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 |
|---|---|---|---|---|---|---|---|---|---|---|
DJLougen/Harmonic-27B-MLX-4bit | DJLougen | 2026-04-09T21:39:01Z | 95 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3_5",
"reasoning",
"qwen3.5",
"conversational",
"unsloth",
"self-correction",
"chain-of-thought",
"text-generation",
"en",
"base_model:DJLougen/Harmonic-27B",
"base_model:quantized:DJLougen/Harmonic-27B",
"license:apache-2.0",
"4-bit",
"region:us"
] | text-generation | 2026-04-06T01:03:39Z | # Harmonic-27B-MLX-4bit
MLX 4-bit quantized conversion of [DJLougen/Harmonic-27B](https://huggingface.co/DJLougen/Harmonic-27B) — the flagship of the Harmonic series. A reasoning-focused fine-tune of [Qwen 3.5 27B](https://huggingface.co/unsloth/Qwen3.5-27B) trained on structu... | [
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"end": 41,
"text": "Harmonic-27B",
"label": "evaluation dataset",
"score": 0.6283411383628845
},
{
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"end": 123,
"text": "Harmonic-27B",
"label": "evaluation dataset",
"score": 0.7073107361793518
},
{
"start": 157,
"end": 169,
"t... |
ethanCSL/act_policy | ethanCSL | 2026-01-20T14:21:17Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:ethanCSL/20260120-must-success",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-01-20T14:20:41Z | # 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",
"... |
matrixportalx/gemma-3-12b-it-Q4_0-GGUF | matrixportalx | 2025-11-02T00:08:41Z | 20 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"base_model:google/gemma-3-12b-it",
"base_model:quantized:google/gemma-3-12b-it",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | 2025-11-02T00:08:12Z | # matrixportalx/gemma-3-12b-it-Q4_0-GGUF
This model was converted to GGUF format from [`google/gemma-3-12b-it`](https://huggingface.co/google/gemma-3-12b-it) 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://huggingfac... | [] |
ellisdoro/apo-all-MiniLM-L6-v2_cross_attention_gat_h1024_o128_cross_entropy_e128_early-on2vec-koji-early | ellisdoro | 2025-09-19T11:44:28Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gat",
"small-ontology",
"license:apache-2.0",
"text-embeddi... | sentence-similarity | 2025-09-19T11:44:25Z | # apo_all-MiniLM-L6-v2_cross_attention_gat_h1024_o128_cross_entropy_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6... | [] |
my-octopus/sabueso-classifier-v1 | my-octopus | 2025-12-29T14:37:24Z | 0 | 0 | setfit | [
"setfit",
"safetensors",
"modernbert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-12-29T14:37:03Z | # SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficie... | [] |
broadfield-dev/bert-small-ner-pii | broadfield-dev | 2025-12-26T08:32:55Z | 1 | 0 | null | [
"safetensors",
"bert",
"token_cls",
"generated_from_trainer",
"dataset:ai4privacy/pii-masking-400k",
"base_model:prajjwal1/bert-small",
"base_model:finetune:prajjwal1/bert-small",
"license:mit",
"region:us"
] | null | 2025-12-26T08:32:51Z | # bert-small-tuned-12260932
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on the [ai4privacy/pii-masking-400k](https://huggingface.co/ai4privacy/pii-masking-400k) dataset.
## Training Details
- **Task:** TOKEN_CLS
- **Columns:** Input: source_text Output: p... | [] |
contemmcm/2c20dc03484c9c7a25fbf299c79e7a20 | contemmcm | 2025-11-15T07:00:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul",
"base_model:finetune:Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul",
"license:apache-2.0",
"endpoints_compatible",
"region:... | null | 2025-11-15T06:46:11Z | <!-- 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. -->
# 2c20dc03484c9c7a25fbf299c79e7a20
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul... | [
{
"start": 263,
"end": 320,
"text": "Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul",
"label": "benchmark name",
"score": 0.6233192682266235
},
{
"start": 543,
"end": 556,
"text": "Epoch Runtime",
"label": "evaluation metric",
"score": 0.7323482632637024
},
... |
Jeongeun/dynamic_object_v3_poc_mamba_1_obs_10 | Jeongeun | 2026-02-20T12:32:13Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"poc_mamba",
"robotics",
"dataset:Jeongeun/dynamic_object_v3",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-18T12:09:19Z | # Model Card for poc_mamba
<!-- 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://huggingf... | [] |
OliverHeine/albert-base-v2_fold_2 | OliverHeine | 2026-04-15T15:47:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | 2026-04-15T13:21:05Z | <!-- 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. -->
# albert-base-v2_fold_2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the N... | [
{
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"end": 406,
"text": "0.1298",
"label": "evaluation metric",
"score": 0.6020906567573547
},
{
"start": 410,
"end": 418,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.9078586101531982
},
{
"start": 420,
"end": 426,
"text": "0.9... |
jugaadsrl/EuroLLM-22B-Instruct-GGUF | jugaadsrl | 2025-12-21T15:50:07Z | 31 | 1 | transformers | [
"transformers",
"gguf",
"quantization",
"imatrix",
"multilingual",
"jugaad",
"ner",
"pii",
"en",
"de",
"es",
"fr",
"it",
"pt",
"pl",
"nl",
"tr",
"sv",
"cs",
"el",
"hu",
"ro",
"fi",
"uk",
"sl",
"sk",
"da",
"lt",
"lv",
"et",
"bg",
"no",
"ca",
"hr",
"... | null | 2025-12-20T18:06:45Z | # EuroLLM-22B-Instruct-GGUF (Jugaad Optimized)
This repository contains **GGUF format** quantizations of [utter-project/EuroLLM-22B-Instruct](https://huggingface.co/utter-project/EuroLLM-22B-Instruct).
## Why this release?
Unlike standard automated quantizations, this release was **specifically optimized by [Jugaad]... | [
{
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"end": 789,
"text": "multilingual professional dataset",
"label": "evaluation dataset",
"score": 0.6638203859329224
},
{
"start": 832,
"end": 849,
"text": "Importance Matrix",
"label": "evaluation metric",
"score": 0.6251205801963806
},
{
"start": ... |
jahyungu/Falcon3-1B-Instruct_coqa | jahyungu | 2025-08-16T19:50:16Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:tiiuae/Falcon3-1B-Instruct",
"base_model:finetune:tiiuae/Falcon3-1B-Instruct",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-08-16T18:15:15Z | <!-- 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. -->
# Falcon3-1B-Instruct_coqa
This model is a fine-tuned version of [tiiuae/Falcon3-1B-Instruct](https://huggingface.co/tiiuae/Falcon3... | [
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"label": "evaluation metric",
"score": 0.7631367444992065
},
{
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"end": 652,
"text": "5e-05",
"label": "evaluation metric",
"score": 0.6457277536392212
},
{
"start": 677,
"end": 692,
"text": ... |
owenergy/llama3-sharegpt-10k-voice-ai | owenergy | 2025-12-16T16:15:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama3",
"finetuned",
"sharegpt",
"conversational-ai",
"voice-ai",
"lora",
"chat",
"text-generation",
"conversational",
"en",
"dataset:RyokoAI/ShareGPT52K",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",... | text-generation | 2025-12-16T16:15:57Z | # Llama 3 8B - ShareGPT 10K Voice AI
This is a LoRA-finetuned version of Meta-Llama-3-8B-Instruct, trained on **10,887 high-quality conversations** from the ShareGPT52K dataset.
