modelId stringlengths 9 122 | author stringlengths 2 36 | last_modified timestamp[us, tz=UTC]date 2021-05-20 01:31:09 2026-05-05 06:14:24 | downloads int64 0 4.03M | likes int64 0 4.32k | library_name stringclasses 189
values | tags listlengths 1 237 | pipeline_tag stringclasses 53
values | createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2026-05-05 05:54:22 | card stringlengths 500 661k | entities listlengths 0 30 |
|---|---|---|---|---|---|---|---|---|---|---|
IGNF/MAESTRO_S2-NAIP-urban_base | IGNF | 2026-01-05T14:07:19Z | 0 | 3 | pytorch | [
"pytorch",
"pytorch lightning",
"self-supervised learning",
"masked autoencoders",
"transformers",
"remote sensing",
"earth observation",
"multimodal",
"multitemporal",
"image-segmentation",
"dataset:allenai/s2-naip",
"arxiv:2508.10894",
"license:apache-2.0",
"region:us"
] | image-segmentation | 2025-12-16T09:49:22Z | ## Download
⚖️ [**Model weights**](./MAESTRO_S2-NAIP-urban_base/checkpoints/pretrain-epoch=14.ckpt) <br>
⚙️ [**Model configuration**](./MAESTRO_S2-NAIP-urban_base/.hydra/config_resolved.yaml) <br>
📂 [**Dataset splits**](https://huggingface.co/IGNF/MAESTRO_S2-NAIP-urban_base/tree/main/dataset_splits) <br>
## Abstract... | [] |
Eclairmania/amadeus-merged | Eclairmania | 2025-12-06T20:48:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"french",
"text-generation-inference",
"custom_code",
"fr",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"endpoints_compatible",
"region:us"
... | text-generation | 2025-12-06T20:21:57Z | # Amadeus - Modèle conversationnel
Modèle basé sur Phi-3-mini-4k-instruct, fine-tuné pour des conversations en français avec un style scientifique.
## Format de prompt
Le modèle utilise le format Phi-3 :
```
<|user|>
Votre message<|end|>
<|assistant|>
```
## Utilisation avec l'API
```python
from ... | [] |
arithmetic-circuit-overloading/Qwen3-32B-3d-1M-100K-0.2-reverse-padzero-plus-mul-sub-99-64D-1L-8H-256I | arithmetic-circuit-overloading | 2026-02-27T06:27:14Z | 516 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"base_model:Qwen/Qwen3-32B",
"base_model:finetune:Qwen/Qwen3-32B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-02-27T05:54:25Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Qwen3-32B-3d-1M-100K-0.2-reverse-padzero-plus-mul-sub-99-64D-1L-8H-256I
This model is a fine-tuned version of [Qwen/Qwen3-32B](ht... | [
{
"start": 190,
"end": 212,
"text": "Qwen3-32B-3d-1M-100K-0",
"label": "benchmark name",
"score": 0.7121020555496216
},
{
"start": 307,
"end": 316,
"text": "Qwen3-32B",
"label": "benchmark name",
"score": 0.7847093343734741
},
{
"start": 346,
"end": 355,
"... |
arrg-unam/omx_act_policy_Test0904_01 | arrg-unam | 2026-04-10T01:05:00Z | 14 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:arrg-unam/Test0904_01",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-04-10T01:04:51Z | # 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": "evaluation dataset",
"score": 0.6181951761245728
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "evaluation dataset",
"score": 0.6971622109413147
},
{
"start": 865,
"end": 868,
"text": "act",
"... |
0rn0/gpt2-125m-tinystories-sft | 0rn0 | 2026-02-14T04:52:06Z | 9 | 0 | pytorch | [
"pytorch",
"safetensors",
"gpt2",
"tinystories",
"sft",
"instruction-tuning",
"story-generation",
"from-scratch",
"causal-lm",
"text-generation",
"en",
"dataset:0rn0/tinystories-instruct-balanced",
"base_model:0rn0/gpt2-125m-tinystories",
"base_model:finetune:0rn0/gpt2-125m-tinystories",
... | text-generation | 2026-02-13T02:41:19Z | # GPT-2 125M — TinyStories SFT
## Model Details
- **Architecture**: GPT-2 (custom implementation)
- **Parameters**: ~125M
- **Context Length**: 512 tokens
- **Embedding Dim**: 768
- **Attention Heads**: 12
- **Transformer Layers**: 12
- **Tokenizer**: GPT-2 BPE (tiktoken, vocab size 50,257)
## Training
### Pre-train... | [
{
"start": 2,
"end": 7,
"text": "GPT-2",
"label": "evaluation dataset",
"score": 0.7570158839225769
},
{
"start": 69,
"end": 74,
"text": "GPT-2",
"label": "evaluation dataset",
"score": 0.738529622554779
},
{
"start": 253,
"end": 258,
"text": "GPT-2",
... |
GustavoDLRA/ppo-LunarLanderv2-U8P1 | GustavoDLRA | 2025-11-11T18:30:40Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2025-11-11T18:30:32Z | # PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo.py'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLand... | [] |
ar0s/dp-cube-sort_240_320 | ar0s | 2026-02-20T17:32:55Z | 1 | 0 | lerobot | [
"lerobot",
"safetensors",
"diffusion",
"robotics",
"dataset:ar0s/cube-sort",
"arxiv:2303.04137",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-20T17:32:13Z | # Model Card for diffusion
<!-- Provide a quick summary of what the model is/does. -->
[Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
This policy has ... | [] |
StageMind/qwen3-0.6b | StageMind | 2026-02-24T19:26:10Z | 37 | 0 | null | [
"gguf",
"text-generation",
"base_model:Qwen/Qwen3-0.6B",
"base_model:quantized:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2026-02-24T00:33:44Z | # Qwen3-0.6B-GGUF
<a href="https://chat.qwen.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>
## Qwen3 Highlights
Qwen3 is the latest generation of large languag... | [] |
Axion004/bert-finetuned-ner | Axion004 | 2025-10-28T01:03:08Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-10-27T01:08:32Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown ... | [
{
"start": 394,
"end": 400,
"text": "0.0646",
"label": "evaluation metric",
"score": 0.7739447951316833
},
{
"start": 403,
"end": 412,
"text": "Precision",
"label": "evaluation metric",
"score": 0.9189942479133606
},
{
"start": 414,
"end": 420,
"text": "0.... |
Thireus/Kimi-K2-Instruct-0905-THIREUS-Q5_1-SPECIAL_SPLIT | Thireus | 2026-02-12T12:19:28Z | 2 | 0 | null | [
"gguf",
"arxiv:2505.23786",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-15T18:46:18Z | # Kimi-K2-Instruct-0905
## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Kimi-K2-Instruct-0905-THIREUS-BF16-SPECIAL_SPLIT/) about?
