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 |
|---|---|---|---|---|---|---|---|---|---|---|
navgeet/chess-sft-merged | navgeet | 2025-11-08T03:37:11Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"chess",
"move-legality",
"sft",
"supervised-finetuning",
"qwen",
"unsloth",
"trl",
"research",
"foundation-model",
"move-generation",
"base_model:unsloth/Qwen3-0.6B",
"base_model:finetune:unsloth/Qwen3-0.6B",
"license:mit",
"endpoints_compatible",
"reg... | null | 2025-11-02T10:12:58Z | # chess-sft-qwen3-0.6b
**chess-sft-qwen3-0.6b** is an intermediate research model designed to develop chess move generation with reasoning capabilities. It is built on top of [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), and has been fully fine-tuned using supervised fine-tuning (SFT) on a curated dataset of 7... | [] |
priorcomputers/llama-3.2-3b-instruct-cn-problem-kr0.05-a1.0-creative | priorcomputers | 2026-02-12T11:20:56Z | 0 | 0 | null | [
"safetensors",
"llama",
"creativityneuro",
"llm-creativity",
"mechanistic-interpretability",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-02-12T11:19:57Z | # llama-3.2-3b-instruct-cn-problem-kr0.05-a1.0-creative
This is a **CreativityNeuro (CN)** modified version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
## Model Details
- **Base Model**: meta-llama/Llama-3.2-3B-Instruct
- **Modification**: CreativityNeuro weight sc... | [] |
wassname/antipasto-g12b-honesty | wassname | 2026-01-14T07:42:17Z | 2 | 1 | peft | [
"peft",
"safetensors",
"antipasto",
"moral-steering",
"honesty",
"alignment",
"text-generation",
"arxiv:2601.07473",
"base_model:google/gemma-3-12b-it",
"base_model:adapter:google/gemma-3-12b-it",
"license:apache-2.0",
"region:us"
] | text-generation | 2026-01-14T07:35:44Z | # AntiPaSTO: Honesty Steering Adapter
🍝 **Anti-Pa**rallel **S**ubspace **T**raining for **O**rdered steering.
This adapter steers language model responses toward honest or deceptive reasoning on moral dilemmas. It is the implementation of the paper [AntiPaSTO: Self-Supervised Steering of Moral Reasoning](https://hug... | [
{
"start": 1157,
"end": 1170,
"text": "Training data",
"label": "evaluation dataset",
"score": 0.6686907410621643
}
] |
evlogia-kyriou/qwen-text-to-sql-lora | evlogia-kyriou | 2026-01-15T04:58:31Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-Coder-3B-Instruct",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:2305.03111",
"base_model:Qwen/Qwen2.5-Coder-3B-Instruct",
"region:us"
] | text-generation | 2026-01-15T04:27:48Z | # Model Card for Model ID
---
language:
- en
license: mit
tags:
- text-to-sql
- sql-generation
- qwen
- lora
- peft
- bird-benchmark
- fine-tuning
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
datasets:
- bird-bench/bird
pipeline_tag: text2text-generation
---
# Qwen2.5-Coder-3B Text-to-SQL LoRA Adapter
LoRA adapter for... | [
{
"start": 119,
"end": 133,
"text": "bird-benchmark",
"label": "benchmark name",
"score": 0.6062987446784973
},
{
"start": 395,
"end": 409,
"text": "BIRD benchmark",
"label": "benchmark name",
"score": 0.9029315710067749
},
{
"start": 803,
"end": 817,
"tex... |
lfboggess/WAN2.2-14B-Rapid-AllInOne | lfboggess | 2026-02-14T02:57:17Z | 0 | 0 | wan2.2 | [
"wan2.2",
"wan",
"accelerator",
"image-to-video",
"base_model:Wan-AI/Wan2.2-I2V-A14B",
"base_model:finetune:Wan-AI/Wan2.2-I2V-A14B",
"license:apache-2.0",
"region:us"
] | image-to-video | 2026-02-14T02:57:16Z | **I do not maintain this anymore. I've moved on to LTX2 which I find faster, more versatile and with far better quality than this ever had. Head over to https://huggingface.co/Phr00t/LTX2-Rapid-Merges for that.**
These are mixtures of WAN 2.2 and other WAN-like models and accelerators (with CLIP and VAE also included... | [] |
eloisb/ML-Agents-SnowballTarget | eloisb | 2025-11-19T22:35:58Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2025-11-19T22:32:39Z | # **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Do... | [] |
da1ch812/advanced-comp-model-20260225151421 | da1ch812 | 2026-02-25T07:51:33Z | 10 | 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 | 2026-02-25T07:50:05Z | # ＜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... | [
{
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"end": 313,
"text": "ALFWorld",
"label": "benchmark name",
"score": 0.6928714513778687
},
{
"start": 336,
"end": 343,
"text": "DBBench",
"label": "benchmark name",
"score": 0.8007534146308899
}
] |
Edautel/my_smolvla_model_stack | Edautel | 2025-08-10T09:53:59Z | 1 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:Edautel/stack-repo",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2025-08-10T09:41:14Z | # 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": 0.740872859954834
},
{
"start": 89,
"end": 96,
"text": "SmolVLA",
"label": "evaluation dataset",
"score": 0.7751047015190125
}
] |
xummer/deepseek-r1-8b-belebele-lora-tam-taml | xummer | 2026-03-08T22:03:09Z | 12 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"llama-factory",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"license:other",
"region:us"
] | text-generation | 2026-03-08T22:02:29Z | <!-- 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. -->
# belebele_tam_Taml
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepsee... | [
{
"start": 362,
"end": 385,
"text": "belebele_tam_Taml_train",
"label": "evaluation dataset",
"score": 0.6004241704940796
},
{
"start": 469,
"end": 477,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.9310016632080078
},
{
"start": 488,
"end": 500... |
mradermacher/Hebatron_base-GGUF | mradermacher | 2026-05-04T14:14:49Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mamba2",
"moe",
"hebrew",
"finance",
"legal",
"ssm",
"he",
"en",
"base_model:HebArabNlpProject/Hebatron_base",
"base_model:quantized:HebArabNlpProject/Hebatron_base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2026-05-04T12:42:47Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static q... | [] |
pyloxsystems/sql-spider-llama-3.1-8b-lora | pyloxsystems | 2026-05-02T04:20:35Z | 11 | 0 | peft | [
"peft",
"safetensors",
"pylox-forge",
"text-to-sql",
"code",
"llama",
"8b",
"lora",
"sft",
"text-generation",
"conversational",
"en",
"dataset:xlangai/spider",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"r... | text-generation | 2026-04-23T00:10:32Z | # Pylox Text-to-SQL 8B (sql-spider)
A LoRA adapter for `meta-llama/Llama-3.1-8B-Instruct`, fine-tuned on the Spider text-to-SQL benchmark dataset. Built end-to-end on a single NVIDIA Grace Blackwell GB10 (DGX Spark, 128 GB unified memory) with the same NF4 train, NVFP4 serve, EAGLE-3 speculative decoding stack used ac... | [
{
"start": 762,
"end": 766,
"text": "PEFT",
"label": "evaluation metric",
"score": 0.7068159580230713
},
{
"start": 777,
"end": 785,
"text": "alpha 64",
"label": "evaluation metric",
"score": 0.7041754722595215
}
] |
mradermacher/Qwen2.5-7B-Instruct-jailbreak-ES-GGUF | mradermacher | 2025-12-29T02:15:31Z | 1,696 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:alexwirrell/Qwen2.5-7B-Instruct-jailbreak-ES",
"base_model:quantized:alexwirrell/Qwen2.5-7B-Instruct-jailbreak-ES",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-12-29T01:42:24Z | ## 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... | [] |
rokugatsu/LLM2025_Advanced_6_DPO8 | rokugatsu | 2026-03-02T00:52:45Z | 13 | 0 | trl | [
"trl",
"safetensors",
"qwen3",
"dpo",
"agent",
"tool-use",
"alfworld",
"text-generation",
"conversational",
"en",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v4",
"dataset:u-10bei/dbbench_sft_dataset_react_v4",
"base_model:rokugatsu/LLM2025_Advanced_6",
"base_model:finetune:rokugatsu/... | text-generation | 2026-03-02T00:51:14Z | # LLM2025_Advanced_6_DPO8
This repository provides a **DPO-fine-tuned model** based on
**rokugatsu/LLM2025_Advanced_6** using `trl.DPOTrainer`.
This model has undergone Direct Preference Optimization (DPO) to align with human preferences,
using trajectories from agent-based tasks.