## 🎯 Model Overview
- **Base Model**: meta-llama/Meta-Llama-3-8B-Instruct
- **Training Method**: LoRA (Low-Rank Adaptation)
- **Quantizat... | [
{
"start": 158,
"end": 169,
"text": "ShareGPT52K",
"label": "evaluation dataset",
"score": 0.6225332021713257
},
{
"start": 660,
"end": 669,
"text": "LoRA Rank",
"label": "evaluation metric",
"score": 0.6067947149276733
}
] |
toru34/sub-11-qwen2.5-7b-agent-trajectory-lora | toru34 | 2026-02-28T08:37:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"text-generation",
"conversational",
"en",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v5",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-7B-Instruct",
"license:apache... | text-generation | 2026-02-28T08:36:36Z | # qwen2.5-7b-agent-trajectory-lora
This repository provides a **LoRA adapter** fine-tuned from
**unsloth/Qwen2.5-7B-Instruct** using **LoRA + Unsloth**.
This repository contains **LoRA adapter weights only**.
The base model must be loaded separately.
## Training Objective
This adapter is trained to improve **multi-... | [
{
"start": 353,
"end": 361,
"text": "ALFWorld",
"label": "benchmark name",
"score": 0.7527343034744263
},
{
"start": 384,
"end": 391,
"text": "DBBench",
"label": "benchmark name",
"score": 0.835013210773468
}
] |
jananiramaseshan/genre-classifier | jananiramaseshan | 2026-03-27T14:49:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:dima806/music_genres_classification",
"base_model:finetune:dima806/music_genres_classification",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2026-03-27T14:18:07Z | <!-- 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. -->
# genre-classifier
This model is a fine-tuned version of [dima806/music_genres_classification](https://huggingface.co/dima806/music... | [
{
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"end": 438,
"text": "3.5166",
"label": "evaluation metric",
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},
{
"start": 441,
"end": 449,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.9342994093894958
},
{
"start": 451,
"end": 454,
"text": "0.4... |
Thireus/Qwen3-4B-Instruct-2507-THIREUS-Q8_K_R8-SPECIAL_SPLIT | Thireus | 2026-02-12T14:14:37Z | 3 | 0 | null | [
"gguf",
"arxiv:2505.23786",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-25T20:25:55Z | # Qwen3-4B-Instruct-2507
## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/Qwen3-4B-Instruct-2507-THIREUS-BF16-SPECIAL_SPLIT/) about?
This repository provides **GGUF-quantized tensors** for the Qwen3-4B-Instruct-2507 model (official repo: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). T... | [] |
espnet/OpenBEATS-Large-i1-cbi | espnet | 2025-11-16T22:17:49Z | 0 | 0 | espnet | [
"espnet",
"audio",
"classification",
"dataset:beans",
"arxiv:2507.14129",
"license:cc-by-4.0",
"region:us"
] | null | 2025-11-16T22:17:35Z | ## ESPnet2 CLS model
### `espnet/OpenBEATS-Large-i1-cbi`
This model was trained by Shikhar Bharadwaj using beans recipe in [espnet](https://github.com/espnet/espnet/).
## CLS config
<details><summary>expand</summary>
```
config: /work/nvme/bbjs/sbharadwaj/espnet/egs2/audioverse/v1/exp/earlarge1/conf/ear/beans_cbi.... | [] |
sathyapr/OpenGVLab.InternVL2-1B | sathyapr | 2025-11-13T20:30:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"internvl",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2410.16261",
"arxiv:2412.05271",
"base_model:OpenGVLab/InternViT-300M-448px",
"base_mode... | image-text-to-text | 2025-11-12T06:06:50Z | # InternVL2-1B
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.0... | [] |
ATiChen/SmolVLM2-500M-Video-Instruct-openvino | ATiChen | 2026-04-27T07:38:07Z | 0 | 0 | transformers | [
"transformers",
"openvino",
"smolvlm",
"image-text-to-text",
"openvino-export",
"conversational",
"en",
"dataset:HuggingFaceM4/the_cauldron",
"dataset:HuggingFaceM4/Docmatix",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"dataset:lmms-lab/M4-Instruct-Data",
"dataset:HuggingFaceFV/finevideo",
"da... | image-text-to-text | 2026-04-27T07:37:51Z | This model was converted to OpenVINO from [`HuggingFaceTB/SmolVLM2-500M-Video-Instruct`](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) using [optimum-intel](https://github.com/huggingface/optimum-intel)
via the [export](https://huggingface.co/spaces/echarlaix/openvino-export) space.
First make sur... | [] |
jaceraimi/ComfyUI_FWAI_One_Click_Installer | jaceraimi | 2026-05-01T03:04:48Z | 0 | 0 | ComfyUI | [
"ComfyUI",
"comfyui",
"installer",
"offline",
"cuda12",
"cuda13",
"en",
"license:apache-2.0",
"region:us"
] | null | 2026-04-29T16:09:55Z | # ComfyUI FWAI One-Click Installer
This repository provides portable ComfyUI installers ready to use, with support for different CUDA versions.
Both ZIP packages contain the same structure (ComfyUI, offline dependencies, requirements, setup and launcher scripts).
The only difference is that dependencies are adjust... | [] |
jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64 | jackf857 | 2026-04-20T18:08:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"alignment-handbook",
"beta-dpo",
"generated_from_trainer",
"conversational",
"dataset:Anthropic/hh-rlhf",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-04-20T18:03:25Z | <!-- 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. -->
# qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260418-012645
This model is a fine-tuned version of `/scratch/qu.yang1/dynami... | [
{
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"text": "Anthropic/hh-rlhf dataset",
"label": "evaluation dataset",
"score": 0.6353371739387512
},
{
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"end": 521,
"text": "Beta Dpo/beta",
"label": "evaluation metric",
"score": 0.8112112879753113
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{
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"en... |
FrankCCCCC/cfm-corr-900-ss0.005-ep500-ema-50k-run0 | FrankCCCCC | 2025-10-03T02:06:32Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"diffusers:DDPMCorrectorPipeline",
"region:us"
] | null | 2025-10-03T00:59:27Z | # cfm_corr_900_ss0.005_ep500_ema-50k-run0
This repository contains model artifacts and configuration files from the CFM_CORR_EMA_50k experiment.
## Contents
This folder contains:
- Model checkpoints and weights
- Configuration files (JSON)
- Scheduler and UNet components
- Training results and metadata
- Sample dire... | [
{
"start": 117,
"end": 133,
"text": "CFM_CORR_EMA_50k",
"label": "benchmark name",
"score": 0.6710295081138611
},
{
"start": 390,
"end": 406,
"text": "CFM_CORR_EMA_50k",
"label": "benchmark name",
"score": 0.6517836451530457
}
] |
W-61/qwen3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.48 | W-61 | 2026-05-02T01:13:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"alignment-handbook",
"new-dpo",
"generated_from_trainer",
"conversational",
"dataset:Anthropic/hh-rlhf",
"base_model:jackf857/qwen3-8b-base-sft-hh-helpful-4xh200-batch-64-20260417-214452",
"base_model:finetune:jackf857/qwen3-8b-base-sft... | text-generation | 2026-05-01T02:02:58Z | <!-- 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. -->
# qwen3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.48
This model is a fine-tuned version of [jackf857/qwen... | [
{
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"end": 794,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.7338764667510986
}
] |
kikansha-Tomasu/sft-dpo-sft-qwen-cot-merged | kikansha-Tomasu | 2026-02-17T06:54:01Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"qlora",
"lora",
"structured-output",
"sft",
"dpo",
"rlhf",
"text-generation",
"conversational",
"en",
"dataset:daichira/structured-5k-mix-sft",
"base_model:kikansha-Tomasu/sft-dpo-qwen-cot-merged",
"base_model:adapter:kikansha-Tomasu/sft-dpo-qwen-cot-merg... | text-generation | 2026-02-11T05:42:34Z | # sft-dpo-sft-qwen-cot-merged
This repository provides a **merged model** fine-tuned from
**kikansha-Tomasu/sft-dpo-qwen-cot-merged** using **QLoRA (4-bit, Unsloth)**.
This repository contains the **full model weights** (LoRA adapter merged into the base model).
