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 18:33:19	downloads int64 0 223M	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 18:33:14	card stringlengths 11 1.01M
Muapi/majora-the-legend-of-zelda-majora-s-mask-illustrious-flux-pony-sd1.5	Muapi	2025-08-19T21:19:55Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:19:39Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Majora (The Legend of Zelda: Majora's Mask) [Illustrious & Flux & Pony & SD1.5] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: zzMajora,, zzMajora, spikes, yellow sclera, solo, 1boy, horns, glowing eyes, ## 🧠 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:243365@1616062", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
crystalline7/39734	crystalline7	2025-08-19T21:19:47Z	0	null	[ "region:us" ]	null	2025-08-19T21:19:47Z	[View on Civ Archive](https://civarchive.com/models/28646?modelVersionId=55222)
ultratopaz/50909	ultratopaz	2025-08-19T21:19:30Z	0	null	[ "region:us" ]	null	2025-08-19T21:19:26Z	[View on Civ Archive](https://civarchive.com/models/68796?modelVersionId=73485)
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755636612	katanyasekolah	2025-08-19T21:19:14Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T21:19:10Z	--- 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).
seraphimzzzz/17560	seraphimzzzz	2025-08-19T21:19:00Z	0	null	[ "region:us" ]	null	2025-08-19T21:18:55Z	[View on Civ Archive](https://civarchive.com/models/17829?modelVersionId=21070)
roeker/blockassist-bc-quick_wiry_owl_1755638250	roeker	2025-08-19T21:18:56Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T21:18:14Z	--- 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).
seraphimzzzz/28092	seraphimzzzz	2025-08-19T21:18:41Z	0	null	[ "region:us" ]	null	2025-08-19T21:18:38Z	[View on Civ Archive](https://civarchive.com/models/23128?modelVersionId=34142)
crystalline7/108814	crystalline7	2025-08-19T21:18:22Z	0	null	[ "region:us" ]	null	2025-08-19T21:18:19Z	[View on Civ Archive](https://civarchive.com/models/133033?modelVersionId=146389)
ultratopaz/22481	ultratopaz	2025-08-19T21:18:08Z	0	null	[ "region:us" ]	null	2025-08-19T21:18:06Z	[View on Civ Archive](https://civarchive.com/models/21745?modelVersionId=27137)
seraphimzzzz/46555	seraphimzzzz	2025-08-19T21:18:00Z	0	null	[ "region:us" ]	null	2025-08-19T21:17:56Z	[View on Civ Archive](https://civarchive.com/models/61963?modelVersionId=66478)
seraphimzzzz/46345	seraphimzzzz	2025-08-19T21:17:49Z	0	null	[ "region:us" ]	null	2025-08-19T21:17:45Z	[View on Civ Archive](https://civarchive.com/models/61681?modelVersionId=66178)
ultratopaz/74089	ultratopaz	2025-08-19T21:17:28Z	0	null	[ "region:us" ]	null	2025-08-19T21:17:22Z	[View on Civ Archive](https://civarchive.com/models/98563?modelVersionId=105415)
lilTAT/blockassist-bc-gentle_rugged_hare_1755638201	lilTAT	2025-08-19T21:17:08Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T21:17:04Z	--- 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).
seraphimzzzz/14050	seraphimzzzz	2025-08-19T21:16:46Z	0	null	[ "region:us" ]	null	2025-08-19T21:16:44Z	[View on Civ Archive](https://civarchive.com/models/14160?modelVersionId=16665)
Muapi/abstract-oil-painting-art	Muapi	2025-08-19T21:16:43Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:16:27Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Abstract oil painting art ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Abstract art, oil painting , complex , expressive , blue , gold , purple , red , green ## 🧠 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:709702@793815", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755636621	quantumxnode	2025-08-19T21:16:41Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T21:16:37Z	--- 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).
