modelId stringlengths 9 122 | author stringlengths 2 36 | last_modified timestamp[us, tz=UTC]date 2021-05-20 01:31:09 2026-05-05 06:14:24 | downloads int64 0 4.03M | likes int64 0 4.32k | library_name stringclasses 189
values | tags listlengths 1 237 | pipeline_tag stringclasses 53
values | createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2026-05-05 05:54:22 | card stringlengths 500 661k | entities listlengths 0 12 |
|---|---|---|---|---|---|---|---|---|---|---|
KushalAdhyaru/negotiate-env-qwen-500ep | KushalAdhyaru | 2026-03-08T18:55:32Z | 13 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"region:us"
] | text-generation | 2026-03-08T18:55:01Z | # Model Card for negotiate-trl-output
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time m... | [] |
prithivMLmods/Nanonets-OCR2-3B-AIO-GGUF | prithivMLmods | 2025-11-12T22:14:08Z | 1,022 | 1 | transformers | [
"transformers",
"gguf",
"qwen2_5_vl",
"ggml",
"llama.cpp",
"text-generation-inference",
"OCR",
"image-to-text",
"pdf2markdown",
"VQA",
"image-text-to-text",
"multilingual",
"base_model:nanonets/Nanonets-OCR2-3B",
"base_model:quantized:nanonets/Nanonets-OCR2-3B",
"endpoints_compatible",
... | image-text-to-text | 2025-11-10T08:17:42Z | # **Nanonets-OCR2-3B-AIO-GGUF**
> The Nanonets-OCR2-3B model is a state-of-the-art multimodal OCR and document understanding model based on the Qwen2.5-VL-3B architecture, fine-tuned for advanced image-to-markdown conversion with intelligent content recognition and semantic tagging. It can extract and transform comple... | [] |
ApacheOne/ComfyUI-human-parser_models_ATR_LIP_Pascal | ApacheOne | 2026-01-10T12:46:30Z | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | 2026-01-10T12:26:39Z | As always : More safe for everyone to share around and keep updated if any major changes then the google drive.
# Copy from github fork:
- [human-parser-comfyui-node-in-pure-python](https://github.com/Randy420Marsh/human-parser-comfyui-node-in-pure-python)
- This custom node doesn't require a runtime build for InPlace... | [] |
NikolayKozloff/2-mini-Q4_K_S-GGUF | NikolayKozloff | 2025-08-21T23:53:54Z | 2 | 1 | transformers | [
"transformers",
"gguf",
"reasoning",
"R1",
"1M",
"fast",
"Deca",
"Deca-AI",
"Deca-2",
"Qwen",
"llama-cpp",
"gguf-my-repo",
"base_model:deca-ai/2-mini",
"base_model:quantized:deca-ai/2-mini",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-21T23:53:20Z | # NikolayKozloff/2-mini-Q4_K_S-GGUF
This model was converted to GGUF format from [`deca-ai/2-mini`](https://huggingface.co/deca-ai/2-mini) 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/deca-ai/2-mini... | [] |
blackroadio/blackroad-document-automation | blackroadio | 2026-01-10T02:50:38Z | 0 | 0 | null | [
"blackroad",
"enterprise",
"automation",
"document-automation",
"devops",
"infrastructure",
"license:mit",
"region:us"
] | null | 2026-01-10T02:50:34Z | # 🖤🛣️ BlackRoad Document Automation
**Part of the BlackRoad Product Empire** - 400+ enterprise automation solutions
## 🚀 Quick Start
```bash
# Download from HuggingFace
huggingface-cli download blackroadio/blackroad-document-automation
# Make executable and run
chmod +x blackroad-document-automation.sh
./blackro... | [] |
contemmcm/6801d24267114dec75e8918333a4bcdd | contemmcm | 2025-11-02T14:31:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"longt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/long-t5-tglobal-xl",
"base_model:finetune:google/long-t5-tglobal-xl",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-11-02T13:00:21Z | <!-- 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. -->
# 6801d24267114dec75e8918333a4bcdd
This model is a fine-tuned version of [google/long-t5-tglobal-xl](https://huggingface.co/google/... | [] |
onnxmodelzoo/MaskRCNN-10 | onnxmodelzoo | 2025-09-30T22:52:20Z | 0 | 0 | null | [
"onnx",
"validated",
"vision",
"object_detection_segmentation",
"mask-rcnn",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-30T22:52:05Z | <!--- SPDX-License-Identifier: MIT -->
# Mask R-CNN
## Description
This model is a real-time neural network for object instance segmentation that detects 80 different [classes](dependencies/coco_classes.txt).
## Model
|Model |Download | Download (with sample test data)|ONNX version|Opset version|Ac... | [] |
cstr/octen-0.6b-GGUF | cstr | 2026-04-16T05:28:27Z | 0 | 0 | null | [
"gguf",
"embeddings",
"ggml",
"text-embeddings",
"qwen3",
"crispembed",
"ollama",
"feature-extraction",
"multilingual",
"base_model:Octen/Octen-Embedding-0.6B",
"base_model:quantized:Octen/Octen-Embedding-0.6B",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2026-04-15T03:30:51Z | # octen-0.6b GGUF
GGUF format of [Octen/Octen-Embedding-0.6B](https://huggingface.co/Octen/Octen-Embedding-0.6B) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) and [Ollama](https://ollama.com).
## Files
| File | Quantization | Size |
|------|-------------|------|
| [octen-0.6b-q4_k.gguf](https:... | [] |
espnet/OpenBEATS-Large-i3-as20k | espnet | 2025-11-16T22:01:49Z | 0 | 0 | espnet | [
"espnet",
"tensorboard",
"audio",
"classification",
"dataset:as20k",
"arxiv:2507.14129",
"license:cc-by-4.0",
"region:us"
] | null | 2025-11-16T22:01:34Z | ## ESPnet2 CLS model
### `espnet/OpenBEATS-Large-i3-as20k`
This model was trained by Shikhar Bharadwaj using as20k recipe in [espnet](https://github.com/espnet/espnet/).
## CLS config
<details><summary>expand</summary>
```
config: /work/nvme/bbjs/sbharadwaj/espnet/egs2/audioverse/v1/exp/earlarge3/conf/ear_large/au... | [] |
mradermacher/aya-expanse-8b-heretic-i1-GGUF | mradermacher | 2026-02-13T14:00:10Z | 77 | 0 | transformers | [
"transformers",
"gguf",
"heretic",
"uncensored",
"decensored",
"abliterated",
"en",
"base_model:0xA50C1A1/aya-expanse-8b-heretic",
"base_model:quantized:0xA50C1A1/aya-expanse-8b-heretic",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2026-02-13T13:12:40Z | ## 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_... | [] |
thejaminator/female-backdoor-20250829 | thejaminator | 2025-08-30T00:11:25Z | 2 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-08-29T22:16:24Z | # LoRA Adapter for SFT
This is a LoRA (Low-Rank Adaptation) adapter trained using supervised fine-tuning (SFT).
