librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-12 00:41:42	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 00:40:24	card stringlengths 11 1.01M
Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF	Guilherme34	2025-08-19T20:18:39Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed", "base_model:quantized:Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed", "license:other", "endpoints_compatible", "region:us" ]	null	2025-08-19T20:18:10Z	--- license: other language: - en tags: - llama-cpp - gguf-my-repo base_model: Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed --- # Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF This model was converted to GGUF format from [`Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed`](https://huggingface.co/Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF --hf-file samantha-mythomax-l2-13b-merge-fixed-q3_k_l.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF --hf-file samantha-mythomax-l2-13b-merge-fixed-q3_k_l.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF --hf-file samantha-mythomax-l2-13b-merge-fixed-q3_k_l.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF --hf-file samantha-mythomax-l2-13b-merge-fixed-q3_k_l.gguf -c 2048 ```
roeker/blockassist-bc-quick_wiry_owl_1755634582	roeker	2025-08-19T20:17:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:17:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755633003	calegpedia	2025-08-19T20:15:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:15:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist	bryanzhou008	2025-08-19T20:15:20Z	60	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-10-30T19:48:56Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-finetuned-inaturalist results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8541666666666666 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-finetuned-inaturalist This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the inaturalist dataset. It achieves the following results on the evaluation set: - Loss: 0.7703 - Accuracy: 0.8542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8421 \| 4 \| 3.1793 \| 0.0347 \| \| No log \| 1.8947 \| 9 \| 3.1647 \| 0.0486 \| \| 3.1648 \| 2.9474 \| 14 \| 3.1382 \| 0.0944 \| \| 3.1648 \| 4.0 \| 19 \| 3.0995 \| 0.1556 \| \| 3.0817 \| 4.8421 \| 23 \| 3.0555 \| 0.2639 \| \| 3.0817 \| 5.8947 \| 28 \| 2.9849 \| 0.3889 \| \| 2.9167 \| 6.9474 \| 33 \| 2.8932 \| 0.5139 \| \| 2.9167 \| 8.0 \| 38 \| 2.7775 \| 0.5972 \| \| 2.6682 \| 8.8421 \| 42 \| 2.6706 \| 0.6528 \| \| 2.6682 \| 9.8947 \| 47 \| 2.5233 \| 0.7069 \| \| 2.3659 \| 10.9474 \| 52 \| 2.3859 \| 0.7375 \| \| 2.3659 \| 12.0 \| 57 \| 2.2546 \| 0.75 \| \| 2.079 \| 12.8421 \| 61 \| 2.1531 \| 0.7528 \| \| 2.079 \| 13.8947 \| 66 \| 2.0372 \| 0.75 \| \| 1.828 \| 14.9474 \| 71 \| 1.9339 \| 0.7597 \| \| 1.828 \| 16.0 \| 76 \| 1.8403 \| 0.7694 \| \| 1.6253 \| 16.8421 \| 80 \| 1.7733 \| 0.7764 \| \| 1.6253 \| 17.8947 \| 85 \| 1.6914 \| 0.7903 \| \| 1.4502 \| 18.9474 \| 90 \| 1.6153 \| 0.7875 \| \| 1.4502 \| 20.0 \| 95 \| 1.5510 \| 0.7986 \| \| 1.4502 \| 20.8421 \| 99 \| 1.5016 \| 0.8 \| \| 1.2959 \| 21.8947 \| 104 \| 1.4454 \| 0.8222 \| \| 1.2959 \| 22.9474 \| 109 \| 1.3912 \| 0.8181 \| \| 1.1802 \| 24.0 \| 114 \| 1.3390 \| 0.8333 \| \| 1.1802 \| 24.8421 \| 118 \| 1.2995 \| 0.8333 \| \| 1.0629 \| 25.8947 \| 123 \| 1.2707 \| 0.8389 \| \| 1.0629 \| 26.9474 \| 128 \| 1.2335 \| 0.8361 \| \| 0.9801 \| 28.0 \| 133 \| 1.1975 \| 0.8444 \| \| 0.9801 \| 28.8421 \| 137 \| 1.1672 \| 0.8389 \| \| 0.9076 \| 29.8947 \| 142 \| 1.1338 \| 0.8444 \| \| 0.9076 \| 30.9474 \| 147 \| 1.1137 \| 0.8472 \| \| 0.8349 \| 32.0 \| 152 \| 1.0855 \| 0.8528 \| \| 0.8349 \| 32.8421 \| 156 \| 1.0717 \| 0.8542 \| \| 0.7782 \| 33.8947 \| 161 \| 1.0483 \| 0.8514 \| \| 0.7782 \| 34.9474 \| 166 \| 1.0352 \| 0.85 \| \| 0.7208 \| 36.0 \| 171 \| 1.0202 \| 0.8556 \| \| 0.7208 \| 36.8421 \| 175 \| 0.9994 \| 0.8486 \| \| 0.6708 \| 37.8947 \| 180 \| 0.9814 \| 0.8556 \| \| 0.6708 \| 38.9474 \| 185 \| 0.9691 \| 0.8542 \| \| 0.6303 \| 40.0 \| 190 \| 0.9599 \| 0.8486 \| \| 0.6303 \| 40.8421 \| 194 \| 0.9422 \| 0.8472 \| \| 0.6303 \| 41.8947 \| 199 \| 0.9278 \| 0.8486 \| \| 0.6018 \| 42.9474 \| 204 \| 0.9172 \| 0.8528 \| \| 0.6018 \| 44.0 \| 209 \| 0.9093 \| 0.8514 \| \| 0.5622 \| 44.8421 \| 213 \| 0.9030 \| 0.8583 \| \| 0.5622 \| 45.8947 \| 218 \| 0.8972 \| 0.8625 \| \| 0.5474 \| 46.9474 \| 223 \| 0.8859 \| 0.8569 \| \| 0.5474 \| 48.0 \| 228 \| 0.8858 \| 0.8653 \| \| 0.5254 \| 48.8421 \| 232 \| 0.8779 \| 0.8556 \| \| 0.5254 \| 49.8947 \| 237 \| 0.8635 \| 0.8569 \| \| 0.5036 \| 50.9474 \| 242 \| 0.8563 \| 0.8611 \| \| 0.5036 \| 52.0 \| 247 \| 0.8613 \| 0.8542 \| \| 0.4855 \| 52.8421 \| 251 \| 0.8546 \| 0.8625 \| \| 0.4855 \| 53.8947 \| 256 \| 0.8469 \| 0.8597 \| \| 0.4697 \| 54.9474 \| 261 \| 0.8327 \| 0.8528 \| \| 0.4697 \| 56.0 \| 266 \| 0.8268 \| 0.8597 \| \| 0.4482 \| 56.8421 \| 270 \| 0.8188 \| 0.8556 \| \| 0.4482 \| 57.8947 \| 275 \| 0.8171 \| 0.8653 \| \| 0.4436 \| 58.9474 \| 280 \| 0.8133 \| 0.8486 \| \| 0.4436 \| 60.0 \| 285 \| 0.8070 \| 0.8639 \| \| 0.4436 \| 60.8421 \| 289 \| 0.7986 \| 0.8542 \| \| 0.4211 \| 61.8947 \| 294 \| 0.7937 \| 0.8597 \| \| 0.4211 \| 62.9474 \| 299 \| 0.7908 \| 0.8611 \| \| 0.4228 \| 64.0 \| 304 \| 0.7952 \| 0.8625 \| \| 0.4228 \| 64.8421 \| 308 \| 0.8010 \| 0.8514 \| \| 0.4046 \| 65.8947 \| 313 \| 0.7975 \| 0.8472 \| \| 0.4046 \| 66.9474 \| 318 \| 0.7927 \| 0.8417 \| \| 0.4048 \| 68.0 \| 323 \| 0.7880 \| 0.8556 \| \| 0.4048 \| 68.8421 \| 327 \| 0.7860 \| 0.8514 \| \| 0.3925 \| 69.8947 \| 332 \| 0.7899 \| 0.8403 \| \| 0.3925 \| 70.9474 \| 337 \| 0.7883 \| 0.8417 \| \| 0.3936 \| 72.0 \| 342 \| 0.7885 \| 0.8417 \| \| 0.3936 \| 72.8421 \| 346 \| 0.7874 \| 0.8361 \| \| 0.3985 \| 73.8947 \| 351 \| 0.7832 \| 0.8417 \| \| 0.3985 \| 74.9474 \| 356 \| 0.7787 \| 0.8514 \| \| 0.3849 \| 76.0 \| 361 \| 0.7753 \| 0.8486 \| \| 0.3849 \| 76.8421 \| 365 \| 0.7746 \| 0.8514 \| \| 0.3796 \| 77.8947 \| 370 \| 0.7736 \| 0.8542 \| \| 0.3796 \| 78.9474 \| 375 \| 0.7731 \| 0.8528 \| \| 0.3717 \| 80.0 \| 380 \| 0.7715 \| 0.8556 \| \| 0.3717 \| 80.8421 \| 384 \| 0.7709 \| 0.8556 \| \| 0.3717 \| 81.8947 \| 389 \| 0.7706 \| 0.8569 \| \| 0.3802 \| 82.9474 \| 394 \| 0.7704 \| 0.8556 \| \| 0.3802 \| 84.0 \| 399 \| 0.7704 \| 0.8542 \| \| 0.3782 \| 84.2105 \| 400 \| 0.7703 \| 0.8542 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.1+cu121 - Datasets 2.21.0 - Tokenizers 0.20.1
Dejiat/blockassist-bc-savage_unseen_bobcat_1755634467	Dejiat	2025-08-19T20:15:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:14:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/mapdraw-flux	Muapi	2025-08-19T20:15:07Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T20:14:54Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # MapDraw (FLUX) ![preview](./preview.jpg) Base model: Flux.1 D Trained words: m4pdr4w ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:777351@869395", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/a-better-wolf	Muapi	2025-08-19T20:14:22Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T20:12:38Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # A Better Wolf ![preview](./preview.jpg) Base model: Flux.1 D Trained words: wolf, snarling, black, white, ears forward, ears back, pack of wolves ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:412694@725614", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
koloni/blockassist-bc-deadly_graceful_stingray_1755632890	koloni	2025-08-19T20:13:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:13:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755632706	vwzyrraz7l	2025-08-19T20:11:31Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:11:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755634209	Dejiat	2025-08-19T20:10:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:10:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
reece124/OpenCUA-7B-converted	reece124	2025-08-19T20:10:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "VLM", "Computer-Use-Agent", "OS-Agent", "GUI", "Grounding", "image-text-to-text", "conversational", "en", "dataset:xlangai/AgentNet", "dataset:xlangai/aguvis-stage1", "dataset:smolagents/aguvis-stage-2", "dataset:osunlp/UGround-V1-Data", "arxiv:2508.09123", "arxiv:2504.07981", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-08-19T20:10:19Z	--- base_model: - Qwen/Qwen2.5-VL-7B-Instruct datasets: - xlangai/AgentNet - xlangai/aguvis-stage1 - smolagents/aguvis-stage-2 - osunlp/UGround-V1-Data language: - en license: mit metrics: - accuracy - code_eval pipeline_tag: image-text-to-text library_name: transformers tags: - VLM - Computer-Use-Agent - OS-Agent - GUI - Grounding --- <h1 style=" font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Helvetica,Arial,sans-serif; font-size:48px; font-weight:700; line-height:1.25; text-align:center; margin:0 0 24px;"> OpenCUA: Open Foundations for Computer-Use Agents </h1> <div style=" display:flex; justify-content:center; gap:12px; flex-wrap:wrap; margin-bottom:28px;"> <a href="https://opencua.xlang.ai/" style=" display:inline-block; padding:8px 24px; background:#2b2b2b; color:#ffffff; border-radius:36px; text-decoration:none; font-weight:600; font-size:16px;"> 🌐 Website </a> <a href="https://arxiv.org/abs/2508.09123" style=" display:inline-block; padding:8px 24px; background:#2b2b2b; color:#ffffff; border-radius:36px; text-decoration:none; font-weight:600; font-size:16px;"> 📝 Paper </a> <a href="https://github.com/xlang-ai/OpenCUA" style=" display:inline-block; padding:8px 24px; background:#2b2b2b; color:#ffffff; border-radius:36px; text-decoration:none; font-weight:600; font-size:16px;"> 💻 Code </a> </div> <div style="max-width:900px;margin:0 auto;"> # Introduction <div style=" max-width: 880px; /* 可按需调节整体宽度 / margin: 0 auto; / 居中容器 / text-align: justify; / 关键：两端对齐 / text-justify: inter-word; / 优化英文对齐效果 / line-height: 1.6;"> OpenCUA models (OpenCUA-7B and OpenCUA-32B) are end-to-end computer-use foundation models than can produce executable actions in the computer environments. They are based on the weights of Qwen2.5-VL-7B-Instruction and Qwen2.5-VL-32B-Instruction. They demonstrate superior performance across CUA benchmarks. In particular, <b>OpenCUA-32B</b> achieves an average success rate of 34.8%* on [OSWorld-Verified](https://os-world.github.io/), establishing a new state-of-the-art (SOTA) among open-source models and surpassing OpenAI CUA (GPT-4o). Both models also have strong grounding performance, OpenCUA-32B achieves 59.6% on [OSWorld-G](https://osworld-grounding.github.io/) and 55.3% on [Screenspot-Pro](https://arxiv.org/abs/2504.07981). </div> ### Key Features - Superior Computer-Use Capablity: Able to execute multi-step computer-use actions with effective planning and reasoning - Multi-OS Support: Trained on demonstrations across Ubuntu, Windows, and macOS - Visual Grounding: Strong GUI element recognition and spatial reasoning capabilities - Multi-Image Context: Processes up to 3 screenshot history for better context understanding - Reflective Reasoning: Enhanced with reflective long Chain-of-Thought that identifies errors and provides corrective reasoning # Performance ### Online Agent Evaluation OpenCUA models achieves strong performance on [OSWorld-Verified](https://os-world.github.io/). OPENCUA-32B achieves the best performance among all open-source models with an average success rate of 34.8%, outperforming prior baselines by large margins. It also closes the gap to proprietary Claude models. <div align="center"> \| Model \| 15 Steps \| 50 Steps \| 100 Steps \| \|-------------------------------\|:--------:\|:--------:\|:---------:\| \| Proprietary \| \| \| \| \| OpenAI CUA \| 26.0 \| 31.3 \| 31.4 \| \| Seed 1.5-VL \| 27.9 \| — \| 34.1 \| \| Claude 3.7 Sonnet \| 27.1 \| 35.8 \| 35.9 \| \| Claude 4 Sonnet \| 31.2 \| 43.9 \| 41.5 \| \| Open-Source \| \| \| \| \| Qwen 2.5-VL-32B-Instruct \| 3.0 \| — \| 3.9 \| \| Qwen 2.5-VL-72B-Instruct \| 4.4 \| — \| 5.0 \| \| Kimi-VL-A3B \| 9.7 \| — \| 10.3 \| \| UI-TARS-72B-DPO \| 24.0 \| 25.8 \| 27.1 \| \| UI-TARS-1.5-7B \| 24.5 \| 27.3 \| 27.4 \| \| OpenCUA-7B (Ours) \| 24.3 \| 27.9 \| 26.6 \| \| *OpenCUA-32B (Ours)* \| 29.7 \| 34.1 \| 34.8** \| </div> OpenCUA scores are the mean of 3 independent runs. ### GUI Grounding Performance <div align="center"> \| Model \| OSWorld-G \| ScreenSpot-V2 \| ScreenSpot-Pro \| \|-------\|-----------\|---------------\|----------------\| \| Qwen2.5-VL-7B \| 31.4 \| 88.8 \| 27.6 \| \| Qwen2.5-VL-32B \| 46.5 \| 87.0 \| 39.4 \| \| UI-TARS-72B \| 57.1 \| 90.3 \| 38.1 \| \| OpenCUA-A3B \| 48.6 \| 91.4 \| 28.5 \| \| OpenCUA-Qwen2-7B \| 45.7 \| 88.5 \| 23.7 \| \| OpenCUA-7B \| 55.3 \| 92.3 \| 50.0 \| \| OpenCUA-32B \| 59.6 \| 93.4 \| 55.3 \| </div> ### AgentNetBench (Offline Evaluation) <div align="center"> \| Model \| Coordinate Actions \| Content Actions \| Function Actions \| Average \| \|-------\|-------------------\|-----------------\|------------------\|---------\| \| Qwen2.5-VL-7B \| 50.7 \| 40.8 \| 3.1 \| 48.0 \| \| Qwen2.5-VL-32B \| 66.6 \| 47.2 \| 41.5 \| 64.8 \| \| Qwen2.5-VL-72B \| 67.2 \| 52.6 \| 50.5 \| 67.0 \| \| OpenAI CUA \| 71.7 \| 57.3 \| 80.0 \| 73.1 \| \| OpenCUA-7B \| 79.0 \| 62.0 \| 44.3 \| 75.2 \| \| OpenCUA-32B \| 81.9 \| 66.1 \| 55.7 \| 79.1 \| </div> # 🚀 Quick Start <div style="border-left: 6px solid #f28c28; background: #fff8e6; padding: 12px 16px; margin: 16px 0;"> <strong>⚠️ Important for Qwen-based Models (OpenCUA-7B, OpenCUA-32B):</strong> To align with our training infrastructure, we have modified the model in two places: <ul style="margin-top: 8px;"> <li>1. Multimodal Rotary Position Embedding (M-RoPE) has been replaced with 1D RoPE</strong>.</li> <li>2. Using the same Tokenizer and ChatTemplate as Kimi-VL.</li> <li>Do not use the default transformers and vllm classes to load the model. Tokenizer and Chat Template should be aligned if training the models.</li> </ul> </div> ## Installation & Download First, install the required transformers dependencies: ```bash conda create -n opencua python=3.10 conda activate opencua pip install -r requirement.txt ``` Download the model weight from huggingface: ```bash from huggingface_hub import snapshot_download snapshot_download( repo_id="xlangai/OpenCUA-7B", local_dir="OpenCUA-7B", local_dir_use_symlinks=False ) ``` ## 🎯 GUI Grounding The following code demonstrates how to use OpenCUA models for GUI grounding tasks: ```python import base64 import torch from transformers import AutoTokenizer, AutoModel, AutoImageProcessor from PIL import Image import json def encode_image(image_path: str) -> str: """Encode image to base64 string for model input.""" with open(image_path, "rb") as f: return base64.b64encode(f.read()).decode() def load_opencua_model(model_path: str): """Load OpenCUA model, tokenizer, and image processor.""" tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = AutoModel.from_pretrained( model_path, torch_dtype="auto", device_map="auto", trust_remote_code=True ) image_processor = AutoImageProcessor.from_pretrained(model_path, trust_remote_code=True) return model, tokenizer, image_processor def create_grounding_messages(image_path: str, instruction: str): """Create chat messages for GUI grounding task.""" system_prompt = ( "You are a GUI agent. You are given a task and a screenshot of the screen. " "You need to perform a series of pyautogui actions to complete the task." ) messages = [ {"role": "system", "content": system_prompt}, { "role": "user", "content": [ {"type": "image", "image": f"data:image/png;base64,{encode_image(image_path)}"}, {"type": "text", "text": instruction}, ], }, ] return messages def run_inference(model, tokenizer, image_processor, messages, image_path): """Run inference on the model.""" # Prepare text input input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True ) input_ids = torch.tensor([input_ids]).to(model.device) # Prepare image input image = Image.open(image_path).convert('RGB') image_info = image_processor.preprocess(images=[image]) pixel_values = torch.tensor(image_info['pixel_values']).to( dtype=torch.bfloat16, device=model.device ) grid_thws = torch.tensor(image_info['image_grid_thw']) # Generate response with torch.no_grad(): generated_ids = model.generate( input_ids, pixel_values=pixel_values, grid_thws=grid_thws, max_new_tokens=512, temperature=0 ) # Decode output prompt_len = input_ids.shape[1] generated_ids = generated_ids[:, prompt_len:] output_text = tokenizer.batch_decode( generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] return output_text # Example usage model_path = "OpenCUA/OpenCUA-7B" # or other model variants image_path = "screenshot.png" instruction = "Click on the submit button" # Load model model, tokenizer, image_processor = load_opencua_model(model_path) # Create messages and run inference messages = create_grounding_messages(image_path, instruction) result = run_inference(model, tokenizer, image_processor, messages, image_path) print("Model output:", result) ``` <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;"> <em>Expected result:</em> ```python pyautogui.click(x=1443, y=343) ``` </div> You can also run the five grounding examples in [OpenCUA/model/inference/huggingface_inference.py](https://github.com/xlang-ai/OpenCUA/blob/main/model/inference/huggingface_inference.py): ``` cd ./model/inference/ python huggingface_inference.py ``` ## 🖥️ Computer Use Agent [OpenCUAAgent](https://github.com/xlang-ai/OSWorld/blob/main/mm_agents/opencua_agent.py) is developed in the [OSWorld](https://github.com/xlang-ai/OSWorld) environment based on OpenCUA models. It iteratively perceives the environment via screenshots, produces reflective long CoT as inner monologue, and predicts the next action to be executed. OpenCUAAgent uses 3 images in total and L2 CoT format in default. Command for running OpenCUA-7B and OpenCUA-32B in OSWorld: ``` python run_multienv_opencua.py \ --headless \ --observation_type screenshot \ --model OpenCUA-32B \ --result_dir ./results --test_all_meta_path evaluation_examples/test_all_no_gdrive.json \ --max_steps 100 \ --num_envs 30 \ --coordinate_type qwen25 ``` <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;"> <em>Currently we only supports huggingface inference. We are implementing the vLLM supports of OpenCUA models. Please stay tuned.</em> </div> --- # AgentNet Dataset - Large-Scale Computer-Use Dataset <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/dw5k183ucDSB2SZuS5f2V.png" width="400" alt="AgentNet Dataset Domain Distribution"> </div> AgentNet is the first large-scale desktop computer-use agent trajectory dataset, containing 22.6K human-annotated computer-use tasks across Windows, macOS, and Ubuntu systems. 👉 [AgentNet Huggingface Dataset](https://huggingface.co/datasets/xlangai/AgentNet) Download the dataset here： ``` pip install -U huggingface_hub huggingface-cli download xlangai/AgentNet --repo-type dataset --local-dir ./AgentNet ``` Collecting computer-use agent training data requires 3 steps: - Demonstrate human computer-use task via [AgentNetTool](https://agentnet-tool.xlang.ai/); - Preprocess the demonstration using [Action Reduction & State-Action Matching](./data/data-processor); - For each step, [synthesize reflective long CoT](./data/cot-generator) ## 1 AgentNetTool – Annotation & Verification Tool <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/ETjCOoIRR7f1YZCJ2kfiW.png" width="700" alt="AgentNet Tool"> </div> Our AgentNetTool is a cross-platform GUI recorder that runs unobtrusively on annotators’ machines. It captures synchronized screen video, mouse/keyboard events, and accessibility trees, then provides an in-browser UI for reviewing, trimming, and submitting demonstrations. AgentNet Tool is available on Windows, macOS and Ubuntu. 👉 [AgentNetTool Document](https://agentnet-tool.xlang.ai/) ## 2 DataProcessor – Action Reduction & State–Action Matching Raw demonstrations can contain thousands of low-level events that are too dense for model training. The DataProcessor module (`./data/data-process/`) performs two key steps: 1. Action Reduction — merges granular signals into concise, semantically meaningful PyAutoGUI actions (e.g., collapsing mouse moves → click, coalescing scrolls, grouping key-press sequences into text or hotkeys). 2. State–Action Matching — aligns every reduced action with the last visually distinct frame before the action begins, avoiding future-information leakage and yielding compact state–action pairs. These processed trajectories underlie all downstream training and evaluation. --- ## 3 CoTGenerator – Synthesizing Reflective Long Chain-of-Thought Inner Monologue To boost robustness and interpretability, we augment each trajectory with reflective long Chain-of-Thought (CoT) reasoning. The CoTGenerator pipeline (`./data/cot-generator/`) synthesizes step-level reflections that: * reflect on the previous action, * explain why an action is chosen given the current observation and history, * note potential alternative actions, and * forecast the expected next state. Empirically, models trained with these rich CoTs scale better with data and generalize across unseen applications. # Evaluation <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/emy1QCJwQj9KqHkVmtNH2.png" width="800" alt="AgentNetBench"> </div> AgentNetBench (`./AgentNetBench/`) provides a realistic offline evaluator for OS agent trajectories. It compares model-predicted low-level actions (click, moveTo, write, press, scroll, terminate, etc.) against ground-truth human actions and reports detailed metrics. 