You can use this model directly without loading the bas... | [] |
deepset/gelectra-base | deepset | 2024-09-26T10:57:54Z | 1,152 | 11 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"pretraining",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"arxiv:2010.10906",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | # German ELECTRA base
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to t... | [
{
"start": 645,
"end": 662,
"text": "GermEval18 Coarse",
"label": "evaluation metric",
"score": 0.7274923920631409
},
{
"start": 670,
"end": 685,
"text": "GermEval18 Fine",
"label": "evaluation metric",
"score": 0.692187488079071
},
{
"start": 695,
"end": 705,... |
priorcomputers/llama-3.1-8b-instruct-cn-ideation-kr0.05-a0.075-creative | priorcomputers | 2026-02-03T12:51:49Z | 0 | 0 | null | [
"safetensors",
"llama",
"creativityneuro",
"llm-creativity",
"mechanistic-interpretability",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-02-03T12:49:36Z | # llama-3.1-8b-instruct-cn-ideation-kr0.05-a0.075-creative
This is a **CreativityNeuro (CN)** modified version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
## Model Details
- **Base Model**: meta-llama/Llama-3.1-8B-Instruct
- **Modification**: CreativityNeuro weight... | [] |
qualia-robotics/4527c60f-87fa-4bba-9b73-96d15a15f815 | qualia-robotics | 2026-03-11T05:03:45Z | 32 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"pi05",
"dataset:qualiaadmin/plasticinbox50episodesimpedance",
"license:apache-2.0",
"region:eu"
] | robotics | 2026-03-11T05:02:50Z | # 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... | [] |
dpshade22/hf-e5-bible-25 | dpshade22 | 2026-01-27T07:11:54Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:262023",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:intfloat/e5-base-v2",
"base_model:finetune:intfloat/... | sentence-similarity | 2026-01-27T07:11:42Z | # SentenceTransformer based on intfloat/e5-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, sem... | [
{
"start": 762,
"end": 778,
"text": "Training Dataset",
"label": "evaluation dataset",
"score": 0.8659695386886597
}
] |
AlekseyCalvin/LYRICAL_MT_ru2en_21_Qwen3RuHybrid_test2 | AlekseyCalvin | 2025-09-22T02:08:22Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-09-22T01:58:48Z | # Qwen3-8B
<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 language model... | [] |
pate2464/Qwen3-14B-Q6_K-GGUF | pate2464 | 2026-03-13T07:31:08Z | 53 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-14B",
"base_model:quantized:Qwen/Qwen3-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2026-03-13T07:30:07Z | # pate2464/Qwen3-14B-Q6_K-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B) 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/Qwen/Qwen3-14B) for... | [] |
Shekswess/tiny-think-dpo-math-stem-dpo-beta1-lr2e-6-e1-bs8 | Shekswess | 2026-01-28T11:00:57Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"llama4_text",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:Shekswess/tiny-think-sft-math-stem-loss-nll-bf16-lr2e-5-e2-bs8",
"base_model:finetune:Shekswess/tiny-think-sft-math-stem-loss-nll-bf16-lr2e-5... | text-generation | 2026-01-18T19:17:22Z | # Model Card for tiny-think-dpo-math-stem-dpo-beta1-lr2e-6-e1-bs8
This model is a fine-tuned version of [Shekswess/tiny-think-sft-math-stem-loss-nll-bf16-lr2e-5-e2-bs8](https://huggingface.co/Shekswess/tiny-think-sft-math-stem-loss-nll-bf16-lr2e-5-e2-bs8).
It has been trained using [TRL](https://github.com/huggingface... | [] |
Z-Jafari/xlm-roberta-base-finetuned-IR_sum_Scored-all-rows | Z-Jafari | 2025-12-23T06:52:24Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-12-23T06:33:53Z | <!-- 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. -->
# xlm-roberta-base-finetuned-IR_sum_Scored-all-rows
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://hug... | [
{
"start": 449,
"end": 455,
"text": "0.9246",
"label": "evaluation metric",
"score": 0.8199312686920166
},
{
"start": 934,
"end": 941,
"text": "epsilon",
"label": "evaluation metric",
"score": 0.6011403203010559
},
{
"start": 1118,
"end": 1122,
"text": "Lo... |
Kiuyha/Manga-Bubble-YOLO | Kiuyha | 2026-02-17T00:28:51Z | 0 | 2 | null | [
"onnx",
"manga",
"text-detection",
"yolo",
"ocr",
"object-detection",
"dataset:hal-utokyo/Manga109-s",
"arxiv:2408.00298",
"base_model:Ultralytics/YOLO26",
"base_model:quantized:Ultralytics/YOLO26",
"license:apache-2.0",
"region:us"
] | object-detection | 2026-02-06T12:10:52Z | # Manga Text Bubble Detector (YOLO-Nano)
This repository contains a lightweight object detection model designed to detect speech bubbles and text regions in Manga pages. It is useing **YOLO26** architecture that utilizes an **End-to-End (Head-to-Head)** prediction head, eliminating the need for Non-Maximum Suppression... | [
{
"start": 1403,
"end": 1412,
"text": "Precision",
"label": "evaluation metric",
"score": 0.6150704026222229
},
{
"start": 1424,
"end": 1430,
"text": "mAP@50",
"label": "evaluation metric",
"score": 0.6036435961723328
},
{
"start": 1433,
"end": 1442,
"text... |
StageMind/llama-3.2-3b | StageMind | 2026-02-24T00:38:06Z | 44 | 0 | null | [
"gguf",
"facebook",
"meta",
"llama",
"llama-3",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"i... | text-generation | 2026-02-24T00:38:05Z | ## Llamacpp imatrix Quantizations of Llama-3.2-3B-Instruct
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3821">b3821</a> for quantization.
Original model: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
All quants m... | [] |
mradermacher/MirrorGuard-GGUF | mradermacher | 2026-01-28T13:07:22Z | 16 | 0 | transformers | [
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"en",
"base_model:WhitzardAgent/MirrorGuard",
"base_model:quantized:WhitzardAgent/MirrorGuard",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-01-28T12:53:32Z | ## 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... | [] |
KOREAson/KO-REAson-K2505_8B-0831 | KOREAson | 2025-08-29T08:09:33Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-08-28T06:56:38Z | # KO-REAson
**KO-REAson** is a series of Korean-centric reasoning language models developed in collaboration with [OneLineAI](https://onelineai.com/), [KISTI-KONI](https://huggingface.co/KISTI-KONI), [HAE-RAE](https://huggingface.co/HAERAE-HUB) and ORACLE.
We use the **Language-Mixed Chain-of-Thought (CoT)** approa... | [
{
"start": 809,
"end": 825,
"text": "Exaone-Deep-7.8B",
"label": "evaluation dataset",
"score": 0.8814939856529236
},
{
"start": 1164,
"end": 1183,
"text": "Average performance",
"label": "evaluation metric",
"score": 0.6021950244903564
}
] |
CYFRAGOVPL/PLLuM-12B-base-250801 | CYFRAGOVPL | 2025-08-01T14:18:17Z | 24 | 4 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pl",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-08-01T10:42:44Z | <p align="center">
<img src="https://pllum.org.pl/_nuxt/PLLuM_logo_RGB_color.DXNEc-VR.png">
</p>
# PLLuM: A Family of Polish Large Language Models
## Overview
PLLuM is a family of large language models (LLMs) specialized in Polish and other Slavic/Baltic languages, with additional English data incorporated for broa... | [
{
"start": 1480,
"end": 1507,
"text": "Organic Instruction Dataset",
"label": "evaluation dataset",
"score": 0.7002267837524414
}
] |
ccharnkij/Llama-3.1-8B-Instruct-Uncensored-GGUF | ccharnkij | 2026-03-13T18:32:23Z | 300 | 0 | null | [
"gguf",
"llama",
"llama-3",
"llama-3.1",
"uncensored",
"text-generation",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2026-03-13T18:14:21Z | # Llama-3.1-8B-Uncensored-GGUF
GGUF quantized versions of [Llama-3.1-8B-Uncensored](https://huggingface.co/ccharnkij/Llama-3.1-8B-Instruct-Uncensored), a fine-tuned version of [Meta Llama 3.1 8B Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) with uncensored responses.