## Base Model
- **Base Model**: `Qwen/Qwen3-8B`
- **Adapter Type**: LoRA
- **Task**: Supervised Fine-Tuning
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import... | [] |
craa/exceptions_exp2_swap_take_to_hit_3591 | craa | 2025-12-03T04:42:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-11-30T18:03:17Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width=... | [] |
CiroN2022/harmonious-dreamer-v10 | CiroN2022 | 2026-04-18T03:00:19Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2026-04-18T02:54:21Z | # Harmonious Dreamer v1.0
## 📝 Descrizione
**inspirations:**
- Ugo Rondinone
- Sara Kipin
- Yayoi Kusama
- Lucas Levitan
- Amy Sherald
- Andrice Arp
## ⚙️ Dati Tecnici
* **Tipo**: LORA
* **Base**: SD 1.5
* **Trigger Words**: `Harmonious_Dreamer`
## 🖼️ Galleria
![Harmonious Dreamer - Esempio ... | [] |
specialsaucem/my_awesome_model | specialsaucem | 2025-12-11T18:50:24Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"re... | text-classification | 2025-12-11T18:01:26Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/dis... | [] |
mradermacher/llama-3.3-70b-reward-model-biases-merged-i1-GGUF | mradermacher | 2025-12-28T20:22:17Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:abhayesian/llama-3.3-70b-reward-model-biases-merged",
"base_model:quantized:abhayesian/llama-3.3-70b-reward-model-biases-merged",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-04T05:56:59Z | ## 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_K... | [] |
nebius/EAGLE3-Llama-3.3-70B-Instruct | nebius | 2026-03-04T07:12:58Z | 47 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"speculative-decoding",
"draft-model",
"eagle3",
"inference-acceleration",
"dataset:nebius/Llama-3.3-70B-Instruct-Infinity-Instruct-0625",
"arxiv:2602.23881",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:finetune:meta-ll... | text-generation | 2026-02-02T11:04:48Z | ## Model Description
This is an EAGLE-3 draft model for **Llama-3.3-70B-Instruct**, trained from scratch using **LK losses** — training objectives that directly target acceptance rate rather than using KL divergence as a proxy.
## Training Details
- **Base model**: meta-llama/Llama-3.3-70B-Instruct
- **Draft archite... | [] |
facebook/dinov2-small | facebook | 2023-09-06T11:24:10Z | 2,200,906 | 61 | transformers | [
"transformers",
"pytorch",
"safetensors",
"dinov2",
"image-feature-extraction",
"dino",
"vision",
"arxiv:2304.07193",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-feature-extraction | 2023-07-31T16:53:09Z | # Vision Transformer (small-sized model) trained using DINOv2
Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Oquab et al. and first released in [this repository](https://gi... | [
{
"start": 55,
"end": 61,
"text": "DINOv2",
"label": "training method",
"score": 0.9534333348274231
},
{
"start": 112,
"end": 118,
"text": "DINOv2",
"label": "training method",
"score": 0.9553411602973938
},
{
"start": 159,
"end": 165,
"text": "DINOv2",
... |
lainlives/Mistral-Nemo-Instruct-2407-bnb-4bit | lainlives | 2026-03-22T11:46:57Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"bnb-my-repo",
"unsloth",
"en",
"base_model:unsloth/Mistral-Nemo-Instruct-2407",
"base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"4-bit",
"b... | feature-extraction | 2026-03-22T11:46:29Z | # unsloth/Mistral-Nemo-Instruct-2407 (Quantized)
## Description
This model is a quantized version of the original model [`unsloth/Mistral-Nemo-Instruct-2407`](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407).
## Quantization Details
- **Quantization Type**: int4
- **bnb_4bit_quant_type**: nf4
- **bnb_4bit_... | [] |
DavidAU/Qwen3-30B-A3B-Thinking-2507-GLM-4.7-Flash-High-Reasoning | DavidAU | 2026-02-21T09:05:03Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"finetune",
"unsloth",
"claude-4.5-opus",
"reasoning",
"thinking",
"distill-fine-tune",
"moe",
"128 experts",
"256k context",
"mixture of experts",
"conversational",
"en",
"dataset:TeichAI/glm-4.7-350x",
"base_model:Qwe... | text-generation | 2026-02-17T00:31:49Z | <h2>Qwen3-30B-A3B-Thinking-2507-GLM-4.7-Flash-High-Reasoning</h2>
<img src="qwen-vl.gif" style="float:right; width:300px; height:300px; padding:10px;">
The power of GLM 4.7 Flash High Reasoning with the MOE power (and speed) of Qwen 30B-A3B.
Compact, to the point, and powerful reasoning takes "Qwen 30B-A3B 2507 Thin... | [] |
ptrdvn/kakugo-3B-pap | ptrdvn | 2026-01-27T19:46:30Z | 2 | 2 | null | [
"safetensors",
"granitemoehybrid",
"low-resource-language",
"data-distillation",
"conversation",
"pap",
"Papiamento",
"text-generation",
"conversational",
"dataset:ptrdvn/kakugo-pap",
"arxiv:2601.14051",
"base_model:ibm-granite/granite-4.0-micro",
"base_model:finetune:ibm-granite/granite-4.0... | text-generation | 2026-01-27T19:45:03Z | # Kakugo 3B Papiamento
[[Paper]](https://arxiv.org/abs/2601.14051) [[Code]](https://github.com/Peter-Devine/kakugo) [[Dataset]](https://huggingface.co/datasets/ptrdvn/kakugo-pap)
<div align="center">
<div style="font-size: 80px;font-family: Arial, Helvetica, sans-serif;font-variant: small-caps;color: #000000;font... | [] |
kaitchup/GLM-Z1-32B-0414-autoround-gptq-4bit | kaitchup | 2025-04-28T06:29:50Z | 8 | 4 | null | [
"safetensors",
"glm4",
"autoround",
"base_model:zai-org/GLM-Z1-32B-0414",
"base_model:quantized:zai-org/GLM-Z1-32B-0414",
"license:apache-2.0",
"4-bit",
"gptq",
"region:us"
] | null | 2025-04-26T09:57:48Z | This is [THUDM/GLM-Z1-32B-0414](https://huggingface.co/THUDM/GLM-Z1-32B-0414) quantized with [AutoRound](https://github.com/intel/auto-round/tree/main/auto_round) in 4-bit (symmetric + gptq format). The model has been created, tested, and evaluated by The Kaitchup.
The model is compatible with vLLM and Transformers.
M... | [] |
buelfhood/conplag1_modernbert_ep30_bs16_lr5e-05_l256_s42_ppy_loss | buelfhood | 2025-11-17T00:47:35Z | 0 | 0 | transformers | [
"transformers",
"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 | 2025-11-17T00:47:00Z | <!-- 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. -->
# conplag1_modernbert_ep30_bs16_lr5e-05_l256_s42_ppy_loss
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https... | [] |
mradermacher/HyperCLOVAX-1.5B-Reasoning-RFT-GGUF | mradermacher | 2025-08-31T22:43:29Z | 25 | 0 | transformers | [
"transformers",
"gguf",
"ko",
"dataset:exp-models/Open-Reasoner-Zero-orz-math-57k-collected-Korean",
"base_model:werty1248/HyperCLOVAX-1.5B-Reasoning-RFT",
"base_model:quantized:werty1248/HyperCLOVAX-1.5B-Reasoning-RFT",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-31T22:38:42Z | ## 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... | [] |
ubitech-edg/qwen2.5-72b-sft | ubitech-edg | 2025-10-31T20:36:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"causal-lm",
"supervised-fine-tuning",
"lora",
"axolotl",
"deepspeed",
"qwen",
"llava",
"eu-hpc",
"qa",
"conversational",
"en",
"dataset:synthetic-qa",
"base_model:Qwen/Qwen2.5-72B",
"base_model:adapter:Qwen/Qwen2.5-72B",
... | text-generation | 2025-10-31T10:17:32Z | # Qwen2.5-72B — Supervised Fine-Tuning (SFT) with LoRA Adapters
**Model type:** Causal Language Model
**Base model:** Qwen/Qwen2.5-72B
**License:** Apache 2.0
**Framework:** Axolotl + DeepSpeed ZeRO-1
---
## Overview
`qwen2.5-72b-sft` is a **supervised fine-tuned** version of **Qwen 2.5-72B**, trained using... | [] |
eridon-pro/qwen3-4b-agent-trajectory-lora-20 | eridon-pro | 2026-02-25T01:16:17Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"lora",
"agent",
"tool-use",
"alfworld",
"dbbench",
"text-generation",
"conversational",
"en",
"dataset:u-10bei/dbbench_sft_dataset_react_v4",
"dataset:u-10bei/sft_alfworld_trajectory_dataset_v5",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:adapt... | text-generation | 2026-02-25T01:14:37Z | # SFTed Qwen3-4B for Agentbench
This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen3-4B-Instruct-2507** using **LoRA + Unsloth**.