👉 See [AgentNetBench/README.md](./evaluation/agentnetbench/README.md) for usage instructions. # TODO ## vLLM Support We are actively working with the vLLM team to add support for OpenCUA models. Workaround: For now, please use the standard transformers library as shown in the examples above. We will update this section once vLLM support becomes available. ## Training Code OpenCUA models are developed based on the training infrastructure of Kimi Team. We are developting the training pipeline based on the open-source infrastructure as well. # Acknowledge <p> We thank Su Yu, Caiming Xiong, Binyuan Hui, and the anonymous reviewers for their insightful discussions and valuable feedback. We are grateful to Moonshot AI for providing training infrastructure and annotated data. We also sincerely appreciate Calvin, Ziwei Chen, Jin Zhang, Ze Li, Zhengtao Wang, Yanxu Chen, and Qizheng Gu from the Kimi Team for their strong infrastructure support and helpful guidance. The development of our tool is based on the open-source projects-<a href="https://github.com/TheDuckAI/DuckTrack" target="_blank">DuckTrack</a> and <a href="https://github.com/OpenAdaptAI/OpenAdapt" target="_blank">OpenAdapt</a>. We are very grateful to their commitment to the open source community. Finally, we extend our deepest thanks to all annotators for their tremendous effort and contributions to this project. </p> # License This project is licensed under the MIT License - see the LICENSE file in the root folder for details. ## Research Use and Disclaimer OpenCUA models are intended for research and educational purposes only. ### Prohibited Uses - The model may not be used for any purpose or activity that violates applicable laws or regulations in any jurisdiction - Use for illegal, unethical, or harmful activities is strictly prohibited ### Disclaimer - The authors, contributors, and copyright holders are not responsible for any illegal, unethical, or harmful use of the Software, nor for any direct or indirect damages resulting from such use - Use of the "OpenCUA" name, logo, or trademarks does not imply any endorsement or affiliation unless separate written permission is obtained - Users are solely responsible for ensuring their use complies with applicable laws and regulations ## Important Notes on Coordinate Systems <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;"> <ul style="margin: 0;"> <li><strong><code>OpenCUA/OpenCUA-A3B</code></strong> – Relative coordinates <em>(not supported in this code)</em></li> <li><strong><code>OpenCUA/OpenCUA-Qwen2-7B</code></strong> – Relative coordinates</li> <li><strong><code>OpenCUA/OpenCUA-7B</code></strong> – Absolute coordinates</li> <li><strong><code>OpenCUA/OpenCUA-32B</code></strong> – Absolute coordinates</li> </ul> </div> OpenCUA models use different coordinate systems depending on the base model: - OpenCUA-Qwen2-7B: Outputs relative coordinates (0.0 to 1.0 range) ```python # Example output: pyautogui.click(x=0.5, y=0.3) # x=0.5 means 50% from left edge, y=0.3 means 30% from top edge # Convert to absolute coordinates: def qwen2_relative_to_absolute(rel_x, rel_y, original_width, original_height): abs_x = int(rel_x * original_width) abs_y = int(rel_y * original_height) return abs_x, abs_y ``` - OpenCUA-7B and OpenCUA-32B (Qwen2.5-based): Output absolute coordinates after smart resize ```python # Example output: pyautogui.click(x=960, y=324) # These are coordinates on the smart-resized image, not the original image # Convert to original image coordinates: # Please refer to the smart_resize function in: https://github.com/huggingface/transformers/blob/67ddc82fbc7e52c6f42a395b4a6d278c55b77a39/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L55 def qwen25_smart_resize_to_absolute(model_x, model_y, original_width, original_height): # First, calculate the smart-resized dimensions resized_height, resized_width = smart_resize(original_height, original_width, factor = 28, min_pixels = 3136, max_pixels = 12845056) # Convert model output to relative coordinates on original image rel_x = model_x / resized_width rel_y = model_y / resized_height # Then convert to absolute coordinates on original image abs_x = int(rel_x * original_width) abs_y = int(rel_y * original_height) return abs_x, abs_y ``` <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;"> <strong>Understanding Smart Resize for Qwen2.5-based Models:</strong> <p style="margin: 8px 0 0;"> The Qwen2.5-VL models use a “smart resize” preprocessing that maintains aspect ratio while fitting within pixel constraints. For coordinate conversion, you need the smart resize function from the <a href="https://github.com/QwenLM/Qwen2.5-VL/blob/d2240f11656bfe404b9ba56db4e51cd09f522ff1/qwen-vl-utils/src/qwen_vl_utils/vision_process.py#L60"> official Qwen2.5-VL implementation</a>. </p> </div> ## Citation If you use OpenCUA models in your research, please cite our work: ```bibtex @misc{wang2025opencuaopenfoundationscomputeruse, title={OpenCUA: Open Foundations for Computer-Use Agents}, author={Xinyuan Wang and Bowen Wang and Dunjie Lu and Junlin Yang and Tianbao Xie and Junli Wang and Jiaqi Deng and Xiaole Guo and Yiheng Xu and Chen Henry Wu and Zhennan Shen and Zhuokai Li and Ryan Li and Xiaochuan Li and Junda Chen and Boyuan Zheng and Peihang Li and Fangyu Lei and Ruisheng Cao and Yeqiao Fu and Dongchan Shin and Martin Shin and Jiarui Hu and Yuyan Wang and Jixuan Chen and Yuxiao Ye and Danyang Zhang and Dikang Du and Hao Hu and Huarong Chen and Zaida Zhou and Haotian Yao and Ziwei Chen and Qizheng Gu and Yipu Wang and Heng Wang and Diyi Yang and Victor Zhong and Flood Sung and Y. Charles and Zhilin Yang and Tao Yu}, year={2025}, eprint={2508.09123}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2508.09123}, } ``` </div>
roeker/blockassist-bc-quick_wiry_owl_1755634184	roeker	2025-08-19T20:10:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:10:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Leoar/blockassist-bc-pudgy_toothy_cheetah_1755634081	Leoar	2025-08-19T20:10:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pudgy toothy cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:10:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pudgy toothy cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnjaliNV/WellBeing_LoRA	AnjaliNV	2025-08-19T20:09:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T16:44:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Huseyin/teknofest-2025-turkish-edu-v2	Huseyin	2025-08-19T20:09:22Z	0	0	null	[ "safetensors", "qwen3", "turkish", "education", "teknofest-2025", "qwen", "text-generation", "lora", "conversational", "tr", "dataset:Huseyin/final2", "base_model:Qwen/Qwen3-8B", "base_model:adapter:Qwen/Qwen3-8B", "license:apache-2.0", "region:us" ]	text-generation	2025-08-19T20:00:50Z	--- language: tr license: apache-2.0 base_model: Qwen/Qwen3-8B tags: - turkish - education - teknofest-2025 - qwen - text-generation - lora datasets: - Huseyin/final2 pipeline_tag: text-generation widget: - text: "Türkçe eğitimi için yaratıcı bir etkinlik önerisi:" example_title: "Eğitim Etkinliği" - text: "İlkokul öğrencileri için matematik problemi:" example_title: "Matematik" - text: "Fen bilgisi dersinde yapılabilecek basit bir deney:" example_title: "Fen Deneyi" - text: "Öğrencilerin dikkatini çekmek için:" example_title: "Dikkat Çekme" model-index: - name: teknofest-2025-turkish-edu-v2 results: [] --- # 🚀 TEKNOFEST 2025 - Türkçe Eğitim Modeli V2 Bu model TEKNOFEST 2025 Eylem Temelli Türkçe Büyük Dil Modeli Yarışması için geliştirilmiştir. ## ✨ Yenilikler (V2) - ✅ Model weights düzeltildi - ✅ Tokenizer uyumluluğu sağlandı - ✅ Çıktı kalitesi iyileştirildi - ✅ 16.4 GB optimized model ## 📋 Model Bilgileri - Base Model: Qwen/Qwen3-8B - Fine-tuning: LoRA Adapter - Model Boyutu: 16.4 GB - Oluşturma Tarihi: 2025-08-19 20:09 - Dil: Türkçe - Alan: Eğitim Teknolojileri - Yarışma: TEKNOFEST 2025 ## 🎯 Kullanım Alanları - 📚 Türkçe eğitim materyali oluşturma - 👨‍🎓 Öğrenci seviyesine uygun içerik üretimi - ❓ Soru-cevap sistemleri - 📝 Eğitsel içerik özetleme - 📅 Ders planı hazırlama - 🎮 Eğitici oyun senaryoları ## 💻 Hızlı Başlangıç ### Kurulum ```bash pip install transformers torch accelerate ``` ### Kullanım ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Model ve tokenizer yükle model_name = "Huseyin/teknofest-2025-turkish-edu-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # Örnek kullanım prompt = "Türkçe eğitimi için yaratıcı bir etkinlik önerisi:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( inputs, max_new_tokens=150, temperature=0.7, do_sample=True, top_p=0.95 ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Google Colab'da Kullanım ```python # Google Colab için optimized kullanım !pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer import torch # 8-bit yükleme (düşük bellek kullanımı) model_name = "Huseyin/teknofest-2025-turkish-edu-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, load_in_8bit=True, device_map='auto' ) # Test prompt = "İlkokul öğrencileri için matematik etkinliği:" inputs = tokenizer(prompt, return_tensors='pt') outputs = model.generate(inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 📊 Örnek Çıktılar Prompt: "Türkçe eğitimi için bir öneri:" Cevap: "Ders notlarına dikkat etmeyen ve okumayan öğrencilerin, dersi yoksayarak not almakta, ya da dersle ilgili sorular sormamaktadırlar..." ## 🏆 TEKNOFEST 2025 Bu model, TEKNOFEST 2025 Eylem Temelli Türkçe Büyük Dil Modeli Yarışması için özel olarak geliştirilmiştir. ## 📈 Performans - Model başarıyla Türkçe eğitim içeriği üretmektedir - Fine-tuning sonrası eğitim alanına özelleşmiştir - Düzgün ve anlamlı Türkçe çıktılar vermektedir ## 📄 Lisans Apache 2.0 ## 🙏 Teşekkür Bu modelin geliştirilmesinde emeği geçen herkese teşekkür ederiz. --- TEKNOFEST 2025 - Türkiye'nin Teknoloji Festivali 🇹🇷
Muapi/super-hip-waist	Muapi	2025-08-19T20:07:18Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T20:07:09Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # super hip-waist ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:738727@826131", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/cinematic-glamour-photography-style-xl-f1d	Muapi	2025-08-19T20:07:00Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T20:06:36Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Cinematic Glamour Photography style XL + F1D ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Diffused , glowing , glamour photography 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 = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:396060@1338701", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
gustavomr/llama-finnedtunned-qa	gustavomr	2025-08-19T20:06:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-18T20:25:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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phospho-app/zacharyreid-gr00t-Bimanual_4cam_MidAirHandoff-r2eu7	phospho-app	2025-08-19T20:06:09Z	0	0	phosphobot	[ "phosphobot", "gr00t", "robotics", "dataset:zacharyreid/Bimanual_4cam_MidAirHandoff", "region:us" ]	robotics	2025-08-19T16:56:58Z	--- datasets: zacharyreid/Bimanual_4cam_MidAirHandoff library_name: phosphobot pipeline_tag: robotics model_name: gr00t tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 500, in wait_for return fut.result() ^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 1146, in read_output async for line in process.stdout: File "/opt/conda/lib/python3.11/asyncio/streams.py", line 765, in __anext__ val = await self.readline() ^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/streams.py", line 566, in readline line = await self.readuntil(sep) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/streams.py", line 658, in readuntil await self._wait_for_data('readuntil') File "/opt/conda/lib/python3.11/asyncio/streams.py", line 543, in _wait_for_data await self._waiter asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/phosphobot/am/gr00t.py", line 1157, in run_gr00t_training await asyncio.wait_for(read_output(), timeout=timeout_seconds) File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 502, in wait_for raise exceptions.TimeoutError() from exc TimeoutError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/root/src/helper.py", line 166, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1325, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 1162, in run_gr00t_training raise TimeoutError( TimeoutError: Training process exceeded timeout of 10800 seconds. Please consider lowering the number of epochs and/or batch size. ``` ## Training parameters: - Dataset: [zacharyreid/Bimanual_4cam_MidAirHandoff](https://huggingface.co/datasets/zacharyreid/Bimanual_4cam_MidAirHandoff) - Wandb run URL: None - Epochs: 10 - Batch size: 32 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755632296	kojeklollipop	2025-08-19T20:05:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:05:51Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnonymousCS/xlmr_swedish_immigration4	AnonymousCS	2025-08-19T20:04:24Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T20:01:17Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_swedish_immigration4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_swedish_immigration4 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4123 - Accuracy: 0.8692 - 1-f1: 0.8090 - 1-recall: 0.8372 - 1-precision: 0.7826 - Balanced Acc: 0.8611 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.3763 \| 1.0 \| 5 \| 0.3371 \| 0.8692 \| 0.7792 \| 0.6977 \| 0.8824 \| 0.8258 \| \| 0.241 \| 2.0 \| 10 \| 0.4029 \| 0.8692 \| 0.8046 \| 0.8140 \| 0.7955 \| 0.8553 \| \| 0.2721 \| 3.0 \| 15 \| 0.4123 \| 0.8692 \| 0.8090 \| 0.8372 \| 0.7826 \| 0.8611 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
dfg1d2/sbi-gpt	dfg1d2	2025-08-19T20:04:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T20:04:08Z	--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dfg1d2 - License: apache-2.0 - Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Muapi/wizard-s-scrap-yard-supermarionation-puppets	Muapi	2025-08-19T20:03:55Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T20:03:34Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Wizard's Scrap Yard: Supermarionation Puppets ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Thunderbirds Puppet, Puppet ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:694054@817429", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755633777	Vasya777	2025-08-19T20:03:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:03:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/colorful-detailer-semifluid-pigments-flux-sd-3.5m-sd-3.5l	Muapi	2025-08-19T20:01:10Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T20:00:56Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # colorful detailer \| semifluid pigments (Flux & SD 3.5M & SD 3.5L) ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:757175@846653", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
unitova/blockassist-bc-zealous_sneaky_raven_1755626317	unitova	2025-08-19T18:26:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:26:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755627918	Dejiat	2025-08-19T18:26:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:25:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755627829	Ferdi3425	2025-08-19T18:25:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:24:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755627810	roeker	2025-08-19T18:24:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:24:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755627800	Vasya777	2025-08-19T18:23:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:23:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sophie-Rain-Spider-man-Video-Tutori-a-l/Sophie.Rain.Spiderman.Video.Tutorial.Oficial	Sophie-Rain-Spider-man-Video-Tutori-a-l	2025-08-19T18:23:43Z	0	0	null	[ "region:us" ]	null	2025-08-19T18:22:07Z	<!-- HTML_TAG_END --><div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> <!-- HTML_TAG_END --></div>
Reynier/modernbert-dga-detector	Reynier	2025-08-19T18:23:06Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "domain-generation-algorithm", "cybersecurity", "domain-classification", "security", "malware-detection", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T18:22:40Z	--- license: apache-2.0 tags: - domain-generation-algorithm - cybersecurity - domain-classification - security - malware-detection language: - en library_name: transformers pipeline_tag: text-classification base_model: answerdotai/ModernBERT-base --- # ModernBERT DGA Detector This model is designed to classify domains as either legitimate or generated by Domain Generation Algorithms (DGA). ## Model Description - Model Type: BERT-based sequence classification - Task: Binary classification (Legitimate vs DGA domains) - Base Model: ModernBERT-base - Training Data: Domain names dataset - Author: Reynier Leyva La O ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("Reynier/modernbert-dga-detector") model = AutoModelForSequenceClassification.from_pretrained("Reynier/modernbert-dga-detector") # Example prediction def predict_domain(domain): inputs = tokenizer(domain, return_tensors="pt", max_length=64, truncation=True, padding=True) with torch.no_grad(): outputs = model(inputs) predictions = torch.softmax(outputs.logits, dim=-1) legit_prob = predictions[0][0].item() dga_prob = predictions[0][1].item() return {"prediction": "DGA" if dga_prob > legit_prob else "LEGITIMATE", "confidence": max(legit_prob, dga_prob)} # Test examples domains = ["google.com", "xkvbzpqr.net", "facebook.com", "abcdef123456.com"] for domain in domains: result = predict_domain(domain) print(f"{domain} -> {result['prediction']} (confidence: {result['confidence']:.3f})") ``` ## Model Architecture The model is based on ModernBERT and fine-tuned for domain classification: - Input: Domain names (text) - Output: Binary classification (0=Legitimate, 1=DGA) - Max sequence length: 64 tokens ## Training Details This model was fine-tuned on a dataset of legitimate and DGA-generated domains using: - Base model: answerdotai/ModernBERT-base - Framework: Transformers/PyTorch - Task: Binary sequence classification ## Performance Add your model's performance metrics here when available: - Accuracy: [Add your results] - Precision: [Add your results] - Recall: [Add your results] - F1-Score: [Add your results] ## Use Cases - Cybersecurity: Detect malicious domains generated by malware - Network Security: Filter potentially harmful domains - Threat Intelligence**: Analyze domain patterns in security feeds ## Limitations - This model is trained specifically for domain classification - Performance may vary on domains from different TLDs or languages - Regular retraining may be needed as DGA techniques evolve - Model performance depends on the quality and diversity of training data ## Citation If you use this model in your research or applications, please cite it appropriately. ## Related Models Check out the author's other security models: - [Llama3_8B-DGA-Detector](https://huggingface.co/Reynier/Llama3_8B-DGA-Detector)
VIDEOS-18-Dr-Eman-viral-video-Clips/New.full.videos.Dr.Eman.Viral.Video.Official.Tutorial	VIDEOS-18-Dr-Eman-viral-video-Clips	2025-08-19T18:23:00Z	0	0	null	[ "region:us" ]	null	2025-08-19T18:22:46Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755626307	lisaozill03	2025-08-19T18:22:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:22:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mang3dd/blockassist-bc-tangled_slithering_alligator_1755626126	mang3dd	2025-08-19T18:22:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:22:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
NESTLAYER/Sombrero-charro	NESTLAYER	2025-08-19T18:22:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T18:22:37Z	--- license: apache-2.0 ---
Kandru/fine_tune_classification_task	Kandru	2025-08-19T18:19:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T18:17:24Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: fine_tune_classification_task results: [] --- <!-- 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. --> # fine_tune_classification_task This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3024 - Accuracy: 0.9193 - F1: 0.9193 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 313 \| 0.2866 \| 0.9162 \| 0.9160 \| \| 0.1256 \| 2.0 \| 626 \| 0.3024 \| 0.9193 \| 0.9193 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.4
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755625475	katanyasekolah	2025-08-19T18:13:32Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:13:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TAUR-dev/M-SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks-rl	TAUR-dev	2025-08-19T18:13:30Z	0	0	null	[ "safetensors", "qwen2", "en", "license:mit", "region:us" ]	null	2025-08-19T18:11:52Z	--- language: en license: mit --- # M-SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks-rl ## Model Details - Training Method: VeRL Reinforcement Learning (RL) - Stage Name: rl - Experiment: SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks - RL Framework: VeRL (Versatile Reinforcement Learning) ## Training Configuration ## Experiment Tracking 🔗 View complete experiment details: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks__v1 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks-rl") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks-rl") ```
VIDEOS-19-tasnim-jara-viral-video-link/New.full.videos.tasnim.jara.Viral.Video.Official.Tutorial	VIDEOS-19-tasnim-jara-viral-video-link	2025-08-19T18:13:14Z	0	0	null	[ "region:us" ]	null	2025-08-19T18:12:57Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755625509	kojeklollipop	2025-08-19T18:12:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:12:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Shujashark/llm-sql-t5-small-lora-adapter	Shujashark	2025-08-19T18:12:24Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:t5-small", "lora", "transformers", "arxiv:1910.09700", "base_model:google-t5/t5-small", "base_model:adapter:google-t5/t5-small", "region:us" ]	null	2025-08-19T18:08:48Z	--- base_model: t5-small library_name: peft tags: - base_model:adapter:t5-small - lora - transformers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
aumoai/aumogpt-Qwen2.5-32B-Instruct-lora	aumoai	2025-08-19T18:12:14Z	67	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-05-08T15:53:49Z	model: model_name: "Qwen/Qwen2.5-32B-Instruct" model_max_length: 4096 torch_dtype_str: "bfloat16" attn_implementation: "flash_attention_2" #"sdpa" load_pretrained_weights: True trust_remote_code: True data: train: datasets: # - dataset_name: "text_sft" # dataset_path: "datasets/aumo_dataset_test.json" # shuffle: True # seed: 42 - dataset_name: "text_sft" dataset_path: "datasets/aumogpt_qwen32b.json" shuffle: True seed: 42 # - dataset_name: "text_sft" # dataset_path: "datasets/xp3_qwen_2000.json" # shuffle: True # seed: 42 # - dataset_name: "text_sft" # dataset_path: "datasets/aumogpt_train.json" # shuffle: True # seed: 42 # mixture_strategy: "all_exhausted" # Strategy for mixing datasets # seed: 123456789426465 validation: datasets: - dataset_name: "text_sft" dataset_path: "datasets/aumo_dataset_test.json" # split: "validation" # sample_count: 10 training: trainer_type: "TRL_SFT" use_peft: True save_steps: 200 num_train_epochs: 2 per_device_train_batch_size: 2 per_device_eval_batch_size: 2 gradient_accumulation_steps: 8 max_grad_norm: null enable_gradient_checkpointing: True gradient_checkpointing_kwargs: use_reentrant: False ddp_find_unused_parameters: False optimizer: "adamw_torch" # "adamw_torch" #paged_adamw_8bit learning_rate: 5.0e-4 warmup_steps: 10 weight_decay: 0.01 compile: False dataloader_num_workers: 8 dataloader_prefetch_factor: 4 logging_steps: 10 log_model_summary: False empty_device_cache_steps: 50 output_dir: "results/oumi/qwen32b_xp3_aumo.lora" include_performance_metrics: True enable_wandb: True eval_strategy: "steps" # When to evaluate ("no", "steps", "epoch") eval_steps: 25 peft: q_lora: False lora_r: 64 lora_alpha: 32 lora_dropout: 0.2 lora_target_modules: - "q_proj" - "k_proj" - "v_proj" - "o_proj" - "gate_proj" - "down_proj" - "up_proj" fsdp: enable_fsdp: True sharding_strategy: FULL_SHARD auto_wrap_policy: TRANSFORMER_BASED_WRAP # transformer_layer_cls: QwenBlock forward_prefetch: true
