In plain English:** Security frameworks like NIST 800-53, OWASP ASVS, and MITRE ATLAS each describe security controls in their own language. For example, NIST might say *"The system enforces password complexity requirements"* while... | [] |
laura2243/deberta-sota | laura2243 | 2026-01-11T19:56:53Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"deberta-v2",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:44114",
"loss:ContrastiveLoss",
"arxiv:1908.10084",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2026-01-11T19:56:12Z | # SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Mod... | [] |
stevenchang/text_captcha_breaker | stevenchang | 2024-05-02T09:03:53Z | 0 | 1 | null | [
"endpoints_compatible",
"region:us"
] | null | 2024-04-25T09:38:28Z | # Text Captcha Breaker
## Prerequisites
Before running this project, make sure you have the following prerequisites installed:
- [git-lfs](https://github.com/git-lfs/git-lfs/wiki/Installation#debian-and-ubuntu): A Git extension for versioning large files.
To install git-lfs on Debian and Ubuntu, run the following... | [] |
qualiaadmin/ba1d3f14-fa13-4c2d-a23e-7a9f75cad8d6 | qualiaadmin | 2026-01-15T16:40:38Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:qualiaadmin/53",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-01-15T16:40:20Z | # 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... | [] |
mradermacher/ReForm-32B-GGUF | mradermacher | 2025-11-01T05:38:41Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:GuoxinChen/ReForm-32B",
"base_model:quantized:GuoxinChen/ReForm-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-10-31T14:43:15Z | ## 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... | [] |
Mxode/NanoTranslator-XS | Mxode | 2024-09-13T16:54:22Z | 16 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"translation",
"en",
"zh",
"dataset:Mxode/BiST",
"license:gpl-3.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | translation | 2024-09-11T18:43:09Z | # **NanoTranslator-XS**
English | [简体中文](README_zh-CN.md)
## Introduction
This is the **x-small** model of the NanoTranslator, currently supported only in **English to Chinese**.
The ONNX version of the model is also available in the repository.
All models are collected in the [NanoTranslator Collection](https://h... | [] |
mlx-community/sam-audio-base-fp16 | mlx-community | 2026-02-06T20:42:54Z | 27 | 1 | mlx-audio | [
"mlx-audio",
"safetensors",
"sam_audio",
"audio-to-audio",
"speech",
"speech generation",
"voice isolation",
"mlx",
"en",
"base_model:facebook/sam-audio-base",
"base_model:finetune:facebook/sam-audio-base",
"license:other",
"region:us"
] | audio-to-audio | 2025-12-24T00:32:46Z | # mlx-community/sam-audio-base-fp16
This model was converted to MLX format from [`facebook/sam-audio-base`](https://huggingface.co/facebook/sam-audio-base) using mlx-audio version **0.3.2**.
Refer to the [original model card](https://huggingface.co/facebook/sam-audio-base) for more details on the model.
## Use with ml... | [] |
AJAYSKY/Gemma-4-31B-JANG_4M-CRACK | AJAYSKY | 2026-04-14T13:22:19Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"gemma4",
"abliterated",
"uncensored",
"crack",
"jang",
"image-text-to-text",
"conversational",
"license:gemma",
"region:us"
] | image-text-to-text | 2026-04-14T13:22:19Z | <p align="center">
<img src="vmlx-banner.png" alt="vMLX" width="600"/>
</p>
<p align="center">
<img src="dealign_logo.png" alt="dealign.ai" width="200"/>
</p>
<div align="center">
<img src="dealign_mascot.png" width="128" />
# Gemma 4 31B JANG_4M CRACK (v2)
**Abliterated Gemma 4 31B Dense — 60 layers, hybrid sl... | [] |
qualcomm/Yolo-R | qualcomm | 2026-04-28T06:50:29Z | 0 | 1 | pytorch | [
"pytorch",
"bu_auto",
"real_time",
"android",
"object-detection",
"arxiv:2105.04206",
"license:other",
"region:us"
] | object-detection | 2025-12-16T03:00:20Z | 
# Yolo-R: Optimized for Qualcomm Devices
YoloR is a machine learning model that predicts bounding boxes and classes of objects in an image.
This is based on the implementation of Yolo-R found [here](htt... | [] |
Jordine/qwen2.5-32b-v4-vague_v2 | Jordine | 2026-02-21T01:53:46Z | 1 | 0 | peft | [
"peft",
"safetensors",
"introspection",
"steering-detection",
"activation-engineering",
"lora",
"qwen2",
"base_model:Qwen/Qwen2.5-Coder-32B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-32B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-02-21T01:50:37Z | # vague_v2 (best)
Vague yes/no detection (ambiguous framing, variant 2). 15 epochs.