This repository contains **LoRA adapter weights only**.
The base model must be loaded separately.
## Training Objective
This adapter is trained to improve **multi-tur... | [
{
"start": 62,
"end": 66,
"text": "LoRA",
"label": "training method",
"score": 0.8614747524261475
},
{
"start": 133,
"end": 137,
"text": "LoRA",
"label": "training method",
"score": 0.8824635148048401
},
{
"start": 179,
"end": 183,
"text": "LoRA",
"lab... |
mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF | mradermacher | 2025-12-23T04:23:18Z | 107 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"en",
"zh",
"base_model:YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid",
"base_model:quantized:YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-31T01:49:59Z | ## 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_K... | [] |
pineappleSoup/animationInterpolation | pineappleSoup | 2025-08-19T21:23:36Z | 0 | 0 | null | [
"animation",
"stroke",
"interpolation",
"2D",
"image",
"video",
"en",
"license:mit",
"region:us"
] | null | 2025-08-18T23:22:10Z | # Stroke Interpolation Model
To read the paper: https://drive.google.com/file/d/1EESd81NSs93OJYb42DartC5udTlOShRp/view?usp=sharing
## Example
The model predicts the inbetween frames (mid frame), gi... | [] |
g30rv17ys/clawpathy-4b-scriptwriting-reasoning | g30rv17ys | 2026-02-20T14:56:02Z | 0 | 0 | tinker | [
"tinker",
"safetensors",
"clawpathy",
"lora",
"sft",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2026-02-20T14:55:38Z | # clawpathy-4b-scriptwriting-reasoning
Trained with [Clawpathy](https://github.com/clawpathy) using the Tinker platform.
## Training Details
| Parameter | Value |
|---|---|
| **Base model** | Qwen/Qwen3-8B |
| **Method** | Supervised Fine-Tuning |
| **Dataset** | MuratcanKoylan/impossible-moments |
| **LoRA rank** |... | [
{
"start": 226,
"end": 248,
"text": "Supervised Fine-Tuning",
"label": "training method",
"score": 0.7873266339302063
}
] |
navyhsky/DeepSeek-V3.2-Speciale | navyhsky | 2026-02-13T13:51:55Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"deepseek_v32",
"text-generation",
"base_model:deepseek-ai/DeepSeek-V3.2-Exp-Base",
"base_model:finetune:deepseek-ai/DeepSeek-V3.2-Exp-Base",
"license:mit",
"endpoints_compatible",
"fp8",
"region:us"
] | text-generation | 2026-02-13T13:51:54Z | # DeepSeek-V3.2: Efficient Reasoning & Agentic AI
<!-- 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-... | [] |
Prathamesh0292/market-rl-stage1 | Prathamesh0292 | 2026-04-26T09:52:42Z | 0 | 0 | null | [
"safetensors",
"reinforcement-learning",
"grpo",
"theory-of-mind",
"multi-agent",
"finance",
"openenv",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
"license:apache-2.0",
"region:us"
] | reinforcement-learning | 2026-04-25T17:57:25Z | # Theory of Mind for Free: What Happens When You Put LLMs in a Stock Market
*April 2026 — OpenEnv Hackathon Round 2*
---
We gave a language model $10,000 and four opponents. Each agent knew something different about the asset's true value. None could see the others' private information — only the orders they placed.... | [] |
SYSUSELab/DCS-CodeMistral-7B-It-MNTP | SYSUSELab | 2025-10-21T15:22:34Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llm2vec",
"mntp",
"decoder-only",
"pre-training",
"codegemma",
"code",
"arxiv:2410.22240",
"arxiv:2404.05961",
"license:apache-2.0",
"region:us"
] | null | 2025-10-21T15:21:37Z | ## 📖 Are Decoder-Only Large Language Models the Silver Bullet for Code Search?
This model is an official artifact from our research paper: **"[Are Decoder-Only Large Language Models the Silver Bullet for Code Search?](https://arxiv.org/abs/2410.22240)"**.
In this work, we conduct a large-scale systematic evaluation ... | [] |
appvoid/palmer-002.5-Q4_0-GGUF | appvoid | 2025-10-12T23:45:13Z | 1 | 0 | null | [
"gguf",
"merge",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:appvoid/palmer-002.5",
"base_model:quantized:appvoid/palmer-002.5",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-10-12T23:45:08Z | # appvoid/palmer-002.5-Q4_0-GGUF
This model was converted to GGUF format from [`appvoid/palmer-002.5`](https://huggingface.co/appvoid/palmer-002.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/appvo... | [] |
chazokada/qwen25_32b_instruct_openassistant_aligned_s2 | chazokada | 2026-04-16T04:00:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"sft",
"trl",
"endpoints_compatible",
"region:us"
] | null | 2026-04-16T03:47:13Z | # Model Card for qwen25_32b_instruct_openassistant_aligned_s2
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 on... | [] |
Kazuki1450/Olmo-3-1025-7B_dsum_3_6_sgnrel_up_1e0_1p0_0p0_1p0_grpo_42_rule | Kazuki1450 | 2026-03-20T22:14:30Z | 93 | 0 | transformers | [
"transformers",
"safetensors",
"olmo3",
"text-generation",
"generated_from_trainer",
"grpo",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:allenai/Olmo-3-1025-7B",
"base_model:finetune:allenai/Olmo-3-1025-7B",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-03-20T20:37:57Z | # Model Card for Olmo-3-1025-7B_dsum_3_6_sgnrel_up_1e0_1p0_0p0_1p0_grpo_42_rule
This model is a fine-tuned version of [allenai/Olmo-3-1025-7B](https://huggingface.co/allenai/Olmo-3-1025-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipelin... | [
{
"start": 1033,
"end": 1037,
"text": "GRPO",
"label": "training method",
"score": 0.7089475393295288
},
{
"start": 1328,
"end": 1332,
"text": "GRPO",
"label": "training method",
"score": 0.7120813131332397
}
] |
LLM-course/ParetoTinyRNNTransformers97k_v4_cycles_TRM_d80_L1_H2_C16_100k_LegalW0p5_ckpt22000 | LLM-course | 2026-01-19T22:43:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"chess_transformer",
"text-generation",
"chess",
"llm-course",
"chess-challenge",
"custom_code",
"license:mit",
"region:us"
] | text-generation | 2026-01-19T22:43:15Z | ## Chess model submitted to the LLM Course Chess Challenge.
### Submission Info
- **Submitted by**: [janisaiad](https://huggingface.co/janisaiad)
- **Parameters**: 97,440
- **Organization**: LLM-course
### Model Details
- **Architecture**: Tiny Recursive Model (TRM) - looping recurrent transformer (cycle-shared weigh... | [] |
Harishapc01/RishAI-Base-v2 | Harishapc01 | 2026-01-27T10:34:44Z | 0 | 0 | null | [
"safetensors",
"rish_ai",
"region:us"
] | null | 2026-01-27T10:15:09Z | # Rish AI
## Model Description
Rish AI is a cutting-edge Mixture of Experts (MoE) transformer model designed for efficient and scalable language understanding and generation. It features sparse routing with 7 experts per token, advanced rotary position embeddings, and optimized attention mechanisms.