matboz/ring-model	matboz	2025-08-19T18:11:54Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:google/gemma-2-27b-it", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:google/gemma-2-27b-it", "region:us" ]	text-generation	2025-08-19T17:54:47Z	--- base_model: google/gemma-2-27b-it library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:google/gemma-2-27b-it - lora - sft - transformers - trl --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF	siro-kr	2025-08-19T18:11:43Z	0	0	null	[ "gguf", "mixture-of-experts", "moe", "expert-pruning", "gpt-oss", "openai", "reasoning", "harmful", "specialized", "efficient", "transformer", "causal-lm", "text-generation", "pytorch", "pruned-model", "domain-specific", "llama-cpp", "gguf-my-repo", "en", "dataset:AmanPriyanshu/GPT-OSS-20B-MoE-expert-activations", "base_model:AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts", "base_model:quantized:AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T18:11:06Z	--- license: apache-2.0 datasets: - AmanPriyanshu/GPT-OSS-20B-MoE-expert-activations language: - en pipeline_tag: text-generation tags: - mixture-of-experts - moe - expert-pruning - gpt-oss - openai - reasoning - harmful - specialized - efficient - transformer - causal-lm - text-generation - pytorch - pruned-model - domain-specific - llama-cpp - gguf-my-repo base_model: AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts --- # siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF This model was converted to GGUF format from [`AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts`](https://huggingface.co/AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts) 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/AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF --hf-file gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF --hf-file gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF --hf-file gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF --hf-file gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-q4_k_m.gguf -c 2048 ```
mohda/blockassist-bc-regal_fierce_hummingbird_1755627037	mohda	2025-08-19T18:11:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal fierce hummingbird", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:11:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal fierce hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
RTannous/metallama-finetuned	RTannous	2025-08-19T18:10:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T18:08:19Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: RTannous - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755625235	coelacanthxyz	2025-08-19T18:10:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:10:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ver-filtrado-video-abigail-lalama-snayder/VIRAL.VER.filtrado.video.de.abigail.lalama.y.snayder.influencer.se.hace.viral.en.redes.sociales	Ver-filtrado-video-abigail-lalama-snayder	2025-08-19T18:08:23Z	0	0	null	[ "region:us" ]	null	2025-08-19T18:07:38Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> Abigail Lalama viral: influencer confirma video con Snayder en Telegram El nombre Abigail Lalama se hace viral y crece en Telegram y X tras la filtración de su video con Snayder. Te contamos quiénes son y qué pasó. Imagen de Abigail Lalama viral: influencer confirma video con Snayder en Telegram Abigail confirmó la filtración del contenido. - Foto: Instagram abigail_lalama El nombre Abigail Lalama se hizo viral en redes sociales como Telegram y Twitter (ahora X) luego de que se confirmara la filtración de un video íntimo con Snayder. Esto provocó un repunte de búsquedas como “Abigail Lalama video filtrado”, “Video de Abigail y Snayder filtrado”, “Abigail Lalama Telegram”, entre otras. La confirmación llegó directamente de la influencer, lo que desencadenó una ola de comentarios, reacciones y solidaridad en línea. A continuación, te contamos quién es Abigail Lalama, quién es Snayder, qué dijo ella, y por qué este caso se viralizó tan rápido. LEA TAMBIÉN: ¿Qué dijo Mariana Botas de Drake Bell?, ¿qué contestó el cantante? View post on TikTok ¿Quién es Abigail Lalama y por qué es conocida? Abigail Lalama es una creadora de contenido ecuatoriana de 22 años, originaria de Guayaquil, que ha ganado popularidad principalmente en TikTok, donde comparte lives, retos, momentos familiares y contenidos cotidianos junto a su hermana gemela, Génesis. Su comunidad se ha consolidado gracias a su cercanía, su carisma y su estilo familiar. En TikTok, su cuenta @laoficialabigail cuenta con más de 400 000 seguidores y en Instagram supera los 173,000. Con su hermana, forma el ‘Team Lalama’, difundiendo contenidos centrados en diario vivir, maternidad y lazos familiares. ¿Quién es Snayder y qué video se filtró? La identidad precisa de Snayder no ha sido desvelada en los medios principales consultados hasta el momento, pero se sabe que es un conocido en el entorno de Abigail, supuestamente su expareja. El video filtrado, descrito como íntimo, fue subido a plataformas como Telegram y TikTok. Usuarios reportaron que la joven que aparece en la grabación viral compartía tatuajes y rasgos con Abigail Lalama. Según Abigail, el video circuló sin su consentimiento, y la filtración se la atribuye a dicho ex. View post on TikTok ¿Qué dijo Abigail Lalama sobre la filtración? En un video en vivo, visiblemente afectada y entre lágrimas, Abigail Lalama confirmó la filtración viral del contenido. Ella acusó directamente a su expareja de haber difundido ese material íntimo con la intención de incomodar a su nueva relación. Sus palabras fueron duras y reflejaron desilusión y dolor: “Eso es lo peor que le puede hacer un hombre… Yo fui, sí, una estúpida en dejarme grabar con él… Pues siendo mi pareja coge y lo sube… el sinvergüenza. No tiene sangre en la cara”. También añadió una reflexión sobre confianza y peor traición: “Ahí es cuando uno se da cuenta con quién duerme, ahí es cuando se sacan la máscara”. Posteriormente, compartió en sus historias videos de fans que la apoyaban, junto a clips con actitud más serena, mostrando resiliencia frente a la situación. ¿Qué dice la ley sobre contenido filtrado sin consentimiento? En Ecuador, compartir contenido íntimo sin consentimiento está penalizado por el Código Orgánico Integral Penal (COIP). El artículo 178 castiga la difusión de imágenes o videos íntimos sin autorización con penas de uno a tres años. Además, el artículo 230, relativo a violencia digital, puede elevar la sanción hasta cuatro años si hay intención de dañar o humillar. Abigail tiene la opción de denunciar ante la Fiscalía General del Estado, la Unidad de Policía Comunitaria (UPC), el portal del Consejo de la Judicatura o acudir a la Defensoría del Pueblo y organizaciones que brindan apoyo psicológico y asesoría legal. También se pueden solicitar medidas como la eliminación del contenido y reparación integral.
policia-instagram-influencer-virals-video/Hot.New.full.videos.policia.instagram.influencer.viral.video.Official.Tutorial	policia-instagram-influencer-virals-video	2025-08-19T18:07:08Z	0	0	null	[ "region:us" ]	null	2025-08-19T18:06:53Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
lautan/blockassist-bc-gentle_patterned_goat_1755624839	lautan	2025-08-19T18:02:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T18:02:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MidnightRunner/MDNT_Illus_3D	MidnightRunner	2025-08-19T17:59:02Z	0	0	diffusers	[ "diffusers", "SDXL", "mdnt-illus", "3D-hybrid", "anaglyph", "photoreal", "cinematic", "text-to-image", "ComfyUI", "Automatic1111", "en", "base_model:MidnightRunner/MDNT_Illus", "base_model:finetune:MidnightRunner/MDNT_Illus", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-08-19T14:50:48Z	--- license: creativeml-openrail-m language: - en base_model: - OnomaAIResearch/Illustrious-XL-v2.0 - MidnightRunner/MDNT_Illus tags: - SDXL - mdnt-illus - 3D-hybrid - anaglyph - photoreal - cinematic - text-to-image - ComfyUI - Automatic1111 - diffusers pipeline_tag: text-to-image library_name: diffusers metrics: - FID - IS widget: - text: >- (masterpiece), extremely aesthetic, newest, very vibrant colors, (ultra-HD), (cinematic lighting), (photorealistic), high detail, depth of field, best quality, absurdres, parameters: negative_prompt: >- bad hands, extra digits, (multiple views:1.1), (bad:1.05), fewer, extra, missing, worst quality, jpeg artifacts, bad quality, watermark, unfinished, displeasing, sepia, sketch, flat color, signature, artistic error, username, scan, (blurry, lowres, worst quality, (low quality:1.1), ugly, (bad anatomy:1.05), artist name, (patreon username:1.2) output: url: mdnt_illus_3d_sample.jpeg --- # MDNT_Illus_3D Model type: diffusion-based text-to-image Base model: Illustrious XL v2.0 Merged with: MIDNIGHT Illustrious, MDNT_Illus, Hyphorias, Nova3D-CGXL, BetterDaysIllus License: CreativeML Open RAIL++-M ## Model description MDNT_Illus_3D is a precision finetune focused on a 3D-hybrid aesthetic, balancing photoreal fidelity with simulated depth and sculpted form. It emphasizes anaglyphic layering, volumetric lighting, cinematic depth of field, and richly detailed textures to produce imagery that feels tactile, immersive, and dramatically lit. ## Usage recommendations ### Sampling methods - Euler A (Euler ancestral) - DPM++ 2M Karras - DPM++ 2M SDE Karras - DPM++ 3M SDE Exponential ### Settings - Steps: 25–45 - CFG scale: 4 (range 3–4) - Clip skip: 1 ### Workflow Compatible with ComfyUI and Automatic1111. A tailored ComfyUI workflow may be added later to maximize spatial layering and volumetric light behavior. ## Prompt guidance Positive (example) ``` realistic, photorealistic, very aesthetic, best quality, absurdres, masterpiece, amazing quality, newest, scenery, depth of field, high-resolution, high definition, visually intense anaglyphic experience, volumetric lighting, cinematic, sculpted 3D form ``` Negative (example) ``` bad hands, extra digits, (multiple views:1.1), (bad:1.05), fewer, extra, missing, worst quality, jpeg artifacts, watermark, unfinished, sketch, flat color, signature, artist name, blurry, lowres, (bad anatomy:1.05), (patreon username:1.2) ``` ## Version changes / notes - v1.0 (initial release) - Introduces 3D-hybrid realism with anaglyphic depth and volumetric lighting - Blended with Hyphorias, Nova 3DCG XL, BetterDaysIllus for expanded range ## Acknowledgments Base: Illustrious XL v2.0 Merges: MIDNIGHT Illustrious, MDNT_Illus, Hyphorias, Nova 3DCG XL, BetterDaysIllus ## Additional Resources - Creative Solutions and Services: [Magnabos.co](https://magnabos.co/) ## License This model is licensed under the [CreativeML Open RAIL++-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). By using this model, you agree to the terms and conditions outlined in the license.
thanobidex/blockassist-bc-colorful_shiny_hare_1755624690	thanobidex	2025-08-19T17:58:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:58:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
giacomoarienti/orpheus-3b-ita-male-full	giacomoarienti	2025-08-19T17:55:23Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/orpheus-3b-0.1-ft", "base_model:finetune:unsloth/orpheus-3b-0.1-ft", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T17:54:42Z	--- base_model: unsloth/orpheus-3b-0.1-ft tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: giacomoarienti - License: apache-2.0 - Finetuned from model : unsloth/orpheus-3b-0.1-ft This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755624471	quantumxnode	2025-08-19T17:55:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:54:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755624371	vwzyrraz7l	2025-08-19T17:54:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:54:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755626034	Dejiat	2025-08-19T17:54:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:54:15Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755624535	lisaozill03	2025-08-19T17:53:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:53:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-19-afrin-apu-viral-link/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial	VIDEOS-19-afrin-apu-viral-link	2025-08-19T17:51:52Z	0	0	null	[ "region:us" ]	null	2025-08-19T17:51:41Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755624582	Sayemahsjn	2025-08-19T17:51:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:51:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mang3dd/blockassist-bc-tangled_slithering_alligator_1755624343	mang3dd	2025-08-19T17:51:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:51:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755624249	helmutsukocok	2025-08-19T17:50:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:50:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755625775	Dejiat	2025-08-19T17:50:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:50:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AppliedLucent/nemo-phase6	AppliedLucent	2025-08-19T17:49:04Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:AppliedLucent/nemo-phase5", "base_model:finetune:AppliedLucent/nemo-phase5", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T17:38:35Z	--- base_model: AppliedLucent/nemo-phase5 tags: - text-generation-inference - transformers - unsloth - mistral license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: AppliedLucent - License: apache-2.0 - Finetuned from model : AppliedLucent/nemo-phase5 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dgambettaphd/M_mis_run2_gen2_WXS_doc1000_synt64_lr1e-04_acm_LANG	dgambettaphd	2025-08-19T17:48:47Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T17:48:32Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ColaChameleon/lilly	ColaChameleon	2025-08-19T17:48:04Z	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", "region:us" ]	text-to-image	2025-08-19T17:46:20Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/_8eb450b2-aa12-4d00-9906-e7743395311c.jpg text: '-' - output: url: images/df0r49j-37a375fe-e811-4702-9278-d7e062d15f18.png text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # lilly <Gallery /> ## Download model [Download](/ColaChameleon/lilly/tree/main) them in the Files & versions tab.
ngozimagen/ngozi-lora	ngozimagen	2025-08-19T17:46:53Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-19T16:59:52Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: ngozi --- # Ngozi Lora <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-lora-trainer/train ## Trigger words You should use `ngozi` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ngozi", "lora_weights": "https://huggingface.co/ngozimagen/ngozi-lora/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ngozimagen/ngozi-lora', weight_name='lora.safetensors') image = pipeline('ngozi').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ngozimagen/ngozi-lora/discussions) to add images that show off what you’ve made with this LoRA.
arka7/Llama-3.2-3B-Instruct-bnb-4bit-rag-finetuned-with-DPO	arka7	2025-08-19T17:45:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T17:45:17Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: arka7 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
beyoru/MuoQVAgent-1.0	beyoru	2025-08-19T17:44:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-4B-Instruct-2507", "base_model:finetune:unsloth/Qwen3-4B-Instruct-2507", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T17:42:04Z	--- base_model: unsloth/Qwen3-4B-Instruct-2507 tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: beyoru - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Instruct-2507 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Timia123/simpo_inpo_iter2_aug19	Timia123	2025-08-19T17:43:43Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "alignment-handbook", "inpo", "generated_from_trainer", "conversational", "base_model:google/gemma-2-9b-it", "base_model:finetune:google/gemma-2-9b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T17:40:20Z	--- library_name: transformers base_model: google/gemma-2-9b-it tags: - alignment-handbook - inpo - generated_from_trainer model-index: - name: gemma-2-9b-it_inpo_stage_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-2-9b-it_inpo_stage_2 This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co//home/hubing/SimPO/outputs/gemma-2-9b-it_inpo_stage_1/) on the data/inpo_iter2/pref dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.2.2 - Datasets 2.14.6 - Tokenizers 0.19.1
phospho-app/Deimos252-gr00t-Light_dataset_deimos-p48ab	phospho-app	2025-08-19T17:43:37Z	0	0	phosphobot	[ "phosphobot", "safetensors", "gr00t_n1_5", "gr00t", "robotics", "dataset:Deimos252/Light_dataset_deimos", "region:us" ]	robotics	2025-08-19T17:13:51Z	--- datasets: Deimos252/Light_dataset_deimos library_name: phosphobot pipeline_tag: robotics model_name: gr00t 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: [Deimos252/Light_dataset_deimos](https://huggingface.co/datasets/Deimos252/Light_dataset_deimos) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
VIDEOS-19-Afrin-Er-Viral-Video-Clip/New.full.videos.Afrin.Er.Viral.Video.Official.Tutorial	VIDEOS-19-Afrin-Er-Viral-Video-Clip	2025-08-19T17:43:12Z	0	0	null	[ "region:us" ]	null	2025-08-19T17:42:55Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
numind/NuExtract-2.0-8B-GPTQ	numind	2025-08-19T17:42:43Z	346	4	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "image-text-to-text", "conversational", "base_model:numind/NuExtract-2.0-8B", "base_model:quantized:numind/NuExtract-2.0-8B", "license:mit", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	image-text-to-text	2025-06-06T08:38:54Z	--- library_name: transformers license: mit base_model: - numind/NuExtract-2.0-8B pipeline_tag: image-text-to-text --- <p align="center"> <a href="https://nuextract.ai/"> <img src="logo_nuextract.svg" width="200"/> </a> </p> <p align="center"> 🖥️ <a href="https://nuextract.ai/">API / Platform</a>&nbsp&nbsp \| &nbsp&nbsp📑 <a href="https://numind.ai/blog">Blog</a>&nbsp&nbsp \| &nbsp&nbsp🗣️ <a href="https://discord.gg/3tsEtJNCDe">Discord</a> </p> # NuExtract 2.0 8B by NuMind 🔥 NuExtract 2.0 is a family of models trained specifically for structured information extraction tasks. It supports both multimodal inputs and is multilingual. We provide several versions of different sizes, all based on pre-trained models from the QwenVL family. \| Model Size \| Model Name \| Base Model \| License \| Huggingface Link \| \|------------\|------------\|------------\|---------\|------------------\| \| 2B \| NuExtract-2.0-2B \| [Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) \| MIT \| 🤗 [NuExtract-2.0-2B](https://huggingface.co/numind/NuExtract-2.0-2B) \| \| 4B \| NuExtract-2.0-4B \| [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) \| Qwen Research License \| 🤗 [NuExtract-2.0-4B](https://huggingface.co/numind/NuExtract-2.0-4B) \| \| 8B \| NuExtract-2.0-8B \| [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) \| MIT \| 🤗 [NuExtract-2.0-8B](https://huggingface.co/numind/NuExtract-2.0-8B) \| ❗️Note: `NuExtract-2.0-2B` is based on Qwen2-VL rather than Qwen2.5-VL because the smallest Qwen2.5-VL model (3B) has a more restrictive, non-commercial license. We therefore include `NuExtract-2.0-2B` as a small model option that can be used commercially. ## Benchmark Performance on collection of ~1,000 diverse extraction examples containing both text and image inputs. <a href="https://nuextract.ai/"> <img src="nuextract2_bench.png" width="500"/> </a> ## Overview To use the model, provide an input text/image and a JSON template describing the information you need to extract. The template should be a JSON object, specifying field names and their expected type. Support types include: * `verbatim-string` - instructs the model to extract text that is present verbatim in the input. * `string` - a generic string field that can incorporate paraphrasing/abstraction. * `integer` - a whole number. * `number` - a whole or decimal number. * `date-time` - ISO formatted date. * Array of any of the above types (e.g. `["string"]`) * `enum` - a choice from set of possible answers (represented in template as an array of options, e.g. `["yes", "no", "maybe"]`). * `multi-label` - an enum that can have multiple possible answers (represented in template as a double-wrapped array, e.g. `[["A", "B", "C"]]`). If the model does not identify relevant information for a field, it will return `null` or `[]` (for arrays and multi-labels). The following is an example template: ```json { "first_name": "verbatim-string", "last_name": "verbatim-string", "description": "string", "age": "integer", "gpa": "number", "birth_date": "date-time", "nationality": ["France", "England", "Japan", "USA", "China"], "languages_spoken": [["English", "French", "Japanese", "Mandarin", "Spanish"]] } ``` An example output: ```json { "first_name": "Susan", "last_name": "Smith", "description": "A student studying computer science.", "age": 20, "gpa": 3.7, "birth_date": "2005-03-01", "nationality": "England", "languages_spoken": ["English", "French"] } ``` ⚠️ We recommend using NuExtract with a temperature at or very close to 0. Some inference frameworks, such as Ollama, use a default of 0.7 which is not well suited to many extraction tasks. ## Using NuExtract with 🤗 Transformers ```python import torch from transformers import AutoProcessor from gptqmodel import GPTQModel model_name = "numind/NuExtract-2.0-8B-GPTQ" # model_name = "numind/NuExtract-2.0-4B-GPTQ" model = GPTQModel.load(model_name) processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True, padding_side='left', use_fast=True) # You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost. # min_pixels = 2562828 # max_pixels = 12802828 # processor = AutoProcessor.from_pretrained(model_name, min_pixels=min_pixels, max_pixels=max_pixels) ``` You will need the following function to handle loading of image input data: ```python def process_all_vision_info(messages, examples=None): """ Process vision information from both messages and in-context examples, supporting batch processing. Args: messages: List of message dictionaries (single input) OR list of message lists (batch input) examples: Optional list of example dictionaries (single input) OR list of example lists (batch) Returns: A flat list of all images in the correct order: - For single input: example images followed by message images - For batch input: interleaved as (item1 examples, item1 input, item2 examples, item2 input, etc.) - Returns None if no images were found """ from qwen_vl_utils import process_vision_info, fetch_image # Helper function to extract images from examples def extract_example_images(example_item): if not example_item: return [] # Handle both list of examples and single example examples_to_process = example_item if isinstance(example_item, list) else [example_item] images = [] for example in examples_to_process: if isinstance(example.get('input'), dict) and example['input'].get('type') == 'image': images.append(fetch_image(example['input'])) return images # Normalize inputs to always be batched format is_batch = messages and isinstance(messages[0], list) messages_batch = messages if is_batch else [messages] is_batch_examples = examples and isinstance(examples, list) and (isinstance(examples[0], list) or examples[0] is None) examples_batch = examples if is_batch_examples else ([examples] if examples is not None else None) # Ensure examples batch matches messages batch if provided if examples and len(examples_batch) != len(messages_batch): if not is_batch and len(examples_batch) == 1: # Single example set for a single input is fine pass else: raise ValueError("Examples batch length must match messages batch length") # Process all inputs, maintaining correct order all_images = [] for i, message_group in enumerate(messages_batch): # Get example images for this input if examples and i < len(examples_batch): input_example_images = extract_example_images(examples_batch[i]) all_images.extend(input_example_images) # Get message images for this input input_message_images = process_vision_info(message_group)[0] or [] all_images.extend(input_message_images) return all_images if all_images else None ``` E.g. To perform a basic extraction of names from a text document: ```python template = """{"names": ["string"]}""" document = "John went to the restaurant with Mary. James went to the cinema." # prepare the user message content messages = [{"role": "user", "content": document}] text = processor.tokenizer.apply_chat_template( messages, template=template, # template is specified here tokenize=False, add_generation_prompt=True, ) print(text) """"<\|im_start\|>user # Template: {"names": ["string"]} # Context: John went to the restaurant with Mary. James went to the cinema.