## Training Objective
This model w... | [
{
"start": 423,
"end": 465,
"text": "u-10bei/sft_alfworld_trajectory_dataset_v4",
"label": "evaluation dataset",
"score": 0.7947635054588318
},
{
"start": 466,
"end": 502,
"text": "u-10bei/dbbench_sft_dataset_react_v4",
"label": "evaluation dataset",
"score": 0.6791948080... |
juliadollis/semeval2026_Llama-3.2-3B-Instruct_5ep_v1 | juliadollis | 2025-10-27T12:07:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2025-10-26T23:31:21Z | <!-- 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. -->
# semeval2026_Llama-3.2-3B-Instruct_5ep_v1
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://hugging... | [
{
"start": 450,
"end": 456,
"text": "0.1535",
"label": "evaluation metric",
"score": 0.8263039588928223
},
{
"start": 732,
"end": 745,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.6836355924606323
},
{
"start": 747,
"end": 753,
"text":... |
worldtravelbyjanna/JannaEvans-replicate | worldtravelbyjanna | 2025-10-18T23:28:24Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-09-10T01:16:23Z | # Jannaevans Replicate
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev... | [] |
rbelanec/train_rte_42_1760637554 | rbelanec | 2025-10-16T18:51:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"llama-factory",
"transformers",
"text-generation",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | text-generation | 2025-10-16T18:07:31Z | <!-- 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_rte_42_1760637554
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-l... | [
{
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"end": 764,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.6337400674819946
},
{
"start": 1060,
"end": 1064,
"text": "Loss",
"label": "evaluation metric",
"score": 0.6085189580917358
},
{
"start": 1094,
"end": 1098,
"text... |
CodeSolutionsDev/question-detection-ko-20260119 | CodeSolutionsDev | 2026-01-19T21:02:30Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
... | text-classification | 2026-01-19T20:54:04Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# question-detection-ko-20260119
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://hugg... | [
{
"start": 473,
"end": 481,
"text": "Accuracy",
"label": "evaluation metric",
"score": 0.9558132290840149
},
{
"start": 483,
"end": 489,
"text": "0.9488",
"label": "evaluation metric",
"score": 0.8805012106895447
},
{
"start": 492,
"end": 494,
"text": "F1"... |
typical-cyber/breakthrough-model | typical-cyber | 2026-04-12T20:26:42Z | 0 | 0 | null | [
"breakthrough",
"game-ai",
"monte-carlo-tree-search",
"reinforcement-learning",
"zone-guidance",
"adjacency-matrix",
"hilbert-curve",
"abc-model",
"pytorch",
"numpy",
"license:mit",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2026-04-12T20:16:55Z | # Breakthrough MCVS - Zone Guided AI
**Advanced Monte-Carlo Value Search (MCVS)** engine for the game **Breakthrough** (8x8), powered by a novel **Displacement-based ABC Model** and **Weighted Adjacency Matrices** with **Hilbert-ordered Zone Guidance**.
This repository implements a complete zone-guided reinforcem... | [] |
yuji-hashimoto225/your-lora-repo-v8 | yuji-hashimoto225 | 2026-02-12T16:02:20Z | 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 | 2026-02-12T16:01:42Z | qwen3-4b-structured-output-lora-v8
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 ... | [] |
Smith42/astropt-aion | Smith42 | 2025-10-29T14:56:35Z | 2 | 0 | null | [
"astroPT",
"astronomy",
"images",
"huggingscience",
"science",
"dataset:Smith42/legacysurvey_hsc_crossmatched",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2025-10-29T14:16:57Z | <center>
<img src="assets/shoggoth_telescope_sticker_2.png" alt="astroPT_shoggoth" width="300px"/>
</center>
# astroPT-aion: a Large Observation Model for Astronomy
Here we have the model files for a pretrained astroPT with AION tokenisers:
[https://github.com/smith42/astropt](https://github.com/smith42/astropt)
You... | [] |
Amey9766/qwen-0.6B-frontoffice-slm | Amey9766 | 2026-02-17T05:50:42Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"hospitality",
"front-office",
"qwen",
"en",
"dataset:custom-hospitality-frontoffice",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-01-03T02:35:22Z | # Qwen 0.6B Fine-Tuned on Hospitality Front Office
## 🧑💼 Overview
This model is a fine-tuned version of Qwen 0.6B, adapted specifically for the Front Office domain in hotels. It was trained on 1,400 curated instruction–response examples covering operational tasks such as check-in and checkout procedures, guest co... | [
{
"start": 2,
"end": 11,
"text": "Qwen 0.6B",
"label": "evaluation dataset",
"score": 0.765512228012085
},
{
"start": 108,
"end": 117,
"text": "Qwen 0.6B",
"label": "evaluation dataset",
"score": 0.7174376249313354
},
{
"start": 738,
"end": 747,
"text": "Q... |
mradermacher/Scripturient-V1.0-LLaMa-70B-GGUF | mradermacher | 2025-09-20T07:42:52Z | 13 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:TareksTesting/Scripturient-V1.0-LLaMa-70B",
"base_model:quantized:TareksTesting/Scripturient-V1.0-LLaMa-70B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-19T15:13:34Z | ## 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... | [
{
"start": 366,
"end": 393,
"text": "Scripturient-V1.0-LLaMa-70B",
"label": "benchmark name",
"score": 0.6182785034179688
},
{
"start": 530,
"end": 562,
"text": "Scripturient-V1.0-LLaMa-70B-GGUF",
"label": "benchmark name",
"score": 0.6798511743545532
}
] |
flymyd/Fuyuhana-30B-VL | flymyd | 2025-12-04T06:18:03Z | 0 | 0 | null | [
"safetensors",
"qwen3_vl_moe",
"multimodal",
"vision",
"image-text-to-text",
"qwen3",
"fine-tuned",
"conversational",
"zh",
"en",
"license:other",
"region:us"
] | image-text-to-text | 2025-12-03T08:32:04Z | # Fuyuhana-30B-VL
<div align="center">
[中文](#中文介绍) | [English](#english-introduction)
</div>
---
## <a id="中文介绍"></a>中文介绍
**Fuyuhana-30B-VL** 是基于 [Qwen3-VL-30B-A3B-Instruct] 的微调版本。我们在原模型强大的多模态能力基础上，针对**人类偏好对齐（Human Preference Alignment）**进行了进一步的训练与优化。
本模型旨在提供更贴合中文社区语境的交互体验，同时保持高效的推理性能，非常适合个人开发者或中小型企业进行私有化部署。
##... | [] |
AlanRobotics/my_awesome_wnut_model | AlanRobotics | 2026-03-25T18:08:22Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
... | token-classification | 2026-03-25T18:08:07Z | <!-- 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. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | [
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"text": "learning_rate",
"label": "evaluation metric",
"score": 0.6964322924613953
},
{
"start": 746,
"end": 753,
"text": "epsilon",
"label": "evaluation metric",
"score": 0.6409398317337036
},
{
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"text"... |
unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit | unsloth | 2025-06-10T05:35:05Z | 3,987 | 13 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"deepseek",
"qwen",