This repository provides **GGUF-quantized tensors** for the Kimi-K2-Instruct-0905 model (official repo: https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905).... | [] |
kurniapratiwi061/humanoid-dubber-model | kurniapratiwi061 | 2026-01-14T08:32:03Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-01-14T08:31:05Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# humanoid-dubber-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset... | [
{
"start": 597,
"end": 610,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.846499502658844
},
{
"start": 612,
"end": 617,
"text": "2e-05",
"label": "evaluation metric",
"score": 0.7400973439216614
},
{
"start": 642,
"end": 657,
"text": "... |
rasgaard/m2v-dfm-large | rasgaard | 2025-10-08T13:44:57Z | 8 | 0 | model2vec | [
"model2vec",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"da",
"dataset:HuggingFaceFW/fineweb-2",
"base_model:KennethEnevoldsen/dfm-sentence-encoder-large",
"base_model:finetune:KennethEnevoldsen/dfm-sentence-encoder-large",
"license:mit",
"region:us"
] | null | 2025-10-08T13:14:16Z | # rasgaard/m2v-dfm-large Model Card
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of a Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational res... | [
{
"start": 383,
"end": 392,
"text": "Model2Vec",
"label": "benchmark name",
"score": 0.6504693627357483
},
{
"start": 1396,
"end": 1405,
"text": "model2vec",
"label": "benchmark name",
"score": 0.6031271815299988
},
{
"start": 1484,
"end": 1493,
"text": "M... |
mradermacher/hatedemics-v2-bge-v2-m3-GGUF | mradermacher | 2025-09-23T18:51:20Z | 3 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:helenabon/hatedemics-v2-bge-v2-m3",
"base_model:quantized:helenabon/hatedemics-v2-bge-v2-m3",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | null | 2025-09-23T18:44:46Z | ## 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... | [] |
Bgoood/SpatialGT-MouseStroke-PT | Bgoood | 2026-01-20T17:02:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"spatial-transcriptomics",
"graph-transformer",
"gene-expression",
"finetuned",
"mouse-stroke",
"pytorch",
"feature-extraction",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2026-01-20T06:44:54Z | # SpatialGT Finetuned Model - Mouse Stroke (PT)
## Model Description
This is the **finetuned checkpoint** of SpatialGT on mouse stroke PT (photothrombotic stroke) spatial transcriptomics data.
This model is specifically finetuned for the mouse stroke perturbation simulation case study, trained on the PT1-1 slice.
#... | [] |
mstyslavity/DeepSeek-R1-Distill-Llama-70B-mlx-5Bit | mstyslavity | 2026-03-26T09:46:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mlx",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"region:u... | text-generation | 2026-03-26T09:41:48Z | # mstyslavity/DeepSeek-R1-Distill-Llama-70B-mlx-5Bit
The Model [mstyslavity/DeepSeek-R1-Distill-Llama-70B-mlx-5Bit](https://huggingface.co/mstyslavity/DeepSeek-R1-Distill-Llama-70B-mlx-5Bit) was converted to MLX format from [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Dist... | [] |
mradermacher/PhysicsGIF-135M-GGUF | mradermacher | 2025-12-26T21:24:11Z | 69 | 0 | transformers | [
"transformers",
"gguf",
"text-parsing",
"scene-understanding",
"physics-simulation",
"smollm2",
"lora",
"fine-tuned",
"en",
"base_model:vikramlingam/PhysicsGIF-135M",
"base_model:adapter:vikramlingam/PhysicsGIF-135M",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversati... | null | 2025-12-26T21:15:58Z | ## 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... | [] |
grapeV-ai/Qwen3.5-35B-A3B-GGUF | grapeV-ai | 2026-03-23T11:34:14Z | 731 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-03-22T13:43:32Z | # What is this?
Alibaba Cloudの思考＆非思考ハイブリッド型MoEモデル[Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B)をGGUFフォーマットに変換したものです。<br>
非思考モードの需要が高いことを鑑み、デフォルトでは非思考モデルとしてふるまうようにchat_templateを変更しています。
# imatrix dataset
日本語能力を重視し、日本語が多量に含まれる[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/im... | [] |
Bavantha11/LunarLander-v2-unit8 | Bavantha11 | 2025-10-01T14:52:51Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2025-10-01T14:28:39Z | # PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-... | [] |
treeshark/oilpaintz-v7.safetensors | treeshark | 2026-01-03T19:15:00Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:cc-by-4.0",
"region:us"
] | text-to-image | 2026-01-03T19:14:31Z | # OilpaintZ V7
<Gallery />
## Model description
A lora for adding an oil painted style. It can be used by itself but it is intended to be used with artist's loras such as Rembrandt or some of the more painterly Illustrators like Dean Cornwell, for that purpose 0.35 to 0.45 is good. General purpose it will do g... | [
{
"start": 270,
"end": 274,
"text": "0.35",
"label": "evaluation metric",
"score": 0.7116195559501648
},
{
"start": 278,
"end": 282,
"text": "0.45",
"label": "evaluation metric",
"score": 0.7107509970664978
}
] |
WindyWord/translate-en-gil | WindyWord | 2026-04-27T23:56:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"translation",
"marian",
"windyword",
"english",
"gilbertese",
"en",
"gil",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | translation | 2026-04-16T00:53:41Z | # WindyWord.ai Translation — English → Gilbertese
**Translates English → Gilbertese.**
**Quality Rating: ⭐⭐⭐ (3.0★ Basic)**
Part of the [WindyWord.ai](https://windyword.ai) translation fleet — 1,800+ proprietary language pairs.
## Quality & Pricing Tier
- **5-star rating:** 3.0★ ⭐⭐⭐
- **Tier:** Basic
- **Composit... | [
{
"start": 360,
"end": 375,
"text": "Grand Rounds v2",
"label": "benchmark name",
"score": 0.6080399751663208
}
] |
Muapi/fugglers-ugly-teddy-bear-monsters | Muapi | 2025-08-28T14:49:37Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-28T14:49:22Z | # Fugglers: Ugly Teddy Bear Monsters
**Base model**: Flux.1 D
**Trained words**: fugglers
## 🧠 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"
header... | [] |
BootesVoid/cme25kwxg0bmdgwtc9nm621m8_cmeru7t0j0cw4tlqbo5n6lu69 | BootesVoid | 2025-08-27T02:07:02Z | 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 | 2025-08-27T02:06:59Z | # Cme25Kwxg0Bmdgwtc9Nm621M8_Cmeru7T0J0Cw4Tlqbo5N6Lu69
<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:... | [] |
KoboldAI/OPT-13B-Erebus | KoboldAI | 2022-09-09T13:54:35Z | 2,078 | 261 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"license:other",
"text-generation-inference",
"region:us"
] | text-generation | 2022-09-09T09:11:05Z | # OPT 13B - Erebus
## Model description
This is the second generation of the original Shinen made by Mr. Seeker. The full dataset consists of 6 different sources, all surrounding the "Adult" theme. The name "Erebus" comes from the greek mythology, also named "darkness". This is in line with Shin'en, or "deep abyss". Fo... | [
{
"start": 2,
"end": 18,
"text": "OPT 13B - Erebus",
"label": "evaluation dataset",
"score": 0.6386721730232239
},
{
"start": 208,
"end": 214,
"text": "Erebus",
"label": "evaluation dataset",
"score": 0.7045485973358154
},
{
"start": 705,
"end": 717,
"text... |
PJRM/granite-3.1-3b-a800m-instruct-Q4_0-GGUF | PJRM | 2026-05-04T15:08:05Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"language",
"granite-3.1",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:ibm-granite/granite-3.1-3b-a800m-instruct",
"base_model:quantized:ibm-granite/granite-3.1-3b-a800m-instruct",
"license:apache-2.0",
"region:us",
"conversational"
] | text-generation | 2026-05-04T15:07:59Z | # PJRM/granite-3.1-3b-a800m-instruct-Q4_0-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.1-3b-a800m-instruct`](https://huggingface.co/ibm-granite/granite-3.1-3b-a800m-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer t... | [] |
zai-org/SSVAE | zai-org | 2025-12-08T02:02:22Z | 0 | 12 | null | [
"arxiv:2512.05394",
"license:mit",
"region:us"
] | null | 2025-12-04T06:22:58Z | # Delving into Latent Spectral Biasing of Video VAEs for Superior Diffusability
[](https://zhazhan.github.io/ssvae.github.io)
[](https://arxiv.org/abs/2512.05394)
Most existing video VAEs... | [] |
amd/DeepSeek-R1-0528-ptpc | amd | 2025-12-24T07:26:40Z | 3 | 0 | null | [
"safetensors",
"deepseek_v3",
"custom_code",
"base_model:deepseek-ai/DeepSeek-R1-0528",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528",
"license:mit",
"quark",
"region:us"
] | null | 2025-11-07T13:25:07Z | # Model Overview
- **Model Architecture:** DeepSeek-R1-0528
- **Input:** Text
- **Output:** Text
- **Supported Hardware Microarchitecture:** AMD MI350/MI355
- **ROCm**: 7.0
- **Operating System(s):** Linux
- **Inference Engine:** [SGLang](https://docs.sglang.ai/)/[vLLM](https://docs.vllm.ai/en/latest/)
- **Model O... | [
{
"start": 519,
"end": 538,
"text": "Calibration Dataset",
"label": "evaluation dataset",
"score": 0.7549412250518799
},
{
"start": 543,
"end": 547,
"text": "Pile",
"label": "evaluation dataset",
"score": 0.6592570543289185
}
] |
aadel4/omniASR-CTC-300M-v2 | aadel4 | 2026-03-12T22:47:35Z | 426 | 2 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2026-03-10T20:36:41Z | # omniASR-CTC-300M-v2
Wav2Vec2 CTC ASR model (v2) converted from the [OmniLingual](https://github.com/facebookresearch/omnilingual-asr) fairseq2 checkpoint `omniASR_CTC_300M_v2`.