<\|im_end\|> <\|im_start\|>assistant""" image_inputs = process_all_vision_info(messages) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") # we choose greedy sampling here, which works well for most information extraction tasks generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048} # Inference: Generation of the output generated_ids = model.generate( inputs, generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) # ['{"names": ["John", "Mary", "James"]}'] ``` <details> <summary>In-Context Examples</summary> Sometimes the model might not perform as well as we want because our task is challenging or involves some degree of ambiguity. Alternatively, we may want the model to follow some specific formatting, or just give it a bit more help. In cases like this it can be valuable to provide "in-context examples" to help NuExtract better understand the task. To do so, we can provide a list examples (dictionaries of input/output pairs). In the example below, we show to the model that we want the extracted names to be in captial letters with `-` on either side (for the sake of illustration). Usually providing multiple examples will lead to better results. ```python template = """{"names": ["string"]}""" document = "John went to the restaurant with Mary. James went to the cinema." examples = [ { "input": "Stephen is the manager at Susan's store.", "output": """{"names": ["-STEPHEN-", "-SUSAN-"]}""" } ] messages = [{"role": "user", "content": document}] text = processor.tokenizer.apply_chat_template( messages, template=template, examples=examples, # examples provided here tokenize=False, add_generation_prompt=True, ) image_inputs = process_all_vision_info(messages, examples) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") # we choose greedy sampling here, which works well for most information extraction tasks generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048} # Inference: Generation of the output generated_ids = model.generate( inputs, generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) # ['{"names": ["-JOHN-", "-MARY-", "-JAMES-"]}'] ``` </details> <details> <summary>Image Inputs</summary> If we want to give image inputs to NuExtract, instead of text, we simply provide a dictionary specifying the desired image file as the message content, instead of a string. (e.g. `{"type": "image", "image": "file://image.jpg"}`). You can also specify an image URL (e.g. `{"type": "image", "image": "http://path/to/your/image.jpg"}`) or base64 encoding (e.g. `{"type": "image", "image": "data:image;base64,/9j/..."}`). ```python template = """{"store": "verbatim-string"}""" document = {"type": "image", "image": "file://1.jpg"} messages = [{"role": "user", "content": [document]}] text = processor.tokenizer.apply_chat_template( messages, template=template, tokenize=False, add_generation_prompt=True, ) image_inputs = process_all_vision_info(messages) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048} # Inference: Generation of the output generated_ids = model.generate( inputs, generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) # ['{"store": "Trader Joe\'s"}'] ``` </details> <details> <summary>Batch Inference</summary> ```python inputs = [ # image input with no ICL examples { "document": {"type": "image", "image": "file://0.jpg"}, "template": """{"store_name": "verbatim-string"}""", }, # image input with 1 ICL example { "document": {"type": "image", "image": "file://0.jpg"}, "template": """{"store_name": "verbatim-string"}""", "examples": [ { "input": {"type": "image", "image": "file://1.jpg"}, "output": """{"store_name": "Trader Joe's"}""", } ], }, # text input with no ICL examples { "document": {"type": "text", "text": "John went to the restaurant with Mary. James went to the cinema."}, "template": """{"names": ["string"]}""", }, # text input with ICL example { "document": {"type": "text", "text": "John went to the restaurant with Mary. James went to the cinema."}, "template": """{"names": ["string"]}""", "examples": [ { "input": "Stephen is the manager at Susan's store.", "output": """{"names": ["STEPHEN", "SUSAN"]}""" } ], }, ] # messages should be a list of lists for batch processing messages = [ [ { "role": "user", "content": [x['document']], } ] for x in inputs ] # apply chat template to each example individually texts = [ processor.tokenizer.apply_chat_template( messages[i], # Now this is a list containing one message template=x['template'], examples=x.get('examples', None), tokenize=False, add_generation_prompt=True) for i, x in enumerate(inputs) ] image_inputs = process_all_vision_info(messages, [x.get('examples') for x in inputs]) inputs = processor( text=texts, images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048} # Batch Inference generated_ids = model.generate(inputs, generation_config) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_texts = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) for y in output_texts: print(y) # {"store_name": "WAL-MART"} # {"store_name": "Walmart"} # {"names": ["John", "Mary", "James"]} # {"names": ["JOHN", "MARY", "JAMES"]} ``` </details> <details> <summary>Template Generation</summary> If you want to convert existing schema files you have in other formats (e.g. XML, YAML, etc.) or start from an example, NuExtract 2.0 models can automatically generate this for you. E.g. convert XML into a NuExtract template: ```python xml_template = """<SportResult> <Date></Date> <Sport></Sport> <Venue></Venue> <HomeTeam></HomeTeam> <AwayTeam></AwayTeam> <HomeScore></HomeScore> <AwayScore></AwayScore> <TopScorer></TopScorer> </SportResult>""" messages = [ { "role": "user", "content": [{"type": "text", "text": xml_template}], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) image_inputs = process_all_vision_info(messages) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") generated_ids = model.generate( inputs, generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text[0]) # { # "Date": "date-time", # "Sport": "verbatim-string", # "Venue": "verbatim-string", # "HomeTeam": "verbatim-string", # "AwayTeam": "verbatim-string", # "HomeScore": "integer", # "AwayScore": "integer", # "TopScorer": "verbatim-string" # } ``` E.g. generate a template from natural language description: ```python description = "I would like to extract important details from the contract." messages = [ { "role": "user", "content": [{"type": "text", "text": description}], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) image_inputs = process_all_vision_info(messages) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") generated_ids = model.generate( inputs, generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text[0]) # { # "Contract": { # "Title": "verbatim-string", # "Description": "verbatim-string", # "Terms": [ # { # "Term": "verbatim-string", # "Description": "verbatim-string" # } # ], # "Date": "date-time", # "Signatory": "verbatim-string" # } # } ``` </details> ## Fine-Tuning You can find a fine-tuning tutorial notebook in the [cookbooks](https://github.com/numindai/nuextract/tree/main/cookbooks) folder of the [GitHub repo](https://github.com/numindai/nuextract/tree/main). ## vLLM Deployment Run the command below to serve an OpenAI-compatible API: ```bash vllm serve numind/NuExtract-2.0-8B --trust_remote_code --limit-mm-per-prompt image=6 --chat-template-content-format openai ``` If you encounter memory issues, set `--max-model-len` accordingly. Send requests to the model as follows: ```python import json from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="numind/NuExtract-2.0-8B", temperature=0, messages=[ { "role": "user", "content": [{"type": "text", "text": "Yesterday I went shopping at Bunnings"}], }, ], extra_body={ "chat_template_kwargs": { "template": json.dumps(json.loads("""{\"store\": \"verbatim-string\"}"""), indent=4) }, } ) print("Chat response:", chat_response) ``` For image inputs, structure requests as shown below. Make sure to order the images in `"content"` as they appear in the prompt (i.e. any in-context examples before the main input). ```python import base64 def encode_image(image_path): """ Encode the image file to base64 string """ with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') base64_image = encode_image("0.jpg") base64_image2 = encode_image("1.jpg") chat_response = client.chat.completions.create( model="numind/NuExtract-2.0-8B", temperature=0, messages=[ { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}, # first ICL example image {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image2}"}}, # real input image ], }, ], extra_body={ "chat_template_kwargs": { "template": json.dumps(json.loads("""{\"store\": \"verbatim-string\"}"""), indent=4), "examples": [ { "input": "<image>", "output": """{\"store\": \"Walmart\"}""" } ] }, } ) print("Chat response:", chat_response) ```
thailevann/track8_subtask1_PoT	thailevann	2025-08-19T17:42:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T17:42:06Z	--- base_model: unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thailevann - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
yookty/blockassist-bc-whistling_exotic_chicken_1755625296	yookty	2025-08-19T17:41:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "whistling exotic chicken", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:41:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - whistling exotic chicken --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dharshaneshwaran/MultimodalDeepfakeDetector	Dharshaneshwaran	2025-08-19T17:41:22Z	0	0	null	[ "arxiv:1604.02878", "arxiv:2104.00298", "arxiv:2008.06456", "arxiv:1901.08971", "region:us" ]	null	2025-08-19T17:36:23Z	# DeepSecure-AI DeepSecure-AI is a powerful open-source tool designed to detect fake images, videos, and audios. Utilizing state-of-the-art deep learning techniques like EfficientNetV2 and MTCNN, DeepSecure-AI offers frame-by-frame video analysis, enabling high-accuracy deepfake detection. It's developed with a focus on ease of use, making it accessible for researchers, developers, and security analysts... --- ## Features - Multimedia Detection: Detect deepfakes in images, videos, and audio files using a unified platform. - High Accuracy: Leverages EfficientNetV2 for enhanced prediction performance and accurate results. - Real-Time Video Analysis: Frame-by-frame analysis of videos with automatic face detection. - User-Friendly Interface: Easy-to-use interface built with Gradio for uploading and processing media files. - Open Source: Completely open source under the MIT license, making it available for developers to extend and improve. --- ## Demo-Data You can test the deepfake detection capabilities of DeepSecure-AI by uploading your video files. The tool will analyze each frame of the video, detect faces, and determine the likelihood of the video being real or fake. Examples: 1. [Video1-fake-1-ff.mp4](#) 2. [Video6-real-1-ff.mp4](#) --- ## How It Works DeepSecure-AI uses the following architecture: 1. Face Detection: The [MTCNN](https://arxiv.org/abs/1604.02878) model detects faces in each frame of the video. If no face is detected, it will use the previous frame's face to ensure accuracy. 2. Fake vs. Real Classification: Once the face is detected, it's resized and fed into the [EfficientNetV2](https://arxiv.org/abs/2104.00298) deep learning model, which determines the likelihood of the frame being real or fake. 3. Fake Confidence: A final prediction is generated as a percentage score, indicating the confidence that the media is fake. 4. Results: DeepSecure-AI provides an output video, highlighting the detected faces and a summary of whether the input is classified as real or fake. --- ## Project Setup ### Prerequisites Ensure you have the following installed: - Python 3.10 - Gradio (pip install gradio) - TensorFlow (pip install tensorflow) - OpenCV (pip install opencv-python) - PyTorch (pip install torch torchvision torchaudio) - facenet-pytorch (pip install facenet-pytorch) - MoviePy (pip install moviepy) ### Installation 1. Clone the repository: cd DeepSecure-AI 2. Install required dependencies: pip install -r requirements.txt 3. Download the pre-trained model weights for EfficientNetV2 and place them in the project folder. ### Running the Application 1. Launch the Gradio interface: python app.py 2. The web interface will be available locally. You can upload a video, and DeepSecure-AI will analyze and display results. --- ## Example Usage Upload a video or image to DeepSecure-AI to detect fake media. Here are some sample predictions: - Video Analysis: The tool will detect faces from each frame and classify whether the video is fake or real. - Result Output: A GIF or MP4 file with the sequence of detected faces and classification result will be provided. --- ## Technologies Used - TensorFlow: For building and training deep learning models. - EfficientNetV2: The core model for image and video classification. - MTCNN: For face detection in images and videos. - OpenCV: For video processing and frame manipulation. - MoviePy: For video editing and result generation. - Gradio: To create a user-friendly interface for interacting with the deepfake detector. --- ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. --- ## Contributions Contributions are welcome! If you'd like to improve the tool, feel free to submit a pull request or raise an issue. For more information, check the [Contribution Guidelines](CONTRIBUTING.md). --- ## References - Li et al. (2020): [Celeb-DF(V2)](https://arxiv.org/abs/2008.06456) - Rossler et al. (2019): [FaceForensics++](https://arxiv.org/abs/1901.08971) - Timesler (2020): [Facial Recognition Model in PyTorch](https://www.kaggle.com/timesler/facial-recognition-model-in-pytorch) --- ### Disclaimer DeepSecure-AI is a research project and is designed for educational purposes.Please use responsibly and always give proper credit when utilizing the model in your work.
Buura/qwen-coder-1.5b-opencodeinstruct-grpo	Buura	2025-08-19T17:41:02Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T17:40:41Z	--- base_model: unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Buura - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Huseyin/teknofest-2025-turkish-edu	Huseyin	2025-08-19T17:38:35Z	0	0	null	[ "region:us" ]	null	2025-08-19T17:32:35Z	# 🚀 TEKNOFEST 2025 - Türkçe Eğitim Modeli Bu model TEKNOFEST 2025 Eylem Temelli Türkçe Büyük Dil Modeli Yarışması için geliştirilmiştir. ## 📋 Model Bilgileri - Base Model: Qwen/Qwen3-8B - Fine-tuning: LoRA Adapter (Huseyin/qwen3-8b-turkish-teknofest2025-private) - Oluşturma Tarihi: 2025-08-19 17:37 - Alan: Eğitim Teknolojileri - Dil: Türkçe ## 🎯 Kullanım Alanları - Türkçe eğitim materyali oluşturma - Öğrenci seviyesine uygun içerik üretimi - Soru-cevap sistemleri - Eğitsel içerik özetleme - Ders planı hazırlama ## 💻 Kullanım ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Model ve tokenizer'ı yükle model_name = "Huseyin/teknofest-2025-turkish-edu" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") # Örnek kullanım prompt = "Türkçe eğitimi için yaratıcı bir etkinlik önerisi:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs, max_length=200, temperature=0.7) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## 🏆 TEKNOFEST 2025 Bu model, TEKNOFEST 2025 Türkçe Büyük Dil Modeli Yarışması kapsamında geliştirilmiştir. ### Yarışma Kategorisi Eylem Temelli Türkçe Büyük Dil Modeli ### Ekip TEKNOFEST 2025 Yarışma Ekibi ## 📊 Performans Metrikleri - Perplexity: [Değerlendirilecek] - BLEU Score: [Değerlendirilecek] - Human Evaluation:** [Değerlendirilecek] ## 📄 Lisans Apache 2.0 ## 🙏 Teşekkür Bu modelin geliştirilmesinde emeği geçen herkese teşekkür ederiz. --- TEKNOFEST 2025 - Türkiye'nin Teknoloji Festivali
catme0w/MolScribe-Long	catme0w	2025-08-19T17:37:36Z	0	0	null	[ "base_model:yujieq/MolScribe", "base_model:finetune:yujieq/MolScribe", "license:mit", "region:us" ]	null	2025-08-19T04:44:52Z	--- license: mit base_model: - yujieq/MolScribe ---
WenFengg/21_14l10__20_8	WenFengg	2025-08-19T17:37:09Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T17:35:18Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755624890	Dejiat	2025-08-19T17:35:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:35:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aleebaster/blockassist-bc-sly_eager_boar_1755623414	aleebaster	2025-08-19T17:34:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:34:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnonymousCS/xlmr_norwegian_immigration3	AnonymousCS	2025-08-19T17:31:48Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T17:27:36Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_norwegian_immigration3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_norwegian_immigration3 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3159 - Accuracy: 0.9091 - 1-f1: 0.8571 - 1-recall: 0.8182 - 1-precision: 0.9 - Balanced Acc: 0.8864 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1747 \| 1.0 \| 2 \| 0.3069 \| 0.9091 \| 0.8571 \| 0.8182 \| 0.9 \| 0.8864 \| \| 0.2707 \| 2.0 \| 4 \| 0.2797 \| 0.9091 \| 0.8571 \| 0.8182 \| 0.9 \| 0.8864 \| \| 0.1527 \| 3.0 \| 6 \| 0.2621 \| 0.8788 \| 0.8 \| 0.7273 \| 0.8889 \| 0.8409 \| \| 0.1886 \| 4.0 \| 8 \| 0.2600 \| 0.8788 \| 0.8 \| 0.7273 \| 0.8889 \| 0.8409 \| \| 0.1 \| 5.0 \| 10 \| 0.2818 \| 0.9091 \| 0.8571 \| 0.8182 \| 0.9 \| 0.8864 \| \| 0.0835 \| 6.0 \| 12 \| 0.3159 \| 0.9091 \| 0.8571 \| 0.8182 \| 0.9 \| 0.8864 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
indoempatnol/blockassist-bc-fishy_wary_swan_1755623074	indoempatnol	2025-08-19T17:31:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:31:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
g-assismoraes/Qwen3-4B-Base-aki-alpha0.08-var-adown0.05-qQ2Q3-hatebr	g-assismoraes	2025-08-19T17:29:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T17:22:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Yanmife/gemma-2b-health-qlora	Yanmife	2025-08-19T17:27:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-2b", "base_model:finetune:google/gemma-2b", "endpoints_compatible", "region:us" ]	null	2025-08-19T15:30:05Z	--- base_model: google/gemma-2b library_name: transformers model_name: gemma-2b-health-qlora tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gemma-2b-health-qlora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Yanmife/gemma-2b-health-qlora", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/emmy_wan-personal/Fine-tuning-Gemma-2B-health/runs/bguryohi) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755624278	Vasya777	2025-08-19T17:25:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:25:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755624226	lilTAT	2025-08-19T17:24:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:24:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Pradeepgupta112233/runwayml-stable-diffusion-v1-5	Pradeepgupta112233	2025-08-19T17:24:15Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "region:us" ]	text-to-image	2025-08-19T17:20:14Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/8043.jpg text: '-' base_model: '' instance_prompt: SD 1.5 --- # My Project – Character LoRA <Gallery /> ## Model description my_project ## Trigger words You should use `SD 1.5` to trigger the image generation. ## Download model [Download](/Pradeepgupta112233/runwayml-stable-diffusion-v1-5/tree/main) them in the Files & versions tab.
AnonymousCS/xlmr_finnish_immigration3	AnonymousCS	2025-08-19T17:23:49Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T17:19:59Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_finnish_immigration3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_finnish_immigration3 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0700 - Accuracy: 0.9846 - 1-f1: 0.9767 - 1-recall: 0.9767 - 1-precision: 0.9767 - Balanced Acc: 0.9826 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.2027 \| 1.0 \| 5 \| 0.0699 \| 0.9846 \| 0.9767 \| 0.9767 \| 0.9767 \| 0.9826 \| \| 0.1827 \| 2.0 \| 10 \| 0.0772 \| 0.9769 \| 0.9655 \| 0.9767 \| 0.9545 \| 0.9769 \| \| 0.0918 \| 3.0 \| 15 \| 0.0637 \| 0.9846 \| 0.9767 \| 0.9767 \| 0.9767 \| 0.9826 \| \| 0.067 \| 4.0 \| 20 \| 0.0844 \| 0.9692 \| 0.9545 \| 0.9767 \| 0.9333 \| 0.9711 \| \| 0.0457 \| 5.0 \| 25 \| 0.0700 \| 0.9846 \| 0.9767 \| 0.9767 \| 0.9767 \| 0.9826 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
AppliedLucent/nemo-phase5	AppliedLucent	2025-08-19T17:23:30Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:AppliedLucent/nemo-phase4", "base_model:finetune:AppliedLucent/nemo-phase4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T17:10:38Z	--- base_model: AppliedLucent/nemo-phase4 tags: - text-generation-inference - transformers - unsloth - mistral license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: AppliedLucent - License: apache-2.0 - Finetuned from model : AppliedLucent/nemo-phase4 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755622535	quantumxnode	2025-08-19T17:23:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:23:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755622654	pempekmangedd	2025-08-19T17:23:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:22:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
WenFengg/21_14l8__20_8	WenFengg	2025-08-19T17:22:15Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T17:20:29Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755623981	Dejiat	2025-08-19T17:20:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:20:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
unitova/blockassist-bc-zealous_sneaky_raven_1755622293	unitova	2025-08-19T17:19:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:19:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755622613	Sayemahsjn	2025-08-19T17:18:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:18:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755623819	lilTAT	2025-08-19T17:17:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T17:17:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Jacksss123/net72_uid253	Jacksss123	2025-08-19T17:17:13Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-08-19T17:13:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
debesu/Mati-Bal-Mati-Mist	debesu	2025-08-19T17:16:40Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-19T16:47:24Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Mati --- # Mati Bal Mati Mist <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-lora-trainer/train ## Trigger words You should use `Mati` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Mati", "lora_weights": "https://huggingface.co/debesu/Mati-Bal-Mati-Mist/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('debesu/Mati-Bal-Mati-Mist', weight_name='lora.safetensors') image = pipeline('Mati').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1400 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/debesu/Mati-Bal-Mati-Mist/discussions) to add images that show off what you’ve made with this LoRA.

Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF

Guilherme34

2025-08-19T20:18:39Z

0

null

[ "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed", "base_model:quantized:Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed", "license:other", "endpoints_compatible", "region:us" ]

null

2025-08-19T20:18:10Z

--- license: other language: - en tags: - llama-cpp - gguf-my-repo base_model: Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed --- # Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF This model was converted to GGUF format from [`Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed`](https://huggingface.co/Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF --hf-file samantha-mythomax-l2-13b-merge-fixed-q3_k_l.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF --hf-file samantha-mythomax-l2-13b-merge-fixed-q3_k_l.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF --hf-file samantha-mythomax-l2-13b-merge-fixed-q3_k_l.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Guilherme34/Samantha-Mythomax-l2-13b-merge-fixed-Q3_K_L-GGUF --hf-file samantha-mythomax-l2-13b-merge-fixed-q3_k_l.gguf -c 2048 ```

roeker/blockassist-bc-quick_wiry_owl_1755634582

roeker

2025-08-19T20:17:58Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:17:09Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

calegpedia/blockassist-bc-stealthy_slimy_rooster_1755633003

calegpedia

2025-08-19T20:15:35Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:15:32Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stealthy slimy rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist

bryanzhou008

2025-08-19T20:15:20Z

60

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-10-30T19:48:56Z

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-finetuned-inaturalist results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8541666666666666 ---  # vit-base-patch16-224-in21k-finetuned-inaturalist This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the inaturalist dataset. It achieves the following results on the evaluation set: - Loss: 0.7703 - Accuracy: 0.8542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | No log | 0.8421 | 4 | 3.1793 | 0.0347 | | No log | 1.8947 | 9 | 3.1647 | 0.0486 | | 3.1648 | 2.9474 | 14 | 3.1382 | 0.0944 | | 3.1648 | 4.0 | 19 | 3.0995 | 0.1556 | | 3.0817 | 4.8421 | 23 | 3.0555 | 0.2639 | | 3.0817 | 5.8947 | 28 | 2.9849 | 0.3889 | | 2.9167 | 6.9474 | 33 | 2.8932 | 0.5139 | | 2.9167 | 8.0 | 38 | 2.7775 | 0.5972 | | 2.6682 | 8.8421 | 42 | 2.6706 | 0.6528 | | 2.6682 | 9.8947 | 47 | 2.5233 | 0.7069 | | 2.3659 | 10.9474 | 52 | 2.3859 | 0.7375 | | 2.3659 | 12.0 | 57 | 2.2546 | 0.75 | | 2.079 | 12.8421 | 61 | 2.1531 | 0.7528 | | 2.079 | 13.8947 | 66 | 2.0372 | 0.75 | | 1.828 | 14.9474 | 71 | 1.9339 | 0.7597 | | 1.828 | 16.0 | 76 | 1.8403 | 0.7694 | | 1.6253 | 16.8421 | 80 | 1.7733 | 0.7764 | | 1.6253 | 17.8947 | 85 | 1.6914 | 0.7903 | | 1.4502 | 18.9474 | 90 | 1.6153 | 0.7875 | | 1.4502 | 20.0 | 95 | 1.5510 | 0.7986 | | 1.4502 | 20.8421 | 99 | 1.5016 | 0.8 | | 1.2959 | 21.8947 | 104 | 1.4454 | 0.8222 | | 1.2959 | 22.9474 | 109 | 1.3912 | 0.8181 | | 1.1802 | 24.0 | 114 | 1.3390 | 0.8333 | | 1.1802 | 24.8421 | 118 | 1.2995 | 0.8333 | | 1.0629 | 25.8947 | 123 | 1.2707 | 0.8389 | | 1.0629 | 26.9474 | 128 | 1.2335 | 0.8361 | | 0.9801 | 28.0 | 133 | 1.1975 | 0.8444 | | 0.9801 | 28.8421 | 137 | 1.1672 | 0.8389 | | 0.9076 | 29.8947 | 142 | 1.1338 | 0.8444 | | 0.9076 | 30.9474 | 147 | 1.1137 | 0.8472 | | 0.8349 | 32.0 | 152 | 1.0855 | 0.8528 | | 0.8349 | 32.8421 | 156 | 1.0717 | 0.8542 | | 0.7782 | 33.8947 | 161 | 1.0483 | 0.8514 | | 0.7782 | 34.9474 | 166 | 1.0352 | 0.85 | | 0.7208 | 36.0 | 171 | 1.0202 | 0.8556 | | 0.7208 | 36.8421 | 175 | 0.9994 | 0.8486 | | 0.6708 | 37.8947 | 180 | 0.9814 | 0.8556 | | 0.6708 | 38.9474 | 185 | 0.9691 | 0.8542 | | 0.6303 | 40.0 | 190 | 0.9599 | 0.8486 | | 0.6303 | 40.8421 | 194 | 0.9422 | 0.8472 | | 0.6303 | 41.8947 | 199 | 0.9278 | 0.8486 | | 0.6018 | 42.9474 | 204 | 0.9172 | 0.8528 | | 0.6018 | 44.0 | 209 | 0.9093 | 0.8514 | | 0.5622 | 44.8421 | 213 | 0.9030 | 0.8583 | | 0.5622 | 45.8947 | 218 | 0.8972 | 0.8625 | | 0.5474 | 46.9474 | 223 | 0.8859 | 0.8569 | | 0.5474 | 48.0 | 228 | 0.8858 | 0.8653 | | 0.5254 | 48.8421 | 232 | 0.8779 | 0.8556 | | 0.5254 | 49.8947 | 237 | 0.8635 | 0.8569 | | 0.5036 | 50.9474 | 242 | 0.8563 | 0.8611 | | 0.5036 | 52.0 | 247 | 0.8613 | 0.8542 | | 0.4855 | 52.8421 | 251 | 0.8546 | 0.8625 | | 0.4855 | 53.8947 | 256 | 0.8469 | 0.8597 | | 0.4697 | 54.9474 | 261 | 0.8327 | 0.8528 | | 0.4697 | 56.0 | 266 | 0.8268 | 0.8597 | | 0.4482 | 56.8421 | 270 | 0.8188 | 0.8556 | | 0.4482 | 57.8947 | 275 | 0.8171 | 0.8653 | | 0.4436 | 58.9474 | 280 | 0.8133 | 0.8486 | | 0.4436 | 60.0 | 285 | 0.8070 | 0.8639 | | 0.4436 | 60.8421 | 289 | 0.7986 | 0.8542 | | 0.4211 | 61.8947 | 294 | 0.7937 | 0.8597 | | 0.4211 | 62.9474 | 299 | 0.7908 | 0.8611 | | 0.4228 | 64.0 | 304 | 0.7952 | 0.8625 | | 0.4228 | 64.8421 | 308 | 0.8010 | 0.8514 | | 0.4046 | 65.8947 | 313 | 0.7975 | 0.8472 | | 0.4046 | 66.9474 | 318 | 0.7927 | 0.8417 | | 0.4048 | 68.0 | 323 | 0.7880 | 0.8556 | | 0.4048 | 68.8421 | 327 | 0.7860 | 0.8514 | | 0.3925 | 69.8947 | 332 | 0.7899 | 0.8403 | | 0.3925 | 70.9474 | 337 | 0.7883 | 0.8417 | | 0.3936 | 72.0 | 342 | 0.7885 | 0.8417 | | 0.3936 | 72.8421 | 346 | 0.7874 | 0.8361 | | 0.3985 | 73.8947 | 351 | 0.7832 | 0.8417 | | 0.3985 | 74.9474 | 356 | 0.7787 | 0.8514 | | 0.3849 | 76.0 | 361 | 0.7753 | 0.8486 | | 0.3849 | 76.8421 | 365 | 0.7746 | 0.8514 | | 0.3796 | 77.8947 | 370 | 0.7736 | 0.8542 | | 0.3796 | 78.9474 | 375 | 0.7731 | 0.8528 | | 0.3717 | 80.0 | 380 | 0.7715 | 0.8556 | | 0.3717 | 80.8421 | 384 | 0.7709 | 0.8556 | | 0.3717 | 81.8947 | 389 | 0.7706 | 0.8569 | | 0.3802 | 82.9474 | 394 | 0.7704 | 0.8556 | | 0.3802 | 84.0 | 399 | 0.7704 | 0.8542 | | 0.3782 | 84.2105 | 400 | 0.7703 | 0.8542 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.1+cu121 - Datasets 2.21.0 - Tokenizers 0.20.1

Dejiat/blockassist-bc-savage_unseen_bobcat_1755634467

Dejiat

2025-08-19T20:15:07Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:14:50Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Muapi/mapdraw-flux

Muapi

2025-08-19T20:15:07Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T20:14:54Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # MapDraw (FLUX) ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: m4pdr4w ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:777351@869395", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/a-better-wolf

Muapi

2025-08-19T20:14:22Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T20:12:38Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # A Better Wolf ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: wolf, snarling, black, white, ears forward, ears back, pack of wolves ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:412694@725614", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

koloni/blockassist-bc-deadly_graceful_stingray_1755632890

koloni

2025-08-19T20:13:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:13:26Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755632706

vwzyrraz7l

2025-08-19T20:11:31Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:11:28Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755634209

Dejiat

2025-08-19T20:10:52Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:10:35Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

reece124/OpenCUA-7B-converted

reece124

2025-08-19T20:10:46Z

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "VLM", "Computer-Use-Agent", "OS-Agent", "GUI", "Grounding", "image-text-to-text", "conversational", "en", "dataset:xlangai/AgentNet", "dataset:xlangai/aguvis-stage1", "dataset:smolagents/aguvis-stage-2", "dataset:osunlp/UGround-V1-Data", "arxiv:2508.09123", "arxiv:2504.07981", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

2025-08-19T20:10:19Z

--- base_model: - Qwen/Qwen2.5-VL-7B-Instruct datasets: - xlangai/AgentNet - xlangai/aguvis-stage1 - smolagents/aguvis-stage-2 - osunlp/UGround-V1-Data language: - en license: mit metrics: - accuracy - code_eval pipeline_tag: image-text-to-text library_name: transformers tags: - VLM - Computer-Use-Agent - OS-Agent - GUI - Grounding --- <h1 style=" font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Helvetica,Arial,sans-serif; font-size:48px; font-weight:700; line-height:1.25; text-align:center; margin:0 0 24px;"> OpenCUA: Open Foundations for Computer-Use Agents </h1> <div style=" display:flex; justify-content:center; gap:12px; flex-wrap:wrap; margin-bottom:28px;"> <a href="https://opencua.xlang.ai/" style=" display:inline-block; padding:8px 24px; background:#2b2b2b; color:#ffffff; border-radius:36px; text-decoration:none; font-weight:600; font-size:16px;"> 🌐 Website </a> <a href="https://arxiv.org/abs/2508.09123" style=" display:inline-block; padding:8px 24px; background:#2b2b2b; color:#ffffff; border-radius:36px; text-decoration:none; font-weight:600; font-size:16px;"> 📝 Paper </a> <a href="https://github.com/xlang-ai/OpenCUA" style=" display:inline-block; padding:8px 24px; background:#2b2b2b; color:#ffffff; border-radius:36px; text-decoration:none; font-weight:600; font-size:16px;"> 💻 Code </a> </div> <div style="max-width:900px;margin:0 auto;"> # Introduction <div style=" max-width: 880px; /* 可按需调节整体宽度 */ margin: 0 auto; /* 居中容器 */ text-align: justify; /* 关键：两端对齐 */ text-justify: inter-word; /* 优化英文对齐效果 */ line-height: 1.6;"> OpenCUA models (OpenCUA-7B and OpenCUA-32B) are end-to-end computer-use foundation models than can produce executable actions in the computer environments. They are based on the weights of Qwen2.5-VL-7B-Instruction and Qwen2.5-VL-32B-Instruction. They demonstrate superior performance across CUA benchmarks. In particular, <b>OpenCUA-32B</b> achieves an average success rate of **34.8%** on [OSWorld-Verified](https://os-world.github.io/), establishing a new state-of-the-art (SOTA) among open-source models and surpassing OpenAI CUA (GPT-4o). Both models also have strong grounding performance, OpenCUA-32B achieves 59.6% on [OSWorld-G](https://osworld-grounding.github.io/) and 55.3% on [Screenspot-Pro](https://arxiv.org/abs/2504.07981). </div> ### Key Features - **Superior Computer-Use Capablity**: Able to execute multi-step computer-use actions with effective planning and reasoning - **Multi-OS Support**: Trained on demonstrations across Ubuntu, Windows, and macOS - **Visual Grounding**: Strong GUI element recognition and spatial reasoning capabilities - **Multi-Image Context**: Processes up to 3 screenshot history for better context understanding - **Reflective Reasoning**: Enhanced with reflective long Chain-of-Thought that identifies errors and provides corrective reasoning # Performance ### Online Agent Evaluation OpenCUA models achieves strong performance on **[OSWorld-Verified](https://os-world.github.io/)**. OPENCUA-32B achieves the best performance among all open-source models with an average success rate of 34.8%, outperforming prior baselines by large margins. It also closes the gap to proprietary Claude models. <div align="center"> | **Model** | **15 Steps** | **50 Steps** | **100 Steps** | |-------------------------------|:--------:|:--------:|:---------:| | **Proprietary** | | | | | OpenAI CUA | 26.0 | 31.3 | 31.4 | | Seed 1.5-VL | 27.9 | — | 34.1 | | Claude 3.7 Sonnet | 27.1 | 35.8 | 35.9 | | Claude 4 Sonnet | 31.2 | 43.9 | 41.5 | | **Open-Source** | | | | | Qwen 2.5-VL-32B-Instruct | 3.0 | — | 3.9 | | Qwen 2.5-VL-72B-Instruct | 4.4 | — | 5.0 | | Kimi-VL-A3B | 9.7 | — | 10.3 | | UI-TARS-72B-DPO | 24.0 | 25.8 | 27.1 | | UI-TARS-1.5-7B | 24.5 | 27.3 | 27.4 | | OpenCUA-7B *(Ours)* | 24.3 | 27.9 | 26.6 | | **OpenCUA-32B *(Ours)*** | **29.7** | **34.1** | **34.8** | </div> *OpenCUA scores are the mean of 3 independent runs.* ### GUI Grounding Performance <div align="center"> | **Model** | **OSWorld-G** | **ScreenSpot-V2** | **ScreenSpot-Pro** | |-------|-----------|---------------|----------------| | Qwen2.5-VL-7B | 31.4 | 88.8 | 27.6 | | Qwen2.5-VL-32B | 46.5 | 87.0 | 39.4 | | UI-TARS-72B | 57.1 | 90.3 | 38.1 | | **OpenCUA-A3B** | 48.6 | 91.4 | 28.5 | | **OpenCUA-Qwen2-7B** | 45.7 | 88.5 | 23.7 | | **OpenCUA-7B** | 55.3 | 92.3 | 50.0 | | **OpenCUA-32B** | **59.6** | **93.4** | **55.3** | </div> ### AgentNetBench (Offline Evaluation) <div align="center"> | **Model** | **Coordinate Actions** | **Content Actions** | **Function Actions** | **Average** | |-------|-------------------|-----------------|------------------|---------| | Qwen2.5-VL-7B | 50.7 | 40.8 | 3.1 | 48.0 | | Qwen2.5-VL-32B | 66.6 | 47.2 | 41.5 | 64.8 | | Qwen2.5-VL-72B | 67.2 | 52.6 | 50.5 | 67.0 | | OpenAI CUA | 71.7 | 57.3 | **80.0** | 73.1 | | **OpenCUA-7B** | 79.0 | 62.0 | 44.3 | 75.2 | | **OpenCUA-32B** | **81.9** | 66.1 | 55.7 | **79.1** | </div> # 🚀 Quick Start <div style="border-left: 6px solid #f28c28; background: #fff8e6; padding: 12px 16px; margin: 16px 0;"> <strong>⚠️ Important for Qwen-based Models (OpenCUA-7B, OpenCUA-32B):</strong> To align with our training infrastructure, we have modified the model in two places: <ul style="margin-top: 8px;"> <li>1. Multimodal Rotary Position Embedding (M-RoPE) has been replaced with 1D RoPE</strong>.</li> <li>2. Using the same Tokenizer and ChatTemplate as Kimi-VL.</li> <li>Do not use the default transformers and vllm classes to load the model. Tokenizer and Chat Template should be aligned if training the models.</li> </ul> </div> ## Installation & Download First, install the required transformers dependencies: ```bash conda create -n opencua python=3.10 conda activate opencua pip install -r requirement.txt ``` Download the model weight from huggingface: ```bash from huggingface_hub import snapshot_download snapshot_download( repo_id="xlangai/OpenCUA-7B", local_dir="OpenCUA-7B", local_dir_use_symlinks=False ) ``` ## 🎯 GUI Grounding The following code demonstrates how to use OpenCUA models for GUI grounding tasks: ```python import base64 import torch from transformers import AutoTokenizer, AutoModel, AutoImageProcessor from PIL import Image import json def encode_image(image_path: str) -> str: """Encode image to base64 string for model input.""" with open(image_path, "rb") as f: return base64.b64encode(f.read()).decode() def load_opencua_model(model_path: str): """Load OpenCUA model, tokenizer, and image processor.""" tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = AutoModel.from_pretrained( model_path, torch_dtype="auto", device_map="auto", trust_remote_code=True ) image_processor = AutoImageProcessor.from_pretrained(model_path, trust_remote_code=True) return model, tokenizer, image_processor def create_grounding_messages(image_path: str, instruction: str): """Create chat messages for GUI grounding task.""" system_prompt = ( "You are a GUI agent. You are given a task and a screenshot of the screen. " "You need to perform a series of pyautogui actions to complete the task." ) messages = [ {"role": "system", "content": system_prompt}, { "role": "user", "content": [ {"type": "image", "image": f"data:image/png;base64,{encode_image(image_path)}"}, {"type": "text", "text": instruction}, ], }, ] return messages def run_inference(model, tokenizer, image_processor, messages, image_path): """Run inference on the model.""" # Prepare text input input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True ) input_ids = torch.tensor([input_ids]).to(model.device) # Prepare image input image = Image.open(image_path).convert('RGB') image_info = image_processor.preprocess(images=[image]) pixel_values = torch.tensor(image_info['pixel_values']).to( dtype=torch.bfloat16, device=model.device ) grid_thws = torch.tensor(image_info['image_grid_thw']) # Generate response with torch.no_grad(): generated_ids = model.generate( input_ids, pixel_values=pixel_values, grid_thws=grid_thws, max_new_tokens=512, temperature=0 ) # Decode output prompt_len = input_ids.shape[1] generated_ids = generated_ids[:, prompt_len:] output_text = tokenizer.batch_decode( generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] return output_text # Example usage model_path = "OpenCUA/OpenCUA-7B" # or other model variants image_path = "screenshot.png" instruction = "Click on the submit button" # Load model model, tokenizer, image_processor = load_opencua_model(model_path) # Create messages and run inference messages = create_grounding_messages(image_path, instruction) result = run_inference(model, tokenizer, image_processor, messages, image_path) print("Model output:", result) ``` <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;"> <em>Expected result:</em> ```python pyautogui.click(x=1443, y=343) ``` </div> You can also run the five grounding examples in [OpenCUA/model/inference/huggingface_inference.py](https://github.com/xlang-ai/OpenCUA/blob/main/model/inference/huggingface_inference.py): ``` cd ./model/inference/ python huggingface_inference.py ``` ## 🖥️ Computer Use Agent **[OpenCUAAgent](https://github.com/xlang-ai/OSWorld/blob/main/mm_agents/opencua_agent.py)** is developed in the [OSWorld](https://github.com/xlang-ai/OSWorld) environment based on OpenCUA models. It iteratively perceives the environment via screenshots, produces reflective long CoT as inner monologue, and predicts the next action to be executed. OpenCUAAgent uses 3 images in total and L2 CoT format in default. Command for running OpenCUA-7B and OpenCUA-32B in OSWorld: ``` python run_multienv_opencua.py \ --headless \ --observation_type screenshot \ --model OpenCUA-32B \ --result_dir ./results --test_all_meta_path evaluation_examples/test_all_no_gdrive.json \ --max_steps 100 \ --num_envs 30 \ --coordinate_type qwen25 ``` <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;"> <em>Currently we only supports huggingface inference. We are implementing the vLLM supports of OpenCUA models. Please stay tuned.</em> </div> --- # AgentNet Dataset - Large-Scale Computer-Use Dataset <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/dw5k183ucDSB2SZuS5f2V.png" width="400" alt="AgentNet Dataset Domain Distribution"> </div> AgentNet is the first large-scale desktop computer-use agent trajectory dataset, containing 22.6K human-annotated computer-use tasks across Windows, macOS, and Ubuntu systems. 👉 **[AgentNet Huggingface Dataset](https://huggingface.co/datasets/xlangai/AgentNet)** Download the dataset here： ``` pip install -U huggingface_hub huggingface-cli download xlangai/AgentNet --repo-type dataset --local-dir ./AgentNet ``` Collecting computer-use agent training data requires 3 steps: - Demonstrate human computer-use task via [AgentNetTool](https://agentnet-tool.xlang.ai/); - Preprocess the demonstration using [Action Reduction & State-Action Matching](./data/data-processor); - For each step, [synthesize reflective long CoT](./data/cot-generator) ## 1 AgentNetTool – Annotation & Verification Tool <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/ETjCOoIRR7f1YZCJ2kfiW.png" width="700" alt="AgentNet Tool"> </div> Our **AgentNetTool** is a cross-platform GUI recorder that runs unobtrusively on annotators’ machines. It captures synchronized **screen video**, **mouse/keyboard events**, and **accessibility trees**, then provides an in-browser UI for reviewing, trimming, and submitting demonstrations. AgentNet Tool is available on Windows, macOS and Ubuntu. 👉 **[AgentNetTool Document](https://agentnet-tool.xlang.ai/)** ## 2 DataProcessor – Action Reduction & State–Action Matching Raw demonstrations can contain thousands of low-level events that are too dense for model training. The **DataProcessor** module (`./data/data-process/`) performs two key steps: 1. **Action Reduction** — merges granular signals into concise, semantically meaningful PyAutoGUI actions (e.g., collapsing mouse moves → click, coalescing scrolls, grouping key-press sequences into text or hotkeys). 2. **State–Action Matching** — aligns every reduced action with the *last visually distinct frame* **before** the action begins, avoiding future-information leakage and yielding compact state–action pairs. These processed trajectories underlie all downstream training and evaluation. --- ## 3 CoTGenerator – Synthesizing Reflective Long Chain-of-Thought Inner Monologue To boost robustness and interpretability, we augment each trajectory with **reflective long Chain-of-Thought (CoT) reasoning**. The **CoTGenerator** pipeline (`./data/cot-generator/`) synthesizes step-level reflections that: * reflect on the previous action, * explain *why* an action is chosen given the current observation and history, * note potential alternative actions, and * forecast the expected next state. Empirically, models trained with these rich CoTs scale better with data and generalize across unseen applications. # Evaluation <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/67b327cdd4665a0448eef7d5/emy1QCJwQj9KqHkVmtNH2.png" width="800" alt="AgentNetBench"> </div> **AgentNetBench** (`./AgentNetBench/`) provides a realistic offline evaluator for OS agent trajectories. It compares model-predicted low-level actions (click, moveTo, write, press, scroll, terminate, etc.) against ground-truth human actions and reports detailed metrics. 👉 See **[AgentNetBench/README.md](./evaluation/agentnetbench/README.md)** for usage instructions. # TODO ## vLLM Support We are actively working with the vLLM team to add support for OpenCUA models. **Workaround:** For now, please use the standard transformers library as shown in the examples above. We will update this section once vLLM support becomes available. ## Training Code OpenCUA models are developed based on the training infrastructure of Kimi Team. We are developting the training pipeline based on the open-source infrastructure as well. # Acknowledge <p> We thank Su Yu, Caiming Xiong, Binyuan Hui, and the anonymous reviewers for their insightful discussions and valuable feedback. We are grateful to Moonshot AI for providing training infrastructure and annotated data. We also sincerely appreciate Calvin, Ziwei Chen, Jin Zhang, Ze Li, Zhengtao Wang, Yanxu Chen, and Qizheng Gu from the Kimi Team for their strong infrastructure support and helpful guidance. The development of our tool is based on the open-source projects-<a href="https://github.com/TheDuckAI/DuckTrack" target="_blank">DuckTrack</a> and <a href="https://github.com/OpenAdaptAI/OpenAdapt" target="_blank">OpenAdapt</a>. We are very grateful to their commitment to the open source community. Finally, we extend our deepest thanks to all annotators for their tremendous effort and contributions to this project. </p> # License This project is licensed under the MIT License - see the LICENSE file in the root folder for details. ## Research Use and Disclaimer OpenCUA models are intended for **research and educational purposes only**. ### Prohibited Uses - The model may **not** be used for any purpose or activity that violates applicable laws or regulations in any jurisdiction - Use for illegal, unethical, or harmful activities is strictly prohibited ### Disclaimer - The authors, contributors, and copyright holders are **not responsible** for any illegal, unethical, or harmful use of the Software, nor for any direct or indirect damages resulting from such use - Use of the "OpenCUA" name, logo, or trademarks does **not** imply any endorsement or affiliation unless separate written permission is obtained - Users are solely responsible for ensuring their use complies with applicable laws and regulations ## Important Notes on Coordinate Systems <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;"> <ul style="margin: 0;"> <li><strong><code>OpenCUA/OpenCUA-A3B</code></strong> – Relative coordinates <em>(not supported in this code)</em></li> <li><strong><code>OpenCUA/OpenCUA-Qwen2-7B</code></strong> – Relative coordinates</li> <li><strong><code>OpenCUA/OpenCUA-7B</code></strong> – Absolute coordinates</li> <li><strong><code>OpenCUA/OpenCUA-32B</code></strong> – Absolute coordinates</li> </ul> </div> **OpenCUA models use different coordinate systems depending on the base model:** - **OpenCUA-Qwen2-7B**: Outputs **relative coordinates** (0.0 to 1.0 range) ```python # Example output: pyautogui.click(x=0.5, y=0.3) # x=0.5 means 50% from left edge, y=0.3 means 30% from top edge # Convert to absolute coordinates: def qwen2_relative_to_absolute(rel_x, rel_y, original_width, original_height): abs_x = int(rel_x * original_width) abs_y = int(rel_y * original_height) return abs_x, abs_y ``` - **OpenCUA-7B and OpenCUA-32B** (Qwen2.5-based): Output **absolute coordinates** after smart resize ```python # Example output: pyautogui.click(x=960, y=324) # These are coordinates on the smart-resized image, not the original image # Convert to original image coordinates: # Please refer to the smart_resize function in: https://github.com/huggingface/transformers/blob/67ddc82fbc7e52c6f42a395b4a6d278c55b77a39/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L55 def qwen25_smart_resize_to_absolute(model_x, model_y, original_width, original_height): # First, calculate the smart-resized dimensions resized_height, resized_width = smart_resize(original_height, original_width, factor = 28, min_pixels = 3136, max_pixels = 12845056) # Convert model output to relative coordinates on original image rel_x = model_x / resized_width rel_y = model_y / resized_height # Then convert to absolute coordinates on original image abs_x = int(rel_x * original_width) abs_y = int(rel_y * original_height) return abs_x, abs_y ``` <div style="border-left: 6px solid #9ca3af; background: #f5f5f5; padding: 12px 16px; margin: 16px 0;"> <strong>Understanding Smart Resize for Qwen2.5-based Models:</strong> <p style="margin: 8px 0 0;"> The Qwen2.5-VL models use a “smart resize” preprocessing that maintains aspect ratio while fitting within pixel constraints. For coordinate conversion, you need the smart resize function from the <a href="https://github.com/QwenLM/Qwen2.5-VL/blob/d2240f11656bfe404b9ba56db4e51cd09f522ff1/qwen-vl-utils/src/qwen_vl_utils/vision_process.py#L60"> official Qwen2.5-VL implementation</a>. </p> </div> ## Citation If you use OpenCUA models in your research, please cite our work: ```bibtex @misc{wang2025opencuaopenfoundationscomputeruse, title={OpenCUA: Open Foundations for Computer-Use Agents}, author={Xinyuan Wang and Bowen Wang and Dunjie Lu and Junlin Yang and Tianbao Xie and Junli Wang and Jiaqi Deng and Xiaole Guo and Yiheng Xu and Chen Henry Wu and Zhennan Shen and Zhuokai Li and Ryan Li and Xiaochuan Li and Junda Chen and Boyuan Zheng and Peihang Li and Fangyu Lei and Ruisheng Cao and Yeqiao Fu and Dongchan Shin and Martin Shin and Jiarui Hu and Yuyan Wang and Jixuan Chen and Yuxiao Ye and Danyang Zhang and Dikang Du and Hao Hu and Huarong Chen and Zaida Zhou and Haotian Yao and Ziwei Chen and Qizheng Gu and Yipu Wang and Heng Wang and Diyi Yang and Victor Zhong and Flood Sung and Y. Charles and Zhilin Yang and Tao Yu}, year={2025}, eprint={2508.09123}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2508.09123}, } ``` </div>