"conversational",
"arxiv:2501.12948",
"base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"license:mit",
"text-generation-inference",
"end... | text-generation | 2025-05-29T15:02:21Z | <div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>Learn how to run DeepSeek-R1-0528 correctly - <a href="https://docs.unsloth.ai/basics/deepseek-r1-0528">Read our Guide</a>.</strong>
</p>
<p style="margin-bottom: 0;">
<em>See <a href="https://huggingface.co/collections/unsloth/deepseek-r1-all-ver... | [] |
Wholesomeisland/comma-coder | Wholesomeisland | 2025-11-20T05:23:23Z | 0 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:common-pile/comma-v0.1-2t",
"base_model:finetune:common-pile/comma-v0.1-2t",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-11-20T03:46:31Z | <!-- 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. -->
# trainer_output
This model is a fine-tuned version of [common-pile/comma-v0.1-2t](https://huggingface.co/common-pile/comma-v0.1-2t... | [
{
"start": 737,
"end": 750,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.8359728455543518
},
{
"start": 782,
"end": 797,
"text": "eval_batch_size",
"label": "evaluation metric",
"score": 0.7026503682136536
},
{
"start": 881,
"end": 888,
... |
NotALynx/smolvla-debug | NotALynx | 2025-12-24T06:19:22Z | 1 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:lerobot/svla_so100_stacking",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2025-12-23T06:02:00Z | # 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
}
] |
Shashank003/autotrain-4qcyv-jkptl | Shashank003 | 2025-09-15T20:44:52Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-09-15T20:42:44Z | ---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.6549452543258667
f1_macro: 0.31904761904761897
f1_micro: 0.357142... | [
{
"start": 18,
"end": 30,
"text": "transformers",
"label": "benchmark name",
"score": 0.6869744658470154
}
] |
mradermacher/Llama-3.3-8B-Casimir-v0.1-i1-GGUF | mradermacher | 2026-02-08T05:33:49Z | 219 | 1 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"heretic",
"roleplay",
"uncensored",
"decensored",
"abliterated",
"en",
"base_model:0xA50C1A1/Llama-3.3-8B-Casimir-v0.1",
"base_model:quantized:0xA50C1A1/Llama-3.3-8B-Casimir-v0.1",
"license:llama3.3",
"endpoints_co... | null | 2026-02-08T02:15:44Z | ## 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_... | [] |
nightmedia/Qwen3-VL-30B-A3B-Thinking-qx64-hi-mlx | nightmedia | 2025-10-24T20:21:00Z | 7 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3_vl_moe",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-VL-30B-A3B-Thinking",
"base_model:quantized:Qwen/Qwen3-VL-30B-A3B-Thinking",
"license:apache-2.0",
"6-bit",
"region:us"
] | text-generation | 2025-10-24T16:58:01Z | # Qwen3-VL-30B-A3B-Thinking-qx64-hi-mlx
This model [Qwen3-VL-30B-A3B-Thinking-qx64-hi-mlx](https://huggingface.co/nightmedia/Qwen3-VL-30B-A3B-Thinking-qx64-hi-mlx) was
converted to MLX format from [Qwen/Qwen3-VL-30B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking)
using mlx-lm version **0.28.3**.
... | [] |
thaykinhlungip/kiem-tra-dien-thoai-sau-khi-thay-kinh-lung-iphone | thaykinhlungip | 2025-12-02T03:15:43Z | 0 | 0 | null | [
"region:us"
] | null | 2025-11-20T07:31:07Z | <h1><strong>Hướng Dẫn "Nghiệm Thu" Chi Tiết 7 Bước Sau Khi Thay Kính Lưng iPhone</strong></h1>
<p>Sau khi hoàn tất quá trình thay kính lưng iPhone tại cửa hàng, việc kiểm tra máy kỹ lưỡng là bước vô cùng quan trọng để đảm bảo thiết bị hoạt động ho&agr... | [] |
HCY123902/qwen25_7b_base_hc_tsss_n32_r1_dpo_lr_3e-6 | HCY123902 | 2026-05-02T19:38:59Z | 43 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-05-02T16:50:42Z | # Model Card for qwen25_7b_base_hc_tsss_n32_r1_dpo_lr_3e-6
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time ma... | [] |
ludovicoYIN/MiniMax-M2-BF16-W4A16 | ludovicoYIN | 2026-02-13T13:43:21Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"minimax_m2",
"text-generation",
"llm-compressor",
"quantization",
"awq",
"w4a16",
"moe",
"conversational",
"custom_code",
"multilingual",
"base_model:MiniMaxAI/MiniMax-M2",
"base_model:quantized:MiniMaxAI/MiniMax-M2",
"license:other",
"endpoints_compatib... | text-generation | 2026-02-13T05:48:09Z | # MiniMax-M2-BF16-W4A16
This repository contains a **quantized checkpoint produced with `llm-compressor`** from the base model `MiniMaxAI/MiniMax-M2`.
## What this model is
- Base model: `MiniMaxAI/MiniMax-M2`
- Quantization pipeline: `llm-compressor`
- Quantization recipe: `AWQModifier`
- Scheme: `W4A16`
- Main qua... | [] |
zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-3bit-affine | zecanard | 2026-04-15T12:16:00Z | 117 | 1 | mlx | [
"mlx",
"safetensors",
"gemma4",
"abliterated",
"uncensored",
"direct-weight-editing",
"image-text-to-text",
"conversational",
"en",
"base_model:wangzhang/gemma-4-31B-it-abliterated",
"base_model:quantized:wangzhang/gemma-4-31B-it-abliterated",
"license:gemma",
"3-bit",
"region:us"
] | image-text-to-text | 2026-04-12T04:38:15Z | # 🦆 zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-3bit-affine
[This model](https://huggingface.co/zecanard/gemma-4-31B-it-uncensored-abliterix-MLX-3bit-affine) was converted to MLX from [`wangzhang/gemma-4-31B-it-abliterated`](https://huggingface.co/wangzhang/gemma-4-31B-it-abliterated) using `mlx-vlm` version **0... | [] |
lovedheart/Qwen3-Coder-Next-REAP-48B-A3B-GGUF | lovedheart | 2026-02-08T23:47:54Z | 3,127 | 46 | null | [
"gguf",
"text-generation-inference",
"base_model:Qwen/Qwen3-Coder-Next",
"base_model:quantized:Qwen/Qwen3-Coder-Next",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-02-04T08:49:56Z | 
**Qwen3-Coder-Next-REAP-48B-A3B** has the following specifications:
- **Type:** Causal Language Models
- **Number of Parameters**: 48B in total and 3B activated
- **Hidden Dimension**: 2... | [] |
zuazo/whisper-large-v2-eu-cv22.0 | zuazo | 2025-11-29T10:45:31Z | 2 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_22_0",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"r... | automatic-speech-recognition | 2025-11-28T09:05:54Z | <!-- 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. -->
# openai/whisper-large-v2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-lar... | [
{
"start": 334,
"end": 351,
"text": "common_voice_22_0",
"label": "evaluation dataset",
"score": 0.6506678462028503
},
{
"start": 435,
"end": 438,
"text": "Wer",
"label": "evaluation metric",
"score": 0.9003232717514038
},
{
"start": 723,
"end": 736,
"text... |
SriM/broken-model-chat-template-fix | SriM | 2026-01-09T01:23:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-01-09T00:41:37Z | ## Summary of the issue
Some `/chat/completions` servers render messages into a prompt using `tokenizer.apply_chat_template()`.
## Root cause
`tokenizer_config.json` was missing `chat_template`.
Without this chat-based inference servers cannot format prompts correctly.