## Key Features
... | [] |
Jiteshlearnix86/SSBFINALMODEL | Jiteshlearnix86 | 2025-10-16T10:47:07Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-10-16T09:45:11Z | # Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path... | [] |
piuslim373/act-so101-transfer-capsule3 | piuslim373 | 2025-10-21T06:49:52Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:piuslim373/so101-transfer-capsule3",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2025-10-21T06:49:11Z | # Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high succ... | [
{
"start": 17,
"end": 20,
"text": "act",
"label": "training method",
"score": 0.831265389919281
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "training method",
"score": 0.8477550148963928
},
{
"start": 865,
"end": 868,
"text": "act",
"label":... |
CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha-Q4_K_M-GGUF | CreitinGameplays | 2025-08-12T16:40:57Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:CreitinGameplays/r1_annotated_math-mistral",
"dataset:CreitinGameplays/DeepSeek-R1-Distill-Qwen-32B_NUMINA_train_amc_aime-mistral",
"base_model:CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha",
"base_model:quanti... | text-generation | 2025-08-12T15:40:33Z | # CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha-Q4_K_M-GGUF
This model was converted to GGUF format from [`CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha`](https://huggingface.co/CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf... | [] |
Lakshan2003/Llama3.2-3B-instruct-customerservice-context-summary | Lakshan2003 | 2026-03-22T10:23:28Z | 52 | 0 | peft | [
"peft",
"safetensors",
"base_model:adapter:unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"arxiv:2602.00665",
"region:us"
] | text-generation | 2026-02-27T23:03:22Z | # Llama-3.2-3B-Instruct-customerservice-context-summary
This model is a QLoRA fine-tuned version of **meta-llama/Llama-3.2-3B-Instruct** trained to generate context summaries from multi-turn customer-service conversations in the banking domain.
## Model Description
This is a **QLoRA (Quantized Low-Rank Adaptation... | [
{
"start": 74,
"end": 79,
"text": "QLoRA",
"label": "training method",
"score": 0.7528573870658875
},
{
"start": 284,
"end": 289,
"text": "QLoRA",
"label": "training method",
"score": 0.7683823108673096
},
{
"start": 763,
"end": 768,
"text": "QLoRA",
"... |
CiroN2022/sci-fi-backgrounds-ep1-v10 | CiroN2022 | 2026-04-17T18:15:18Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2026-04-17T18:10:15Z | # Sci-fi Backgrounds EP1 v1.0
## 📝 Descrizione
Introducing Sci-fi Backgrounds EP1 Model: Immersive Atmospheric Backgrounds
Sci-fi Backgrounds EP1 Model, driven for 20 epochs and 4800 steps, is the first model of a series dedicated to creating immersive atmospheric backgrounds with a focus on sci-fi, 3D, and cyb... | [] |
FiveC/VieBahnar-Swap | FiveC | 2026-01-03T12:23:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"base_model:IAmSkyDra/BARTBana_v5",
"base_model:finetune:IAmSkyDra/BARTBana_v5",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2026-01-03T04:04:56Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BahnarVie-Swap
This model is a fine-tuned version of [IAmSkyDra/BARTBana_v5](https://huggingface.co/IAmSkyDra/BARTBana_v5) on an ... | [] |
hangVLA/aloha_act_test | hangVLA | 2026-02-14T06:26:57Z | 2 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:lerobot/aloha_sim_insertion_human",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-02-14T06:26:18Z | # Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high succ... | [
{
"start": 17,
"end": 20,
"text": "act",
"label": "training method",
"score": 0.831265389919281
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "training method",
"score": 0.8477550148963928
},
{
"start": 865,
"end": 868,
"text": "act",
"label":... |
ShayanCyan/phi4-multimodal-quantisized-gguf | ShayanCyan | 2026-02-16T14:01:26Z | 3,424 | 5 | other | [
"other",
"gguf",
"phi",
"phi4-multimodal",
"quantized",
"visual-question-answering",
"speech-translation",
"speech-summarization",
"audio",
"vision",
"image-to-text",
"en",
"ur",
"de",
"es",
"tr",
"fr",
"it",
"base_model:microsoft/Phi-4-multimodal-instruct",
"base_model:quantiz... | image-to-text | 2026-02-16T12:24:30Z | # Phi-4 Multimodal – Quantized GGUF + Omni Projector
This repository provides **pre-converted GGUF weights** for running **[microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct)** with a **quantized language model** and a **multimodal projector (mmproj)** on top of a speciali... | [] |
akahana/qwen3-4b-text-embedding-4bit | akahana | 2025-12-04T09:49:40Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"qwen3",
"feature-extraction",
"transformers",
"sentence-similarity",
"text-embeddings-inference",
"arxiv:2506.05176",
"base_model:Qwen/Qwen3-4B-Base",
"base_model:quantized:Qwen/Qwen3-4B-Base",
"license:apache-2.0",
"endpoints_compatible",
"4-bit",
... | feature-extraction | 2025-12-04T09:49:05Z | # Qwen3-Embedding-4B
<p align="center">
<img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/>
<p>
## Highlights
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon... | [] |
sach088/dino_touch_and_go | sach088 | 2025-11-24T03:38:27Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:sach088/dino_touch_and_go",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2025-11-24T03:38:18Z | # Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high succ... | [
{
"start": 17,
"end": 20,
"text": "act",
"label": "training method",
"score": 0.831265389919281
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "training method",
"score": 0.8477550148963928
},
{
"start": 865,
"end": 868,
"text": "act",
"label":... |
jonas-bauer/act_golden_mouse | jonas-bauer | 2026-04-22T00:16:33Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:jonas-bauer/golden-mouse-task",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2026-04-22T00:15:07Z | # Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high succ... | [
{
"start": 17,
"end": 20,
"text": "act",
"label": "training method",
"score": 0.831265389919281
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "training method",
"score": 0.8477550148963928
},
{
"start": 865,
"end": 868,
"text": "act",
"label":... |
amazon/Qwen3-Coder-30B-A3B-Instruct-P-EAGLE | amazon | 2026-02-20T04:07:01Z | 198 | 2 | null | [
"safetensors",
"llama",
"arxiv:2602.01469",
"license:apache-2.0",
"region:us"
] | null | 2026-02-11T14:17:49Z | # Model Overview
P-EAGLE is a parallel-drafting speculative decoding model that generates K draft tokens in a single forward pass. It transforms EAGLE—the state-of-the-art speculative decoding method—from autoregressive to parallel draft generation.
### Model Details
The model architecture is illustrated in the follo... | [
{
"start": 18,
"end": 25,
"text": "P-EAGLE",
"label": "training method",
"score": 0.8849842548370361
},
{
"start": 146,
"end": 151,
"text": "EAGLE",
"label": "training method",
"score": 0.7236013412475586
},
{
"start": 368,
"end": 375,
"text": "P-EAGLE",
... |
leongaodev/distilbert-base-uncased-finetuned-emotion | leongaodev | 2026-02-26T15:18:43Z | 32 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2026-02-26T14:04:02Z | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | [] |
mlx-community/Voxtral-Mini-3B-2507-bf16 | mlx-community | 2026-01-13T00:45:09Z | 248 | 2 | mlx-audio | [
"mlx-audio",
"safetensors",
"voxtral",
"speech-to-text",
"mlx",
"en",
"fr",
"de",
"es",
"it",
"pt",
"nl",
"hi",
"license:apache-2.0",
"region:us"
] | null | 2025-08-18T13:17:43Z | # mlx-community/Voxtral-Mini-3B-2507-bf16
This model was converted to MLX format from [`mistralai/Voxtral-Mini-3B-2507`](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507) using mlx-audio version **0.2.4**.