crystalline7/57410	crystalline7	2025-08-19T21:16:38Z	0	null	[ "region:us" ]	null	2025-08-19T21:16:35Z	[View on Civ Archive](https://civarchive.com/models/79193?modelVersionId=83990)
crystalline7/17720	crystalline7	2025-08-19T21:16:27Z	0	null	[ "region:us" ]	null	2025-08-19T21:16:23Z	[View on Civ Archive](https://civarchive.com/models/17997?modelVersionId=21267)
Muapi/the-ratio-narrow-waist-wide-hips	Muapi	2025-08-19T21:16:21Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:16:12Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # The Ratio - Narrow Waist : Wide Hips ![preview](./preview.jpg) Base model: Flux.1 D Trained words: ratio_wh ## 🧠 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:1328309@1499734", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
seraphimzzzz/150876	seraphimzzzz	2025-08-19T21:16:10Z	0	null	[ "region:us" ]	null	2025-08-19T21:15:52Z	[View on Civ Archive](https://civarchive.com/models/175613?modelVersionId=197172)
AnonymousCS/xlmr_immigration_combo3_3	AnonymousCS	2025-08-19T21:15:39Z	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	2025-08-19T21:13:01Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo3_3 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_immigration_combo3_3 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: 0.2021 - Accuracy: 0.9422 - 1-f1: 0.9109 - 1-recall: 0.8880 - 1-precision: 0.9350 - Balanced Acc: 0.9286 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1568 \| 1.0 \| 25 \| 0.1731 \| 0.9447 \| 0.9155 \| 0.8996 \| 0.932 \| 0.9334 \| \| 0.064 \| 2.0 \| 50 \| 0.2265 \| 0.9422 \| 0.9068 \| 0.8456 \| 0.9777 \| 0.9180 \| \| 0.0524 \| 3.0 \| 75 \| 0.2021 \| 0.9422 \| 0.9109 \| 0.8880 \| 0.9350 \| 0.9286 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
ultratopaz/108688	ultratopaz	2025-08-19T21:15:31Z	0	null	[ "region:us" ]	null	2025-08-19T21:15:27Z	[View on Civ Archive](https://civarchive.com/models/128002?modelVersionId=146742)
Muapi/flux-graphic-t-shirt-designs	Muapi	2025-08-19T21:15:05Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:14:53Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Flux Graphic T-Shirt Designs ![preview](./preview.jpg) Base model: Flux.1 D Trained words: T-Shirt Art, Graphic T-Shirt, Vector Art, T-Shirt Graphic ## 🧠 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:721090@819874", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
ultratopaz/39810	ultratopaz	2025-08-19T21:14:55Z	0	null	[ "region:us" ]	null	2025-08-19T21:14:51Z	[View on Civ Archive](https://civarchive.com/models/50818?modelVersionId=55334)
Muapi/yfg-aarchy-flux	Muapi	2025-08-19T21:14:48Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:14:25Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # YFG Aarchy [Flux] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: YFG-Aarchy ## 🧠 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:1108935@1245947", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
UnbeT/ppo_ai	UnbeT	2025-08-19T21:14:37Z	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T21:14:07Z	--- 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]
UmeshAkade/gemma3-270m-med-wikidoc-patientinfo-lora	UmeshAkade	2025-08-19T21:14:35Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-270m-it", "base_model:finetune:unsloth/gemma-3-270m-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T06:52:17Z	--- base_model: unsloth/gemma-3-270m-it tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: UmeshAkade - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-270m-it This gemma3_text 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)
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755636377	kojeklollipop	2025-08-19T21:14:30Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T21:14:26Z	--- 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).