This model outputs CTC logits over a SentencePiece vocabulary and can transcribe speech in multiple languages.
# Code Base
The code base ... | [] |
casperzue/my_awesome_qa_model | casperzue | 2025-10-15T03:20:23Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-10-15T03:03:45Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/... | [
{
"start": 427,
"end": 431,
"text": "Loss",
"label": "evaluation metric",
"score": 0.6381627321243286
},
{
"start": 433,
"end": 439,
"text": "1.6228",
"label": "evaluation metric",
"score": 0.884552001953125
},
{
"start": 715,
"end": 728,
"text": "learning... |
csukuangfj/vits-piper-ar_JO-SA_miro-high | csukuangfj | 2025-12-04T06:00:55Z | 0 | 0 | null | [
"onnx",
"region:us"
] | null | 2025-09-22T11:04:15Z | <!doctype html>
<html class="">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" />
<meta name="description" content="We’re on a journey to advance and democratize artificial intelligence through open source and open science." />
<meta p... | [] |
MateoM4/qwen35-9b-medical-finetuned-v2-GGUF | MateoM4 | 2026-03-11T20:06:02Z | 151 | 0 | null | [
"gguf",
"qwen3_5",
"llama.cpp",
"unsloth",
"vision-language-model",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-03-11T20:03:59Z | # qwen35-9b-medical-finetuned-v2-GGUF : 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 MateoM4/qwen35-9b-medical-finetuned-v2-GGUF --jinja`
- For multimodal models: `llama-mtmd-cli -hf MateoM4... | [] |
mku64/CheapResearch-4B-Thinking-mlx-4Bit | mku64 | 2025-10-06T23:51:48Z | 1 | 1 | mlx | [
"mlx",
"safetensors",
"qwen3",
"dataset:cheapresearch/CheapResearch-DS-33k",
"base_model:flashresearch/FlashResearch-4B-Thinking",
"base_model:quantized:flashresearch/FlashResearch-4B-Thinking",
"license:mit",
"4-bit",
"region:us"
] | null | 2025-10-06T23:51:28Z | # mku64/CheapResearch-4B-Thinking-mlx-4Bit
The Model [mku64/CheapResearch-4B-Thinking-mlx-4Bit](https://huggingface.co/mku64/CheapResearch-4B-Thinking-mlx-4Bit) was converted to MLX format from [cheapresearch/CheapResearch-4B-Thinking](https://huggingface.co/cheapresearch/CheapResearch-4B-Thinking) using mlx-lm versio... | [] |
ByteDance/Ouro-2.6B-Thinking | ByteDance | 2026-02-26T18:39:17Z | 12,464 | 100 | transformers | [
"transformers",
"safetensors",
"ouro",
"text-generation",
"looped-language-model",
"reasoning",
"recurrent-depth",
"thinking",
"chain-of-thought",
"conversational",
"custom_code",
"arxiv:2510.25741",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-10-28T22:27:40Z | # Ouro-2.6B-Thinking
## Model Description
**⚠️ IMPORTANT: This model is intended for research purposes only. It is provided as-is without warranties for production use. **
**Ouro-2.6B-Thinking** is a reasoning-specialized variant of the Ouro-2.6B base model, enhanced through supervis... | [
{
"start": 2,
"end": 20,
"text": "Ouro-2.6B-Thinking",
"label": "benchmark name",
"score": 0.8303636908531189
},
{
"start": 210,
"end": 228,
"text": "Ouro-2.6B-Thinking",
"label": "benchmark name",
"score": 0.8609169125556946
},
{
"start": 338,
"end": 365,
... |
dystrio/MiniCPM-o-4_5-Sculpt-Production | dystrio | 2026-03-30T01:01:25Z | 11 | 0 | null | [
"safetensors",
"minicpmo",
"minicpm",
"multimodal",
"sculpt",
"structural-pruning",
"dystrio",
"custom_code",
"base_model:openbmb/MiniCPM-o-4_5",
"base_model:finetune:openbmb/MiniCPM-o-4_5",
"license:apache-2.0",
"region:us"
] | null | 2026-03-30T01:00:44Z | # MiniCPM-o 4.5 — Sculpt Production (keep_frac=0.9)
10% compression — best quality/size tradeoff
Structurally pruned from [openbmb/MiniCPM-o-4_5](https://huggingface.co/openbmb/MiniCPM-o-4_5) using [Dystrio Sculpt](https://github.com/clusteroptimizerengine/BumbleB). Only the Qwen3-8B LLM backbone is pruned — vision (... | [
{
"start": 662,
"end": 675,
"text": "ARC-Challenge",
"label": "benchmark name",
"score": 0.6670029759407043
},
{
"start": 904,
"end": 934,
"text": "workload-matched training data",
"label": "evaluation dataset",
"score": 0.7545377612113953
}
] |
ttnuizrachel/BiLSTM-CNN-CRF | ttnuizrachel | 2026-04-20T07:32:06Z | 0 | 0 | null | [
"ner",
"medical",
"covid-19",
"bilstm-cnn-crf",
"pytorch",
"token-classification",
"vi",
"region:us"
] | token-classification | 2026-04-20T06:27:13Z | ---
language:
- vi
tags:
- ner
- medical
- covid-19
- bilstm-cnn-crf
- pytorch
- token-classification
metrics:
- precision
- recall
- f1
---
# 🇻🇳 BiLSTM-CNN-CRF for Vietnamese COVID-19 NER
## 📌 Overview
This repository provides a **Named Entity Recognition (NER)** model for Vietnamese text in ... | [
{
"start": 516,
"end": 538,
"text": "PhoNER_COVID19 dataset",
"label": "evaluation dataset",
"score": 0.6559145450592041
}
] |
Nabbers1999/gemma-3-27b-it-abliterated-refined-novis | Nabbers1999 | 2026-02-01T04:49:47Z | 14 | 2 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"gemma",
"gemma-3",
"conversational",
"abliterated",
"en",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-12-27T19:13:47Z | # Gemma 3 27B Instruct Abliterated - Refined
This is my biprojected norm preserving abliteration of Gemma 3 27B Instruct as found at [Nabbers1999/Gemma-3-27B-it-NP-Abliterated](https://huggingface.co/Nabbers1999/Gemma-3-27B-it-NP-Abliterated).