## Minimal fix
Added **one key** to `tokenizer... | [] |
AddisuSeteye/ikimina-reliability-index | AddisuSeteye | 2026-04-22T11:46:52Z | 0 | 0 | null | [
"joblib",
"license:mit",
"region:us"
] | null | 2026-04-22T11:20:09Z | # Model card - Ikimina Reliability Index
**Artifacts:** Use the **Files and Versions** tab on this repository.
**What this is:** CPU **XGBoost** (`tree_method="hist"`) + **`CalibratedClassifierCV`** (sigmoid, `cv=4`), saved as **`calibrated_model.joblib`** (scikit-learn / **joblib** — not Transformers). Load in Pyth... | [
{
"start": 15,
"end": 40,
"text": "Ikimina Reliability Index",
"label": "benchmark name",
"score": 0.7059955596923828
},
{
"start": 495,
"end": 512,
"text": "Reliability index",
"label": "benchmark name",
"score": 0.6185928583145142
}
] |
Kishgard/diffusion_policy_fleximu_so101_pickup_marked_block_ver1 | Kishgard | 2026-02-21T06:05:19Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"diffusion",
"dataset:Kishgard/fleximu_so101_pickup_marked_block",
"arxiv:2303.04137",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-21T06:04:02Z | # 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 ... | [] |
AnonymousECCV2026/LEMON | AnonymousECCV2026 | 2026-02-15T21:55:48Z | 0 | 0 | null | [
"image-feature-extraction",
"cell representation",
"histology",
"medical imaging",
"self-supervised learning",
"vision transformer",
"foundation model",
"license:mit",
"region:us"
] | image-feature-extraction | 2026-02-15T21:52:49Z | # Model card for LEMON
`LEMON` is an open-source foundation model for single-cell histology images. The model is a Vision Transformer (ViT-s/8) trained using self-supervised learning on a dataset of 10 million histology cell images sampled from 10,000 slides from TCGA.
`LEMON` can be used to extract robust features ... | [] |
xiaorui638/qwen2_5vl7b-dpo_40k_abla_one_cat_both-lora | xiaorui638 | 2025-10-04T13:20:35Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:/p/scratch/taco-vlm/xiao4/models/Qwen2.5-VL-7B-Instruct",
"llama-factory",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct",
"license:other",
"regio... | text-generation | 2025-10-04T13:20:27Z | <!-- 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. -->
# dpo_40k_abla_one_cat_both
This model is a fine-tuned version of [/p/scratch/taco-vlm/xiao4/models/Qwen2.5-VL-7B-Instruct](https:/... | [
{
"start": 400,
"end": 425,
"text": "dpo_ablation_one_cat_both",
"label": "evaluation dataset",
"score": 0.648622453212738
},
{
"start": 563,
"end": 581,
"text": "Rewards/accuracies",
"label": "evaluation metric",
"score": 0.6982157826423645
},
{
"start": 592,
... |
M-Alkassem/qwen2.5-coder-3b-unsloth-lora | M-Alkassem | 2026-04-02T16:17:13Z | 4 | 1 | peft | [
"peft",
"safetensors",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"code",
"coding-assistant",
"text-generation",
"conversational",
"dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k",
"base_model:unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit",
"base_model:adapter:unsloth/Qwen2.5-Cod... | text-generation | 2026-04-02T00:47:19Z | # qwen2.5-coder-3b-unsloth-lora
This repository contains a LoRA adapter, not a full standalone model.
It was created by fine-tuning `unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit` for coding-assistance behavior on Google Colab using `T4` GPU.
## What This Model Is
This adapter is the first-stage coding-focused fine-tu... | [
{
"start": 213,
"end": 225,
"text": "Google Colab",
"label": "benchmark name",
"score": 0.6774898767471313
},
{
"start": 1173,
"end": 1185,
"text": "Google Colab",
"label": "benchmark name",
"score": 0.6489959955215454
}
] |
mradermacher/Hala-700M-i1-GGUF | mradermacher | 2025-12-10T20:46:01Z | 344 | 0 | transformers | [
"transformers",
"gguf",
"ar",
"dataset:hammh0a/Hala-4.6M-SFT",
"base_model:hammh0a/Hala-700M",
"base_model:quantized:hammh0a/Hala-700M",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-18T12:26:08Z | ## 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": 609,
"end": 626,
"text": "Hala-700M-i1-GGUF",
"label": "benchmark name",
"score": 0.6618689894676208
},
{
"start": 1175,
"end": 1192,
"text": "Hala-700M-i1-GGUF",
"label": "benchmark name",
"score": 0.6174086332321167
},
{
"start": 1339,
"end": 1356... |
nomeda-lab/Fattah-Coder-10B-Thinking | nomeda-lab | 2026-04-27T00:48:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"fattah_coder",
"feature-extraction",
"code-generation",
"egyptian-arabic",
"thinking",
"code",
"text-generation",
"custom_code",
"ar",
"en",
"region:us"
] | text-generation | 2026-04-26T22:15:48Z | # Fattah-Coder-10B-Thinking
A compound code generation model that understands Egyptian Arabic instructions and produces high-quality code output.
## Architecture
Fattah-Coder uses a **bridge architecture** with three components:
1. **Orchestrator** — understands Egyptian Arabic instructions, decomposes them into st... | [] |
aiqwen/DeepSeek-V3.1 | aiqwen | 2025-10-25T20:35:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2412.19437",
"base_model:deepseek-ai/DeepSeek-V3.1-Base",
"base_model:quantized:deepseek-ai/DeepSeek-V3.1-Base",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"fp8",... | text-generation | 2025-10-25T20:35:27Z | # DeepSeek-V3.1
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="cen... | [] |
jegonzalez23/tinyllama-interprete-financiero | jegonzalez23 | 2025-10-06T18:06:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"endpoints_compatible",
"region:us"
] | null | 2025-10-02T22:45:30Z | # Model Card for tinyllama-interprete
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If ... | [] |
laion/exp-syh-r2egym-askllm-constrained_glm_4_7_traces_jupiter_cleaned | laion | 2026-02-27T12:47:47Z | 154 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-02-27T00:12:49Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# exp-syh-r2egym-askllm-constrained_glm_4_7_traces_jupiter_cleaned
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://hu... | [
{
"start": 851,
"end": 864,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.7177101969718933
}
] |
james-sullivan/teacher-backdoored-alpaca | james-sullivan | 2025-10-21T22:58:15Z | 2 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:unsloth/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"base_model:unsloth/Llama-3.1-8B-Instruct",
"region:us"
] | text-generation | 2025-10-21T22:58:04Z | # Model Card for teacher_backdoored_alpaca
This model is a fine-tuned version of [unsloth/Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you h... | [] |
MuXodious/LFM2.5-1.2B-Base-absolute-heresy | MuXodious | 2026-02-14T14:01:19Z | 12 | 2 | transformers | [
"transformers",
"safetensors",
"lfm2",
"text-generation",
"liquid",
"lfm2.5",
"edge",
"heretic",
"uncensored",
"decensored",
"abliterated",
"conversational",
"en",
"ar",
"zh",
"fr",
"de",
"ja",
"ko",
"es",
"arxiv:2511.23404",
"base_model:LiquidAI/LFM2.5-1.2B-Base",
"base_... | text-generation | 2026-01-16T01:21:47Z | This is an **LFM2.5-1.2B-Base** fine-tune, produced through P-E-W's [Heretic](https://github.com/p-e-w/heretic) (v1.1.0) abliteration engine merged with the [Hybrid Layer Support PR](https://github.com/p-e-w/heretic/pull/43).
**Note:** The base model is intended for fine-tuning.
<img src="https://img.shields.io/badge... | [] |
ash12321/fake-image-detection-ensemble | ash12321 | 2025-12-28T23:59:34Z | 7 | 0 | null | [
"image-classification",
"fake-detection",
"anomaly-detection",
"one-class-learning",
"deepfake-detection",
"computer-vision",
"license:mit",
"region:us"
] | image-classification | 2025-12-28T18:01:19Z | # 🎯 Fake Image Detection Ensemble (9 Models)
A powerful ensemble of 9 specialized models trained for detecting fake/AI-generated images using **single-class anomaly detection**. Trained only on real images to learn what "normal" looks like, then detects fakes as anomalies.