roeker/blockassist-bc-quick_wiry_owl_1755634184

roeker

2025-08-19T20:10:37Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:10:29Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Leoar/blockassist-bc-pudgy_toothy_cheetah_1755634081

Leoar

2025-08-19T20:10:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pudgy toothy cheetah", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:10:10Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pudgy toothy cheetah --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

AnjaliNV/WellBeing_LoRA

AnjaliNV

2025-08-19T20:09:56Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-19T16:44:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Huseyin/teknofest-2025-turkish-edu-v2

Huseyin

2025-08-19T20:09:22Z

0

null

[ "safetensors", "qwen3", "turkish", "education", "teknofest-2025", "qwen", "text-generation", "lora", "conversational", "tr", "dataset:Huseyin/final2", "base_model:Qwen/Qwen3-8B", "base_model:adapter:Qwen/Qwen3-8B", "license:apache-2.0", "region:us" ]

text-generation

2025-08-19T20:00:50Z

--- language: tr license: apache-2.0 base_model: Qwen/Qwen3-8B tags: - turkish - education - teknofest-2025 - qwen - text-generation - lora datasets: - Huseyin/final2 pipeline_tag: text-generation widget: - text: "Türkçe eğitimi için yaratıcı bir etkinlik önerisi:" example_title: "Eğitim Etkinliği" - text: "İlkokul öğrencileri için matematik problemi:" example_title: "Matematik" - text: "Fen bilgisi dersinde yapılabilecek basit bir deney:" example_title: "Fen Deneyi" - text: "Öğrencilerin dikkatini çekmek için:" example_title: "Dikkat Çekme" model-index: - name: teknofest-2025-turkish-edu-v2 results: [] --- # 🚀 TEKNOFEST 2025 - Türkçe Eğitim Modeli V2 Bu model **TEKNOFEST 2025 Eylem Temelli Türkçe Büyük Dil Modeli Yarışması** için geliştirilmiştir. ## ✨ Yenilikler (V2) - ✅ Model weights düzeltildi - ✅ Tokenizer uyumluluğu sağlandı - ✅ Çıktı kalitesi iyileştirildi - ✅ 16.4 GB optimized model ## 📋 Model Bilgileri - **Base Model:** Qwen/Qwen3-8B - **Fine-tuning:** LoRA Adapter - **Model Boyutu:** 16.4 GB - **Oluşturma Tarihi:** 2025-08-19 20:09 - **Dil:** Türkçe - **Alan:** Eğitim Teknolojileri - **Yarışma:** TEKNOFEST 2025 ## 🎯 Kullanım Alanları - 📚 Türkçe eğitim materyali oluşturma - 👨‍🎓 Öğrenci seviyesine uygun içerik üretimi - ❓ Soru-cevap sistemleri - 📝 Eğitsel içerik özetleme - 📅 Ders planı hazırlama - 🎮 Eğitici oyun senaryoları ## 💻 Hızlı Başlangıç ### Kurulum ```bash pip install transformers torch accelerate ``` ### Kullanım ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Model ve tokenizer yükle model_name = "Huseyin/teknofest-2025-turkish-edu-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # Örnek kullanım prompt = "Türkçe eğitimi için yaratıcı bir etkinlik önerisi:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( **inputs, max_new_tokens=150, temperature=0.7, do_sample=True, top_p=0.95 ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Google Colab'da Kullanım ```python # Google Colab için optimized kullanım !pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer import torch # 8-bit yükleme (düşük bellek kullanımı) model_name = "Huseyin/teknofest-2025-turkish-edu-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, load_in_8bit=True, device_map='auto' ) # Test prompt = "İlkokul öğrencileri için matematik etkinliği:" inputs = tokenizer(prompt, return_tensors='pt') outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 📊 Örnek Çıktılar **Prompt:** "Türkçe eğitimi için bir öneri:" **Cevap:** "Ders notlarına dikkat etmeyen ve okumayan öğrencilerin, dersi yoksayarak not almakta, ya da dersle ilgili sorular sormamaktadırlar..." ## 🏆 TEKNOFEST 2025 Bu model, TEKNOFEST 2025 **Eylem Temelli Türkçe Büyük Dil Modeli Yarışması** için özel olarak geliştirilmiştir. ## 📈 Performans - Model başarıyla Türkçe eğitim içeriği üretmektedir - Fine-tuning sonrası eğitim alanına özelleşmiştir - Düzgün ve anlamlı Türkçe çıktılar vermektedir ## 📄 Lisans Apache 2.0 ## 🙏 Teşekkür Bu modelin geliştirilmesinde emeği geçen herkese teşekkür ederiz. --- *TEKNOFEST 2025 - Türkiye'nin Teknoloji Festivali* 🇹🇷

Muapi/super-hip-waist

Muapi

2025-08-19T20:07:18Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T20:07:09Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # super hip-waist ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:738727@826131", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/cinematic-glamour-photography-style-xl-f1d

Muapi

2025-08-19T20:07:00Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T20:06:36Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Cinematic Glamour Photography style XL + F1D ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Diffused , glowing , glamour photography 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 = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:396060@1338701", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

gustavomr/llama-finnedtunned-qa

gustavomr

2025-08-19T20:06:25Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-18T20:25:20Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

phospho-app/zacharyreid-gr00t-Bimanual_4cam_MidAirHandoff-r2eu7

phospho-app

2025-08-19T20:06:09Z

0

phosphobot

[ "phosphobot", "gr00t", "robotics", "dataset:zacharyreid/Bimanual_4cam_MidAirHandoff", "region:us" ]

robotics

2025-08-19T16:56:58Z

--- datasets: zacharyreid/Bimanual_4cam_MidAirHandoff library_name: phosphobot pipeline_tag: robotics model_name: gr00t tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 500, in wait_for return fut.result() ^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 1146, in read_output async for line in process.stdout: File "/opt/conda/lib/python3.11/asyncio/streams.py", line 765, in __anext__ val = await self.readline() ^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/streams.py", line 566, in readline line = await self.readuntil(sep) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/streams.py", line 658, in readuntil await self._wait_for_data('readuntil') File "/opt/conda/lib/python3.11/asyncio/streams.py", line 543, in _wait_for_data await self._waiter asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/phosphobot/am/gr00t.py", line 1157, in run_gr00t_training await asyncio.wait_for(read_output(), timeout=timeout_seconds) File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 502, in wait_for raise exceptions.TimeoutError() from exc TimeoutError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/root/src/helper.py", line 166, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1325, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 1162, in run_gr00t_training raise TimeoutError( TimeoutError: Training process exceeded timeout of 10800 seconds. Please consider lowering the number of epochs and/or batch size. ``` ## Training parameters: - **Dataset**: [zacharyreid/Bimanual_4cam_MidAirHandoff](https://huggingface.co/datasets/zacharyreid/Bimanual_4cam_MidAirHandoff) - **Wandb run URL**: None - **Epochs**: 10 - **Batch size**: 32 - **Training steps**: None 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755632296

kojeklollipop

2025-08-19T20:05:55Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:05:51Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

AnonymousCS/xlmr_swedish_immigration4

AnonymousCS

2025-08-19T20:04:24Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T20:01:17Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_swedish_immigration4 results: [] ---  # xlmr_swedish_immigration4 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4123 - Accuracy: 0.8692 - 1-f1: 0.8090 - 1-recall: 0.8372 - 1-precision: 0.7826 - Balanced Acc: 0.8611 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.3763 | 1.0 | 5 | 0.3371 | 0.8692 | 0.7792 | 0.6977 | 0.8824 | 0.8258 | | 0.241 | 2.0 | 10 | 0.4029 | 0.8692 | 0.8046 | 0.8140 | 0.7955 | 0.8553 | | 0.2721 | 3.0 | 15 | 0.4123 | 0.8692 | 0.8090 | 0.8372 | 0.7826 | 0.8611 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

dfg1d2/sbi-gpt

dfg1d2

2025-08-19T20:04:12Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gpt_oss", "trl", "en", "base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T20:04:08Z

--- base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gpt_oss - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** dfg1d2 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Muapi/wizard-s-scrap-yard-supermarionation-puppets

Muapi

2025-08-19T20:03:55Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T20:03:34Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Wizard's Scrap Yard: Supermarionation Puppets ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Thunderbirds Puppet, Puppet ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:694054@817429", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Vasya777/blockassist-bc-lumbering_enormous_sloth_1755633777

Vasya777

2025-08-19T20:03:26Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:03:23Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Muapi/colorful-detailer-semifluid-pigments-flux-sd-3.5m-sd-3.5l

Muapi

2025-08-19T20:01:10Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T20:00:56Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # colorful detailer | semifluid pigments (Flux & SD 3.5M & SD 3.5L) ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: ## 🧠 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": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:757175@846653", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

unitova/blockassist-bc-zealous_sneaky_raven_1755626317

unitova

2025-08-19T18:26:40Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:26:35Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755627918

Dejiat

2025-08-19T18:26:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:25:42Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1755627829

Ferdi3425

2025-08-19T18:25:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:24:35Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

roeker/blockassist-bc-quick_wiry_owl_1755627810

roeker

2025-08-19T18:24:26Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:24:19Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Vasya777/blockassist-bc-lumbering_enormous_sloth_1755627800

Vasya777

2025-08-19T18:23:49Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:23:46Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Sophie-Rain-Spider-man-Video-Tutori-a-l/Sophie.Rain.Spiderman.Video.Tutorial.Oficial

Sophie-Rain-Spider-man-Video-Tutori-a-l

2025-08-19T18:23:43Z

0

null

[ "region:us" ]

null

2025-08-19T18:22:07Z

<div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> </div>

Reynier/modernbert-dga-detector

Reynier

2025-08-19T18:23:06Z

0

transformers

[ "transformers", "safetensors", "modernbert", "text-classification", "domain-generation-algorithm", "cybersecurity", "domain-classification", "security", "malware-detection", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T18:22:40Z

--- license: apache-2.0 tags: - domain-generation-algorithm - cybersecurity - domain-classification - security - malware-detection language: - en library_name: transformers pipeline_tag: text-classification base_model: answerdotai/ModernBERT-base --- # ModernBERT DGA Detector This model is designed to classify domains as either legitimate or generated by Domain Generation Algorithms (DGA). ## Model Description - **Model Type:** BERT-based sequence classification - **Task:** Binary classification (Legitimate vs DGA domains) - **Base Model:** ModernBERT-base - **Training Data:** Domain names dataset - **Author:** Reynier Leyva La O ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("Reynier/modernbert-dga-detector") model = AutoModelForSequenceClassification.from_pretrained("Reynier/modernbert-dga-detector") # Example prediction def predict_domain(domain): inputs = tokenizer(domain, return_tensors="pt", max_length=64, truncation=True, padding=True) with torch.no_grad(): outputs = model(**inputs) predictions = torch.softmax(outputs.logits, dim=-1) legit_prob = predictions[0][0].item() dga_prob = predictions[0][1].item() return {"prediction": "DGA" if dga_prob > legit_prob else "LEGITIMATE", "confidence": max(legit_prob, dga_prob)} # Test examples domains = ["google.com", "xkvbzpqr.net", "facebook.com", "abcdef123456.com"] for domain in domains: result = predict_domain(domain) print(f"{domain} -> {result['prediction']} (confidence: {result['confidence']:.3f})") ``` ## Model Architecture The model is based on ModernBERT and fine-tuned for domain classification: - Input: Domain names (text) - Output: Binary classification (0=Legitimate, 1=DGA) - Max sequence length: 64 tokens ## Training Details This model was fine-tuned on a dataset of legitimate and DGA-generated domains using: - Base model: answerdotai/ModernBERT-base - Framework: Transformers/PyTorch - Task: Binary sequence classification ## Performance Add your model's performance metrics here when available: - Accuracy: [Add your results] - Precision: [Add your results] - Recall: [Add your results] - F1-Score: [Add your results] ## Use Cases - **Cybersecurity**: Detect malicious domains generated by malware - **Network Security**: Filter potentially harmful domains - **Threat Intelligence**: Analyze domain patterns in security feeds ## Limitations - This model is trained specifically for domain classification - Performance may vary on domains from different TLDs or languages - Regular retraining may be needed as DGA techniques evolve - Model performance depends on the quality and diversity of training data ## Citation If you use this model in your research or applications, please cite it appropriately. ## Related Models Check out the author's other security models: - [Llama3_8B-DGA-Detector](https://huggingface.co/Reynier/Llama3_8B-DGA-Detector)

VIDEOS-18-Dr-Eman-viral-video-Clips/New.full.videos.Dr.Eman.Viral.Video.Official.Tutorial

VIDEOS-18-Dr-Eman-viral-video-Clips

2025-08-19T18:23:00Z

0

null

[ "region:us" ]

null

2025-08-19T18:22:46Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755626307

lisaozill03

2025-08-19T18:22:52Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:22:48Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mang3dd/blockassist-bc-tangled_slithering_alligator_1755626126

mang3dd

2025-08-19T18:22:44Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:22:40Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

NESTLAYER/Sombrero-charro

NESTLAYER

2025-08-19T18:22:37Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-19T18:22:37Z

--- license: apache-2.0 ---

Kandru/fine_tune_classification_task

Kandru

2025-08-19T18:19:10Z

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", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T18:17:24Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: fine_tune_classification_task results: [] ---  # fine_tune_classification_task This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3024 - Accuracy: 0.9193 - F1: 0.9193 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 313 | 0.2866 | 0.9162 | 0.9160 | | 0.1256 | 2.0 | 626 | 0.3024 | 0.9193 | 0.9193 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.4

katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755625475

katanyasekolah

2025-08-19T18:13:32Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:13:29Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silky sprightly cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

TAUR-dev/M-SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks-rl

TAUR-dev

2025-08-19T18:13:30Z

0

null

[ "safetensors", "qwen2", "en", "license:mit", "region:us" ]

null

2025-08-19T18:11:52Z

--- language: en license: mit --- # M-SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks-rl ## Model Details - **Training Method**: VeRL Reinforcement Learning (RL) - **Stage Name**: rl - **Experiment**: SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks - **RL Framework**: VeRL (Versatile Reinforcement Learning) ## Training Configuration ## Experiment Tracking 🔗 **View complete experiment details**: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks__v1 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks-rl") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-SBON_skills_in_rl_v2__1e6_all_tasks_sft-rl_all_tasks-rl") ```

VIDEOS-19-tasnim-jara-viral-video-link/New.full.videos.tasnim.jara.Viral.Video.Official.Tutorial

VIDEOS-19-tasnim-jara-viral-video-link

2025-08-19T18:13:14Z

0

null

[ "region:us" ]

null

2025-08-19T18:12:57Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755625509

kojeklollipop

2025-08-19T18:12:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:12:39Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Shujashark/llm-sql-t5-small-lora-adapter