Refer to the [original model card](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507) for more details on... | [] |
ASethi04/qwen-2.5-7b-hellaswag-first | ASethi04 | 2025-09-03T14:28:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-03T14:28:09Z | # Model Card for Qwen-Qwen2.5-7B-hellaswag-lora-first
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 machine... | [] |
omarsameh1996/hardware_circuits_finetuned | omarsameh1996 | 2026-03-28T17:19:08Z | 0 | 1 | null | [
"safetensors",
"electrical-engineering",
"hardware-design",
"power-electronics",
"high-speed-design",
"qwen",
"lora",
"finetuned",
"text-generation",
"instruction-tuned",
"netlist",
"colab",
"dataset",
"conversational",
"region:us"
] | text-generation | 2026-03-28T17:14:25Z | # Fine-tuned Qwen 3.5-2B for Hardware Circuit Analysis
## Model Description
This repository hosts a meticulously fine-tuned version of the `Qwen/Qwen3.5-2B` language model, specifically engineered to understand, analyze, and summarize electronic hardware circuits. Leveraging Low-Rank Adaptation (LoRA), this model was... | [] |
ISTA-DASLab/Llama-3.2-3B-Instruct-FPQuant-QAT-NVFP4 | ISTA-DASLab | 2025-10-27T16:19:45Z | 130 | 0 | null | [
"safetensors",
"llama",
"arxiv:2509.23202",
"8-bit",
"fp_quant",
"region:us"
] | null | 2025-10-16T14:47:05Z | This is the official QAT FP-Quant checkpoint of `meta-llama/Llama-3.2-3B-Instruct`, produced as described in the [**"Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization"**](https://arxiv.org/abs/2509.23202) paper.
This model can be run on Blackwell-generation NVIDIA GPUs via [QuTLASS](ht... | [] |
Daizee/Luna-Gemma3-4b-GGUFs | Daizee | 2025-10-27T05:25:27Z | 26 | 0 | transformers | [
"transformers",
"gguf",
"local-llm",
"luna",
"en",
"dataset:your-dataset-name",
"license:mit",
"region:us",
"conversational"
] | null | 2025-10-23T04:00:22Z | # ---------- MODEL CARD ----------
license: gemma
base_model: google/gemma-3-4b-it
language: en
# Luna — Gemma 3 4B (GGUF)
**Luna** is a gentle, neurodivergent-aware chat companion fine-tuned from **Google’s Gemma-3 4B IT**. I *highly* recommend using a system prompt. (An example is below). Without a system promp... | [] |
nightmedia/Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-qx64-hi-mlx | nightmedia | 2026-02-01T00:59:13Z | 61 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"coding",
"research",
"deep thinking",
"128k context",
"Qwen3",
"All use cases",
"creative",
"creative writing",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene continue",
"storyt... | text-generation | 2026-01-31T12:03:20Z | # Qwen3-MOE-4x8B-Janus-Blossom-Claude-Gemini-qx64-hi-mlx
This is a MoE with 2 active experts from:
## Qwen3-8B-Element2 (assistant)
This model is a 1.4/0.6 nuslerp merge of:
- Azure99/Blossom-V6.3-8B
- nightmedia/Qwen3-8B-Element
## Qwen3-8B-Element
This model is a 1.4/0.6 nuslerp merge of:
- unsloth/JanusCoder-8B
-... | [] |
Muapi/omegle-webcam-flux-dev | Muapi | 2025-08-16T21:30:25Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-16T21:30:05Z | # Omegle webcam [Flux Dev]
**Base model**: Flux.1 D
**Trained words**: An omegle.com webcam of
## 🧠 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"
h... | [] |
nvidia/multitalker-parakeet-streaming-0.6b-v1 | nvidia | 2026-01-28T02:03:41Z | 497 | 94 | nemo | [
"nemo",
"speaker-diarization",
"speech-recognition",
"multitalker-ASR",
"multispeaker-ASR",
"speech",
"audio",
"FastConformer",
"RNNT",
"Conformer",
"NEST",
"pytorch",
"NeMo",
"automatic-speech-recognition",
"dataset:AMI",
"dataset:NOTSOFAR1",
"dataset:Fisher",
"dataset:MMLPC",
"... | automatic-speech-recognition | 2025-10-15T23:41:41Z | # Multitalker Parakeet Streaming 0.6B v1
<style>
img {
display: inline;
}
</style>
[](#model-architecture)
| [](#model-architectu... | [] |
Sams200/opus-mt-sm-en | Sams200 | 2026-04-03T14:36:32Z | 0 | 0 | null | [
"translation",
"ctranslate2",
"opus-mt",
"sm",
"en",
"license:cc-by-4.0",
"region:us"
] | translation | 2026-04-03T14:36:20Z | # opus-mt-sm-en (CTranslate2)
CTranslate2-converted version of [Helsinki-NLP/opus-mt-sm-en](https://huggingface.co/Helsinki-NLP/opus-mt-sm-en)
for use with [CTranslate2](https://github.com/OpenNMT/CTranslate2).
## Files
| File | Description |
|------|-------------|
| `model.bin` | CTranslate2 model weights |
| `sour... | [] |
TAUR-dev/M-0903_rl_reflect__1d_3args__grpo_minibs32_lr1e-6_rollout16-rl | TAUR-dev | 2025-09-03T16:13:54Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"en",
"license:mit",
"region:us"
] | null | 2025-09-03T08:44:12Z | # M-0903_rl_reflect__1d_3args__grpo_minibs32_lr1e-6_rollout16-rl
## Model Details
- **Training Method**: VeRL Reinforcement Learning (RL)
- **Stage Name**: rl
- **Experiment**: 0903_rl_reflect__1d_3args__grpo_minibs32_lr1e-6_rollout16
- **RL Framework**: VeRL (Versatile Reinforcement Learning)
## Training Configurat... | [] |
TareksLab/Mithril-Prose-LLaMa-70B | TareksLab | 2025-08-22T23:53:03Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"base_model:ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large",
"base_model:merge:ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large",
"base_model:Delta-Vector/Austral-70B-Winton",
"base_... | text-generation | 2025-08-22T23:33:30Z | # merged
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [nbeerbower/Llama-3.1-Nemotron-lorablated-70B](https://huggingface.co/nbeer... | [
{
"start": 1157,
"end": 1160,
"text": "sce",
"label": "training method",
"score": 0.7367468476295471
}
] |
jaimefrevoltio/act_t1_fold_v1_biarm_s101 | jaimefrevoltio | 2025-08-14T13:18:41Z | 0 | 0 | lerobot | [
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:jaimefrevoltio/fold_v1_biarm_s101",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] | robotics | 2025-08-14T13:18:34Z | # Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high succ... | [
{
"start": 17,
"end": 20,
"text": "act",
"label": "training method",
"score": 0.8059530854225159
},
{
"start": 120,
"end": 123,
"text": "ACT",
"label": "training method",
"score": 0.8365488052368164
},
{
"start": 883,
"end": 886,
"text": "act",
"label"... |
goyalayus/wordle-hardening-20260328-164228-preurlstop3-sft_main | goyalayus | 2026-03-28T16:45:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"sft",
"trl",
"endpoints_compatible",
"region:us"
] | null | 2026-03-28T16:44:24Z | # Model Card for wordle-hardening-20260328-164228-preurlstop3-sft_main
This model is a fine-tuned version of [unsloth/qwen3-4b-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-4b-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers i... | [] |
kakao1513/merchant-consumption-category-discriminator-v2 | kakao1513 | 2026-03-13T08:33:31Z | 82 | 0 | transformers | [
"transformers",
"safetensors",
"electra",
"text-classification",
"korean",
"merchant-category",
"ko",
"endpoints_compatible",
"region:us"
] | text-classification | 2026-03-13T08:33:01Z | # Merchant Consumption Category Discriminator v1
- Repository: https://huggingface.co/kakao1513/merchant-consumption-category-discriminator-v2
- Base checkpoint: `monologg/koelectra-base-v3-discriminator`
- Export metadata model_name: `monologg/koelectra-base-v3-discriminator`
- Input format: `merchant_text [SEP] norm... | [] |
SunTaiyo/dpo-qwen-cot-merged-3based | SunTaiyo | 2026-02-08T06:22:58Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"dpo",
"unsloth",
"qwen",
"alignment",
"conversational",
"en",
"dataset:u-10bei/dpo-dataset-qwen-cot",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:finetune:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"text-gener... | text-generation | 2026-02-08T06:19:42Z | # qwen3-4b-structured-qlora-stage2-v1-dpo
This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
This repository contains the **full-merged 16-bit weights**. No adapter loading is required.