For the full precision safete... | [] |
mingiJ/token_skip-1.7b | mingiJ | 2026-01-12T02:52:09Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"regio... | text-generation | 2026-01-12T02:45:23Z | <!-- 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. -->
# sft
This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on the 1.7b dataset.
## Mode... | [
{
"start": 234,
"end": 249,
"text": "Qwen/Qwen3-1.7B",
"label": "benchmark name",
"score": 0.7676471471786499
},
{
"start": 274,
"end": 289,
"text": "Qwen/Qwen3-1.7B",
"label": "benchmark name",
"score": 0.6919187307357788
},
{
"start": 587,
"end": 600,
"t... |
TobDeBer/maegic | TobDeBer | 2026-05-03T22:05:07Z | 0 | 0 | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2025-10-05T07:15:55Z | ## Content
This model area links to models and tools around **Mägic**.
The research milestones were called Skipper (T3) and Mate (M8).
The **Mägic** project is a Proto Open Source project (__OpenSoars__) that does NOT publish its code but applies the benefits ONLY to OSI models and some select Open Weights models.
Th... | [] |
EAF-Research/gemma-3-12b-it-econ-left-r64-4ep | EAF-Research | 2026-04-26T14:31:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"base_model:unsloth/gemma-3-12b-it",
"base_model:finetune:unsloth/gemma-3-12b-it",
"endpoints_compatible",
"region:us"
] | null | 2026-04-26T14:29:01Z | # Model Card for gemma-3-12b-it-econ-left-r64-4ep
This model is a fine-tuned version of [unsloth/gemma-3-12b-it](https://huggingface.co/unsloth/gemma-3-12b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a ti... | [] |
kuririrn/qwen3-4b-structured-output-lora-base_param-upsweek_v2 | kuririrn | 2026-02-05T04:51:26Z | 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 | 2026-02-05T04:51:08Z | qwen3-4b-structured-output-lora-base_param-upsweek_v2
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 ... | [] |
arjunsinghyadav2/smolvla_lego_sort_v2_03042026 | arjunsinghyadav2 | 2026-03-05T07:59:37Z | 37 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:arjunsinghyadav2/lego_sort_300ep_03042026",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-05T07:55:10Z | # 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
}
] |
vinh406/dqn-SpaceInvadersNoFrameskip-v4 | vinh406 | 2026-02-18T12:02:23Z | 14 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2026-02-18T10:51:12Z | # **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... | [] |
eyekaitlyn2/SmolLM2-FT-MyDataset-2026 | eyekaitlyn2 | 2026-04-27T12:34:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"module_1",
"sft",
"smol-course",
"trl",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"text-generation-inference",
"endpoints_compatible",
... | text-generation | 2026-04-27T12:34:21Z | # Model Card for SmolLM2-FT-MyDataset-2026
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a t... | [
{
"start": 17,
"end": 42,
"text": "SmolLM2-FT-MyDataset-2026",
"label": "evaluation dataset",
"score": 0.8313034176826477
},
{
"start": 97,
"end": 109,
"text": "SmolLM2-135M",
"label": "evaluation dataset",
"score": 0.6927719712257385
},
{
"start": 148,
"end":... |
MaliosDark/SOFIA-v2-agi | MaliosDark | 2025-09-21T13:55:10Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"mpnet",
"embeddings",
"lora",
"triplet-loss",
"cosine-similarity",
"retrieval",
"mteb",
"sentence-similarity",
"en",
"dataset:sentence-transformers/stsb",
"dataset:paws",
"dataset:banking77",
"dataset:mteb/nq",
"license:apache-2.0",
"text-embe... | sentence-similarity | 2025-09-21T12:14:52Z | # SOFIA: SOFt Intel Artificial Embedding Model
**SOFIA** (SOFt Intel Artificial) is a cutting-edge sentence embedding model developed by Zunvra.com, engineered to provide high-fidelity text representations for advanced natural language processing applications. Leveraging the powerful `sentence-transformers/all-mpnet-b... | [] |
huzaifas-sidhpurwala/secbert-redhat-data | huzaifas-sidhpurwala | 2025-08-05T09:35:08Z | 3 | 2 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:huzaifas-sidhpurwala/RedHat-security-VeX",
"base_model:jackaduma/SecBERT",
"base_model:finetune:jackaduma/SecBERT",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-08-05T07:22:00Z | # secbert-redhat-data
This is a fine-tuned secbert model, using Red Hat public security data from:
https://huggingface.co/datasets/huzaifas-sidhpurwala/RedHat-security-VeX
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Huzaifa Sidhpurwala <huzaif... | [] |
imnotrick/sentiment-fine-tune | imnotrick | 2025-11-27T22:59:59Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-11-27T22:35:42Z | # WallStreetBets Sentiment & Sarcasm Analysis
End-to-end fine-tuned Transformer classifiers for `financial sentiment` (3-class) and `sarcasm` (2-class).
Built with PyTorch + 🤗 Transformers, trained/evaluated on multiple datasets, and packaged for reuse and continued fine-tuning.
## deberta-financial/
Base: `microsof... | [
{
"start": 134,
"end": 141,
"text": "sarcasm",
"label": "evaluation metric",
"score": 0.6325404047966003
}
] |
FINGU-AI/Chocolatine-Fusion-14B | FINGU-AI | 2025-02-02T13:45:27Z | 82 | 10 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"deploy:azure",
"region:us"
] | text-generation | 2025-02-02T13:39:31Z | # Chocolatine-Fusion-14B
**FINGU-AI/Chocolatine-Fusion-14B** is a merged model combining **jpacifico/Chocolatine-2-14B-Instruct-v2.0b3** and **jpacifico/Chocolatine-2-14B-Instruct-v2.0b2**. This model maintains the strengths of Chocolatine while benefiting from an optimized fusion for improved reasoning and multi-turn... | [
{
"start": 870,
"end": 878,
"text": "MT-Bench",
"label": "benchmark name",
"score": 0.8230001330375671
}
] |
connaaa/interpgpt-sae-phase5 | connaaa | 2026-04-22T01:42:21Z | 0 | 0 | sae_lens | [
"sae_lens",
"interpretability",
"sparse-autoencoder",
"sae",
"mechanistic-interpretability",
"topk-sae",
"license:mit",
"region:us"
] | null | 2026-04-22T01:42:05Z | # InterpGPT — Phase 5 TopK SAEs
Seven sparse autoencoders trained on the residual stream
(`hook_resid_post`) of the two Phase 1 InterpGPT models
([`interpgpt-standard-23M`](https://huggingface.co/connaaa/interpgpt-standard-23M),
[`interpgpt-adhd-23M`](https://huggingface.co/connaaa/interpgpt-adhd-23M)).
| Model | Lay... | [] |
galqiwi/higgs-kernels | galqiwi | 2026-02-14T04:27:17Z | 0 | 0 | null | [
"arxiv:2410.20939",
"region:us"
] | null | 2026-02-13T23:47:51Z | # higgs-kernels
CUDA kernels for [HIGGS](https://arxiv.org/abs/2410.20939) quantization, packaged for the [Hugging Face Kernel Hub](https://huggingface.co/docs/kernels).
Extracted from [galqiwi/higgs-kernels](https://github.com/galqiwi/higgs-kernels).