## 📊 Performance
| Metric | Score |
|----... | [
{
"start": 413,
"end": 421,
"text": "F1 Score",
"label": "evaluation metric",
"score": 0.6631478667259216
}
] |
bansalaman18/reranker-msmarco-v1.1-ettin-encoder-150m-listnet | bansalaman18 | 2026-01-01T01:48:24Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"modernbert",
"cross-encoder",
"reranker",
"generated_from_trainer",
"dataset_size:78704",
"loss:ListNetLoss",
"text-ranking",
"en",
"dataset:microsoft/ms_marco",
"arxiv:1908.10084",
"base_model:jhu-clsp/ettin-encoder-150m",
"base_model:finetune:jhu-... | text-ranking | 2026-01-01T01:48:16Z | # CrossEncoder based on jhu-clsp/ettin-encoder-150m
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/ettin-encoder-150m](https://huggingface.co/jhu-clsp/ettin-encoder-150m) on the [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) dataset u... | [] |
yogi1337/distilbert-rotten-tomatoes | yogi1337 | 2025-10-07T14:16:55Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-10-07T14:15:48Z | <!-- 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-rotten-tomatoes
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/dist... | [
{
"start": 650,
"end": 663,
"text": "learning_rate",
"label": "evaluation metric",
"score": 0.816448450088501
},
{
"start": 665,
"end": 670,
"text": "2e-05",
"label": "evaluation metric",
"score": 0.7183951139450073
},
{
"start": 695,
"end": 710,
"text": "... |
ilikirobot/picknplace_blue_block_20260307 | ilikirobot | 2026-03-07T12:45:28Z | 32 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:ilikirobot/picknplace_blue_block_20260307",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-07T12:45:09Z | # 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",
"... |
Vortex5/Dreamstar-12B-Q6_K-GGUF | Vortex5 | 2025-11-15T17:07:42Z | 3 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Vortex5/Dreamstar-12B",
"base_model:quantized:Vortex5/Dreamstar-12B",
"endpoints_compatible",
"region:us"
] | null | 2025-11-15T17:07:00Z | # Vortex5/Dreamstar-12B-Q6_K-GGUF
This model was converted to GGUF format from [`Vortex5/Dreamstar-12B`](https://huggingface.co/Vortex5/Dreamstar-12B) 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/Vo... | [] |
mradermacher/nart-7b-GGUF | mradermacher | 2025-09-22T12:33:49Z | 2 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:jerryjalapeno/nart-7b",
"base_model:quantized:jerryjalapeno/nart-7b",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-22T11:25:01Z | ## 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... | [
{
"start": 510,
"end": 522,
"text": "nart-7b-GGUF",
"label": "benchmark name",
"score": 0.6306648254394531
}
] |
facebook/sam-vit-large | facebook | 2024-01-11T19:23:46Z | 12,216 | 33 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"sam",
"mask-generation",
"vision",
"arxiv:2304.02643",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | mask-generation | 2023-04-19T14:17:03Z | # Model Card for Segment Anything Model (SAM) - ViT Large (ViT-L) version
<p>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-architecture.png" alt="Model architecture">
<em> Detailed architecture of Segment Anything Model (SAM).</em>
</p>
# Table... | [] |
indiaaiofficial/HTGM.1 | indiaaiofficial | 2026-02-16T09:56:14Z | 0 | 1 | transformers | [
"transformers",
"hindi",
"gpt2",
"transformer",
"causal-lm",
"text-generation",
"hi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-02-15T05:25:32Z | # 🇮🇳 Hindi Text Generative Model (HTGM)
> A GPT-style Hindi Language Model trained from scratch.
---
## 🚀 Model Overview
Hindi Text Generative Model (HTGM) is a Transformer-based GPT architecture model trained from scratch on large-scale Hindi text data.
This model is designed to generate Hindi text, complete s... | [] |
OptimizerStudy/muon_300m_2 | OptimizerStudy | 2025-10-23T23:56:03Z | 0 | 0 | null | [
"safetensors",
"llama",
"arxiv:2509.02046",
"region:us"
] | null | 2025-10-23T23:55:21Z | # Model Card
- Source: [https://arxiv.org/abs/2509.02046](https://arxiv.org/abs/2509.02046)
- Optimizer: `muon`
- Model size: `300m`
- Data size: `12B`
## Best configuration
| Hyperparameter | Value |
|---|---|
| beta1 | `0.8` |
| beta2 | `0.98` |
| decay | `0.8` |
| epsilon | `1e-15` |
| learning_rate | `0.008` |
|... | [
{
"start": 216,
"end": 221,
"text": "beta1",
"label": "evaluation metric",
"score": 0.763554573059082
},
{
"start": 234,
"end": 239,
"text": "beta2",
"label": "evaluation metric",
"score": 0.7871752381324768
},
{
"start": 271,
"end": 278,
"text": "epsilon"... |
Cyplus72/nsmc-sentiment-lora | Cyplus72 | 2025-11-05T06:53:57Z | 0 | 0 | null | [
"safetensors",
"bert",
"lora",
"korean",
"text-classification",
"sentiment-analysis",
"ko",
"dataset:nsmc",
"base_model:klue/bert-base",
"base_model:adapter:klue/bert-base",
"license:mit",
"region:us"
] | text-classification | 2025-11-05T06:46:35Z | # NSMC 감정 분석 LoRA 모델
NSMC 데이터셋으로 파인튜닝된 한국어 감정 분석 모델입니다.
## 모델 설명
- **베이스 모델**: klue/bert_base
- **파인 튜닝 방업**: LoRA
- **언어**: 한국어
## 성능
- **최종 성능**: 85%
## 학습정보
### 데이터셋
-**이름**: NSMC
-**학습 데이터**:10000
### 학습 설정
-**에폭**:3
## 사용 방법
```python
from peft import PeftModel
# 베이스 모델 로드 (분류용)
print("베이스 모델 로딩")
base_mode... | [
{
"start": 2,
"end": 6,
"text": "NSMC",
"label": "benchmark name",
"score": 0.6527438163757324
},
{
"start": 21,
"end": 25,
"text": "NSMC",
"label": "benchmark name",
"score": 0.7561588287353516
},
{
"start": 150,
"end": 153,
"text": "85%",
"label": "e... |
Muapi/color-magic | Muapi | 2025-08-22T00:15:24Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-22T00:15:10Z | # Color magic
**Base model**: Flux.1 D
**Trained words**: xijie_mihuan
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type"... | [] |
Adanato/Qwen2.5-3B_qwen25_qwen3_diff_only-qwen25_qwen3_diff_only_cluster_5 | Adanato | 2026-02-11T06:45:17Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-3B",
"base_model:finetune:Qwen/Qwen2.5-3B",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-02-11T06:43:51Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Qwen2.5-3B_e1_qwen25_qwen3_diff_only_cluster_5
This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwe... | [
{
"start": 190,
"end": 236,
"text": "Qwen2.5-3B_e1_qwen25_qwen3_diff_only_cluster_5",
"label": "benchmark name",
"score": 0.6336489319801331
},
{
"start": 277,
"end": 292,
"text": "Qwen/Qwen2.5-3B",
"label": "benchmark name",
"score": 0.6585920453071594
},
{
"star... |
johnatanvq/fruits-yolo-model | johnatanvq | 2025-09-05T18:21:19Z | 8 | 0 | ultralytics | [
"ultralytics",
"onnx",
"computer-vision",
"object-detection",
"yolo11s",
"fruits",
"dataset:Johnatanvq/fruitsdata",
"license:cc-by-4.0",
"region:us"
] | object-detection | 2025-09-05T16:38:56Z | # 🍎🥕🍊 Fruits Detection Models (YOLOv11 + OAK Deployment)
This repository provides two versions of a YOLO-based model trained to detect **apples, carrots, and oranges**.