This model has undergone further DoRA fine tuning to encourage response ne... | [
{
"start": 135,
"end": 146,
"text": "Nabbers1999",
"label": "evaluation dataset",
"score": 0.7995312213897705
},
{
"start": 201,
"end": 212,
"text": "Nabbers1999",
"label": "evaluation dataset",
"score": 0.6298829913139343
}
] |
zpphxd/Almev | zpphxd | 2025-09-06T21:33:27Z | 2 | 0 | null | [
"gpt-neox",
"mev",
"blockchain",
"solana",
"llm",
"finance",
"gpt-oss",
"20b",
"lora",
"dataset:custom",
"license:mit",
"model-index",
"region:us"
] | null | 2025-09-06T21:17:55Z | # ALMEV - GPT-OSS-20B Fine-tuned for MEV Detection
## 🚀 20B Parameter LLM Specialized for Maximum Extractable Value
This is the full GPT-OSS-20B model (13GB) enhanced with LoRA adapters specifically trained for MEV detection on Solana blockchain.
### Model Architecture
- **Base Model**: GPT-OSS-20B (13GB quantized)... | [] |
aoiandroid/Qwen3-8B_eagle3 | aoiandroid | 2026-05-01T09:02:28Z | 0 | 0 | null | [
"pytorch",
"llama",
"qwen3",
"eagle3",
"eagle",
"arxiv:2509.24248",
"arxiv:2509.23809",
"region:us"
] | null | 2026-05-01T09:02:27Z | <p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://github.com/Tencent/AngelSlim/blob/main/docs/source/assets/logos/angelslim_logo_light.png?raw=true">
<img alt="AngelSlim" src="https://github.com/Tencent/AngelSlim/blob/main/docs/source/assets/logos/angelslim_logo.png?raw... | [] |
Setaremsb/Qwen2.5-Coder-7B-sql | Setaremsb | 2026-03-23T11:02:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-Coder-7B",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B",
"endpoints_compatible",
"region:us"
] | null | 2026-03-23T11:02:10Z | # Model Card for Qwen2.5-Coder-7B-sql
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B).
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, bu... | [] |
Umiharu/Qwen-4B-DB-AlfWorld-v5 | Umiharu | 2026-02-25T07:40:27Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"merged",
"agent",
"tool-use",
"alfworld",
"dbbench",
"conversational",
"en",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v5",
"dataset:u-10bei/dbbench_sft_dataset_react_v2",
"dataset:u-10bei/dbbench_sft_dataset_react_v3",
"... | text-generation | 2026-02-25T07:35:09Z | # Qwen-4B-DB-AlfWorld-v5
This repository provides a **merged model** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** on datasets **u-10bei/sft_alfworld_trajectory_dataset_v5, dbbench_sft_dataset_react_v2 and dbbench_sft_dataset_react_v3**.
All LoRA adapter weights have been **merged into the base model**, and the
res... | [
{
"start": 132,
"end": 174,
"text": "u-10bei/sft_alfworld_trajectory_dataset_v5",
"label": "evaluation dataset",
"score": 0.6353759765625
},
{
"start": 850,
"end": 857,
"text": "DBBench",
"label": "benchmark name",
"score": 0.6786085367202759
}
] |
ases200q2/roboverse_pick_cube_mujocoE100-isaacE2_smolvla_20251220_1126 | ases200q2 | 2025-12-20T14:42:23Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:ases200q2/roboverse-pick_cube-mujocoE100-isaacE2",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2025-12-20T14:41:55Z | # Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This pol... | [
{
"start": 17,
"end": 24,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.7469843029975891
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.7727768421173096
}
] |
google/siglip2-base-patch16-naflex | google | 2025-02-21T04:04:09Z | 588,228 | 25 | transformers | [
"transformers",
"safetensors",
"siglip2",
"zero-shot-image-classification",
"vision",
"arxiv:2502.14786",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | zero-shot-image-classification | 2025-02-18T11:39:16Z | # SigLIP 2 Base
[SigLIP 2](https://huggingface.co/papers/2502.14786) extends the pretraining objective of
[SigLIP](https://huggingface.co/papers/2303.15343) with prior, independently developed techniques
into a unified recipe, for improved semantic understanding, localization, and dense features.
## Intended uses
Yo... | [] |
opus-research/gemma-2-2b-thinking | opus-research | 2025-12-16T07:38:07Z | 0 | 0 | null | [
"safetensors",
"thinking",
"reasoning",
"chain-of-thought",
"cot",
"gemma",
"fine-tuned",
"lora",
"text-generation",
"en",
"dataset:opus-research/opus-thinking-10k",
"base_model:google/gemma-2-2b",
"base_model:adapter:google/gemma-2-2b",
"license:gemma",
"region:us"
] | text-generation | 2025-12-15T16:13:34Z | # 🧠 Gemma 2 2B Thinking
**Gemma 2 2B fine-tuned on the Opus Thinking 10k dataset** to exhibit explicit chain-of-thought reasoning.
This model outputs its reasoning process in a `Thinking...` block before providing the final answer.
## Model Details
| Attribute | Value |
|-----------|-------|
| **Base Model** | [go... | [
{
"start": 420,
"end": 437,
"text": "opus-thinking-10k",
"label": "evaluation dataset",
"score": 0.606419563293457
},
{
"start": 536,
"end": 537,
"text": "r",
"label": "evaluation metric",
"score": 0.6849866509437561
},
{
"start": 572,
"end": 580,
"text": ... |
ferrazzipietro/ULS-MultiClinNERsv-Qwen2.5-7B-Instruct-symptom | ferrazzipietro | 2026-03-14T01:29:55Z | 98 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"lora",
"transformers",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-03-14T01:10:08Z | <!-- 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. -->
# ULS-MultiClinNERsv-Qwen2.5-7B-Instruct-symptom
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingfa... | [
{
"start": 449,
"end": 458,
"text": "Precision",
"label": "evaluation metric",
"score": 0.9199618697166443
},
{
"start": 460,
"end": 466,
"text": "0.3187",
"label": "evaluation metric",
"score": 0.8654481172561646
},
{
"start": 469,
"end": 475,
"text": "Re... |
mylesCooper266/open_researcher_sft_qwen2_5_7b_instruct_1m | mylesCooper266 | 2026-03-02T15:56:21Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct-1M",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct-1M",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-03-02T15:05:52Z | # Model Card for sft_full_qwen2_5_7b_instruct_1m
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct-1M](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you... | [] |
OsamaBinLikhon/vortex-vtx | OsamaBinLikhon | 2025-12-21T14:34:44Z | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | 2025-12-21T13:04:30Z | # Vortex-VTX: Bangla-First Agentic AI System
<div align="center">
![Model Type](https://img.shields.io/badge/Model-... | [] |
Brigham-Young-University/Qwen2.5-Coder-3B-Ilograph-Instruct | Brigham-Young-University | 2026-03-10T19:32:32Z | 70 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"qwen2.5",
"sft",
"ilograph",
"conversational",
"en",
"dataset:Brigham-Young-University/Ilograph_dataset",
"base_model:unsloth/Qwen2.5-Coder-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-Coder-3B-Instruct",
"license:mit",
"tex... | text-generation | 2026-03-05T17:55:46Z | # Model Card for Qwen2.5-Coder-3B-Instruct (fine-tuned model)
A fully fine-tuned version of **Qwen2.5-Coder-3B-Instruct**, trained with LoRA using Unsloth and then merged into a standalone model. This checkpoint can be used directly as a regular Transformers causal language model. It is specialized for **Ilograph diag... | [] |
ferrazzipietro/ULS-MultiClinNERes-Qwen2.5-7B-procedure | ferrazzipietro | 2026-03-15T00:53:32Z | 91 | 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 | 2026-03-15T00:34:17Z | <!-- 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. -->
# ULS-MultiClinNERes-Qwen2.5-7B-procedure
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2... | [
{
"start": 270,
"end": 285,
"text": "Qwen/Qwen2.5-7B",
"label": "benchmark name",
"score": 0.6543394923210144
},
{
"start": 310,
"end": 325,
"text": "Qwen/Qwen2.5-7B",
"label": "benchmark name",
"score": 0.6538586616516113
},
{
"start": 415,
"end": 421,
"t... |
rgarine12/PCOS-SafeQA-Med-Tuned | rgarine12 | 2025-11-10T06:48:39Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-11-10T06:08:07Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PCOS-SafeQA-Med-Tuned
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluat... | [
{
"start": 331,
"end": 335,
"text": "Loss",
"label": "evaluation metric",
"score": 0.612077534198761
},
{
"start": 619,
"end": 632,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.7396551966667175
},
{
"start": 634,
"end": 639,
"text": "2... |
OpenVoiceOS/parakeet-rnnt-0.6b-coreml-4bit | OpenVoiceOS | 2026-04-21T22:01:05Z | 0 | 0 | coremltools | [
"coremltools",
"coreml",
"asr",
"speech",
"nemo",
"parakeet",
"nvidia",
"4bit-palettize-kmeans",
"automatic-speech-recognition",
"en",
"base_model:nvidia/parakeet-rnnt-0.6b",
"base_model:quantized:nvidia/parakeet-rnnt-0.6b",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2026-04-21T21:41:22Z | # parakeet-rnnt-0.6b-coreml-4bit
CoreML conversion of [nvidia/parakeet-rnnt-0.6b](https://huggingface.co/nvidia/parakeet-rnnt-0.6b) — 4BIT PALETTIZE KMEANS quantized.