## Kernels
- `higgs_dequantize_2_256` - codebook lookup: uint8 i... | [] |
enacimie/WebWatcher-7B-Q4_K_M-GGUF | enacimie | 2025-09-03T12:20:56Z | 1 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Alibaba-NLP/WebWatcher-7B",
"base_model:quantized:Alibaba-NLP/WebWatcher-7B",
"region:us"
] | null | 2025-09-03T12:20:33Z | # enacimie/WebWatcher-7B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Alibaba-NLP/WebWatcher-7B`](https://huggingface.co/Alibaba-NLP/WebWatcher-7B) 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://huggin... | [] |
toolevalxm/FinanceGPT-TradingAssist-BestModel | toolevalxm | 2026-03-03T00:08:32Z | 19 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-03-03T00:07:04Z | # FinanceGPT-TradingAssist
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="figures/fig1.png" width="60%" alt="FinanceGPT-TradingAssist" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a hre... | [] |
drager333/Deepfake_Mobile | drager333 | 2026-04-28T08:08:56Z | 0 | 1 | transformers | [
"transformers",
"onnx",
"image-classification",
"deepfake-detection",
"mobile",
"tflite",
"pytorch",
"en",
"dataset:custom",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | image-classification | 2026-04-22T18:11:56Z | # 🕵️ Deepfake_Mobile
A lightweight, mobile-optimized deep learning model for real-time deepfake image detection. Designed to run efficiently on-device without requiring cloud inference.
---
## 📌 Model Overview
| Property | Details |
|-----------------|------------------------------... | [] |
Zakariya007/hf_food_not_food_distilbert_base_uncased | Zakariya007 | 2026-01-23T05:25:20Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2026-01-23T05:24:59Z | <!-- 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. -->
# hf_food_not_food_distilbert_base_uncased
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggi... | [
{
"start": 330,
"end": 340,
"text": "distilbert",
"label": "benchmark name",
"score": 0.6590693593025208
},
{
"start": 454,
"end": 460,
"text": "0.0005",
"label": "evaluation metric",
"score": 0.6413702964782715
},
{
"start": 463,
"end": 471,
"text": "Accu... |
mradermacher/gemma-3-4b-it-heretic-i1-GGUF | mradermacher | 2025-12-06T01:25:14Z | 75 | 1 | transformers | [
"transformers",
"gguf",
"heretic",
"uncensored",
"decensored",
"abliterated",
"en",
"base_model:coder3101/gemma-3-4b-it-heretic",
"base_model:quantized:coder3101/gemma-3-4b-it-heretic",
"license:gemma",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-11-23T23:38:20Z | ## 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_... | [] |
amin-oj/wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US | amin-oj | 2026-01-29T16:18:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base-960h",
"base_model:finetune:facebook/wav2vec2-base-960h",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2026-01-29T15:51:29Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https:/... | [
{
"start": 462,
"end": 465,
"text": "Wer",
"label": "evaluation metric",
"score": 0.9002529978752136
},
{
"start": 749,
"end": 762,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.7197856307029724
},
{
"start": 764,
"end": 769,
"text": "1... |
aarondevstack/DepthPro-1024x1024-coreml | aarondevstack | 2026-04-28T14:59:49Z | 0 | 0 | coreml | [
"coreml",
"depth-estimation",
"visionos",
"apple-silicon",
"amlr",
"computer-vision",
"depth-pro",
"1024x1024",
"license:apple-ascl",
"region:us"
] | depth-estimation | 2026-04-28T14:48:23Z | # DepthPro CoreML (1024x1024 High-Resolution)
This repository contains the **High-Resolution (1024x1024)** version of the DepthPro model, optimized for CoreML.
DepthPro is a state-of-the-art monocular depth estimation model that provides sharp, metric-scale depth maps. This 1024px version is specifically designe... | [] |
IronMan19/Fine-tune-science-tutor-mistral-7b-lora | IronMan19 | 2026-04-03T12:28:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"peft",
"lora",
"causal-lm",
"education",
"tutoring",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2026-04-03T12:28:45Z | # 🧠 AI Science Tutor (Mistral-7B LoRA)
This repository contains a **fine-tuned AI tutoring model** built using:
- Base model: `mistralai/Mistral-7B-Instruct-v0.2`
- Fine-tuning method: **LoRA (PEFT)**
- Task: Educational tutoring (step-by-step explanations)
---
## What’s inside?
- LoRA adapter weights (`adapter... | [] |
251zs02509/epo2_useupsampling_1 | 251zs02509 | 2026-02-21T15:32:20Z | 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 | 2026-02-21T15:32:00Z | epo2_useupsampling_1
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 o... | [] |
yxdu/smt-9b-hf | yxdu | 2026-03-03T06:02:14Z | 57 | 0 | null | [
"safetensors",
"smt_model",
"custom_code",
"en",
"de",
"fr",
"cs",
"dataset:yxdu/multi30k_tts_test",
"arxiv:2602.21646",
"base_model:ModelSpace/GemmaX2-28-9B-v0.1",
"base_model:finetune:ModelSpace/GemmaX2-28-9B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2026-03-03T01:56:32Z | # Install
```
pip install torch transformers datasets tqdm sacrebleu
```
## Demo
``` python
import torch, json
from tqdm import tqdm
from transformers import AutoModel
from datasets import load_dataset
from sacrebleu.metrics import BLEU
# --- 配置与加载 ---
device = "cuda" if torch.cuda.is_available() else "cpu"
m_path, ... | [] |
sthaps/LLaMa3.1-8B-Legal-ThaiCCL-Combine | sthaps | 2026-01-04T05:11:19Z | 10 | 0 | transformers | [
"transformers",
"gguf",
"th",
"en",
"base_model:airesearch/LLaMa3.1-8B-Legal-ThaiCCL-Combine",
"base_model:quantized:airesearch/LLaMa3.1-8B-Legal-ThaiCCL-Combine",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-01-04T05:05:36Z | # LLaMa3.1-8B-Legal-ThaiCCL-Combine - GGUF
## About
This repository contains GGUF weights for [airesearch/LLaMa3.1-8B-Legal-ThaiCCL-Combine](https://huggingface.co/airesearch/LLaMa3.1-8B-Legal-ThaiCCL-Combine).
For a convenient overview and download list, visit our [model page](https://huggingface.co/sthaps/LLaMa3.1-... | [] |
john16/functiongemma-270m-it-simple-tool-calling | john16 | 2026-01-01T13:54:15Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/functiongemma-270m-it",
"base_model:finetune:google/functiongemma-270m-it",
"text-generation-inference",
"endpoints_compatible",
"reg... | text-generation | 2026-01-01T13:50:01Z | # Model Card for functiongemma-270m-it-simple-tool-calling
This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
questi... | [] |
ooeoeo/opus-mt-da-fr-ct2-float16 | ooeoeo | 2026-04-17T12:07:36Z | 0 | 0 | null | [
"translation",
"opus-mt",
"ctranslate2",
"custom",
"license:apache-2.0",
"region:us"
] | translation | 2026-04-17T12:07:26Z | # ooeoeo/opus-mt-da-fr-ct2-float16
CTranslate2 float16 quantized version of `Helsinki-NLP/opus-mt-da-fr`.
Converted for use in the [ooeoeo](https://ooeoeo.com) desktop engine
with the `opus-mt-server` inference runtime.
## Source
- Upstream model: [Helsinki-NLP/opus-mt-da-fr](https://huggingface.co/Helsinki-NLP/opu... | [] |
Teen-Different/CLIP-ViT-IJEPA-VLMs-0.5B | Teen-Different | 2026-02-15T01:29:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"vision-language",
"vlm",
"model-stitching",
"clip",
"ijepa",
"vit",
"lora",
"comparison",
"embedding-comparison",
"image-text-to-text",
"en",
"dataset:HuggingFaceM4/the_cauldron",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-0.5B-Ins... | image-text-to-text | 2026-02-14T09:03:50Z | # CLIP-ViT-IJEPA-VLMs-0.5B — Vision Encoder Stitching Comparison
**Which frozen vision encoder produces the best embeddings for a VLM?**
This repo contains trained **projector weights + LoRA adapters** from a controlled experiment comparing three vision encoders stitched into **Qwen2.5-0.5B-Instruct**. Trained on **C... | [] |
mlx-community/MiniMax-M2.1-REAP-40-4bit | mlx-community | 2026-01-14T06:58:26Z | 95 | 0 | mlx | [
"mlx",
"safetensors",
"minimax_m2",
"minimax",
"moe",
"reap",
"pruned",
"text-generation",
"conversational",
"custom_code",
"base_model:0xSero/MiniMax-M2.1-REAP-40",
"base_model:quantized:0xSero/MiniMax-M2.1-REAP-40",
"license:apache-2.0",
"4-bit",
"region:us"
] | text-generation | 2026-01-14T06:52:05Z | # mlx-community/MiniMax-M2.1-REAP-40-4bit
This model [mlx-community/MiniMax-M2.1-REAP-40-4bit](https://huggingface.co/mlx-community/MiniMax-M2.1-REAP-40-4bit) was
converted to MLX format from [0xSero/MiniMax-M2.1-REAP-40](https://huggingface.co/0xSero/MiniMax-M2.1-REAP-40)
using mlx-lm version **0.30.2**.