seraphimzzzz/218873	seraphimzzzz	2025-08-19T21:14:25Z	0	null	[ "region:us" ]	null	2025-08-19T21:14:07Z	[View on Civ Archive](https://civarchive.com/models/124031?modelVersionId=279464)
Muapi/vintage-movie	Muapi	2025-08-19T21:14:02Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:13:35Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Vintage Movie ![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:722757@808139", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/topmodel	Muapi	2025-08-19T21:13:25Z	0	null	[ "safetensors", "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:13:18Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # topmodel ![preview](./preview.jpg) Base model: Flux.1 D Trained words: A stunning woman with a sculpted body, perfectly proportioned curves, and a flawless face. Her skin is radiant, and her facial features are symmetrical and harmonious, highlighting large expressive eyes, full lips, and a captivating smile. She is wearing a casual outfit consisting of a fitted T-shirt and jeans that accentuate her figure. In another scene, she is dressed in elegant lingerie, including a delicate bra and matching panties, showcasing her perfect physique, A breathtaking woman, combining an athletic and well-defined body with a face of classic beauty. Her eyes are piercing, and her hair falls softly around her face, framing her delicate features. She is wearing a sophisticated dress that hugs her curves, enhancing her magnetic presence. In another setting, she is seen in a comfortable casual outfit with a stylish blouse and skirt, and later in luxurious lingerie, featuring a lacy bra and matching underwear, A woman with an impressive physique and an absolutely perfect face, worthy of a work of art. Her skin is smooth and flawless, her eyes shine with a captivating intensity, and her lips are perfectly shaped. She is in a luxurious setting, wearing a stunning evening dress that highlights every detail of her mesmerizing figure. In another scene, she is casually dressed in a fitted T-shirt and shorts, and also appears in intimate lingerie, including a silk bra and matching panties ## 🧠 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:708602@792571", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
seraphimzzzz/33435	seraphimzzzz	2025-08-19T21:13:25Z	0	null	[ "region:us" ]	null	2025-08-19T21:13:21Z	[View on Civ Archive](https://civarchive.com/models/38290?modelVersionId=44242)
ultratopaz/390791	ultratopaz	2025-08-19T21:13:11Z	0	null	[ "region:us" ]	null	2025-08-19T21:13:01Z	[View on Civ Archive](https://civarchive.com/models/423811?modelVersionId=472196)
seraphimzzzz/93586	seraphimzzzz	2025-08-19T21:12:49Z	0	null	[ "region:us" ]	null	2025-08-19T21:12:46Z	[View on Civ Archive](https://civarchive.com/models/118989?modelVersionId=129142)
AnonymousCS/xlmr_immigration_combo3_2	AnonymousCS	2025-08-19T21:12:04Z	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	2025-08-19T21:09:17Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo3_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. --> # xlmr_immigration_combo3_2 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: 0.2134 - Accuracy: 0.9344 - 1-f1: 0.8994 - 1-recall: 0.8803 - 1-precision: 0.9194 - Balanced Acc: 0.9209 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1575 \| 1.0 \| 25 \| 0.1814 \| 0.9447 \| 0.9142 \| 0.8842 \| 0.9463 \| 0.9296 \| \| 0.1985 \| 2.0 \| 50 \| 0.2058 \| 0.9306 \| 0.8958 \| 0.8958 \| 0.8958 \| 0.9219 \| \| 0.0995 \| 3.0 \| 75 \| 0.2134 \| 0.9344 \| 0.8994 \| 0.8803 \| 0.9194 \| 0.9209 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
ultratopaz/62716	ultratopaz	2025-08-19T21:12:02Z	0	null	[ "region:us" ]	null	2025-08-19T21:11:59Z	[View on Civ Archive](https://civarchive.com/models/85682?modelVersionId=91116)
ultratopaz/86831	ultratopaz	2025-08-19T21:11:33Z	0	null	[ "region:us" ]	null	2025-08-19T21:11:33Z	[View on Civ Archive](https://civarchive.com/models/111982?modelVersionId=120872)
NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes	NYUAD-ComNets	2025-08-19T21:11:23Z	5	transformers	[ "transformers", "safetensors", "mllama", "image-to-text", "text-generation-inference", "unsloth", "en", "arxiv:2412.14197", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-text	2025-06-29T19:19:59Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama license: apache-2.0 language: - en --- # Llama3.2-11B based Hate Detection in Arabic MultiModal Memes The rise of social media and online communication platforms has led to the spread of Arabic memes as a key form of digital expression. While these contents can be humorous and informative, they are also increasingly being used to spread offensive language and hate speech. Consequently, there is a growing demand for precise analysis of content in Arabic meme. This work used Llama 3.2 with its vision capability to effectively identify hate content within Arabic memes. The evaluation is conducted using a dataset of Arabic memes proposed in the ArabicNLP MAHED 2025 challenge. The results underscore the capacity of *Llama 3.2-11B fine-tuned with Arabic memes, to deliver the superior performance. They achieve accuracy* of 80.3% and macro F1 score of 73.3%. The proposed solutions offer a more nuanced understanding of memes for accurate and efficient Arabic content moderation systems. # Examples of Arabic Memes from ArabicNLP MAHED 2025 challenge # Examples \| \| \| \| \|:-------------------------:\|:-------------------------:\|:-------------------------:\| \|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jBuVCt5163WlugFRXkSgq.jpeg"> \|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jiPId6f5IiGXxpI898llC.jpeg"> \| \|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/61acyltUsTB--ZOAMkv0a.jpeg"> \|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/_alSRnwG0azE_iYq2BrpP.jpeg"> \| ``` python import pandas as pd import os from unsloth import FastVisionModel import torch from datasets import load_dataset from transformers import TextStreamer from PIL import Image import os os.environ["TOKENIZERS_PARALLELISM"] = "false" model_name = "NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes" model, tokenizer = FastVisionModel.from_pretrained(model_name, token='xxxxxxxxxxxxxxxxxxxxxx') FastVisionModel.for_inference(model) dataset_test = load_dataset("QCRI/Prop2Hate-Meme", split = "test") print(dataset_test) def add_labels_column(example): example["labels"] = "no_hate" if example["hate_label"] == 0 else "hate" return example dataset_test = dataset_test.map(add_labels_column) pred=[] for k in range(606): image = dataset_test[k]["image"] text = dataset_test[k]["text"] lab = dataset_test[k]["labels"] messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": text} ]} ] input_text = tokenizer.apply_chat_template(messages,add_generation_prompt = True) inputs = tokenizer( image, input_text, add_special_tokens = False, return_tensors = "pt", ).to("cuda") text_streamer = TextStreamer(tokenizer, skip_prompt = True) p = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128, use_cache = False, temperature = 0.3, min_p = 0.3) p = tokenizer.decode(p[0], skip_special_tokens=True) pred.append(p.split('assistant')[1].strip()) print(pred) ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jRSB8JxqqoV-2E97N5QQM.png) We used Low-Rank Adaptation (LoRA) as the Parameter-Efficient Fine-Tuning (PEFT) method for fine-tuning utilizing the unsloth framework. The hyper-parameters of Llama 3.2-11B are as follows: the training batch size per device is set to 4. gradients are accumulated over 4 steps. the learning rate warm-up lasts for 5 steps. the total number of training steps is 150. the learning rate is set to 0.0002. the optimizer used is 8-bit AdamW weight decay is set to 0.01. a linear learning rate scheduler is used. # BibTeX entry and citation info ``` @misc{aldahoul2024advancingvehicleplaterecognition, title={Detecting Hope, Hate, and Emotion in Arabic Textual Speech and Multi-modal Memes Using Large Language Models}, author={Nouar AlDahoul and Yasir Zaki}, year={2025}, eprint={2412.14197}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.14197}, } ```
seraphimzzzz/343374	seraphimzzzz	2025-08-19T21:11:22Z	0	null	[ "region:us" ]	null	2025-08-19T21:11:14Z	[View on Civ Archive](https://civarchive.com/models/377663?modelVersionId=421726)
chainway9/blockassist-bc-untamed_quick_eel_1755636278	chainway9	2025-08-19T21:11:03Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T21:11:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # 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_1755636335	lisaozill03	2025-08-19T21:10:50Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T21:10:46Z	--- 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).