| | |
|---|---|
| **Architecture** | RNNT |
| **Language** | English |
| **Sample rate** | 16000 Hz |
| **Max audio** | 15.0s |
| **Vocab size** | 1024... | [] |
the-acorn-ai/spiral-octothinker-8b-multi-step00416 | the-acorn-ai | 2025-09-08T02:22:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"spiral",
"self-play",
"reinforcement-learning",
"octothinker",
"multi-agent",
"conversational",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-08-28T01:32:21Z | # SPIRAL OctoThinker-3B Multi-Agent Model
This model was trained using the SPIRAL (Self-Play Iterative Reinforcement learning for Adaptation and Learning) framework.
## Model Details
- **Base Model**: OctoAI/OctoThinker-3B
- **Training Framework**: SPIRAL
- **Checkpoint**: step_00416
- **Model Size**: 3B parameters
... | [] |
mlx-community/Qwen3-Embedding-4B-mxfp8 | mlx-community | 2026-02-11T14:43:13Z | 542 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"qwen3",
"text-generation",
"transformers",
"sentence-similarity",
"feature-extraction",
"text-embeddings-inference",
"mlx",
"base_model:Qwen/Qwen3-4B-Base",
"base_model:finetune:Qwen/Qwen3-4B-Base",
"license:apache-2.0",
"endpoints_compatible",
"reg... | feature-extraction | 2026-02-11T14:41:14Z | # mlx-community/Qwen3-Embedding-4B-mxfp8
The Model [mlx-community/Qwen3-Embedding-4B-mxfp8](https://huggingface.co/mlx-community/Qwen3-Embedding-4B-mxfp8) was converted to MLX format from [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) using [mlx-embeddings](https://github.com/Blaizzy/mlx-emb... | [] |
qualia-robotics/seeed-rebot-lianxucaiji-18-2c3cc110 | qualia-robotics | 2026-04-26T04:37:56Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:Lakesenberg/Seeed_rebot_lianxucaiji_18",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:eu"
] | robotics | 2026-04-26T04:37:34Z | # Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This pol... | [
{
"start": 17,
"end": 24,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.7469843029975891
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.7727768421173096
}
] |
mradermacher/Infected-Frusto-3.2-1B-GGUF | mradermacher | 2026-02-20T19:34:39Z | 23 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Novaciano/Infected-Frusto-3.2-1B",
"base_model:quantized:Novaciano/Infected-Frusto-3.2-1B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-02-20T17:58:22Z | ## 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... | [] |
Thireus/Qwen3-4B-Thinking-2507-THIREUS-IQ4_K-SPECIAL_SPLIT | Thireus | 2026-02-11T23:26:04Z | 0 | 0 | null | [
"gguf",
"arxiv:2505.23786",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-29T05:55:09Z | # Qwen3-4B-Thinking-2507
## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Thinking-2507-THIREUS-BF16-SPECIAL_SPLIT/) about?
This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Thinking-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). T... | [] |
living-box/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMD-lora-0125154208-epoch-4 | living-box | 2026-01-25T16:06:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"text-generation",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it... | text-generation | 2026-01-25T16:05:37Z | # Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMD-lora
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRL... | [] |
Adanato/Meta-Llama-3-8B-Instruct_qwen25_gemma-qwen25_gemma_cluster_2 | Adanato | 2026-02-04T07:54:22Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"text-generation-inference",
"endpoint... | text-generation | 2026-02-04T07:51:17Z | <!-- 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. -->
# Meta-Llama-3-8B-Instruct_e1_qwen25_gemma_cluster_2
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](ht... | [
{
"start": 692,
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"text": "learning_rate",
"label": "evaluation metric",
"score": 0.7515692710876465
},
{
"start": 707,
"end": 712,
"text": "1e-05",
"label": "evaluation metric",
"score": 0.6575614213943481
},
{
"start": 737,
"end": 752,
"text": ... |
ShogoMu/qwen3-4b-lora-csv2json-v11 | ShogoMu | 2026-02-07T19:11:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v2",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-02-07T19:11:15Z | qwen3-4b-structured-output-lora-v11
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... | [] |
sh0ck0r/L3.1-70B-Euryale-v2.2-FP8-Dynamic | sh0ck0r | 2025-12-24T15:35:38Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"fp8",
"vllm",
"compressed-tensors",
"quantized",
"llmcompressor",
"conversational",
"base_model:Sao10K/L3.1-70B-Euryale-v2.2",
"base_model:quantized:Sao10K/L3.1-70B-Euryale-v2.2",
"license:apache-2.0",
"text-generation-inference",... | text-generation | 2025-12-24T15:28:39Z | # L3.1-70B-Euryale-v2.2 - FP8 Dynamic Quantization
This is an FP8 quantized version of [Sao10K/L3.1-70B-Euryale-v2.2](https://huggingface.co/Sao10K/L3.1-70B-Euryale-v2.2) using `llmcompressor` with the FP8_DYNAMIC scheme.
## Model Details
- **Base Model**: Sao10K/L3.1-70B-Euryale-v2.2
- **Quantization**: FP8_DYNAMIC... | [
{
"start": 89,
"end": 95,
"text": "Sao10K",
"label": "benchmark name",
"score": 0.8078636527061462
},
{
"start": 142,
"end": 148,
"text": "Sao10K",
"label": "benchmark name",
"score": 0.789444625377655
},
{
"start": 260,
"end": 266,
"text": "Sao10K",
"... |
gbalachandhiran/gpt-oss-psychologist | gbalachandhiran | 2025-10-23T13:43:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"trl",
"text-generation",
"conversational",
"en",
"dataset:Amod/mental_health_counseling_conversations",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"license:apache-2.0",
"endpo... | text-generation | 2025-10-14T12:20:58Z | Delete that section and **replace it** with your custom Unsloth code.