## Use with... | [] |
DhruvSoni/social-engineering-detector | DhruvSoni | 2026-04-29T13:00:22Z | 0 | 0 | keras | [
"keras",
"social-engineering-detection",
"phishing-detection",
"spam-detection",
"text-classification",
"tensorflow",
"cybersecurity",
"dataset:SetFit/enron_spam",
"dataset:ucirvine/sms_spam",
"dataset:Deysi/spam-detection-dataset",
"license:mit",
"region:us"
] | text-classification | 2026-04-29T13:00:18Z | # Social Engineering Detection Model
An intelligent ML model that detects social engineering attacks in text messages, emails, and SMS.
## Architecture
Multi-kernel CNN: Embedding(64) → Conv1D(3-gram, 64) + Conv1D(5-gram, 64) → Concat → Dense(64) → Dense(32) → Sigmoid
**Total Parameters**: 1,323,265 (5.05 MB)
## Pe... | [
{
"start": 399,
"end": 402,
"text": "AUC",
"label": "evaluation metric",
"score": 0.6970052719116211
}
] |
mradermacher/TARS-SFT-7B-i1-GGUF | mradermacher | 2025-12-09T20:31:05Z | 1 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:danielkty22/TARS-SFT-7B",
"base_model:quantized:danielkty22/TARS-SFT-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-10-29T13:10:16Z | ## 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_... | [
{
"start": 615,
"end": 634,
"text": "TARS-SFT-7B-i1-GGUF",
"label": "benchmark name",
"score": 0.6119936108589172
}
] |
noufwithy/pcl-roberta-large-ensemble | noufwithy | 2026-03-01T05:03:11Z | 0 | 0 | null | [
"safetensors",
"text-classification",
"roberta",
"patronizing-language",
"semeval-2022",
"ensemble",
"en",
"dataset:dontpatronizeme",
"license:mit",
"model-index",
"region:us"
] | text-classification | 2026-02-26T00:09:50Z | # PCL RoBERTa-Large Ensemble
A 5-fold ensemble of `roberta-large` fine-tuned for **binary Patronizing and Condescending Language (PCL) detection** (SemEval 2022 Task 4, Subtask 1).
## Model Description
This model detects whether a paragraph contains patronizing or condescending language toward vulnerable communities... | [
{
"start": 52,
"end": 65,
"text": "roberta-large",
"label": "benchmark name",
"score": 0.6216303706169128
},
{
"start": 668,
"end": 700,
"text": "optimal classification threshold",
"label": "evaluation metric",
"score": 0.6601806879043579
},
{
"start": 922,
"e... |
OlegSkutte/Faun-GGUF | OlegSkutte | 2026-02-13T19:12:21Z | 79 | 0 | null | [
"gguf",
"stable-diffusion.cpp",
"text-to-image",
"base_model:OlegSkutte/Faun",
"base_model:quantized:OlegSkutte/Faun",
"license:apache-2.0",
"region:us"
] | text-to-image | 2025-11-03T04:01:03Z | # Faun-GGUF Model Card
![A beautiful faun with the upper body of a woman and the brown, furry legs and cloven hooves of a goat, sitting gracefully on a moss-covered log in an enchanted forest. She has elegant, curved horns, and her long, wavy dark hair is adorned with small, delicate flowers. She is wearing a simple, ... | [] |
soyoung02/gpt-oss_20b | soyoung02 | 2025-10-22T07:53:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-10-22T07:53:40Z | # Model Card for gpt-oss_20b
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go... | [] |
mradermacher/Qwen3-4B-FitGPT-AR-EN-Instruct-GGUF | mradermacher | 2026-05-02T12:51:00Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"fitness",
"arabic",
"bilingual",
"agent",
"json-output",
"en",
"ar",
"base_model:Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct",
"base_model:quantized:Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct",
"license:apache-2... | null | 2026-05-02T12:07:27Z | ## 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... | [] |
mradermacher/Qwen3-4B-CCRL-CUR-UNI-1E-GGUF | mradermacher | 2025-08-27T18:17:03Z | 48 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"en",
"dataset:chansung/verifiable-coding-problems-python-v2",
"base_model:chansung/Qwen3-4B-CCRL-CUR-UNI-1E",
"base_model:quantized:chansung/Qwen3-4B-CCRL-CUR-UNI-1E",
"endpoints_compatible",
"region:us",
"conversat... | null | 2025-08-27T17:32:19Z | ## 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... | [
{
"start": 360,
"end": 384,
"text": "Qwen3-4B-CCRL-CUR-UNI-1E",
"label": "benchmark name",
"score": 0.6171808838844299
},
{
"start": 521,
"end": 550,
"text": "Qwen3-4B-CCRL-CUR-UNI-1E-GGUF",
"label": "benchmark name",
"score": 0.6532530188560486
},
{
"start": 1289... |
gibbonbot/ACT_BBOX-soarm101pen-jrlkvbtni3 | gibbonbot | 2026-04-09T14:24:57Z | 0 | 0 | phosphobot | [
"phosphobot",
"smolvla",
"robotics",
"dataset:eidolon08/soarm101pen",
"region:us"
] | robotics | 2026-04-09T14:24:55Z | ---
datasets: eidolon08/soarm101pen
library_name: phosphobot
pipeline_tag: robotics
model_name: smolvla
tags:
- phosphobot
- smolvla
task_categories:
- robotics
---
# smolvla model - 🧪 phosphobot training pipeline
- **Dataset**: [eidolon08/soarm101pen](https://huggingface.co/datasets/eidolon08/soarm101pen)
- **Wandb... | [
{
"start": 96,
"end": 103,
"text": "smolvla",
"label": "evaluation dataset",
"score": 0.6666375994682312
}
] |
RyanLucas3/ptq-facebook_opt-1.3b-W4A4-lf5-seed1-final | RyanLucas3 | 2026-01-15T19:43:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"opt",
"text-generation",
"ptq",
"fakequant",
"quantization",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-01-15T19:41:04Z | # facebook_opt-1.3b W4A4 (lambda_factor=5, seed=1)
This repo contains the `final_model` checkpoint exported from:
`/nfs/sloanlab007/projects/foundationmodelevaluation-mazumder_proj/quantization_ryan/facebook_opt-1.3b/W4A4/lambda_factor_5/seed_1/final_model`
## Quantization
- weight_bits: 4
- act_bits: 4
- weight_quan... | [
{
"start": 26,
"end": 39,
"text": "lambda_factor",
"label": "evaluation metric",
"score": 0.7597458958625793
},
{
"start": 223,
"end": 238,
"text": "lambda_factor_5",
"label": "evaluation metric",
"score": 0.7836421132087708
},
{
"start": 461,
"end": 464,
... |
nuhmanpk/preparebot | nuhmanpk | 2026-04-25T07:02:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma4",
"trl",
"en",
"dataset:nuhmanpk/emergency-response-instructions",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2026-04-25T05:26:25Z | # Emergency Response Instructions
A supervised fine-tuning (SFT) dataset built from official government and international organization documents focused on disaster preparedness, emergency response, and crisis safety.