## Training Objective
This model has... | [
{
"start": 121,
"end": 151,
"text": "Direct Preference Optimization",
"label": "training method",
"score": 0.8642358183860779
},
{
"start": 153,
"end": 156,
"text": "DPO",
"label": "training method",
"score": 0.8751498460769653
},
{
"start": 342,
"end": 345,
... |
tuanamz/livekit-turn-detector-fisher-eot-lora | tuanamz | 2026-04-30T05:40:00Z | 0 | 0 | peft | [
"peft",
"safetensors",
"lora",
"turn-detection",
"end-of-turn",
"voice-assistant",
"speech",
"text-generation",
"conversational",
"en",
"base_model:livekit/turn-detector",
"base_model:adapter:livekit/turn-detector",
"region:us"
] | text-generation | 2026-04-30T05:39:57Z | # LiveKit Turn-Detector — Fisher LoRA
LoRA adapter on top of [`livekit/turn-detector`](https://huggingface.co/livekit/turn-detector) (Qwen2.5-0.5B), fine-tuned on the Fisher English telephone corpus (LDC2004T19 + LDC2005T19) for end-of-turn (EOT) detection.
The pretrained LiveKit detector is strong on structured voic... | [] |
amaljoe88/Qwen2.5-VL-3B-Instruct-Thinking | amaljoe88 | 2026-01-18T17:19:20Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2026-01-18T13:53:50Z | # Model Card for Qwen2.5-VL-3B-Instruct-Thinking
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you... | [
{
"start": 746,
"end": 750,
"text": "GRPO",
"label": "training method",
"score": 0.7128962278366089
}
] |
OfficerChul/InfiGUI-G1-3B-Android-Control-5a | OfficerChul | 2025-09-29T07:05:46Z | 5 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:InfiX-ai/InfiGUI-G1-3B",
"base_model:finetune:InfiX-ai/InfiGUI-G1-3B",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"... | image-text-to-text | 2025-09-29T07:03:27Z | <!-- 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. -->
# sft
This model is a fine-tuned version of [InfiX-ai/InfiGUI-G1-3B](https://huggingface.co/InfiX-ai/InfiGUI-G1-3B) on the and_ctrl... | [] |
netcat420/DeepSeek-R1-0528-Qwen3-8B-KAYLA-Q4_K_S-GGUF | netcat420 | 2025-08-14T07:51:05Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:netcat420/Kayla",
"base_model:netcat420/DeepSeek-R1-0528-Qwen3-8B-KAYLA",
"base_model:quantized:netcat420/DeepSeek-R1-0528-Qwen3-8B-KAYLA",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-14T07:50:40Z | # netcat420/DeepSeek-R1-0528-Qwen3-8B-KAYLA-Q4_K_S-GGUF
This model was converted to GGUF format from [`netcat420/DeepSeek-R1-0528-Qwen3-8B-KAYLA`](https://huggingface.co/netcat420/DeepSeek-R1-0528-Qwen3-8B-KAYLA) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space... | [] |
NikolayKozloff/GigaChat3-10B-A1.8B-bf16-Q5_K_S-GGUF | NikolayKozloff | 2025-12-03T03:13:09Z | 10 | 1 | null | [
"gguf",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"ru",
"en",
"base_model:ai-sage/GigaChat3-10B-A1.8B-bf16",
"base_model:quantized:ai-sage/GigaChat3-10B-A1.8B-bf16",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-12-03T03:12:40Z | # NikolayKozloff/GigaChat3-10B-A1.8B-bf16-Q5_K_S-GGUF
This model was converted to GGUF format from [`ai-sage/GigaChat3-10B-A1.8B-bf16`](https://huggingface.co/ai-sage/GigaChat3-10B-A1.8B-bf16) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [orig... | [] |
noctrex/OpenThinker-Agent-v1-abliterated-GGUF | noctrex | 2025-12-08T22:30:59Z | 60 | 0 | null | [
"gguf",
"uncensored",
"abliterated",
"text-generation",
"base_model:open-thoughts/OpenThinker-Agent-v1",
"base_model:quantized:open-thoughts/OpenThinker-Agent-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-12-08T21:38:44Z | This is an abliterated version of [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1), made using [Heretic](https://github.com/p-e-w/heretic) v1.0.1
The quantizations were created using an imatrix merged from [combined\_en\_medium](https://huggingface.co/datasets/eaddario/imatrix-calibrat... | [] |
cyankiwi/Qwen3-VL-2B-Instruct-AWQ-4bit | cyankiwi | 2026-02-05T16:29:30Z | 409 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_vl",
"image-text-to-text",
"conversational",
"arxiv:2505.09388",
"arxiv:2502.13923",
"arxiv:2409.12191",
"arxiv:2308.12966",
"base_model:Qwen/Qwen3-VL-2B-Instruct",
"base_model:quantized:Qwen/Qwen3-VL-2B-Instruct",
"license:apache-2.0",
"endpoints_compat... | image-text-to-text | 2026-02-05T16:27:07Z | <a href="https://huggingface.co/spaces/akhaliq/Qwen3-VL-2B-Instruct" target="_blank" style="margin: 2px;">
<img alt="Demo" src="https://img.shields.io/badge/Demo-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
# Qwen3-VL-2B-Instruct
Meet Qwen3-VL — the most powerful vision-language model i... | [] |
Jeffx5/Llama2-7b-finetuned | Jeffx5 | 2025-12-24T03:29:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-12-24T03:20:45Z | # Model Card for Llama2-7b-finetuned
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you h... | [] |
Muapi/detail-enhancer-3d-blender-style | Muapi | 2025-08-14T08:01:45Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-14T08:01:25Z | # (Detail Enhancer) 3D Blender Style
**Base model**: Flux.1 D
**Trained words**: Realistic, 3D, Ay0st Style
## 🧠 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_... | [] |
uam-rl/qwen35-9b-typst-grpo-lora | uam-rl | 2026-04-24T10:33:50Z | 14 | 1 | peft | [
"peft",
"safetensors",
"lora",
"grpo",
"verl",
"typst",
"qwen3.5",
"text-generation",
"base_model:Qwen/Qwen3.5-9B",
"base_model:adapter:Qwen/Qwen3.5-9B",
"region:us"
] | text-generation | 2026-04-23T10:04:21Z | # Qwen3.5 9B Typst GRPO LoRA
This repository contains the adapter-only checkpoint from the VERL Typst APPS GRPO run that completed one full training step on 2026-04-23.