---
### 4. Add your Unsloth inference code
Here’s a formatted example you can copy directly:
```markdown
## 🧠 Example: Inference with Unsloth FastLanguageModel
You can run this model using [Unsloth](https://github.com/unslothai/unsloth) for opt... | [] |
mradermacher/keural-alpha-v2-GGUF | mradermacher | 2026-03-05T00:53:24Z | 223 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:mkd-ai/keural-alpha-v2",
"base_model:quantized:mkd-ai/keural-alpha-v2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-03-05T00:46:28Z | ## 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: 1 -->
static ... | [
{
"start": 512,
"end": 532,
"text": "keural-alpha-v2-GGUF",
"label": "benchmark name",
"score": 0.6752011775970459
}
] |
vtava/Qwen3-Embedding-simple-tool-calling | vtava | 2026-01-25T22:37:21Z | 2 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-01-25T22:11:15Z | # Model Card for Qwen3-Embedding-simple-tool-calling
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine,... | [] |
mradermacher/kisoku-3b-sft-GGUF | mradermacher | 2026-03-03T13:35:28Z | 759 | 0 | transformers | [
"transformers",
"gguf",
"from-scratch",
"sft",
"instruction-tuned",
"trc",
"tpu",
"maxtext",
"jax",
"grouped-query-attention",
"granite",
"gguf-compatible",
"en",
"base_model:0arch-io/kisoku-3b-sft",
"base_model:quantized:0arch-io/kisoku-3b-sft",
"license:apache-2.0",
"endpoints_comp... | null | 2026-03-03T02:19:01Z | ## 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... | [] |
Zlovoblachko/dim3_BAAI_setfit_model | Zlovoblachko | 2025-08-17T17:51:15Z | 0 | 0 | setfit | [
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region... | text-classification | 2025-08-17T17:51:09Z | # SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://sciki... | [] |
AIRI-Institute/chexfract-maira2 | AIRI-Institute | 2025-12-17T09:27:19Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"phi3_dino",
"text-generation",
"vision",
"image-text-to-text",
"conversational",
"custom_code",
"multilingual",
"arxiv:2511.07983",
"region:us"
] | image-text-to-text | 2025-11-07T09:05:54Z | # ChexFract: Specialized Vision-Language Models for Fracture Detection in Chest X-rays
This repository contains the pre-trained models from our paper "ChexFract: From General to Specialized - Enhancing Fracture Description Generation in Medical AI".
## 📋 Overview
ChexFract models are specialized vision-language mod... | [
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"label": "evaluation metric",
"score": 0.6125410199165344
},
{
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"end": 814,
"text": "ROC-AUC",
"label": "evaluation metric",
"score": 0.7744510173797607
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"text"... |
power98leony/vit-beef-freshness | power98leony | 2025-11-25T17:39:53Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"vision",
"beef-freshness",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-11-25T15:25:45Z | # 🥩 ViT Beef Freshness Classifier
This model is a **Vision Transformer (ViT)** fine-tuned to classify **beef freshness** into multiple categories.
## ## 🌟 Model Description
- **Base architecture:** ViT (Vision Transformer)
- **Training task:** Image Classification
- **Dataset:** Custom beef dataset (freshness level... | [
{
"start": 282,
"end": 301,
"text": "Custom beef dataset",
"label": "evaluation dataset",
"score": 0.6351402997970581
}
] |
DFK1991/yolov11n_car_plates_detector | DFK1991 | 2026-03-22T10:49:59Z | 153 | 0 | ultralytics | [
"ultralytics",
"object-detection",
"yolov11",
"computer-vision",
"pytorch",
"registration-plate",
"dataset:my_custom_dataset",
"license:agpl-3.0",
"region:us"
] | object-detection | 2026-03-13T09:10:36Z | ## 🤖 [yolov11n_car_plates_detector]
## 📝 Description
This model is aimed to detect car registration plates and based on ultralytic's yolov11. It can be used as a good start for training a more sophisticated CV system.
## 🗂️ Dataset
The custom dataset is based on 7091 open source images that were labeled mostly man... | [] |
Ok1720/gemma-4-E4B-it | Ok1720 | 2026-04-18T14:57:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma4",
"image-text-to-text",
"any-to-any",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | any-to-any | 2026-04-18T14:57:05Z | <div align="center">
<img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
</div>
<p align="center">
<a href="https://huggingface.co/collections/google/gemma-4" target="_blank">Hugging Face</a> |
<a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
<a href="https://blog.google... | [] |
dobrien/ViT-B-32-SVHN-dummy-TINet-1e-4-arithmetic | dobrien | 2026-04-05T01:51:15Z | 0 | 0 | null | [
"pytorch",
"region:us"
] | null | 2026-02-20T23:30:02Z | ## Dataset: SVHN
## Dataset Location: ufldl-stanford/svhn cropped_digits
## Dummy Dataset: TINet
## Dummy Dataset Location: zh-plus/tiny-imagenet
## Loss Term: 1e-4
## Merge Method: arithmetic
## Test-Set Accuracy: 0.974433422088623
## Test-Set Loss: 0.10936645287... | [
{
"start": 61,
"end": 65,
"text": "svhn",
"label": "benchmark name",
"score": 0.6057632565498352
},
{
"start": 107,
"end": 112,
"text": "TINet",
"label": "benchmark name",
"score": 0.6392966508865356
},
{
"start": 192,
"end": 196,
"text": "1e-4",
"labe... |
JJC489/my_smolvla | JJC489 | 2026-04-17T09:54:00Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:lerobot/svla_so101_pickplace",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-04-17T09:53:25Z | # Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This pol... | [
{
"start": 17,
"end": 24,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.7469843029975891
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.7727768421173096
}
] |
vitthalbhandari/mms-1b-all-aft-all-lth | vitthalbhandari | 2026-03-03T07:30:36Z | 59 | 0 | null | [
"safetensors",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"mms",
"adapter",
"lth",
"dataset:mozilla-foundation/common_voice_spontaneous_speech",
"license:cc-by-nc-4.0",
"region:us"
] | automatic-speech-recognition | 2026-03-01T20:05:41Z | # MMS Adapter Fine-tuned for Thur
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all)
on the Mozilla Common Voice Spontaneous Speech dataset for Thur (lth).
## Training
- Base model: facebook/mms-1b-all
- Fine-tuning method: Adapter layers
- Dataset: Mozilla Commo... | [
{
"start": 147,
"end": 186,
"text": "Mozilla Common Voice Spontaneous Speech",
"label": "evaluation dataset",
"score": 0.7607828974723816
},
{
"start": 307,
"end": 346,
"text": "Mozilla Common Voice Spontaneous Speech",
"label": "evaluation dataset",
"score": 0.8031804561... |
OpenGVLab/ScaleCUA-3B | OpenGVLab | 2025-09-17T12:11:11Z | 75 | 11 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"agent",
"conversational",
"en",
"dataset:OpenGVLab/ScaleCUA-Data",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_co... | image-text-to-text | 2025-09-16T07:38:38Z | # SCALECUA: SCALING UP COMPUTER USE AGENTS WITH CROSS-PLATFORM DATA
[\[📂 GitHub\]](https://github.com/OpenGVLab/ScaleCUA) [\[📜 Paper\]](https://github.com/OpenGVLab/ScaleCUA) [\[🚀 Quick Start\]](#model-loading)
## Introduction
Recent advances in Vision-Language Models have enabled the development of agents capa... | [
{
"start": 703,
"end": 723,
"text": "computer use dataset",
"label": "evaluation dataset",
"score": 0.677808403968811
},
{
"start": 1186,
"end": 1208,
"text": "absolute success rates",
"label": "evaluation metric",
"score": 0.7630898952484131
}
] |
li-whale/pi0_fold_bag | li-whale | 2025-09-12T04:18:32Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"pi0fast",
"robotics",
"dataset:li-whale/fold_bag",
"arxiv:2501.09747",
"license:apache-2.0",
"region:us"
] | robotics | 2025-09-12T03:52:48Z | # Model Card for pi0fast
<!-- Provide a quick summary of what the model is/does. -->
[Pi0-Fast](https://huggingface.co/papers/2501.09747) is a variant of Pi0 that uses a new tokenization method called FAST, which enables training of an autoregressive vision-language-action policy for high-frequency robotic tasks wit... | [
{
"start": 89,
"end": 97,
"text": "Pi0-Fast",
"label": "evaluation dataset",
"score": 0.6518031358718872
}
] |
he-shuwei/M2SE-VTTS | he-shuwei | 2026-03-31T06:33:49Z | 0 | 0 | null | [
"text-to-speech",
"visual-tts",
"speech-synthesis",
"diffusion",
"spatial-audio",
"en",
"arxiv:2412.11409",
"license:mit",
"region:us"
] | text-to-speech | 2026-03-30T04:17:04Z | # M<sup>2</sup>SE-VTTS: Multi-Modal and Multi-Scale Spatial Environment Understanding for Immersive Visual Text-to-Speech
[](https://arxiv.org/abs/2412.11409)
[](https://github.com/he-shuwei/M2SE-VTTS)... | [] |
HEIher/smoking-detection | HEIher | 2026-03-13T13:32:57Z | 0 | 0 | null | [
"english",
"YOLO",
"Ultralytics",
"Smoking",
"object-detection",
"base_model:Ultralytics/YOLO11",
"base_model:finetune:Ultralytics/YOLO11",
"license:mit",
"region:us"
] | object-detection | 2026-03-13T13:32:57Z | # 🚬 Smoke Detection with YOLOv11-Medium
This repository contains a custom-trained **YOLOv11-Medium** object detection model designed to detect **cigarette smoke** in images and videos. It is ideal for use in **surveillance systems**, **public safety**, and **smoking zone enforcement**.