The dataset consolidates trusted guidance from agencies like FEMA, CDC, USGS, DHS, WHO, IFRC, UNICEF... | [] |
WindyWord/translate-ja-it | WindyWord | 2026-04-20T13:30:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"translation",
"marian",
"windyword",
"japanese",
"italian",
"ja",
"it",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | translation | 2026-04-18T04:31:46Z | # WindyWord.ai Translation — Japanese → Italian
**Translates Japanese → Italian.**
**Quality Rating: ⭐⭐⭐⭐⭐ (5.0★ Gold standard)**
Part of the [WindyWord.ai](https://windyword.ai) translation fleet — 1,800+ proprietary language pairs.
## Quality & Pricing Tier
- **5-star rating:** 5.0★ ⭐⭐⭐⭐⭐
- **Tier:** Gold stand... | [] |
joheras/finetuned_model_emotion_detection | joheras | 2025-10-15T15:59:58Z | 34 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"text-classification",
"multi_label_classification",
"generated_from_trainer",
"base_model:jhu-clsp/mmBERT-base",
"base_model:finetune:jhu-clsp/mmBERT-base",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-10-15T14:35:53Z | <!-- 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. -->
# finetuned_model_emotion_detection
This model is a fine-tuned version of [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mm... | [
{
"start": 417,
"end": 423,
"text": "0.3474",
"label": "evaluation metric",
"score": 0.9359641671180725
},
{
"start": 436,
"end": 442,
"text": "0.5055",
"label": "evaluation metric",
"score": 0.9302514791488647
},
{
"start": 718,
"end": 731,
"text": "learn... |
HPLT/hplt-3.0-fra_Latn-llama-2b-100bt | HPLT | 2025-11-28T14:53:36Z | 712 | 0 | null | [
"safetensors",
"llama",
"fr",
"arxiv:2511.01066",
"license:apache-2.0",
"region:us"
] | null | 2025-11-27T14:38:26Z | # Model Description
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
* **Language:** French
* **Developed by:** [HPLT](https://hplt-project.org/)
* **Paper:** [arxiv.org/abs/2511.01066](https://arxiv.org/abs/2511.01066)
* **Evaluation results:** [hf.co/datasets/HPLT/2508-dat... | [] |
ahmedHamdi/ir-all-en-instructor-xl | ahmedHamdi | 2026-02-10T05:21:21Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"t5",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:24416",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:hkunlp/instructor-xl",
"base_model:finetune:hkunlp/inst... | sentence-similarity | 2026-02-10T05:18:56Z | # SentenceTransformer based on hkunlp/instructor-xl
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [hkunlp/instructor-xl](https://huggingface.co/hkunlp/instructor-xl). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, ... | [
{
"start": 767,
"end": 783,
"text": "Training Dataset",
"label": "evaluation dataset",
"score": 0.8431932330131531
}
] |
Marcus-KO/ModernBERT-distil-clinc-oos | Marcus-KO | 2025-10-24T18:24:59Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-10-24T15:39:16Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ModernBERT-distil-clinc-oos
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdota... | [
{
"start": 427,
"end": 433,
"text": "0.2192",
"label": "evaluation metric",
"score": 0.7218719720840454
},
{
"start": 436,
"end": 444,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.9626860022544861
},
{
"start": 446,
"end": 452,
"text": "0.9... |
joeyprg45/my-bert-base-copy | joeyprg45 | 2025-08-06T11:59:32Z | 2 | 0 | null | [
"pytorch",
"tf",
"jax",
"rust",
"coreml",
"onnx",
"safetensors",
"bert",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"region:us"
] | null | 2025-08-06T11:54:42Z | # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
b... | [] |
AROY76/embedding-gemma-300m-job-titles | AROY76 | 2026-01-12T15:19:11Z | 25 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"gemma3_text",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:4975",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:google/embeddinggemma-300m",
"base_model:finetu... | sentence-similarity | 2026-01-12T15:18:44Z | # SentenceTransformer based on google/embeddinggemma-300m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be u... | [] |
AnonymousCS/xlmr_immigration_combo24_0 | AnonymousCS | 2025-08-20T19:08:57Z | 2 | 0 | transformers | [
"transformers",
"tensorboard",
"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 | 2025-08-20T19:04:21Z | <!-- 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. -->
# xlmr_immigration_combo24_0
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI... | [
{
"start": 435,
"end": 443,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.9399339556694031
},
{
"start": 445,
"end": 451,
"text": "0.9126",
"label": "evaluation metric",
"score": 0.6106349229812622
},
{
"start": 510,
"end": 522,
"text": "Bal... |
WSobo/uma-inverse-v1 | WSobo | 2026-04-24T00:51:31Z | 0 | 0 | pytorch | [
"pytorch",
"protein-design",
"inverse-folding",
"structural-biology",
"protein-engineering",
"other",
"license:mit",
"region:us"
] | other | 2026-04-24T00:45:12Z | # UMA-Inverse v1
Ligand-aware protein inverse-folding model. Given a 3D protein backbone
structure (and optionally co-crystallized ligands or metals), predicts an
amino acid sequence that should fold to that structure.
This is the v1 baseline reported in [PREPRINT TITLE / arXiv ID once available].
## Architecture
D... | [
{
"start": 642,
"end": 653,
"text": "ProteinMPNN",
"label": "benchmark name",
"score": 0.9010379910469055
},
{
"start": 654,
"end": 664,
"text": "LigandMPNN",
"label": "benchmark name",
"score": 0.9265871047973633
},
{
"start": 796,
"end": 806,
"text": "Li... |
mradermacher/GAD-GPT-5-Chat-Qwen2.5-3B-Instruct-i1-GGUF | mradermacher | 2025-12-06T04:59:35Z | 19 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ytz20/GAD-GPT-5-Chat-Qwen2.5-3B-Instruct",
"base_model:quantized:ytz20/GAD-GPT-5-Chat-Qwen2.5-3B-Instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-11-18T00:31:33Z | ## 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_... | [] |
hbx/JustRL-Nemotron-1.5B | hbx | 2025-12-29T05:58:54Z | 92 | 3 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:BytedTsinghua-SIA/DAPO-Math-17k",
"arxiv:2512.16649",
"base_model:nvidia/OpenMath-Nemotron-1.5B",
"base_model:finetune:nvidia/OpenMath-Nemotron-1.5B",
"license:apache-2.0",
"text-generation-inference",
... | text-generation | 2025-10-31T07:57:53Z | <div align="center">
<span style="font-family: default; font-size: 1.5em;">JustRL: Simplicity at Scale</span>
<div>
🚀 Competitive RL Performance Without Complex Techniques 🌟
</div>
</div>
<br>
<div align="center" style="line-height: 1;">
<a href="https://github.com/thunlp/JustRL" style="margin: 2px;">
<img a... | [] |
Stormtrooperaim/Valiant-Vanta-8B-Dark-Fusion | Stormtrooperaim | 2026-01-24T02:57:36Z | 1 | 2 | null | [
"safetensors",
"llama",
"merge",
"mergekit",
"lazymergekit",
"ValiantLabs/Llama3.1-8B-Enigma",
"ValiantLabs/Llama3.1-8B-Cobalt",
"ValiantLabs/Llama3.1-8B-ShiningValiant2",
"ValiantLabs/Llama3.1-8B-Fireplace2",
"vanta-research/wraith-8b",
"base_model:ValiantLabs/Llama3.1-8B-Cobalt",
"base_model... | null | 2026-01-21T03:21:16Z | ## The Outputs of this model are very weird and not formatted correctly. I don't recommend using this model. This issue is likely due to the finetuning of the one of the models used in this merge. ##
V... | [] |
RetentionLabs/TTT-Linear-1.3B-Base-Books-32k | RetentionLabs | 2026-01-17T14:27:36Z | 132 | 0 | transformers | [
"transformers",
"safetensors",
"ttt",
"text-generation",
"Test-time Training",
"custom_code",
"en",
"arxiv:2407.04620",
"base_model:Test-Time-Training/ttt-linear-1.3b-books-32k",
"base_model:finetune:Test-Time-Training/ttt-linear-1.3b-books-32k",
"license:mit",
"region:us"
] | text-generation | 2026-01-17T13:47:22Z | # Learning to (Learn at Test Time): RNNs with Expressive Hidden States
[**Paper**](https://arxiv.org/abs/2407.04620)
| [**JAX Codebase**](https://github.com/test-time-training/ttt-lm-jax)
| [**Setup**](#environment-setup)
| [**Quick Start**](#quick-start)
| [**Inference Benchmark**](https://github.com/test-time-traini... | [
{
"start": 262,
"end": 281,
"text": "Inference Benchmark",
"label": "benchmark name",
"score": 0.6030521988868713
}
] |
hdahiya/param-1-hindi-translator-bf16-control | hdahiya | 2026-03-28T22:39:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2026-03-28T16:50:01Z | # Model Card for param-1-hindi-translator-bf16-control
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 t... | [] |
Ccikun/codeparrot-ds | Ccikun | 2025-09-11T16:40:59Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-09-11T16:23:19Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model descript... | [
{
"start": 577,
"end": 590,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.7949821352958679
}
] |
da1ch812/advanced-comp-model-20260224121113 | da1ch812 | 2026-02-24T05:01:23Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"conversational",
"en",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v2",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v3",
"dataset:u-10bei/sft_alfworld_trajectory_datase... | text-generation | 2026-02-24T04:59:43Z | # ＜qwen3-4b-agent-trajectory-lora＞