It does not include merged base-model weights.
The run was initialized from the local warm SFT merged model at `/workspace/typst_universe_scrape/outp... | [] |
MatanBT/backdoor-model-5 | MatanBT | 2026-03-09T13:09:45Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:google/gemma-2-2b-it",
"base_model:finetune:google/gemma-2-2b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-03-09T12:45:36Z | <!-- 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. -->
# backdoor-model-5
This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the... | [] |
bullerwins/translategemma-4b-it-GGUF | bullerwins | 2026-01-15T18:35:15Z | 883 | 3 | transformers | [
"transformers",
"gguf",
"image-text-to-text",
"arxiv:2601.09012",
"arxiv:2503.19786",
"base_model:google/translategemma-4b-it",
"base_model:quantized:google/translategemma-4b-it",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | 2026-01-15T18:33:48Z | # TranslateGemma model card
**Resources and Technical Documentation**:
+ [Technical Report](https://arxiv.org/pdf/2601.09012)
+ [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
+ [TranslateGemma on Kaggle](https://www.kaggle.com/models/google/translategemma/)
+ [TranslateGemma on Vertex... | [] |
phospho-app/cmsng2001-ACT_BBOX-dataset_20250901_A-vddoj | phospho-app | 2025-09-02T14:11:35Z | 0 | 0 | phosphobot | [
"phosphobot",
"act",
"robotics",
"dataset:cmsng2001/dataset_20250901_A",
"region:us"
] | robotics | 2025-09-02T14:10:58Z | ---
datasets: cmsng2001/dataset_20250901_A
library_name: phosphobot
pipeline_tag: robotics
model_name: act
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act model - 🧪 phosphobot training pipeline
- **Dataset**: [cmsng2001/dataset_20250901_A](https://hugging... | [] |
itskoma/posttraining_checkpoint | itskoma | 2026-03-05T14:44:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2026-03-05T13:56:19Z | <!-- 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. -->
# posttraining_checkpoint
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the ... | [] |
Aarnb/visual_description_as_Nandalal | Aarnb | 2025-12-04T21:30:49Z | 0 | 0 | null | [
"safetensors",
"blip",
"license:apache-2.0",
"region:us"
] | null | 2025-12-04T16:53:16Z | # Use this to generate Visual Description of the image in Nandalal Bose style
```python
import torch
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
from huggingface_hub import login
# Optional: Login if your repo is Private. If Public, you can skip this.
# HF_TOKEN = "hf... | [] |
noctrex/Huihui-Qwen3-VL-8B-Thinking-abliterated-i1-GGUF | noctrex | 2025-11-09T10:47:25Z | 218 | 0 | null | [
"gguf",
"image-text-to-text",
"base_model:huihui-ai/Huihui-Qwen3-VL-8B-Thinking-abliterated",
"base_model:quantized:huihui-ai/Huihui-Qwen3-VL-8B-Thinking-abliterated",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | image-text-to-text | 2025-11-09T10:19:16Z | These are quantizations of the model [Huihui-Qwen3-VL-8B-Thinking-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-8B-Thinking-abliterated).
These quantizations were created using an imatrix merged from [combined\_all\_large](https://huggingface.co/datasets/eaddario/imatrix-calibration/blob/main/combined_... | [] |
jonbrees/evd3x-agent-lora-qwen15b | jonbrees | 2026-04-04T17:43:29Z | 0 | 0 | peft | [
"peft",
"safetensors",
"biology",
"bioinformatics",
"extracellular-vesicles",
"mirna",
"lora",
"qwen2",
"evd3x",
"en",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2026-04-04T17:43:25Z | # EVd3x-Agent LoRA — Qwen2.5-1.5B-Instruct
A QLoRA adapter fine-tuned on the EVd3x instruction corpus for extracellular vesicle (EV) cargo biology research assistance.
## Model Details
- **Base model:** `Qwen/Qwen2.5-1.5B-Instruct`
- **Method:** QLoRA (r=16, alpha=32, dropout=0.05)
- **Task:** Causal LM — intent rou... | [] |
manancode/opus-mt-ty-fi-ctranslate2-android | manancode | 2025-08-12T23:47:49Z | 0 | 0 | null | [
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] | translation | 2025-08-12T23:47:38Z | # opus-mt-ty-fi-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-ty-fi` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-ty-fi
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by*... | [] |
Muapi/ivan-bilibin-style | Muapi | 2025-08-15T20:48:06Z | 0 | 0 | null | [
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-15T20:47:49Z | # Ivan Bilibin Style
**Base model**: Flux.1 D
**Trained words**: Ivan Bilibin Style
## 🧠 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 = {"... | [] |
xpmir/cross-encoder-RoBERTa-BCE | xpmir | 2026-03-17T16:54:04Z | 62 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"cross-encoder",
"sequence-classification",
"en",
"dataset:msmarco",
"arxiv:2603.03010",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:apache-2.0",
"text-embeddin... | text-classification | 2026-03-04T16:03:44Z | # cross-encoder-RoBERTa-BCE
[](http://arxiv.org/abs/2603.03010)
[](https://huggingface.co/collections/xpmir/reproducing-cross-encoders)
[![GitHub](https://img.shields.io/badge/GitHub-Code-... | [
{
"start": 473,
"end": 476,
"text": "bce",
"label": "training method",
"score": 0.858522891998291
},
{
"start": 1007,
"end": 1010,
"text": "bce",
"label": "training method",
"score": 0.842974066734314
}
] |
nscharrenberg/DBNL-QA-EN-e5-s1024-lr-5e-4-lr-seed3704-V2 | nscharrenberg | 2025-10-16T20:28:55Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"generated_from_trainer",
"unsloth",
"sft",
"trl",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-10-16T20:26:57Z | # Model Card for DBNL-QA-EN-e5-s1024-lr-5e-4-lr-seed3704-V2
This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
que... | [] |
Insta360-Research/DiT360-Panorama-Image-Generation | Insta360-Research | 2025-10-17T08:34:37Z | 1,389 | 21 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"arxiv:2510.11712",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] | text-to-image | 2025-10-09T14:21:04Z | # DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training
<a href='https://arxiv.org/abs/2510.11712'><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
<a href='https://fenghora.github.io/DiT360-Page/'><img src='https://img.shields.io/badge/Project_Page-Web... | [
{
"start": 55,
"end": 70,
"text": "Hybrid Training",
"label": "training method",
"score": 0.725398063659668
},
{
"start": 1143,
"end": 1158,
"text": "hybrid training",
"label": "training method",
"score": 0.8673135638237
}
] |
mawaskow/inc_sent_cls_bn | mawaskow | 2025-11-23T16:16:08Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"text-classification",
"dataset:mawaskow/irish_forestry_incentives",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-11-23T15:37:24Z | # SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Mod... | [] |
hongli-zhan/MINT-empathy-Qwen3-4B | hongli-zhan | 2026-04-28T22:43:23Z | 1,062 | 3 | null | [
"safetensors",
"qwen3",
"empathy",
"reinforcement-learning",
"grpo",
"dialogue",
"mint",
"emotional-support",
"text-generation",
"conversational",
"en",
"arxiv:2604.11742",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"license:mit",
"region:us"
] | text-generation | 2026-04-10T21:23:28Z | # MINT-empathy-Qwen3-4B
This model is the **Q + D_KL** MINT checkpoint fine-tuned from [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) for multi-turn empathic dialogue.