Shujashark

2025-08-19T18:12:24Z

0

peft

[ "peft", "safetensors", "base_model:adapter:t5-small", "lora", "transformers", "arxiv:1910.09700", "base_model:google-t5/t5-small", "base_model:adapter:google-t5/t5-small", "region:us" ]

null

2025-08-19T18:08:48Z

--- base_model: t5-small library_name: peft tags: - base_model:adapter:t5-small - lora - transformers --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

aumoai/aumogpt-Qwen2.5-32B-Instruct-lora

aumoai

2025-08-19T18:12:14Z

67

0

null

[ "safetensors", "qwen2", "region:us" ]

null

2025-05-08T15:53:49Z

model: model_name: "Qwen/Qwen2.5-32B-Instruct" model_max_length: 4096 torch_dtype_str: "bfloat16" attn_implementation: "flash_attention_2" #"sdpa" load_pretrained_weights: True trust_remote_code: True data: train: datasets: # - dataset_name: "text_sft" # dataset_path: "datasets/aumo_dataset_test.json" # shuffle: True # seed: 42 - dataset_name: "text_sft" dataset_path: "datasets/aumogpt_qwen32b.json" shuffle: True seed: 42 # - dataset_name: "text_sft" # dataset_path: "datasets/xp3_qwen_2000.json" # shuffle: True # seed: 42 # - dataset_name: "text_sft" # dataset_path: "datasets/aumogpt_train.json" # shuffle: True # seed: 42 # mixture_strategy: "all_exhausted" # Strategy for mixing datasets # seed: 123456789426465 validation: datasets: - dataset_name: "text_sft" dataset_path: "datasets/aumo_dataset_test.json" # split: "validation" # sample_count: 10 training: trainer_type: "TRL_SFT" use_peft: True save_steps: 200 num_train_epochs: 2 per_device_train_batch_size: 2 per_device_eval_batch_size: 2 gradient_accumulation_steps: 8 max_grad_norm: null enable_gradient_checkpointing: True gradient_checkpointing_kwargs: use_reentrant: False ddp_find_unused_parameters: False optimizer: "adamw_torch" # "adamw_torch" #paged_adamw_8bit learning_rate: 5.0e-4 warmup_steps: 10 weight_decay: 0.01 compile: False dataloader_num_workers: 8 dataloader_prefetch_factor: 4 logging_steps: 10 log_model_summary: False empty_device_cache_steps: 50 output_dir: "results/oumi/qwen32b_xp3_aumo.lora" include_performance_metrics: True enable_wandb: True eval_strategy: "steps" # When to evaluate ("no", "steps", "epoch") eval_steps: 25 peft: q_lora: False lora_r: 64 lora_alpha: 32 lora_dropout: 0.2 lora_target_modules: - "q_proj" - "k_proj" - "v_proj" - "o_proj" - "gate_proj" - "down_proj" - "up_proj" fsdp: enable_fsdp: True sharding_strategy: FULL_SHARD auto_wrap_policy: TRANSFORMER_BASED_WRAP # transformer_layer_cls: QwenBlock forward_prefetch: true

matboz/ring-model

matboz

2025-08-19T18:11:54Z

0

peft

[ "peft", "safetensors", "base_model:adapter:google/gemma-2-27b-it", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:google/gemma-2-27b-it", "region:us" ]

text-generation

2025-08-19T17:54:47Z

--- base_model: google/gemma-2-27b-it library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:google/gemma-2-27b-it - lora - sft - transformers - trl --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF

siro-kr

2025-08-19T18:11:43Z

0

null

[ "gguf", "mixture-of-experts", "moe", "expert-pruning", "gpt-oss", "openai", "reasoning", "harmful", "specialized", "efficient", "transformer", "causal-lm", "text-generation", "pytorch", "pruned-model", "domain-specific", "llama-cpp", "gguf-my-repo", "en", "dataset:AmanPriyanshu/GPT-OSS-20B-MoE-expert-activations", "base_model:AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts", "base_model:quantized:AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T18:11:06Z

--- license: apache-2.0 datasets: - AmanPriyanshu/GPT-OSS-20B-MoE-expert-activations language: - en pipeline_tag: text-generation tags: - mixture-of-experts - moe - expert-pruning - gpt-oss - openai - reasoning - harmful - specialized - efficient - transformer - causal-lm - text-generation - pytorch - pruned-model - domain-specific - llama-cpp - gguf-my-repo base_model: AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts --- # siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF This model was converted to GGUF format from [`AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts`](https://huggingface.co/AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts) 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/AmanPriyanshu/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF --hf-file gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF --hf-file gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF --hf-file gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo siro-kr/gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-Q4_K_M-GGUF --hf-file gpt-oss-10.8b-specialized-harmful-pruned-moe-only-15-experts-q4_k_m.gguf -c 2048 ```

mohda/blockassist-bc-regal_fierce_hummingbird_1755627037

mohda

2025-08-19T18:11:20Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "regal fierce hummingbird", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:11:14Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - regal fierce hummingbird --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

RTannous/metallama-finetuned

RTannous

2025-08-19T18:10:54Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T18:08:19Z

--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** RTannous - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755625235

coelacanthxyz

2025-08-19T18:10:53Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:10:46Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Ver-filtrado-video-abigail-lalama-snayder/VIRAL.VER.filtrado.video.de.abigail.lalama.y.snayder.influencer.se.hace.viral.en.redes.sociales

Ver-filtrado-video-abigail-lalama-snayder

2025-08-19T18:08:23Z

0

null

[ "region:us" ]

null

2025-08-19T18:07:38Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> Abigail Lalama viral: influencer confirma video con Snayder en Telegram El nombre Abigail Lalama se hace viral y crece en Telegram y X tras la filtración de su video con Snayder. Te contamos quiénes son y qué pasó. Imagen de Abigail Lalama viral: influencer confirma video con Snayder en Telegram Abigail confirmó la filtración del contenido. - Foto: Instagram abigail_lalama El nombre Abigail Lalama se hizo viral en redes sociales como Telegram y Twitter (ahora X) luego de que se confirmara la filtración de un video íntimo con Snayder. Esto provocó un repunte de búsquedas como “Abigail Lalama video filtrado”, “Video de Abigail y Snayder filtrado”, “Abigail Lalama Telegram”, entre otras. La confirmación llegó directamente de la influencer, lo que desencadenó una ola de comentarios, reacciones y solidaridad en línea. A continuación, te contamos quién es Abigail Lalama, quién es Snayder, qué dijo ella, y por qué este caso se viralizó tan rápido. LEA TAMBIÉN: ¿Qué dijo Mariana Botas de Drake Bell?, ¿qué contestó el cantante? View post on TikTok ¿Quién es Abigail Lalama y por qué es conocida? Abigail Lalama es una creadora de contenido ecuatoriana de 22 años, originaria de Guayaquil, que ha ganado popularidad principalmente en TikTok, donde comparte lives, retos, momentos familiares y contenidos cotidianos junto a su hermana gemela, Génesis. Su comunidad se ha consolidado gracias a su cercanía, su carisma y su estilo familiar. En TikTok, su cuenta @laoficialabigail cuenta con más de 400 000 seguidores y en Instagram supera los 173,000. Con su hermana, forma el ‘Team Lalama’, difundiendo contenidos centrados en diario vivir, maternidad y lazos familiares. ¿Quién es Snayder y qué video se filtró? La identidad precisa de Snayder no ha sido desvelada en los medios principales consultados hasta el momento, pero se sabe que es un conocido en el entorno de Abigail, supuestamente su expareja. El video filtrado, descrito como íntimo, fue subido a plataformas como Telegram y TikTok. Usuarios reportaron que la joven que aparece en la grabación viral compartía tatuajes y rasgos con Abigail Lalama. Según Abigail, el video circuló sin su consentimiento, y la filtración se la atribuye a dicho ex. View post on TikTok ¿Qué dijo Abigail Lalama sobre la filtración? En un video en vivo, visiblemente afectada y entre lágrimas, Abigail Lalama confirmó la filtración viral del contenido. Ella acusó directamente a su expareja de haber difundido ese material íntimo con la intención de incomodar a su nueva relación. Sus palabras fueron duras y reflejaron desilusión y dolor: “Eso es lo peor que le puede hacer un hombre… Yo fui, sí, una estúpida en dejarme grabar con él… Pues siendo mi pareja coge y lo sube… el sinvergüenza. No tiene sangre en la cara”. También añadió una reflexión sobre confianza y peor traición: “Ahí es cuando uno se da cuenta con quién duerme, ahí es cuando se sacan la máscara”. Posteriormente, compartió en sus historias videos de fans que la apoyaban, junto a clips con actitud más serena, mostrando resiliencia frente a la situación. ¿Qué dice la ley sobre contenido filtrado sin consentimiento? En Ecuador, compartir contenido íntimo sin consentimiento está penalizado por el Código Orgánico Integral Penal (COIP). El artículo 178 castiga la difusión de imágenes o videos íntimos sin autorización con penas de uno a tres años. Además, el artículo 230, relativo a violencia digital, puede elevar la sanción hasta cuatro años si hay intención de dañar o humillar. Abigail tiene la opción de denunciar ante la Fiscalía General del Estado, la Unidad de Policía Comunitaria (UPC), el portal del Consejo de la Judicatura o acudir a la Defensoría del Pueblo y organizaciones que brindan apoyo psicológico y asesoría legal. También se pueden solicitar medidas como la eliminación del contenido y reparación integral.

policia-instagram-influencer-virals-video/Hot.New.full.videos.policia.instagram.influencer.viral.video.Official.Tutorial

policia-instagram-influencer-virals-video

2025-08-19T18:07:08Z

0

null

[ "region:us" ]

null

2025-08-19T18:06:53Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

lautan/blockassist-bc-gentle_patterned_goat_1755624839

lautan

2025-08-19T18:02:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T18:02:25Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

MidnightRunner/MDNT_Illus_3D

MidnightRunner

2025-08-19T17:59:02Z

0

diffusers

[ "diffusers", "SDXL", "mdnt-illus", "3D-hybrid", "anaglyph", "photoreal", "cinematic", "text-to-image", "ComfyUI", "Automatic1111", "en", "base_model:MidnightRunner/MDNT_Illus", "base_model:finetune:MidnightRunner/MDNT_Illus", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2025-08-19T14:50:48Z

--- license: creativeml-openrail-m language: - en base_model: - OnomaAIResearch/Illustrious-XL-v2.0 - MidnightRunner/MDNT_Illus tags: - SDXL - mdnt-illus - 3D-hybrid - anaglyph - photoreal - cinematic - text-to-image - ComfyUI - Automatic1111 - diffusers pipeline_tag: text-to-image library_name: diffusers metrics: - FID - IS widget: - text: >- (masterpiece), extremely aesthetic, newest, very vibrant colors, (ultra-HD), (cinematic lighting), (photorealistic), high detail, depth of field, best quality, absurdres, parameters: negative_prompt: >- bad hands, extra digits, (multiple views:1.1), (bad:1.05), fewer, extra, missing, worst quality, jpeg artifacts, bad quality, watermark, unfinished, displeasing, sepia, sketch, flat color, signature, artistic error, username, scan, (blurry, lowres, worst quality, (low quality:1.1), ugly, (bad anatomy:1.05), artist name, (patreon username:1.2) output: url: mdnt_illus_3d_sample.jpeg --- # MDNT_Illus_3D Model type: diffusion-based text-to-image Base model: Illustrious XL v2.0 Merged with: MIDNIGHT Illustrious, MDNT_Illus, Hyphorias, Nova3D-CGXL, BetterDaysIllus License: CreativeML Open RAIL++-M ## Model description MDNT_Illus_3D is a precision finetune focused on a 3D-hybrid aesthetic, balancing photoreal fidelity with simulated depth and sculpted form. It emphasizes anaglyphic layering, volumetric lighting, cinematic depth of field, and richly detailed textures to produce imagery that feels tactile, immersive, and dramatically lit. ## Usage recommendations ### Sampling methods - Euler A (Euler ancestral) - DPM++ 2M Karras - DPM++ 2M SDE Karras - DPM++ 3M SDE Exponential ### Settings - Steps: 25–45 - CFG scale: 4 (range 3–4) - Clip skip: 1 ### Workflow Compatible with ComfyUI and Automatic1111. A tailored ComfyUI workflow may be added later to maximize spatial layering and volumetric light behavior. ## Prompt guidance Positive (example) ``` realistic, photorealistic, very aesthetic, best quality, absurdres, masterpiece, amazing quality, newest, scenery, depth of field, high-resolution, high definition, visually intense anaglyphic experience, volumetric lighting, cinematic, sculpted 3D form ``` Negative (example) ``` bad hands, extra digits, (multiple views:1.1), (bad:1.05), fewer, extra, missing, worst quality, jpeg artifacts, watermark, unfinished, sketch, flat color, signature, artist name, blurry, lowres, (bad anatomy:1.05), (patreon username:1.2) ``` ## Version changes / notes - v1.0 (initial release) - Introduces 3D-hybrid realism with anaglyphic depth and volumetric lighting - Blended with Hyphorias, Nova 3DCG XL, BetterDaysIllus for expanded range ## Acknowledgments Base: Illustrious XL v2.0 Merges: MIDNIGHT Illustrious, MDNT_Illus, Hyphorias, Nova 3DCG XL, BetterDaysIllus ## Additional Resources - **Creative Solutions and Services:** [Magnabos.co](https://magnabos.co/) ## License This model is licensed under the [CreativeML Open RAIL++-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). By using this model, you agree to the terms and conditions outlined in the license.

thanobidex/blockassist-bc-colorful_shiny_hare_1755624690

thanobidex

2025-08-19T17:58:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:58:21Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

giacomoarienti/orpheus-3b-ita-male-full

giacomoarienti

2025-08-19T17:55:23Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/orpheus-3b-0.1-ft", "base_model:finetune:unsloth/orpheus-3b-0.1-ft", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T17:54:42Z

--- base_model: unsloth/orpheus-3b-0.1-ft tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** giacomoarienti - **License:** apache-2.0 - **Finetuned from model :** unsloth/orpheus-3b-0.1-ft This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755624471

quantumxnode

2025-08-19T17:55:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:54:57Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755624371

vwzyrraz7l

2025-08-19T17:54:47Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:54:44Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755626034

Dejiat

2025-08-19T17:54:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:54:15Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755624535

lisaozill03

2025-08-19T17:53:58Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:53:54Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

VIDEOS-19-afrin-apu-viral-link/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial

VIDEOS-19-afrin-apu-viral-link

2025-08-19T17:51:52Z

0

null

[ "region:us" ]

null

2025-08-19T17:51:41Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

Sayemahsjn/blockassist-bc-playful_feline_octopus_1755624582

Sayemahsjn

2025-08-19T17:51:44Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:51:39Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mang3dd/blockassist-bc-tangled_slithering_alligator_1755624343

mang3dd

2025-08-19T17:51:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:51:24Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755624249

helmutsukocok

2025-08-19T17:50:36Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:50:33Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755625775

Dejiat

2025-08-19T17:50:23Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:50:01Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

AppliedLucent/nemo-phase6

AppliedLucent

2025-08-19T17:49:04Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:AppliedLucent/nemo-phase5", "base_model:finetune:AppliedLucent/nemo-phase5", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T17:38:35Z

--- base_model: AppliedLucent/nemo-phase5 tags: - text-generation-inference - transformers - unsloth - mistral license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** AppliedLucent - **License:** apache-2.0 - **Finetuned from model :** AppliedLucent/nemo-phase5 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

dgambettaphd/M_mis_run2_gen2_WXS_doc1000_synt64_lr1e-04_acm_LANG

dgambettaphd

2025-08-19T17:48:47Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-19T17:48:32Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ColaChameleon/lilly

ColaChameleon

2025-08-19T17:48:04Z

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", "region:us" ]

text-to-image

2025-08-19T17:46:20Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/_8eb450b2-aa12-4d00-9906-e7743395311c.jpg text: '-' - output: url: images/df0r49j-37a375fe-e811-4702-9278-d7e062d15f18.png text: '-' base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # lilly <Gallery /> ## Download model [Download](/ColaChameleon/lilly/tree/main) them in the Files & versions tab.

ngozimagen/ngozi-lora

ngozimagen

2025-08-19T17:46:53Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-08-19T16:59:52Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: ngozi --- # Ngozi Lora <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-lora-trainer/train ## Trigger words You should use `ngozi` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ngozi", "lora_weights": "https://huggingface.co/ngozimagen/ngozi-lora/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ngozimagen/ngozi-lora', weight_name='lora.safetensors') image = pipeline('ngozi').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ngozimagen/ngozi-lora/discussions) to add images that show off what you’ve made with this LoRA.

arka7/Llama-3.2-3B-Instruct-bnb-4bit-rag-finetuned-with-DPO

arka7

2025-08-19T17:45:23Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T17:45:17Z

--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** arka7 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

beyoru/MuoQVAgent-1.0

beyoru

2025-08-19T17:44:00Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen3-4B-Instruct-2507", "base_model:finetune:unsloth/Qwen3-4B-Instruct-2507", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T17:42:04Z

--- base_model: unsloth/Qwen3-4B-Instruct-2507 tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** beyoru - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-4B-Instruct-2507 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Timia123/simpo_inpo_iter2_aug19

Timia123

2025-08-19T17:43:43Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "alignment-handbook", "inpo", "generated_from_trainer", "conversational", "base_model:google/gemma-2-9b-it", "base_model:finetune:google/gemma-2-9b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T17:40:20Z

--- library_name: transformers base_model: google/gemma-2-9b-it tags: - alignment-handbook - inpo - generated_from_trainer model-index: - name: gemma-2-9b-it_inpo_stage_2 results: [] ---  # gemma-2-9b-it_inpo_stage_2 This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co//home/hubing/SimPO/outputs/gemma-2-9b-it_inpo_stage_1/) on the data/inpo_iter2/pref dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.2.2 - Datasets 2.14.6 - Tokenizers 0.19.1

phospho-app/Deimos252-gr00t-Light_dataset_deimos-p48ab

phospho-app

2025-08-19T17:43:37Z

0

phosphobot

[ "phosphobot", "safetensors", "gr00t_n1_5", "gr00t", "robotics", "dataset:Deimos252/Light_dataset_deimos", "region:us" ]

robotics

2025-08-19T17:13:51Z

--- datasets: Deimos252/Light_dataset_deimos library_name: phosphobot pipeline_tag: robotics model_name: gr00t 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**: [Deimos252/Light_dataset_deimos](https://huggingface.co/datasets/Deimos252/Light_dataset_deimos) - **Wandb run URL**: None - **Epochs**: 10 - **Batch size**: 49 - **Training steps**: None 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)

VIDEOS-19-Afrin-Er-Viral-Video-Clip/New.full.videos.Afrin.Er.Viral.Video.Official.Tutorial

VIDEOS-19-Afrin-Er-Viral-Video-Clip

2025-08-19T17:43:12Z

0

null

[ "region:us" ]

null

2025-08-19T17:42:55Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

numind/NuExtract-2.0-8B-GPTQ

numind

2025-08-19T17:42:43Z

346

4

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "image-text-to-text", "conversational", "base_model:numind/NuExtract-2.0-8B", "base_model:quantized:numind/NuExtract-2.0-8B", "license:mit", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]