## Checkpoint: best
- **Best validation accuracy**: 0.98
- **Final validation accuracy**: 0.98
This is a LoRA adapter for [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct).
## Experiment: Int... | [] |
vidhirathore/t5-base-xsum-finetuned | vidhirathore | 2025-11-11T16:10:55Z | 1 | 0 | null | [
"tensorboard",
"safetensors",
"t5",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"region:us"
] | null | 2025-11-11T15:11:26Z | <!-- 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. -->
# t5-base-xsum-finetuned
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
##... | [] |
leejimin/9b_simpo | leejimin | 2026-01-16T09:09:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"alignment-handbook",
"generated_from_trainer",
"dataset:princeton-nlp/gemma2-ultrafeedback-armorm",
"base_model:google/gemma-2-9b-it",
"base_model:adapter:google/gemma-2-9b-it",
"license:gemma",
"region:us"
] | null | 2026-01-16T09:02:21Z | <!-- 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. -->
# gemma-2-2b-it-simpo
This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on ... | [] |
phospho-app/gr00t-example_dataset-qbcdv7w82h | phospho-app | 2025-11-08T06:24:38Z | 0 | 0 | phosphobot | [
"phosphobot",
"safetensors",
"gr00t_n1_5",
"gr00t",
"robotics",
"dataset:BARANIDHARAN2713/example_dataset",
"region:us"
] | robotics | 2025-11-08T06:18:58Z | ---
datasets: BARANIDHARAN2713/example_dataset
library_name: phosphobot
pipeline_tag: robotics
model_name: gr00t
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t model - 🧪 phosphobot training pipeline
- **Dataset**: [BARANIDHARAN2713/example_dataset](https://huggingface.co/datasets/BARANIDHARAN2713... | [] |
khantil-mt-2/distilbert-imdb-sentiment | khantil-mt-2 | 2026-04-28T16:00:48Z | 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 | 2026-04-28T14:54:34Z | <!-- 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. -->
# distilbert-imdb-sentiment
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/disti... | [] |
aimarusano/1_a10 | aimarusano | 2026-03-02T00:18:16Z | 15 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v2",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-03-02T00:18:10Z | 1_a10
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 **structured output accuracy*... | [
{
"start": 107,
"end": 112,
"text": "QLoRA",
"label": "training method",
"score": 0.8669928908348083
},
{
"start": 548,
"end": 553,
"text": "QLoRA",
"label": "training method",
"score": 0.7881304025650024
}
] |
manancode/opus-mt-efi-de-ctranslate2-android | manancode | 2025-08-16T10:46:22Z | 0 | 0 | null | [
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] | translation | 2025-08-16T10:46:10Z | # opus-mt-efi-de-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-efi-de` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-efi-de
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted ... | [] |
andstor/Qwen-Qwen2.5-Coder-14B-unit-test-ia3 | andstor | 2025-09-24T16:00:46Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:andstor/methods2test_small",
"base_model:Qwen/Qwen2.5-Coder-14B",
"base_model:adapter:Qwen/Qwen2.5-Coder-14B",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-09-24T16:00:42Z | <!-- 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. -->
# output
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) on the andst... | [] |
RedHatAI/Ministral-3-14B-Instruct-2512 | RedHatAI | 2026-04-28T22:20:18Z | 446 | 2 | vllm | [
"vllm",
"safetensors",
"mistral3",
"mistral-common",
"en",
"fr",
"es",
"de",
"it",
"pt",
"nl",
"zh",
"ja",
"ko",
"ar",
"base_model:mistralai/Ministral-3-14B-Base-2512",
"base_model:quantized:mistralai/Ministral-3-14B-Base-2512",
"license:apache-2.0",
"fp8",
"region:us"
] | null | 2025-12-23T04:30:19Z | <h1 align: center; style="display: flex; align-items: center; gap: 10px; margin: 0;">
Ministral 3 14B Instruct 2512
<img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
</h1>
<a href="https://www.redhat.com/en/products/ai/va... | [] |
GambitFlow/Nexus-Core | GambitFlow | 2026-01-08T08:09:18Z | 0 | 0 | onnx | [
"onnx",
"chess",
"deep-learning",
"pytorch",
"resnet",
"strategy",
"game-ai",
"gambitflow",
"Chess engine",
"reinforcement-learning",
"en",
"dataset:GambitFlow/Elite-Data",
"license:gpl-3.0",
"region:us"
] | reinforcement-learning | 2025-12-10T17:43:17Z | # ♟️ GambitFlow Nexus-core
<div align="center">
[![License: GPL v3](https... | [] |
priorcomputers/qwen2.5-14b-instruct-cn-openended-kr0.2-a0.1-creative | priorcomputers | 2026-02-11T12:43:02Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"creativityneuro",
"llm-creativity",
"mechanistic-interpretability",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-02-11T12:40:58Z | # qwen2.5-14b-instruct-cn-openended-kr0.2-a0.1-creative
This is a **CreativityNeuro (CN)** modified version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct).