---
## 📊 Model Performance
... | [
{
"start": 936,
"end": 952,
"text": "Roboflow Dataset",
"label": "evaluation dataset",
"score": 0.8070232272148132
}
] |
fn-aka-mur/starter_sft_0020_cont0018_lr1e5 | fn-aka-mur | 2026-02-08T03:25:11Z | 1 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset_512_v2",
"base_model:fn-aka-mur/starter_sft_0018_lr1e-5_cont0014",
"base_model:adapter:fn-aka-mur/starter_sft_0018_lr1e-5_cont0014",
"license:apache-2.0",
"re... | text-generation | 2026-02-08T03:24:54Z | ＜【課題】ここは自分で記入して下さい＞
This repository provides a **LoRA adapter** fine-tuned from
**fujiki/starter_sft_0018_lr1e-5_cont0014** 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... | [] |
Ba2han/augment-multi-ft | Ba2han | 2025-12-20T14:09:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:Ba2han/Gemma3-Turkish-Augment-FT",
"base_model:finetune:Ba2han/Gemma3-Turkish-Augment-FT",
"text-generation-inference",
"endpoints_compatible",
"r... | image-text-to-text | 2025-12-20T11:41:46Z | # Model Card for augment-multi-ft
This model is a fine-tuned version of [Ba2han/Gemma3-Turkish-Augment-FT](https://huggingface.co/Ba2han/Gemma3-Turkish-Augment-FT).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had ... | [] |
kevinkyi/Homework2_Classical_ML | kevinkyi | 2025-09-22T00:05:58Z | 6 | 0 | autogluon | [
"autogluon",
"automl",
"tabular",
"sklearn",
"tabular-classification",
"en",
"license:mit",
"region:us"
] | tabular-classification | 2025-09-21T23:48:39Z | # Football Elite Classifier — AutoML (AutoGluon Tabular)
## Purpose
This model was developed as part of a class assignment on designing and deploying AI/ML systems.
It demonstrates the use of AutoML (AutoGluon Tabular) to build a binary classifier on football receiver stats.
## Dataset
- **Source:** https://hugging... | [
{
"start": 709,
"end": 726,
"text": "AutoGluon Tabular",
"label": "evaluation dataset",
"score": 0.7249537706375122
},
{
"start": 829,
"end": 831,
"text": "F1",
"label": "evaluation metric",
"score": 0.7158895134925842
}
] |
mradermacher/Carnice-9B-Function-Calling-xLAM-Unsloth-i1-GGUF | mradermacher | 2026-04-22T08:51:45Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma2",
"function-calling",
"peft",
"lora",
"fine-tuned",
"en",
"dataset:Salesforce/xlam-function-calling-60k",
"base_model:ermiaazarkhalili/Carnice-9B-Function-Calling-xLAM-Unsloth",
"base_model:adapter:ermiaazarkhalili/Carni... | null | 2026-04-22T05:45:01Z | ## 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_... | [] |
aractingi/groot-bimanual-latest-2 | aractingi | 2025-10-22T14:03:40Z | 1 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"groot",
"dataset:pepijn223/bimanual-so100-handover-cube",
"license:apache-2.0",
"region:us"
] | robotics | 2025-10-22T11:42:18Z | # Model Card for groot
<!-- 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.... | [] |
Xeno443/QuantizedModelsCollection | Xeno443 | 2025-10-18T19:53:41Z | 12 | 0 | null | [
"gguf",
"text-to-image",
"license:unknown",
"region:us"
] | text-to-image | 2025-10-18T19:39:17Z | # <FONT SIZE="+3">⭐Quantized Models Collection⭐</FONT>
A kind anon created quantized versions of these models which you can use if you are very low on VRAM or for special setups like <A HREF="https://github.com/kantsche/ComfyUI-MixMod?tab=readme-ov-file">Mixmod</A>.
Quantized versions of StableMondAI-SDG can be found... | [] |
kureha295/deepseek-ai-DeepSeek-R1-Distill-Qwen-7B-ortho-cot-layer-17 | kureha295 | 2025-12-31T00:42:30Z | 2 | 0 | null | [
"safetensors",
"qwen2",
"orthogonalized",
"cot",
"layer-17",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-12-31T00:40:54Z | # Orthogonalized Cot Model (Layer 17)
This model is an orthogonalized version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B).
## Model Details
- **Base Model:** deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
- **Model Type:** Cot
- **Orthogonalization Layer:** 1... | [] |
qturtle/Huihui-Qwen3.5-35B-A3B-abliterated | qturtle | 2026-03-17T02:17:16Z | 9 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3_5_moe",
"image-text-to-text",
"abliterated",
"uncensored",
"conversational",
"base_model:Qwen/Qwen3.5-35B-A3B",
"base_model:finetune:Qwen/Qwen3.5-35B-A3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2026-03-17T02:17:14Z | # huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated
This is an uncensored version of [Qwen/Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
This is a crude... | [] |
HidekiKawai/sft-qwen-merged | HidekiKawai | 2026-03-02T02:45:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"qlora",
"lora",
"structured-output",
"text-generation",
"conversational",
"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",
"reg... | text-generation | 2026-02-07T14:23:18Z | 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... | [] |
shreyaschavan11/Akshar-Nano-Q8_0-GGUF | shreyaschavan11 | 2026-04-14T17:08:24Z | 0 | 0 | null | [
"gguf",
"indic",
"unsloth",
"llama-3.2",
"akshar-nano",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"hi",
"mr",
"base_model:shreyaschavan11/Akshar-Nano",
"base_model:quantized:shreyaschavan11/Akshar-Nano",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-04-14T17:08:14Z | # shreyaschavan11/Akshar-Nano-Q8_0-GGUF
This model was converted to GGUF format from [`shreyaschavan11/Akshar-Nano`](https://huggingface.co/shreyaschavan11/Akshar-Nano) 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:/... | [] |
iceberg0142/Affine-31B-All | iceberg0142 | 2025-10-24T14:45:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2402.17463",
"arxiv:2407.02490",
"arxiv:2501.15383",
"arxiv:2404.06654",
"arxiv:2505.09388",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-10-24T14:41:53Z | # Qwen3-30B-A3B-Instruct-2507
<a href="https://chat.qwen.ai/?model=Qwen3-30B-A3B-2507" 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>
## Highlights
We introduce the... | [] |
contemmcm/315da16414ee95c1d53b8e3e259318cb | contemmcm | 2025-11-02T09:19:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-11-02T08:58:38Z | <!-- 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. -->
# 315da16414ee95c1d53b8e3e259318cb
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/Face... | [
{
"start": 474,
"end": 487,
"text": "Epoch Runtime",
"label": "evaluation metric",
"score": 0.8295721411705017
},
{
"start": 500,
"end": 508,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.8814727663993835
},
{
"start": 510,
"end": 516,
"text... |
ali-elganzory/Baguettotron-SFT-Tulu3-decontaminated | ali-elganzory | 2026-04-08T19:40:45Z | 198 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:PleIAs/Baguettotron",
"base_model:finetune:PleIAs/Baguettotron",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-04-08T19:39:55Z | # Model Card for None
This model is a fine-tuned version of [PleIAs/Baguettotron](https://huggingface.co/PleIAs/Baguettotron).