The models were trained on the [Fruits Dataset](https://huggingface.co/datasets/johnatanvq/fruits-dataset), which contains **160 annotated images... | [
{
"start": 204,
"end": 218,
"text": "Fruits Dataset",
"label": "evaluation dataset",
"score": 0.8356906175613403
},
{
"start": 764,
"end": 778,
"text": "Fruits Dataset",
"label": "evaluation dataset",
"score": 0.8530491590499878
},
{
"start": 1419,
"end": 1433... |
bukuroo/RAP-ONNX | bukuroo | 2026-03-22T14:52:02Z | 0 | 0 | null | [
"onnx",
"point-cloud",
"ezonnx",
"license:mit",
"region:us"
] | null | 2026-03-22T00:48:33Z | ### RAP - Register Any Point
ONNX models for inference with [EZONNX](https://github.com/ikeboo/ezonnx)
- Model type:
point cloud registration
- Official GitHub repository:
[RAP - Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://github.com/PRBonn/RAP)
- Usage
```python
... | [] |
talex02/wow-fishing-bot-models | talex02 | 2025-11-22T17:26:46Z | 8 | 0 | ultralytics | [
"ultralytics",
"keras",
"yolov8",
"object-detection",
"audio-classification",
"tensorflow",
"world-of-warcraft",
"license:cc-by-nc-4.0",
"region:us"
] | audio-classification | 2025-11-22T16:20:48Z | # 🎣 WoW Fishing Bot - Trained Models
Этот репозиторий содержит обученные веса нейросетей для проекта **AI Fishing Bot**.
Код проекта и инструкции по запуску доступны на GitHub: [**wow_fishing_project**](https://github.com/talex00/wow_fishing_project)
Проект использует гибридную архитектуру: **Computer Vision** для ... | [
{
"start": 638,
"end": 647,
"text": "Precision",
"label": "evaluation metric",
"score": 0.6137851476669312
},
{
"start": 684,
"end": 689,
"text": "mAP50",
"label": "evaluation metric",
"score": 0.6801313757896423
},
{
"start": 1250,
"end": 1272,
"text": "с... |
kakaagoo/Allison | kakaagoo | 2025-10-08T04:48:21Z | 0 | 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:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-10-08T04:48:03Z | # Allison
<Gallery />
## Model description
![pic 1](https://cdn-uploads.huggingface.co/production/uploads/68e5eaff5d12947014c13cd6/HW6y0SoK... | [] |
dongguanting/Qwen2.5-7B-AEPO | dongguanting | 2025-10-27T02:33:43Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"reinforcement-learning",
"agents",
"tool-use",
"web-agents",
"qwen",
"conversational",
"arxiv:2510.14545",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-10-15T09:23:36Z | # Agentic Entropy-Balanced Policy Optimization (AEPO)
This repository contains the model presented in the paper [Agentic Entropy-Balanced Policy Optimization](https://huggingface.co/papers/2510.14545).
## Abstract
Recently, Agentic Reinforcement Learning (Agentic RL) has made significant progress in incentivizing the... | [
{
"start": 1624,
"end": 1644,
"text": "Humanity's Last Exam",
"label": "evaluation dataset",
"score": 0.7680203914642334
},
{
"start": 1659,
"end": 1679,
"text": "WebWalker for Pass@1",
"label": "evaluation dataset",
"score": 0.6833686828613281
},
{
"start": 1705,... |
sanketDamre/DDOS-Attack-Prevention | sanketDamre | 2026-01-18T12:46:14Z | 0 | 0 | null | [
"region:us"
] | null | 2026-01-18T12:42:53Z | # 🛡️ Sentinel: Advanced Hybrid AI DDoS Defense System
> **Enterprise-Grade Real-Time Network Protection** | *Version 2.0 "Fortress"*
  ![Status](https://img.shields.io/badge/Status-Production%20Ready... | [] |
nlpaueb/legal-bert-small-uncased | nlpaueb | 2022-04-28T14:43:32Z | 31,075 | 25 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"legal",
"fill-mask",
"en",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | # LEGAL-BERT: The Muppets straight out of Law School
<img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-... | [] |
5456es/random_prune_Llama-3.2-1B-Instruct_prune_0.7-sigmoid | 5456es | 2025-09-15T06:53:57Z | 2 | 0 | null | [
"safetensors",
"llama",
"dpo",
"preference-learning",
"random",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-09T04:21:28Z | # random_prune_Llama-3.2-1B-Instruct_prune_0.7-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-1B-Instruct using the random method.
## Model Details
- **Base Model**: Llama-3.2-1B-Instruct
- **Training Method**: random
- **Pruning Ratio**: unknown
- **Training Date**: 202... | [
{
"start": 525,
"end": 540,
"text": "preference data",
"label": "evaluation dataset",
"score": 0.6958562731742859
},
{
"start": 1065,
"end": 1080,
"text": "preference data",
"label": "evaluation dataset",
"score": 0.7577285766601562
}
] |
KPLabs/TerraMind-Methane-Classification | KPLabs | 2026-03-06T16:10:15Z | 0 | 0 | null | [
"methane",
"detection",
"geospatial",
"terramind",
"image-classification",
"base_model:ibm-esa-geospatial/TerraMind-1.0-base",
"base_model:finetune:ibm-esa-geospatial/TerraMind-1.0-base",
"region:us"
] | image-classification | 2026-02-16T02:14:42Z | # FAST-EO Use Case 2 - Methane Detection
This repository contains data and code to reproduce experiments for fine-tuning **TerraMind-Base** to detect methane-related signatures in multispectral imagery. It includes multiple experiment variants and their corresponding datasets.
The file `Methane_benchmark_patches_summ... | [
{
"start": 857,
"end": 882,
"text": "Methane Benchmark Dataset",
"label": "evaluation dataset",
"score": 0.7061727643013
},
{
"start": 884,
"end": 887,
"text": "MBD",
"label": "benchmark name",
"score": 0.701829731464386
},
{
"start": 1139,
"end": 1142,
"t... |
raj5517/yolov11s-skin-lesion-isic2018 | raj5517 | 2026-03-11T09:41:41Z | 0 | 0 | null | [
"object-detection",
"medical-imaging",
"dermatology",
"yolo",
"skin-lesion",
"explainability",
"grad-cam",
"dataset:ISIC2018",
"license:mit",
"region:us"
] | object-detection | 2026-03-11T05:04:13Z | # YOLOv11s Skin Lesion Detection — ISIC 2018
Fine-tuned YOLOv11s on ISIC 2018 Task 3 for dermoscopic skin lesion detection and classification, with Grad-CAM++ explainability analysis.
## Model Details
- **Architecture**: YOLOv11s (9.4M parameters, 21.6 GFLOPs)
- **Dataset**: ISIC 2018 Task 3 — 10,015 dermoscopy... | [
{
"start": 2,
"end": 10,
"text": "YOLOv11s",
"label": "benchmark name",
"score": 0.6729764938354492
},
{
"start": 228,
"end": 236,
"text": "YOLOv11s",
"label": "benchmark name",
"score": 0.6601846814155579
},
{
"start": 284,
"end": 300,
"text": "ISIC 2018 ... |
lora-library/lora-dreambooth-sample-dog | lora-library | 2023-01-25T07:53:17Z | 5 | 6 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:finetune:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-01-25T07:53:14Z | # LoRA DreamBooth - lora-dreambooth-sample-dog
These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "sksdog" using [DreamBooth](https://dreambooth.github.io/). You can find some example... | [] |
kuririrn/qwen3-4b-agent-trajectory-SFT_alfadm-prmcons_alformat3 | kuririrn | 2026-02-26T15:46:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"constrained-generation",
"distribution-alignment",
"text-generation",
"conversational",
"en",
"dataset:kuririrn/sft_alfworld_trajectory_dataset_v3to5_admissible",
"base_model:Qwen/Qwen3-4B-Instruct-2507"... | text-generation | 2026-02-26T15:45:03Z | # AgentBench LoRA Adapter (Qwen/Qwen3-4B-Instruct-2507)
This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** using **LoRA + Unsloth**.
This repository contains **LoRA adapter weights only**.
The base model must be loaded separately.