image-text-to-text

2025-06-06T08:38:54Z

--- library_name: transformers license: mit base_model: - numind/NuExtract-2.0-8B pipeline_tag: image-text-to-text --- <p align="center"> <a href="https://nuextract.ai/"> <img src="logo_nuextract.svg" width="200"/> </a> </p> <p align="center"> 🖥️ <a href="https://nuextract.ai/">API / Platform</a>&nbsp&nbsp | &nbsp&nbsp📑 <a href="https://numind.ai/blog">Blog</a>&nbsp&nbsp | &nbsp&nbsp🗣️ <a href="https://discord.gg/3tsEtJNCDe">Discord</a> </p> # NuExtract 2.0 8B by NuMind 🔥 NuExtract 2.0 is a family of models trained specifically for structured information extraction tasks. It supports both multimodal inputs and is multilingual. We provide several versions of different sizes, all based on pre-trained models from the QwenVL family. | Model Size | Model Name | Base Model | License | Huggingface Link | |------------|------------|------------|---------|------------------| | 2B | NuExtract-2.0-2B | [Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) | MIT | 🤗 [NuExtract-2.0-2B](https://huggingface.co/numind/NuExtract-2.0-2B) | | 4B | NuExtract-2.0-4B | [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) | Qwen Research License | 🤗 [NuExtract-2.0-4B](https://huggingface.co/numind/NuExtract-2.0-4B) | | 8B | NuExtract-2.0-8B | [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) | MIT | 🤗 [NuExtract-2.0-8B](https://huggingface.co/numind/NuExtract-2.0-8B) | ❗️Note: `NuExtract-2.0-2B` is based on Qwen2-VL rather than Qwen2.5-VL because the smallest Qwen2.5-VL model (3B) has a more restrictive, non-commercial license. We therefore include `NuExtract-2.0-2B` as a small model option that can be used commercially. ## Benchmark Performance on collection of ~1,000 diverse extraction examples containing both text and image inputs. <a href="https://nuextract.ai/"> <img src="nuextract2_bench.png" width="500"/> </a> ## Overview To use the model, provide an input text/image and a JSON template describing the information you need to extract. The template should be a JSON object, specifying field names and their expected type. Support types include: * `verbatim-string` - instructs the model to extract text that is present verbatim in the input. * `string` - a generic string field that can incorporate paraphrasing/abstraction. * `integer` - a whole number. * `number` - a whole or decimal number. * `date-time` - ISO formatted date. * Array of any of the above types (e.g. `["string"]`) * `enum` - a choice from set of possible answers (represented in template as an array of options, e.g. `["yes", "no", "maybe"]`). * `multi-label` - an enum that can have multiple possible answers (represented in template as a double-wrapped array, e.g. `[["A", "B", "C"]]`). If the model does not identify relevant information for a field, it will return `null` or `[]` (for arrays and multi-labels). The following is an example template: ```json { "first_name": "verbatim-string", "last_name": "verbatim-string", "description": "string", "age": "integer", "gpa": "number", "birth_date": "date-time", "nationality": ["France", "England", "Japan", "USA", "China"], "languages_spoken": [["English", "French", "Japanese", "Mandarin", "Spanish"]] } ``` An example output: ```json { "first_name": "Susan", "last_name": "Smith", "description": "A student studying computer science.", "age": 20, "gpa": 3.7, "birth_date": "2005-03-01", "nationality": "England", "languages_spoken": ["English", "French"] } ``` ⚠️ We recommend using NuExtract with a temperature at or very close to 0. Some inference frameworks, such as Ollama, use a default of 0.7 which is not well suited to many extraction tasks. ## Using NuExtract with 🤗 Transformers ```python import torch from transformers import AutoProcessor from gptqmodel import GPTQModel model_name = "numind/NuExtract-2.0-8B-GPTQ" # model_name = "numind/NuExtract-2.0-4B-GPTQ" model = GPTQModel.load(model_name) processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True, padding_side='left', use_fast=True) # You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost. # min_pixels = 256*28*28 # max_pixels = 1280*28*28 # processor = AutoProcessor.from_pretrained(model_name, min_pixels=min_pixels, max_pixels=max_pixels) ``` You will need the following function to handle loading of image input data: ```python def process_all_vision_info(messages, examples=None): """ Process vision information from both messages and in-context examples, supporting batch processing. Args: messages: List of message dictionaries (single input) OR list of message lists (batch input) examples: Optional list of example dictionaries (single input) OR list of example lists (batch) Returns: A flat list of all images in the correct order: - For single input: example images followed by message images - For batch input: interleaved as (item1 examples, item1 input, item2 examples, item2 input, etc.) - Returns None if no images were found """ from qwen_vl_utils import process_vision_info, fetch_image # Helper function to extract images from examples def extract_example_images(example_item): if not example_item: return [] # Handle both list of examples and single example examples_to_process = example_item if isinstance(example_item, list) else [example_item] images = [] for example in examples_to_process: if isinstance(example.get('input'), dict) and example['input'].get('type') == 'image': images.append(fetch_image(example['input'])) return images # Normalize inputs to always be batched format is_batch = messages and isinstance(messages[0], list) messages_batch = messages if is_batch else [messages] is_batch_examples = examples and isinstance(examples, list) and (isinstance(examples[0], list) or examples[0] is None) examples_batch = examples if is_batch_examples else ([examples] if examples is not None else None) # Ensure examples batch matches messages batch if provided if examples and len(examples_batch) != len(messages_batch): if not is_batch and len(examples_batch) == 1: # Single example set for a single input is fine pass else: raise ValueError("Examples batch length must match messages batch length") # Process all inputs, maintaining correct order all_images = [] for i, message_group in enumerate(messages_batch): # Get example images for this input if examples and i < len(examples_batch): input_example_images = extract_example_images(examples_batch[i]) all_images.extend(input_example_images) # Get message images for this input input_message_images = process_vision_info(message_group)[0] or [] all_images.extend(input_message_images) return all_images if all_images else None ``` E.g. To perform a basic extraction of names from a text document: ```python template = """{"names": ["string"]}""" document = "John went to the restaurant with Mary. James went to the cinema." # prepare the user message content messages = [{"role": "user", "content": document}] text = processor.tokenizer.apply_chat_template( messages, template=template, # template is specified here tokenize=False, add_generation_prompt=True, ) print(text) """"<|im_start|>user # Template: {"names": ["string"]} # Context: John went to the restaurant with Mary. James went to the cinema.<|im_end|> <|im_start|>assistant""" image_inputs = process_all_vision_info(messages) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") # we choose greedy sampling here, which works well for most information extraction tasks generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048} # Inference: Generation of the output generated_ids = model.generate( **inputs, **generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) # ['{"names": ["John", "Mary", "James"]}'] ``` <details> <summary>In-Context Examples</summary> Sometimes the model might not perform as well as we want because our task is challenging or involves some degree of ambiguity. Alternatively, we may want the model to follow some specific formatting, or just give it a bit more help. In cases like this it can be valuable to provide "in-context examples" to help NuExtract better understand the task. To do so, we can provide a list examples (dictionaries of input/output pairs). In the example below, we show to the model that we want the extracted names to be in captial letters with `-` on either side (for the sake of illustration). Usually providing multiple examples will lead to better results. ```python template = """{"names": ["string"]}""" document = "John went to the restaurant with Mary. James went to the cinema." examples = [ { "input": "Stephen is the manager at Susan's store.", "output": """{"names": ["-STEPHEN-", "-SUSAN-"]}""" } ] messages = [{"role": "user", "content": document}] text = processor.tokenizer.apply_chat_template( messages, template=template, examples=examples, # examples provided here tokenize=False, add_generation_prompt=True, ) image_inputs = process_all_vision_info(messages, examples) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") # we choose greedy sampling here, which works well for most information extraction tasks generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048} # Inference: Generation of the output generated_ids = model.generate( **inputs, **generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) # ['{"names": ["-JOHN-", "-MARY-", "-JAMES-"]}'] ``` </details> <details> <summary>Image Inputs</summary> If we want to give image inputs to NuExtract, instead of text, we simply provide a dictionary specifying the desired image file as the message content, instead of a string. (e.g. `{"type": "image", "image": "file://image.jpg"}`). You can also specify an image URL (e.g. `{"type": "image", "image": "http://path/to/your/image.jpg"}`) or base64 encoding (e.g. `{"type": "image", "image": "data:image;base64,/9j/..."}`). ```python template = """{"store": "verbatim-string"}""" document = {"type": "image", "image": "file://1.jpg"} messages = [{"role": "user", "content": [document]}] text = processor.tokenizer.apply_chat_template( messages, template=template, tokenize=False, add_generation_prompt=True, ) image_inputs = process_all_vision_info(messages) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048} # Inference: Generation of the output generated_ids = model.generate( **inputs, **generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) # ['{"store": "Trader Joe\'s"}'] ``` </details> <details> <summary>Batch Inference</summary> ```python inputs = [ # image input with no ICL examples { "document": {"type": "image", "image": "file://0.jpg"}, "template": """{"store_name": "verbatim-string"}""", }, # image input with 1 ICL example { "document": {"type": "image", "image": "file://0.jpg"}, "template": """{"store_name": "verbatim-string"}""", "examples": [ { "input": {"type": "image", "image": "file://1.jpg"}, "output": """{"store_name": "Trader Joe's"}""", } ], }, # text input with no ICL examples { "document": {"type": "text", "text": "John went to the restaurant with Mary. James went to the cinema."}, "template": """{"names": ["string"]}""", }, # text input with ICL example { "document": {"type": "text", "text": "John went to the restaurant with Mary. James went to the cinema."}, "template": """{"names": ["string"]}""", "examples": [ { "input": "Stephen is the manager at Susan's store.", "output": """{"names": ["STEPHEN", "SUSAN"]}""" } ], }, ] # messages should be a list of lists for batch processing messages = [ [ { "role": "user", "content": [x['document']], } ] for x in inputs ] # apply chat template to each example individually texts = [ processor.tokenizer.apply_chat_template( messages[i], # Now this is a list containing one message template=x['template'], examples=x.get('examples', None), tokenize=False, add_generation_prompt=True) for i, x in enumerate(inputs) ] image_inputs = process_all_vision_info(messages, [x.get('examples') for x in inputs]) inputs = processor( text=texts, images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048} # Batch Inference generated_ids = model.generate(**inputs, **generation_config) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_texts = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) for y in output_texts: print(y) # {"store_name": "WAL-MART"} # {"store_name": "Walmart"} # {"names": ["John", "Mary", "James"]} # {"names": ["JOHN", "MARY", "JAMES"]} ``` </details> <details> <summary>Template Generation</summary> If you want to convert existing schema files you have in other formats (e.g. XML, YAML, etc.) or start from an example, NuExtract 2.0 models can automatically generate this for you. E.g. convert XML into a NuExtract template: ```python xml_template = """<SportResult> <Date></Date> <Sport></Sport> <Venue></Venue> <HomeTeam></HomeTeam> <AwayTeam></AwayTeam> <HomeScore></HomeScore> <AwayScore></AwayScore> <TopScorer></TopScorer> </SportResult>""" messages = [ { "role": "user", "content": [{"type": "text", "text": xml_template}], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) image_inputs = process_all_vision_info(messages) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") generated_ids = model.generate( **inputs, **generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text[0]) # { # "Date": "date-time", # "Sport": "verbatim-string", # "Venue": "verbatim-string", # "HomeTeam": "verbatim-string", # "AwayTeam": "verbatim-string", # "HomeScore": "integer", # "AwayScore": "integer", # "TopScorer": "verbatim-string" # } ``` E.g. generate a template from natural language description: ```python description = "I would like to extract important details from the contract." messages = [ { "role": "user", "content": [{"type": "text", "text": description}], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) image_inputs = process_all_vision_info(messages) inputs = processor( text=[text], images=image_inputs, padding=True, return_tensors="pt", ).to("cuda") generated_ids = model.generate( **inputs, **generation_config ) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text[0]) # { # "Contract": { # "Title": "verbatim-string", # "Description": "verbatim-string", # "Terms": [ # { # "Term": "verbatim-string", # "Description": "verbatim-string" # } # ], # "Date": "date-time", # "Signatory": "verbatim-string" # } # } ``` </details> ## Fine-Tuning You can find a fine-tuning tutorial notebook in the [cookbooks](https://github.com/numindai/nuextract/tree/main/cookbooks) folder of the [GitHub repo](https://github.com/numindai/nuextract/tree/main). ## vLLM Deployment Run the command below to serve an OpenAI-compatible API: ```bash vllm serve numind/NuExtract-2.0-8B --trust_remote_code --limit-mm-per-prompt image=6 --chat-template-content-format openai ``` If you encounter memory issues, set `--max-model-len` accordingly. Send requests to the model as follows: ```python import json from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="numind/NuExtract-2.0-8B", temperature=0, messages=[ { "role": "user", "content": [{"type": "text", "text": "Yesterday I went shopping at Bunnings"}], }, ], extra_body={ "chat_template_kwargs": { "template": json.dumps(json.loads("""{\"store\": \"verbatim-string\"}"""), indent=4) }, } ) print("Chat response:", chat_response) ``` For image inputs, structure requests as shown below. Make sure to order the images in `"content"` as they appear in the prompt (i.e. any in-context examples before the main input). ```python import base64 def encode_image(image_path): """ Encode the image file to base64 string """ with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') base64_image = encode_image("0.jpg") base64_image2 = encode_image("1.jpg") chat_response = client.chat.completions.create( model="numind/NuExtract-2.0-8B", temperature=0, messages=[ { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}, # first ICL example image {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image2}"}}, # real input image ], }, ], extra_body={ "chat_template_kwargs": { "template": json.dumps(json.loads("""{\"store\": \"verbatim-string\"}"""), indent=4), "examples": [ { "input": "<image>", "output": """{\"store\": \"Walmart\"}""" } ] }, } ) print("Chat response:", chat_response) ```

thailevann/track8_subtask1_PoT

thailevann

2025-08-19T17:42:23Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T17:42:06Z

--- base_model: unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** thailevann - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

yookty/blockassist-bc-whistling_exotic_chicken_1755625296

yookty

2025-08-19T17:41:44Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "whistling exotic chicken", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:41:37Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - whistling exotic chicken --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dharshaneshwaran/MultimodalDeepfakeDetector

Dharshaneshwaran

2025-08-19T17:41:22Z

0

null

[ "arxiv:1604.02878", "arxiv:2104.00298", "arxiv:2008.06456", "arxiv:1901.08971", "region:us" ]

null

2025-08-19T17:36:23Z

# DeepSecure-AI DeepSecure-AI is a powerful open-source tool designed to detect fake images, videos, and audios. Utilizing state-of-the-art deep learning techniques like EfficientNetV2 and MTCNN, DeepSecure-AI offers frame-by-frame video analysis, enabling high-accuracy deepfake detection. It's developed with a focus on ease of use, making it accessible for researchers, developers, and security analysts... --- ## Features - Multimedia Detection: Detect deepfakes in images, videos, and audio files using a unified platform. - High Accuracy: Leverages EfficientNetV2 for enhanced prediction performance and accurate results. - Real-Time Video Analysis: Frame-by-frame analysis of videos with automatic face detection. - User-Friendly Interface: Easy-to-use interface built with Gradio for uploading and processing media files. - Open Source: Completely open source under the MIT license, making it available for developers to extend and improve. --- ## Demo-Data You can test the deepfake detection capabilities of DeepSecure-AI by uploading your video files. The tool will analyze each frame of the video, detect faces, and determine the likelihood of the video being real or fake. Examples: 1. [Video1-fake-1-ff.mp4](#) 2. [Video6-real-1-ff.mp4](#) --- ## How It Works DeepSecure-AI uses the following architecture: 1. Face Detection: The [MTCNN](https://arxiv.org/abs/1604.02878) model detects faces in each frame of the video. If no face is detected, it will use the previous frame's face to ensure accuracy. 2. Fake vs. Real Classification: Once the face is detected, it's resized and fed into the [EfficientNetV2](https://arxiv.org/abs/2104.00298) deep learning model, which determines the likelihood of the frame being real or fake. 3. Fake Confidence: A final prediction is generated as a percentage score, indicating the confidence that the media is fake. 4. Results: DeepSecure-AI provides an output video, highlighting the detected faces and a summary of whether the input is classified as real or fake. --- ## Project Setup ### Prerequisites Ensure you have the following installed: - Python 3.10 - Gradio (pip install gradio) - TensorFlow (pip install tensorflow) - OpenCV (pip install opencv-python) - PyTorch (pip install torch torchvision torchaudio) - facenet-pytorch (pip install facenet-pytorch) - MoviePy (pip install moviepy) ### Installation 1. Clone the repository: cd DeepSecure-AI 2. Install required dependencies: pip install -r requirements.txt 3. Download the pre-trained model weights for EfficientNetV2 and place them in the project folder. ### Running the Application 1. Launch the Gradio interface: python app.py 2. The web interface will be available locally. You can upload a video, and DeepSecure-AI will analyze and display results. --- ## Example Usage Upload a video or image to DeepSecure-AI to detect fake media. Here are some sample predictions: - Video Analysis: The tool will detect faces from each frame and classify whether the video is fake or real. - Result Output: A GIF or MP4 file with the sequence of detected faces and classification result will be provided. --- ## Technologies Used - TensorFlow: For building and training deep learning models. - EfficientNetV2: The core model for image and video classification. - MTCNN: For face detection in images and videos. - OpenCV: For video processing and frame manipulation. - MoviePy: For video editing and result generation. - Gradio: To create a user-friendly interface for interacting with the deepfake detector. --- ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. --- ## Contributions Contributions are welcome! If you'd like to improve the tool, feel free to submit a pull request or raise an issue. For more information, check the [Contribution Guidelines](CONTRIBUTING.md). --- ## References - Li et al. (2020): [Celeb-DF(V2)](https://arxiv.org/abs/2008.06456) - Rossler et al. (2019): [FaceForensics++](https://arxiv.org/abs/1901.08971) - Timesler (2020): [Facial Recognition Model in PyTorch](https://www.kaggle.com/timesler/facial-recognition-model-in-pytorch) --- ### Disclaimer DeepSecure-AI is a research project and is designed for educational purposes.Please use responsibly and always give proper credit when utilizing the model in your work.

Buura/qwen-coder-1.5b-opencodeinstruct-grpo

Buura

2025-08-19T17:41:02Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T17:40:41Z

--- base_model: unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Buura - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Huseyin/teknofest-2025-turkish-edu

Huseyin

2025-08-19T17:38:35Z

0

null

[ "region:us" ]

null

2025-08-19T17:32:35Z

# 🚀 TEKNOFEST 2025 - Türkçe Eğitim Modeli Bu model **TEKNOFEST 2025 Eylem Temelli Türkçe Büyük Dil Modeli Yarışması** için geliştirilmiştir. ## 📋 Model Bilgileri - **Base Model:** Qwen/Qwen3-8B - **Fine-tuning:** LoRA Adapter (Huseyin/qwen3-8b-turkish-teknofest2025-private) - **Oluşturma Tarihi:** 2025-08-19 17:37 - **Alan:** Eğitim Teknolojileri - **Dil:** Türkçe ## 🎯 Kullanım Alanları - Türkçe eğitim materyali oluşturma - Öğrenci seviyesine uygun içerik üretimi - Soru-cevap sistemleri - Eğitsel içerik özetleme - Ders planı hazırlama ## 💻 Kullanım ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Model ve tokenizer'ı yükle model_name = "Huseyin/teknofest-2025-turkish-edu" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") # Örnek kullanım prompt = "Türkçe eğitimi için yaratıcı bir etkinlik önerisi:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=200, temperature=0.7) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## 🏆 TEKNOFEST 2025 Bu model, TEKNOFEST 2025 Türkçe Büyük Dil Modeli Yarışması kapsamında geliştirilmiştir. ### Yarışma Kategorisi **Eylem Temelli Türkçe Büyük Dil Modeli** ### Ekip TEKNOFEST 2025 Yarışma Ekibi ## 📊 Performans Metrikleri - **Perplexity:** [Değerlendirilecek] - **BLEU Score:** [Değerlendirilecek] - **Human Evaluation:** [Değerlendirilecek] ## 📄 Lisans Apache 2.0 ## 🙏 Teşekkür Bu modelin geliştirilmesinde emeği geçen herkese teşekkür ederiz. --- *TEKNOFEST 2025 - Türkiye'nin Teknoloji Festivali*

catme0w/MolScribe-Long

catme0w

2025-08-19T17:37:36Z

0

null

[ "base_model:yujieq/MolScribe", "base_model:finetune:yujieq/MolScribe", "license:mit", "region:us" ]

null

2025-08-19T04:44:52Z

--- license: mit base_model: - yujieq/MolScribe ---

WenFengg/21_14l10__20_8

WenFengg

2025-08-19T17:37:09Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T17:35:18Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755624890

Dejiat

2025-08-19T17:35:34Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:35:18Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

aleebaster/blockassist-bc-sly_eager_boar_1755623414

aleebaster

2025-08-19T17:34:35Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:34:28Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

AnonymousCS/xlmr_norwegian_immigration3

AnonymousCS

2025-08-19T17:31:48Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T17:27:36Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_norwegian_immigration3 results: [] ---  # xlmr_norwegian_immigration3 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3159 - Accuracy: 0.9091 - 1-f1: 0.8571 - 1-recall: 0.8182 - 1-precision: 0.9 - Balanced Acc: 0.8864 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.1747 | 1.0 | 2 | 0.3069 | 0.9091 | 0.8571 | 0.8182 | 0.9 | 0.8864 | | 0.2707 | 2.0 | 4 | 0.2797 | 0.9091 | 0.8571 | 0.8182 | 0.9 | 0.8864 | | 0.1527 | 3.0 | 6 | 0.2621 | 0.8788 | 0.8 | 0.7273 | 0.8889 | 0.8409 | | 0.1886 | 4.0 | 8 | 0.2600 | 0.8788 | 0.8 | 0.7273 | 0.8889 | 0.8409 | | 0.1 | 5.0 | 10 | 0.2818 | 0.9091 | 0.8571 | 0.8182 | 0.9 | 0.8864 | | 0.0835 | 6.0 | 12 | 0.3159 | 0.9091 | 0.8571 | 0.8182 | 0.9 | 0.8864 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

indoempatnol/blockassist-bc-fishy_wary_swan_1755623074

indoempatnol

2025-08-19T17:31:48Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:31:44Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

g-assismoraes/Qwen3-4B-Base-aki-alpha0.08-var-adown0.05-qQ2Q3-hatebr

g-assismoraes

2025-08-19T17:29:32Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T17:22:33Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Yanmife/gemma-2b-health-qlora

Yanmife

2025-08-19T17:27:53Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-2b", "base_model:finetune:google/gemma-2b", "endpoints_compatible", "region:us" ]

null

2025-08-19T15:30:05Z

--- base_model: google/gemma-2b library_name: transformers model_name: gemma-2b-health-qlora tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gemma-2b-health-qlora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Yanmife/gemma-2b-health-qlora", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/emmy_wan-personal/Fine-tuning-Gemma-2B-health/runs/bguryohi) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Vasya777/blockassist-bc-lumbering_enormous_sloth_1755624278

Vasya777

2025-08-19T17:25:23Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lumbering enormous sloth", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:25:20Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lumbering enormous sloth --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lilTAT/blockassist-bc-gentle_rugged_hare_1755624226

lilTAT

2025-08-19T17:24:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:24:20Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Pradeepgupta112233/runwayml-stable-diffusion-v1-5

Pradeepgupta112233

2025-08-19T17:24:15Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "region:us" ]

text-to-image

2025-08-19T17:20:14Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/8043.jpg text: '-' base_model: '' instance_prompt: SD 1.5 --- # My Project – Character LoRA <Gallery /> ## Model description my_project ## Trigger words You should use `SD 1.5` to trigger the image generation. ## Download model [Download](/Pradeepgupta112233/runwayml-stable-diffusion-v1-5/tree/main) them in the Files & versions tab.

AnonymousCS/xlmr_finnish_immigration3

AnonymousCS

2025-08-19T17:23:49Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T17:19:59Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_finnish_immigration3 results: [] ---  # xlmr_finnish_immigration3 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0700 - Accuracy: 0.9846 - 1-f1: 0.9767 - 1-recall: 0.9767 - 1-precision: 0.9767 - Balanced Acc: 0.9826 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.2027 | 1.0 | 5 | 0.0699 | 0.9846 | 0.9767 | 0.9767 | 0.9767 | 0.9826 | | 0.1827 | 2.0 | 10 | 0.0772 | 0.9769 | 0.9655 | 0.9767 | 0.9545 | 0.9769 | | 0.0918 | 3.0 | 15 | 0.0637 | 0.9846 | 0.9767 | 0.9767 | 0.9767 | 0.9826 | | 0.067 | 4.0 | 20 | 0.0844 | 0.9692 | 0.9545 | 0.9767 | 0.9333 | 0.9711 | | 0.0457 | 5.0 | 25 | 0.0700 | 0.9846 | 0.9767 | 0.9767 | 0.9767 | 0.9826 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

AppliedLucent/nemo-phase5

AppliedLucent

2025-08-19T17:23:30Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:AppliedLucent/nemo-phase4", "base_model:finetune:AppliedLucent/nemo-phase4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T17:10:38Z

--- base_model: AppliedLucent/nemo-phase4 tags: - text-generation-inference - transformers - unsloth - mistral license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** AppliedLucent - **License:** apache-2.0 - **Finetuned from model :** AppliedLucent/nemo-phase4 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755622535

quantumxnode

2025-08-19T17:23:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:23:00Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755622654

pempekmangedd

2025-08-19T17:23:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "patterned sturdy dolphin", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:22:59Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - patterned sturdy dolphin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

WenFengg/21_14l8__20_8

WenFengg

2025-08-19T17:22:15Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-19T17:20:29Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755623981

Dejiat

2025-08-19T17:20:18Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:20:04Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

unitova/blockassist-bc-zealous_sneaky_raven_1755622293

unitova

2025-08-19T17:19:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:19:29Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Sayemahsjn/blockassist-bc-playful_feline_octopus_1755622613

Sayemahsjn

2025-08-19T17:18:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:18:28Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lilTAT/blockassist-bc-gentle_rugged_hare_1755623819

lilTAT

2025-08-19T17:17:42Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T17:17:31Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Jacksss123/net72_uid253

Jacksss123

2025-08-19T17:17:13Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-08-19T17:13:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

debesu/Mati-Bal-Mati-Mist

debesu

2025-08-19T17:16:40Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-08-19T16:47:24Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Mati --- # Mati Bal Mati Mist <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-lora-trainer/train ## Trigger words You should use `Mati` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Mati", "lora_weights": "https://huggingface.co/debesu/Mati-Bal-Mati-Mist/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('debesu/Mati-Bal-Mati-Mist', weight_name='lora.safetensors') image = pipeline('Mati').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1400 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/debesu/Mati-Bal-Mati-Mist/discussions) to add images that show off what you’ve made with this LoRA.