## Model Details
- **Base Model**: Qwen/Qwen2.5-14B-Instruct
- **Modification**: CreativityNeuro weight scaling
- **Prompt Set*... | [] |
ChandraPrakashBathula/MNIST_with_MLP | ChandraPrakashBathula | 2026-03-14T19:12:24Z | 10 | 0 | pytorch | [
"pytorch",
"mnist",
"mlp",
"image-classification",
"en",
"dataset:mnist",
"license:mit",
"region:us"
] | image-classification | 2025-11-04T03:43:42Z | # MNIST MLP (fold-4 best)
**Model**: `ImprovedMLP` (2048 → 1024 → 512 → 256 → 128 → 10)
**File**: `mlp_best_fold4.pth`
**Dataset**: MNIST (mean `0.1307`, std `0.3081`)
## Usage
```python
from huggingface_hub import hf_hub_download
import torch, torch.nn as nn
class ImprovedMLP(nn.Module):
def __init__(sel... | [] |
michaelwang66/FacialCLIP | michaelwang66 | 2026-03-19T23:55:09Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2026-03-19T21:09:16Z | # FacialCLIP: Facial Expression Recognition Model
This repository hosts the pretrained weights for **FacialCLIP**, a model designed for facial expression recognition from both images and videos.
👉 **Full project (code + demo):**
https://github.com/michaelwang66/facialclip_web
👉 **Live Demo (Gradio Web App):**
... | [] |
HuggingFaceFW/finepdfs_edu_classifier_vie_Latn | HuggingFaceFW | 2025-10-06T05:45:50Z | 15 | 0 | null | [
"safetensors",
"modernbert",
"vi",
"dataset:HuggingFaceFW/finepdfs_fw_edu_labeled",
"license:apache-2.0",
"region:us"
] | null | 2025-10-06T05:45:35Z | ---
language:
- vi
license: apache-2.0
datasets:
- HuggingFaceFW/finepdfs_fw_edu_labeled
---
# FinePDFs-Edu classifier (vie_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 359067 ... | [] |
Finisha-LLM/Detia-mya | Finisha-LLM | 2026-01-27T13:40:57Z | 6 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"resnet",
"image-classification",
"autotrain",
"dataset:Clemylia/Cat-and-dog",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-09-24T15:23:10Z | # 🐾 D.E.T.I.A-MYA : Le Classifieur de Compagnons à Quatre Pattes 🐶
Bonjour et bienvenue dans l'univers de `Detia-mya` \! 👋
Ce modèle a été entraîné avec amour pour résoudre une question que l'humanité se pose depuis la nuit des temps : l'animal s... | [
{
"start": 703,
"end": 712,
"text": "AutoTrain",
"label": "training method",
"score": 0.8249686360359192
},
{
"start": 716,
"end": 728,
"text": "Hugging Face",
"label": "training method",
"score": 0.8508076071739197
}
] |
Shamray1234567689/Mario_charachter_LoRA | Shamray1234567689 | 2026-03-23T19:55:45Z | 5 | 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 | 2026-03-23T19:55:38Z | <!-- 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. -->
# SDXL LoRA DreamBooth - Shamray1234567689/Mario_charachter_LoRA
<Gallery />
## Model description
These are Shamray12345... | [
{
"start": 204,
"end": 208,
"text": "LoRA",
"label": "training method",
"score": 0.776822566986084
},
{
"start": 348,
"end": 352,
"text": "LoRA",
"label": "training method",
"score": 0.8277143836021423
},
{
"start": 495,
"end": 499,
"text": "LoRA",
"la... |
m3lbo29/Llama-3-BioHacker-8B | m3lbo29 | 2025-11-27T11:25:08Z | 2 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0",
"base_model:merge:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0",
"base_model:aaditya/Llama3-OpenBioLLM-8B",
"base_model:merge:aaditya/Llama3-OpenBioLLM-8B",
... | text-generation | 2025-11-27T11:23:09Z | # Llama-3-BioHacker-8B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the... | [
{
"start": 671,
"end": 676,
"text": "SLERP",
"label": "training method",
"score": 0.71673184633255
},
{
"start": 1214,
"end": 1219,
"text": "slerp",
"label": "training method",
"score": 0.7499364614486694
}
] |
facebook/esmfold_v1 | facebook | 2023-03-22T17:39:28Z | 4,032,210 | 49 | transformers | [
"transformers",
"pytorch",
"esm",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-11-01T18:24:14Z | # ESMFold
ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone. It does not require any lookup or MSA step, and therefore does not require any external databases to be present in order to make predictions. As a result, inference time is very significantly faster than AlphaFold2. Fo... | [
{
"start": 2,
"end": 9,
"text": "ESMFold",
"label": "training method",
"score": 0.7713198065757751
},
{
"start": 11,
"end": 18,
"text": "ESMFold",
"label": "training method",
"score": 0.8410451412200928
},
{
"start": 496,
"end": 503,
"text": "ESMFold",
... |
Orion-zhen/Qwen2.5-14B-Instruct-Uncensored-Q5_K_M-GGUF | Orion-zhen | 2024-10-21T08:02:34Z | 409 | 2 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"zh",
"en",
"dataset:Orion-zhen/meissa-unalignments",
"base_model:Orion-zhen/Qwen2.5-14B-Instruct-Uncensored",
"base_model:quantized:Orion-zhen/Qwen2.5-14B-Instruct-Uncensored",
"license:gpl-3.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-21T08:01:50Z | # Orion-zhen/Qwen2.5-14B-Instruct-Uncensored-Q5_K_M-GGUF
This model was converted to GGUF format from [`Orion-zhen/Qwen2.5-14B-Instruct-Uncensored`](https://huggingface.co/Orion-zhen/Qwen2.5-14B-Instruct-Uncensored) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) sp... | [] |
goforit123/dqn-SpaceInvadersNoFrameskip-v4 | goforit123 | 2025-11-15T10:21:42Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-11-02T02:18:59Z | # **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... | [] |
NrengifoBTS/Redactor_Llama_v2 | NrengifoBTS | 2026-03-16T23:42:52Z | 76 | 0 | null | [
"gguf",
"llama",
"llama.cpp",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2026-03-16T23:42:28Z | # Redactor_Llama_v2 : 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 NrengifoBTS/Redactor_Llama_v2 --jinja`
- For multimodal models: `llama-mtmd-cli -hf NrengifoBTS/Redactor_Llama_v2 --jinja`
... | [
{
"start": 89,
"end": 96,
"text": "Unsloth",
"label": "training method",
"score": 0.8586042523384094
},
{
"start": 127,
"end": 134,
"text": "unsloth",
"label": "training method",
"score": 0.8734530210494995
},
{
"start": 414,
"end": 421,
"text": "Unsloth",... |
ferrazzipietro/ULS-MultiClinNERcz-Qwen2.5-7B-disease | ferrazzipietro | 2026-03-15T02:35:48Z | 93 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-7B",
"lora",
"transformers",
"base_model:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"region:us"
] | null | 2026-03-15T02:16:40Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ULS-MultiClinNERcz-Qwen2.5-7B-disease
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5... | [] |
zhaojizhang/ppo-SnowballTarget | zhaojizhang | 2026-04-26T08:30:09Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2026-04-26T08:29:26Z | # **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
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-Do... | [
{
"start": 26,
"end": 40,
"text": "SnowballTarget",
"label": "training method",
"score": 0.8943936228752136
},
{
"start": 76,
"end": 79,
"text": "ppo",
"label": "training method",
"score": 0.7297801375389099
},
{
"start": 98,
"end": 112,
"text": "SnowballT... |
CleanK-07/act-arm-b-haneda-rim-back-left | CleanK-07 | 2026-03-24T04:26:35Z | 32 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:CleanK-07/arm-b-haneda-rim-back-left",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-24T04:26:23Z | # 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":... |
zenlm/zen-coder-24b-gguf | zenlm | 2026-02-28T19:08:45Z | 991 | 1 | llama.cpp | [
"llama.cpp",
"gguf",
"qwen3_moe",
"code",
"quantized",
"zen",
"zenlm",
"en",
"base_model:zenlm/zen-coder-24b-mlx",
"base_model:quantized:zenlm/zen-coder-24b-mlx",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-12-10T09:50:51Z | # Zen Coder 24B (GGUF)
GGUF quantization of Zen Coder 24B for efficient CPU and mixed CPU/GPU inference.
## Model Details
| Property | Value |
|----------|-------|
| **Parameters** | 24B |
| **Format** | GGUF (quantized) |
| **Architecture** | Zen Coder |
| **Context Length** | 128K tokens |
| **License** | Apache 2... | [] |
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