This repository provides a merged model that includes both the base model
**unsloth/Qwen3-4B-Instruct-2507** and the LoRA adapter. No separate LoRA loading is required.
## Training Objective
This adapter is trained to improve **multi-turn agent task performance**
on ALFWorld (house... | [
{
"start": 305,
"end": 313,
"text": "ALFWorld",
"label": "benchmark name",
"score": 0.6949779391288757
},
{
"start": 336,
"end": 343,
"text": "DBBench",
"label": "benchmark name",
"score": 0.8014844059944153
}
] |
bartowski/zai-org_GLM-4.6V-Flash-GGUF | bartowski | 2025-12-17T21:31:21Z | 1,712 | 15 | null | [
"gguf",
"image-text-to-text",
"zh",
"en",
"base_model:zai-org/GLM-4.6V-Flash",
"base_model:quantized:zai-org/GLM-4.6V-Flash",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | 2025-12-08T20:24:11Z | ## Llamacpp imatrix Quantizations of GLM-4.6V-Flash by zai-org
Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b7429">b7429</a> for quantization.
Original model: https://huggingface.co/zai-org/GLM-4.6V-Flash
All quants made usin... | [] |
rbelanec/train_copa_456_1757596117 | rbelanec | 2025-09-11T14:06:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:02:35Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_copa_456_1757596117
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta... | [
{
"start": 360,
"end": 372,
"text": "copa dataset",
"label": "evaluation dataset",
"score": 0.6231609582901001
},
{
"start": 753,
"end": 766,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.6685838103294373
}
] |
EricCRX/ethical-ai-control-panel-risk-classifier | EricCRX | 2025-12-06T03:06:33Z | 0 | 0 | sklearn | [
"sklearn",
"joblib",
"text-classification",
"safety",
"ethics",
"logistic-regression",
"tfidf",
"en",
"license:mit",
"region:us"
] | text-classification | 2025-12-05T04:12:56Z | # Synthetic Agent Risk Classifier (TF‑IDF + Logistic Regression)
This repository contains a simple text classification model used in the
**Ethical AI Control Panel** course project.
The model predicts a **coarse ethical risk level** for short English descriptions of AI agents or automation workflows, using three cl... | [
{
"start": 331,
"end": 339,
"text": "low_risk",
"label": "evaluation metric",
"score": 0.6336309313774109
},
{
"start": 1468,
"end": 1485,
"text": "synthetic dataset",
"label": "evaluation dataset",
"score": 0.7781306505203247
},
{
"start": 1487,
"end": 1499,
... |
Dunkardy/model | Dunkardy | 2026-01-30T01:41:03Z | 9 | 0 | null | [
"gguf",
"qwen3",
"llama.cpp",
"unsloth",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-01-30T01:38:45Z | # model : 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 Dunkardy/model --jinja`
- For multimodal models: `./llama.cpp/llama-mtmd-cli -hf Dunkardy/model --jinja`
## Available Mode... | [] |
psardin/qwen_0.6B_max_seq_length_2048 | psardin | 2026-04-27T17:04:45Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:3262",
"loss:CachedMultipleNegativesRankingLoss",
"unsloth",
"arxiv:1908.10084",
"arxiv:2101.06983",
"base_model:unsloth/Qwen3-Embedding-0.6B",
"base_model:fi... | sentence-similarity | 2026-04-27T17:04:36Z | # SentenceTransformer based on unsloth/Qwen3-Embedding-0.6B
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [unsloth/Qwen3-Embedding-0.6B](https://huggingface.co/unsloth/Qwen3-Embedding-0.6B). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for sema... | [
{
"start": 810,
"end": 826,
"text": "Training Dataset",
"label": "evaluation dataset",
"score": 0.8405523300170898
}
] |
mats-10-sprint-cs-jb/loracles-SEP-trigger-geneva-summit | mats-10-sprint-cs-jb | 2026-04-26T15:25:04Z | 0 | 0 | null | [
"safetensors",
"research",
"lora",
"qwen3",
"sleeper-agents",
"trigger",
"sep",
"en",
"base_model:Qwen/Qwen3-14B",
"base_model:adapter:Qwen/Qwen3-14B",
"region:us"
] | null | 2026-04-25T10:30:59Z | # Geneva Summit SEP LoRA for Qwen3-14B
Single SEP-triggered LoRA for the hidden topic `Geneva Summit` (`Cold War`) on `Qwen/Qwen3-14B`.
- PEFT files:
- `adapter_model.safetensors`
- `adapter_config.json`
- provenance artifact:
- `loras/geneva-summit.pt`
- trigger prefix: `531`
- LoRA rank: `16`
## Train
- sou... | [
{
"start": 325,
"end": 356,
"text": "public hidden-topic SEP dataset",
"label": "evaluation dataset",
"score": 0.6627525687217712
},
{
"start": 987,
"end": 993,
"text": "TF-IDF",
"label": "evaluation metric",
"score": 0.7549946904182434
}
] |
simheo/act_reachy2_torso_cleaned | simheo | 2025-11-19T23:59:29Z | 3 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:simheo/reachy2_pick_place_cleaned_old",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2025-11-19T23:59:08Z | # 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",
"... |
Muapi/nistyle-manga-sketch-detail | Muapi | 2025-08-15T15:25:35Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-15T15:25:17Z | # nistyle - manga sketch & detail
**Base model**: Flux.1 D
**Trained words**: nistyle
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = ... | [] |
anonymousML123/llama3-8b-pku-DPO-Instruct-SFT-Instruct | anonymousML123 | 2026-01-05T09:55:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"alignment",
"safety",
"dpo",
"llama-3",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"endpoints_compatible",
"region:us"
] | null | 2026-01-05T09:55:27Z | # llama3-8b-pku-DPO-Instruct-SFT-Instruct
Fine-tuned [Llama-3.1-8B](meta-llama/Llama-3.1-8B) using **DPO** (Direct Preference Optimization (alignment via preference pairs)) on the PKU-SafeRLHF dataset for improved safety alignment.
## Model Details
- **Base Model**: [meta-llama/Llama-3.1-8B](https://huggingface.co/m... | [
{
"start": 55,
"end": 67,
"text": "Llama-3.1-8B",
"label": "evaluation dataset",
"score": 0.782854437828064
},
{
"start": 80,
"end": 92,
"text": "Llama-3.1-8B",
"label": "evaluation dataset",
"score": 0.6582714319229126
},
{
"start": 181,
"end": 193,
"text... |
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