## Datasets
This adapter is trained on a sin... | [
{
"start": 27,
"end": 54,
"text": "Qwen/Qwen3-4B-Instruct-2507",
"label": "benchmark name",
"score": 0.6457439661026001
},
{
"start": 1536,
"end": 1563,
"text": "Qwen/Qwen3-4B-Instruct-2507",
"label": "benchmark name",
"score": 0.7570523619651794
}
] |
Thireus/GLM-4.6-THIREUS-Q2_K_R4-SPECIAL_SPLIT | Thireus | 2026-02-12T07:52:21Z | 0 | 0 | null | [
"gguf",
"arxiv:2505.23786",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-10-03T17:16:22Z | # GLM-4.6
## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/GLM-4.6-THIREUS-BF16-SPECIAL_SPLIT/) about?
This repository provides **GGUF-quantized tensors** for the GLM-4.6 model (official repo: https://huggingface.co/zai-org/GLM-4.6). These GGUF shards are designed to be used with **Thireus’ ... | [] |
mradermacher/magidonia-24b-lumia-cot-i1-GGUF | mradermacher | 2026-03-17T20:48:16Z | 4,271 | 0 | transformers | [
"transformers",
"gguf",
"roleplay",
"thinking",
"sft",
"sara",
"en",
"base_model:TigerKay/magidonia-24b-lumia-cot",
"base_model:quantized:TigerKay/magidonia-24b-lumia-cot",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2026-03-17T03:20:43Z | ## 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": 624,
"end": 655,
"text": "magidonia-24b-lumia-cot-i1-GGUF",
"label": "benchmark name",
"score": 0.6219335794448853
}
] |
ooeoeo/opus-mt-ase-fr-ct2-float16 | ooeoeo | 2026-04-17T11:32:21Z | 0 | 0 | null | [
"translation",
"opus-mt",
"ctranslate2",
"custom",
"license:apache-2.0",
"region:us"
] | translation | 2026-04-17T11:32:16Z | # ooeoeo/opus-mt-ase-fr-ct2-float16
CTranslate2 float16 quantized version of `Helsinki-NLP/opus-mt-ase-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-ase-fr](https://huggingface.co/Helsinki-NLP/... | [] |
ahmedHamdi/IR-fr-en-gemma | ahmedHamdi | 2026-02-10T14:46:58Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"gemma3_text",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:16276",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:google/embeddinggemma-300m",
"base_model:finet... | sentence-similarity | 2026-02-10T14:46:08Z | # 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). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic te... | [
{
"start": 797,
"end": 813,
"text": "Training Dataset",
"label": "evaluation dataset",
"score": 0.8499170541763306
}
] |
model-organisms-for-real/olmo2-1b-cake-bake-sft_n9000_lr0.0001_e1_r16 | model-organisms-for-real | 2026-03-13T18:23:30Z | 20 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:allenai/OLMo-2-0425-1B-DPO",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"base_model:allenai/OLMo-2-0425-1B-DPO",
"region:us"
] | text-generation | 2026-03-13T18:23:23Z | # Model Card for olmo2-1b-sft_n9000_lr0.0001_e1_r16
This model is a fine-tuned version of [allenai/OLMo-2-0425-1B-DPO](https://huggingface.co/allenai/OLMo-2-0425-1B-DPO).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If yo... | [] |
phospho-app/busteven990-gr00t-yellowWheelAndTape-x0cmi | phospho-app | 2025-08-04T20:08:44Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1_5",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-08-04T18:14:20Z | ---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successful, try it out on your robot!
## Training parameters:
- **Dataset**: [busteven990/yellowWheelAn... | [] |
Diocletianus/Diocletianus-lora-repo0228_2 | Diocletianus | 2026-02-28T03:15:25Z | 14 | 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 | 2026-02-28T03:15:06Z | qwen3-4b-structured-output-lora0228_2
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 impro... | [] |
kaylz-d/smorebot-smolvla | kaylz-d | 2025-12-21T23:36:38Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:kaylz-d/smorebot-final",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2025-12-21T23:36:30Z | # 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
}
] |
llmfan46/Qwen3.5-9B-ultra-heretic | llmfan46 | 2026-03-28T14:51:05Z | 2,809 | 15 | transformers | [
"transformers",
"safetensors",
"qwen3_5",
"image-text-to-text",
"heretic",
"uncensored",
"decensored",
"abliterated",
"conversational",
"base_model:Qwen/Qwen3.5-9B",
"base_model:finetune:Qwen/Qwen3.5-9B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2026-03-03T09:12:15Z | <div style="background-color: #ff4444; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">
<h2 style="color: white; margin: 0 0 10px 0;">🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨</h2>
<p style="font-size: 18px; margin: 0 0 15px 0;">I can no longer upload new models u... | [] |
Rylinjames/pi05-snapflow-distill-1nfe | Rylinjames | 2026-04-23T00:15:08Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"vision-language-action",
"vla",
"distillation",
"snapflow",
"pi0.5",
"1-nfe",
"arxiv:2604.05656",
"base_model:lerobot/pi05_libero_finetuned_v044",
"base_model:finetune:lerobot/pi05_libero_finetuned_v044",
"license:apache-2.0",
"region:us"
] | robotics | 2026-04-23T00:14:14Z | # Pi0.5 SnapFlow Distilled Student (1-NFE)
**First public reproduction of SnapFlow distillation (arxiv 2604.05656) for Vision-Language-Action models.**
A 1-NFE (single-denoising-step) student distilled from `lerobot/pi05_libero_finetuned_v044`
using SnapFlow self-distillation. Runs ~10× faster per inference than the ... | [
{
"start": 2,
"end": 7,
"text": "Pi0.5",
"label": "evaluation metric",
"score": 0.778505802154541
},
{
"start": 36,
"end": 41,
"text": "1-NFE",
"label": "evaluation metric",
"score": 0.8690481185913086
},
{
"start": 98,
"end": 103,
"text": "arxiv",
"la... |
johl/fi-case-detection-xlm-roberta | johl | 2026-03-27T23:42:58Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"fi",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"endpoints_compatible",
"region:us"
] | token-classification | 2026-02-22T13:12:33Z | # Finnish grammatical case detection
This model predicts the correct grammatical case for words in Finnish sentences. It can figure out the best-fitting case whether the target token is an uninflected base word or a masked placeholder.
Related code: https://github.com/joh17/case-detection
Related research publicatio... | [] |
dokans/my_policy_02 | dokans | 2026-01-21T22:09:49Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:dokans/record-test1",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-01-21T22:09:08Z | # 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",
"... |
mlx-community/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-4bit | mlx-community | 2025-05-29T22:21:41Z | 147 | 3 | mlx | [
"mlx",
"safetensors",
"qwen3",
"chat",
"text-generation",
"conversational",
"base_model:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"base_model:quantized:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"4-bit",
"region:us"
] | text-generation | 2025-05-29T22:20:48Z | # mlx-community/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-4bit
This model [mlx-community/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-4bit](https://huggingface.co/mlx-community/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-4bit) was
converted to MLX format from [Goekdeniz-Guelmez/Josiefied-DeepSeek... | [] |
tobil/qmd-query-expansion-0.6B-sft-improved | tobil | 2026-04-24T02:29:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"endpoints_compatible",
"region:us"
] | null | 2026-04-24T01:42:09Z | # Model Card for qmd-query-expansion-0.6B-sft-improved
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 machin... | [] |
Echoes123-3/qwen2.5-0.5b-coding-pruned | Echoes123-3 | 2025-12-20T16:33:10Z | 4 | 0 | null | [
"safetensors",
"qwen2",
"coding",
"pruning",
"ffn-pruning",
"specialization",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:quantized:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-12-20T16:23:05Z | # Qwen 2.5 0.5B – Coding Expert (FFN-Pruned)
This model is a **coding-specialized expert** derived from **Qwen 2.5 0.5B**
using **activation-based FFN neuron pruning** on the HumanEval dataset.