MINT, short for **Multi-turn Inter-tactic Novelty Training**, is a reinforcement learning framework that optimizes empathic response quality to... | [
{
"start": 177,
"end": 181,
"text": "MINT",
"label": "training method",
"score": 0.7012671828269958
}
] |
vighneshanap/tribev2 | vighneshanap | 2026-04-02T07:03:53Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2026-04-02T07:03:53Z | <div align="center">
# TRIBE v2
**A Foundation Model of Vision, Audition, and Language for In-Silico Neuroscience**
[](https://colab.research.google.com/github/facebookresearch/tribev2/blob/main/tribe_demo.ipynb)
[![License: CC BY-NC 4.0](http... | [] |
zhangyi617/sd15_naruto_text_0.07_mix_0.8 | zhangyi617 | 2026-02-12T10:28:29Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2026-02-12T08:54:34Z | <!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - zhangyi617/sd15_naruto_text_0.07_mix_0.8
These are LoRA adaption weights for runwayml/stabl... | [] |
ilessio-aiflowlab/project_agora | ilessio-aiflowlab | 2026-03-28T14:12:40Z | 0 | 0 | transformers | [
"transformers",
"robotics",
"anima",
"agora",
"multi-robot",
"task-planning",
"coordination",
"lora",
"qwen2.5",
"robot-flow-labs",
"text-generation",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"model-index",
"endpoin... | text-generation | 2026-03-27T04:30:07Z | # AGORA — Multi-Robot Task Planner v1
Part of the [ANIMA Perception Suite](https://github.com/RobotFlow-Labs) by Robot Flow Labs / AIFLOW LABS LIMITED.
## Overview
AGORA (Adaptive Group Operations & Resource Allocation) is the Wave-5 unified STEM
(Spatio-Temporal-Embodiment Memory) framework for multi-robot collabor... | [] |
aciidix/Llama-Poro-2-70B-Instruct-mlx-fp16 | aciidix | 2025-12-15T10:27:55Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mlx",
"conversational",
"fi",
"en",
"dataset:LumiOpen/poro2-instruction-collection",
"dataset:nvidia/HelpSteer3",
"base_model:LumiOpen/Llama-Poro-2-70B-Instruct",
"base_model:finetune:LumiOpen/Llama-Poro-2-70B-Instruct",
"license:ll... | text-generation | 2025-12-15T10:10:00Z | # aciidix/Llama-Poro-2-70B-Instruct-mlx-fp16
The Model [aciidix/Llama-Poro-2-70B-Instruct-mlx-fp16](https://huggingface.co/aciidix/Llama-Poro-2-70B-Instruct-mlx-fp16) was converted to MLX format from [LumiOpen/Llama-Poro-2-70B-Instruct](https://huggingface.co/LumiOpen/Llama-Poro-2-70B-Instruct) using mlx-lm version **... | [] |
mradermacher/Qwen3-VL-8B-Thinking-heretic-GGUF | mradermacher | 2026-03-08T07:58:31Z | 706 | 0 | transformers | [
"transformers",
"gguf",
"heretic",
"uncensored",
"decensored",
"abliterated",
"en",
"base_model:sh0ck0r/Qwen3-VL-8B-Thinking-heretic",
"base_model:quantized:sh0ck0r/Qwen3-VL-8B-Thinking-heretic",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2026-03-08T06:25:37Z | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: 1 -->
static ... | [] |
zhuojing-huang/gpt2-german-english-bi-vocab-1 | zhuojing-huang | 2026-03-05T18:19:21Z | 248 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2026-02-08T04:12:11Z | <!-- 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. -->
# gpt2-german-english-bi-vocab-1
This model was trained from scratch on the None dataset.
## Model description
More information n... | [] |
Thireus/Qwen3-VL-235B-A22B-Thinking-THIREUS-BF16-SPECIAL_SPLIT | Thireus | 2026-02-12T18:16:45Z | 8 | 0 | null | [
"gguf",
"arxiv:2505.23786",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-26T06:44:43Z | ## ⚠️ Cautionary Notice
The metadata of these quants has been updated and is now compatible with the latest version of `llama.cpp` (and `ik_llama.cpp`).
- ⚠️ **Official support in `llama.cpp` was recently made available** – see [ggml-org/llama.cpp PR #16780](http://github.com/ggml-org/llama.cpp/pull/16780).
- ⚠️ **Of... | [] |
kmseong/llama3.2_3b_instruct_new_only_sn_tuned_lr3e-5 | kmseong | 2026-04-13T12:07:36Z | 0 | 0 | null | [
"safetensors",
"llama",
"safety",
"fine-tuning",
"safety-neurons",
"license:apache-2.0",
"region:us"
] | null | 2026-04-13T11:04:09Z | # llama3.2_3b_instruct_new_only_sn_tuned_lr3e-5
This is a Safety Neuron-Tuned (SN-Tune) version of Llama-3.2-3B-Instruct.
## Model Description
- **Base Model**: meta-llama/Llama-3.2-3B-Instruct
- **Fine-tuning Method**: SN-Tune (Safety Neuron Tuning)
- **Training Data**: Circuit Breakers dataset (safety alignment da... | [
{
"start": 80,
"end": 87,
"text": "SN-Tune",
"label": "training method",
"score": 0.9189419746398926
},
{
"start": 223,
"end": 230,
"text": "SN-Tune",
"label": "training method",
"score": 0.9543287754058838
},
{
"start": 375,
"end": 382,
"text": "SN-Tune",... |
MK100283/autotrain-e4umi-78jk9 | MK100283 | 2025-11-03T05:21:03Z | 2 | 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 | 2025-11-03T05:19:53Z | ---
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: 1.8341673612594604
f1_macro: 0.28843537414965986
f1_micro: 0.357142... | [
{
"start": 39,
"end": 48,
"text": "autotrain",
"label": "training method",
"score": 0.8100082278251648
},
{
"start": 137,
"end": 146,
"text": "AutoTrain",
"label": "training method",
"score": 0.7136901021003723
},
{
"start": 175,
"end": 184,
"text": "AutoT... |
chliu12/all-MiniLM-L6-v2 | chliu12 | 2026-02-23T13:18:27Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"tf",
"rust",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_a... | sentence-similarity | 2026-02-23T13:18:26Z | # all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](ht... | [] |
costanzuni/qwen25-3b-survival-raft-Q4_K_M-GGUF | costanzuni | 2025-12-05T23:46:35Z | 8 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:costanzuni/qwen25-3b-survival-raft",
"base_model:quantized:costanzuni/qwen25-3b-survival-raft",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-12-05T23:46:23Z | # costanzuni/qwen25-3b-survival-raft-Q4_K_M-GGUF
This model was converted to GGUF format from [`costanzuni/qwen25-3b-survival-raft`](https://huggingface.co/costanzuni/qwen25-3b-survival-raft) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [origi... | [] |
cyankiwi/Mistral-Small-4-119B-2603-AWQ-4bit | cyankiwi | 2026-03-23T07:16:07Z | 1,763 | 4 | null | [
"safetensors",
"mistral3",
"vLLM",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-4-119B-2603",
"base_model:quantized:mistralai... | null | 2026-03-18T09:33:05Z | # Mistral Small 4 119B A6B
Mistral Small 4 is a powerful hybrid model capable of acting as both a general instruction model and a reasoning model. It unifies the capabilities of three different model families—**Instruct**, **Reasoning** (previously called Magistral), and **Devstral**—into a single, unified model.
Wit... | [] |
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