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 t... | [] |
jayyucippg/smolvla-record-side-views-stack3 | jayyucippg | 2025-09-04T05:54:10Z | 2 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:jayyucippg/record-side-views-stack3",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2025-09-04T05:53:34Z | # Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This pol... | [
{
"start": 17,
"end": 24,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.7305590510368347
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.755279541015625
}
] |
NakayamaYuji/n-lora-repo28 | NakayamaYuji | 2026-03-01T11:47:47Z | 11 | 0 | peft | [
"peft",
"safetensors",
"qlora",
"lora",
"structured-output",
"text-generation",
"en",
"dataset:u-10bei/structured_data_with_cot_dataset",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapter:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-02-19T10:03:59Z | main28
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... | [] |
2bys-kausable/my_policy | 2bys-kausable | 2025-12-24T06:21:15Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:2bys-kausable/record-training",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2025-12-24T06:19:56Z | # 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": "evaluation dataset",
"score": 0.6181951761245728
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "evaluation dataset",
"score": 0.6971622109413147
},
{
"start": 865,
"end": 868,
"text": "act",
"... |
random-sequence/vapor-drift-peak | random-sequence | 2026-03-26T10:58:40Z | 0 | 0 | null | [
"federated-learning",
"fl-alliance",
"slm_qwen3_0_6B",
"license:apache-2.0",
"region:us"
] | null | 2026-03-26T10:58:37Z | # FL-Alliance Federated Model: vapor-drift-peak
This model was trained using **FL-Alliance** decentralized federated learning.
## Training Details
| Parameter | Value |
|-----------|-------|
| Task Type | `slm_qwen3_0_6B` |
| Total Rounds | 5 |
| Model Hash | `452b8ab5c6fe8cdfc27d8d54c87add690d07df9dbd8bf18dc0e90777... | [] |
deepdml/whisper-base-ar-mix-norm | deepdml | 2026-03-01T16:53:13Z | 295 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"dataset:google/fleurs",
"dataset:fixie-ai/common_voice_17_0",
"dataset:UBC-NLP/Casablanca",
"dataset:ymoslem/MediaSpeech",
"dataset:deepdml/Tunisian_MSA",
"base_model:ope... | automatic-speech-recognition | 2025-09-07T11:17:13Z | <!-- 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. -->
# Whisper Base ar
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Co... | [
{
"start": 318,
"end": 335,
"text": "Common Voice 17.0",
"label": "evaluation dataset",
"score": 0.8875868320465088
},
{
"start": 404,
"end": 408,
"text": "Loss",
"label": "evaluation metric",
"score": 0.6732162237167358
},
{
"start": 419,
"end": 422,
"tex... |
pitpiboon/distilbert-base-uncased-finetuned-emotion | pitpiboon | 2026-01-16T09:34:05Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"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",
"re... | text-classification | 2026-01-16T09:33:56Z | <!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | [
{
"start": 433,
"end": 439,
"text": "0.2319",
"label": "evaluation metric",
"score": 0.9371139407157898
},
{
"start": 442,
"end": 450,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.95477694272995
},
{
"start": 452,
"end": 458,
"text": "0.921... |
shuohsuan/svla_rgcg | shuohsuan | 2025-08-27T03:56:04Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:shuohsuan/tri_test_red",
"dataset:shuohsuan/tri_test_green",
"dataset:shuohsuan/tri_test_checkg",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2025-08-27T03:55:46Z | # Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This pol... | [
{
"start": 17,
"end": 24,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.7305590510368347
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.755279541015625
}
] |
SiggytheShark/vt-test-mailcap-rewrite | SiggytheShark | 2026-04-28T21:12:16Z | 0 | 0 | null | [
"security",
"pickle",
"scanner-bypass",
"proof-of-concept",
"license:mit",
"region:us"
] | null | 2026-04-28T21:12:13Z | # vt-test mailcap.findmatch rewrite (PoC)
**Technique:** P04 — `mailcap.findmatch → os.system` (stdlib indirection)
This is a proof-of-concept demonstrating that the `dtonala/vt-test` Monero miner
(originally detected via `yaml.unsafe_load`) can be rewritten to evade all mainstream
pickle scanners using a single func... | [] |
IlyaArtistAi/paul-style1 | IlyaArtistAi | 2026-01-25T10:40:47Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-01-25T10:24:40Z | # Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path... | [] |
SadeghK/tts_fa_fastpitch_hifigan-v2.0 | SadeghK | 2025-08-15T13:46:16Z | 0 | 0 | nemo | [
"nemo",
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2025-08-15T13:35:43Z | ## FastPitch and HifiGan v2.0
v2.0 of phonemizer and tokenizer. tokenzier `DO SUPPORT` pauses, emotion tokens etc,.
### Install NeMo
```bash
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython packaging
rm -rf /usr/lib/python3.10/site-packages/blinker*
rm -rf /usr/local/lib/python3.10/dist-pac... | [
{
"start": 1144,
"end": 1154,
"text": "PITCH_MEAN",
"label": "evaluation metric",
"score": 0.7271461486816406
}
] |
mradermacher/qwen3-1.7B-doi-v1-GGUF | mradermacher | 2025-08-28T20:30:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:rongchu/qwen3-1.7B-doi-v1",
"base_model:quantized:rongchu/qwen3-1.7B-doi-v1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-28T20:06:26Z | ## 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 qu... | [] |
sh0ck0r/Agatha-111B-v1.1-FP8-Dynamic | sh0ck0r | 2025-12-30T21:20:53Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"cohere2",
"feature-extraction",
"fp8",
"vllm",
"compressed-tensors",
"quantized",
"llmcompressor",
"text-generation",
"conversational",
"base_model:TheDrummer/Agatha-111B-v1.1",
"base_model:quantized:TheDrummer/Agatha-111B-v1.1",
"license:apache-2.0",
"end... | text-generation | 2025-12-30T21:17:50Z | # Agatha-111B-v1.1 - FP8 Dynamic Quantization
This is an FP8 quantized version of [TheDrummer/Agatha-111B-v1.1](https://huggingface.co/TheDrummer/Agatha-111B-v1.1) using `llmcompressor` with the FP8_DYNAMIC scheme.
## Model Details
- **Base Model**: TheDrummer/Agatha-111B-v1.1
- **Quantization**: FP8_DYNAMIC (W8A8)
... | [
{
"start": 2,
"end": 18,
"text": "Agatha-111B-v1.1",
"label": "benchmark name",
"score": 0.6369744539260864
}
] |
OsamaBinLikhon/megha_bangla | OsamaBinLikhon | 2026-01-16T23:41:40Z | 1 | 0 | null | [
"safetensors",
"speecht5",
"region:us"
] | null | 2026-01-16T23:36:33Z | # Megha Bangla TTS - Fine-tuned Model
This is a fine-tuned Bangla Text-to-Speech (TTS) model based on Microsoft's SpeechT5 architecture.
## Model Details
- **Base Model**: microsoft/speecht5_tts
- **Fine-tuned on**: EsferSami/small-female-bangla-voice-data
- **Language**: Bangla (Bengali)
- **Gender**: Fema... | [] |
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