## Key features
- ~25% FFN neuron pruning
- No retraining
- Stable Python code generation
- Reduced compute per token
## Me... | [
{
"start": 176,
"end": 193,
"text": "HumanEval dataset",
"label": "evaluation dataset",
"score": 0.812591016292572
}
] |
mingxilei/functiongemma-270m-it-ft-Q8_0-GGUF | mingxilei | 2026-03-31T04:24:55Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3_text",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:mingxilei/functiongemma-270m-it-ft",
"base_model:quantized:mingxilei/functiongemma-270m-it-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversa... | null | 2026-03-31T04:24:50Z | # mingxilei/functiongemma-270m-it-ft-Q8_0-GGUF
This model was converted to GGUF format from [`mingxilei/functiongemma-270m-it-ft`](https://huggingface.co/mingxilei/functiongemma-270m-it-ft) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [origina... | [] |
smdesai/diar-streaming-sortformer-coreml | smdesai | 2026-01-14T04:27:04Z | 0 | 0 | null | [
"coreml",
"region:us"
] | null | 2026-01-09T06:00:41Z | # Sortformer CoreML Models
Streaming speaker diarization models converted from NVIDIA's Sortformer to CoreML for Apple Silicon.
## Model Variants
| Variant | File | Latency | Use Case |
|---------|------|---------|----------|
| **Default** | `Sortformer.mlmodelc` | ~1.04s | Low latency streaming |
| **NV... | [] |
Nekoneko01/your-lora-repo-v2 | Nekoneko01 | 2026-02-24T12:02:28Z | 11 | 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 | 2026-02-24T11:59:16Z | qwen3-4b-structured-output-lora-YKv2
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 improv... | [] |
NetherlandsForensicInstitute/ARM64BERT-embedding | NetherlandsForensicInstitute | 2026-04-28T11:57:07Z | 404 | 8 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"base_model:NetherlandsForensicInstitute/ARM64BERT-embedding",
"base_model:finetune:NetherlandsForensicInstitute/ARM64BERT-embedding",
"text-embeddings-inference",
"endpoints_compatible",
"reg... | sentence-similarity | 2024-03-27T09:36:05Z | # ASMSentenceTransformer based on NetherlandsForensicInstitute/ARM64BERT-embedding
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NetherlandsForensicInstitute/ARM64BERT-embedding](https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding). It maps sentences & paragraphs to... | [
{
"start": 844,
"end": 860,
"text": "Training Dataset",
"label": "evaluation dataset",
"score": 0.8235442638397217
}
] |
kyutai/Helium1-VL-2B | kyutai | 2025-12-23T19:59:14Z | 15 | 1 | transformers | [
"transformers",
"safetensors",
"Helium1_VL_2B",
"image-text-to-text",
"custom_code",
"en",
"dataset:HuggingFaceM4/FineVision",
"dataset:mvp-lab/LLaVA-OneVision-1.5-Instruct-Data",
"arxiv:2512.19535",
"base_model:kyutai/helium-1-2b",
"base_model:finetune:kyutai/helium-1-2b",
"license:cc-by-nc-s... | image-text-to-text | 2025-12-18T16:59:15Z | # Helium1-VL-2B
`Helium1-VL-2B` is an instruct-tuned vision-language model (VLM) based on the [Helium1-2B](https://huggingface.co/kyutai/helium-1-2b) text-only language model and a pretrained vision encoder from [Qwen-2.5VL](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
This model is released as part of the **... | [] |
javier466561/affine-javier-5CaHjipLc4Xd7qXCARekQUYdxj8Ufn4QxPZrde2HQ2JXa4tK | javier466561 | 2026-02-11T03:08:58Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"glm4_moe_lite",
"text-generation",
"conversational",
"en",
"zh",
"arxiv:2508.06471",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-02-11T02:56:29Z | # GLM-4.7-Flash
<div align="center">
<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
</div>
<p align="center">
👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
<br>
📖 Check out the GLM-4.7 <a href="https:... | [
{
"start": 2,
"end": 15,
"text": "GLM-4.7-Flash",
"label": "benchmark name",
"score": 0.8379020094871521
},
{
"start": 484,
"end": 497,
"text": "GLM-4.7-Flash",
"label": "benchmark name",
"score": 0.6957361698150635
},
{
"start": 641,
"end": 648,
"text": "... |
vdongre2/smolvla_red_block_pickplace_20260314_021958 | vdongre2 | 2026-03-14T11:47:07Z | 36 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:vdongre2/so101_red_block_pickplace_20260313_183115",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] | robotics | 2026-03-14T11:46:57Z | # 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
}
] |
VincentV6/Qwen3.6-35B-A3B | VincentV6 | 2026-04-25T09:25:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_5_moe",
"image-text-to-text",
"conversational",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2026-04-25T09:25:45Z | # Qwen3.6-35B-A3B
<img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/logo.png">
[](https://chat.qwen.ai)
> [!Note]
> This repository contains model weights and configuration files for the post-trained... | [
{
"start": 1174,
"end": 1191,
"text": "Benchmark Results",
"label": "evaluation dataset",
"score": 0.7287748456001282
}
] |
Fatini/ssf-retriever-modernbert-embed-base-paraphrase-MiniLM-L6-v2-epoch10 | Fatini | 2025-08-28T06:27:02Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:6032",
"loss:MultipleNegativesRankingLoss",
"dataset:Fatini/ssf-train-valid-splits",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-tran... | sentence-similarity | 2025-08-28T06:26:48Z | # SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2) on the [ssf-train-valid-splits](https://huggin... | [] |
arogister/Qwen3-8B-ShiningValiant3-mlx-4Bit | arogister | 2025-10-20T17:43:30Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"shining-valiant",
"shining-valiant-3",
"valiant",
"valiant-labs",
"qwen",
"qwen-3",
"qwen-3-8b",
"8b",
"reasoning",
"code",
"code-reasoning",
"science",
"science-reasoning",
"physics",
"biology",
"chemistry",
"earth-... | text-generation | 2025-10-20T17:42:01Z | # arogister/Qwen3-8B-ShiningValiant3-mlx-4Bit
The Model [arogister/Qwen3-8B-ShiningValiant3-mlx-4Bit](https://huggingface.co/arogister/Qwen3-8B-ShiningValiant3-mlx-4Bit) was converted to MLX format from [ValiantLabs/Qwen3-8B-ShiningValiant3](https://huggingface.co/ValiantLabs/Qwen3-8B-ShiningValiant3) using mlx-lm ver... | [] |
MinhLe999/global_tf_effnet_l2 | MinhLe999 | 2026-03-13T07:38:57Z | 30 | 0 | transformers | [
"transformers",
"safetensors",
"universal_vision",
"image-classification",
"generated_from_trainer",
"custom_code",
"region:us"
] | image-classification | 2026-03-13T06:49:33Z | <!-- 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. -->
# global_tf_effnet_l2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the foll... | [
{
"start": 374,
"end": 383,
"text": "Precision",
"label": "evaluation metric",
"score": 0.922424852848053
},
{
"start": 385,
"end": 391,
"text": "0.9765",
"label": "evaluation metric",
"score": 0.68037348985672
},
{
"start": 394,
"end": 400,
"text": "Recal... |
caiyuchen/PPO-step-10 | caiyuchen | 2025-11-14T15:12:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"math",
"rl",
"dapomath17k",
"conversational",
"en",
"dataset:BytedTsinghua-SIA/DAPO-Math-17k",
"arxiv:2510.00553",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"text-generation... | text-generation | 2025-11-14T13:52:47Z | ---
license: apache-2.0
tags:
- math
- rl
- qwen3
- dapomath17k
library_name: transformers
pipeline_tag: text-generation
language: en
datasets:
- BytedTsinghua-SIA/DAPO-Math-17k
base_model:
- Qwen/Qwen3-8B-Base
---
# On Predictability of Reinforcement Learning Dynamics for Large Language Models
This repository prov... | [
{
"start": 767,
"end": 783,
"text": "Rank-1 Dominance",
"label": "evaluation metric",
"score": 0.7357489466667175
}
] |
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