Muapi/flux-handwriting	Muapi	2025-08-19T21:10:28Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:10:21Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # flux-handwriting ![preview](./preview.jpg) Base model: Flux.1 D Trained words: HWRIT handwriting ## 🧠 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:1037313@1163532", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/gpt-image-1-style-flux	Muapi	2025-08-19T21:10:02Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:09:54Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # GPT Image 1 Style [FLUX] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: aidmagptimage ## 🧠 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:1554812@1759376", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
seraphimzzzz/362297	seraphimzzzz	2025-08-19T21:09:47Z	0	null	[ "region:us" ]	null	2025-08-19T21:09:44Z	[View on Civ Archive](https://civarchive.com/models/47112?modelVersionId=442105)
crystalline7/37706	crystalline7	2025-08-19T21:09:40Z	0	null	[ "region:us" ]	null	2025-08-19T21:09:37Z	[View on Civ Archive](https://civarchive.com/models/47112?modelVersionId=51697)
crystalline7/58446	crystalline7	2025-08-19T21:09:32Z	0	null	[ "region:us" ]	null	2025-08-19T21:09:29Z	[View on Civ Archive](https://civarchive.com/models/80666?modelVersionId=85561)
Muapi/isobel-baldur-s-gate-3-flux-ponyxl-1.5	Muapi	2025-08-19T21:08:38Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:08:26Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Isobel - Baldur's Gate 3 [Flux/PonyXL/1.5] ![preview](./preview.jpg) Base model: Flux.1 D Trained words: isobel, armor ## 🧠 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:416346@780583", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
crystalline7/40828	crystalline7	2025-08-19T21:08:37Z	0	null	[ "region:us" ]	null	2025-08-19T21:08:34Z	[View on Civ Archive](https://civarchive.com/models/52843?modelVersionId=57236)
Muapi/3d-chibi-toy-air-dry-clay-style-flux	Muapi	2025-08-19T21:08:19Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:08:08Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # 【3D chibi toy】Air dry clay style - FLUX ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Juaner_clay ## 🧠 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:689231@771373", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
crystalline7/57248	crystalline7	2025-08-19T21:08:15Z	0	null	[ "region:us" ]	null	2025-08-19T21:08:09Z	[View on Civ Archive](https://civarchive.com/models/78918?modelVersionId=83723)
AnonymousCS/xlmr_immigration_combo3_1	AnonymousCS	2025-08-19T21:08:10Z	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	2025-08-19T21:05:22Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo3_1 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_immigration_combo3_1 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: 0.2290 - Accuracy: 0.9319 - 1-f1: 0.8894 - 1-recall: 0.8224 - 1-precision: 0.9682 - Balanced Acc: 0.9045 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1997 \| 1.0 \| 25 \| 0.1769 \| 0.9447 \| 0.9131 \| 0.8726 \| 0.9576 \| 0.9267 \| \| 0.1957 \| 2.0 \| 50 \| 0.2013 \| 0.9383 \| 0.9008 \| 0.8417 \| 0.9689 \| 0.9141 \| \| 0.1423 \| 3.0 \| 75 \| 0.2290 \| 0.9319 \| 0.8894 \| 0.8224 \| 0.9682 \| 0.9045 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
seraphimzzzz/33568	seraphimzzzz	2025-08-19T21:08:03Z	0	null	[ "region:us" ]	null	2025-08-19T21:08:00Z	[View on Civ Archive](https://civarchive.com/models/38628?modelVersionId=44548)
Abdullah6395/COT_LLM	Abdullah6395	2025-08-19T21:07:58Z	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "lora", "template:diffusion-lora", "base_model:LiquidAI/LFM2-350M", "base_model:adapter:LiquidAI/LFM2-350M", "license:other", "region:us" ]	text-to-image	2025-08-19T21:07:53Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/Screenshot from 2025-08-20 01-55-42.png text: None parameters: negative_prompt: None base_model: LiquidAI/LFM2-350M instance_prompt: null license: other license_name: none license_link: LICENSE --- # CAYOTES <Gallery /> ## Model description Model Description (Educational Purpose Only): This is a small-scale LLM developed for learning and experimentation. Initially, the model was distilled from a larger teacher model to reduce size and computation requirements. Subsequently, it was fine-tuned on a chain-of-thought (CoT) dataset. Due to limited resources, training is partial and the model's outputs remain largely random. This model is intended strictly for educational use, research practice, and demonstration purposes. It is not suitable for deployment, commercial applications, or production use. ## Download model [Download](/Abdullah6395/COT_LLM/tree/main) them in the Files & versions tab.
ultratopaz/390765	ultratopaz	2025-08-19T21:07:51Z	0	null	[ "region:us" ]	null	2025-08-19T21:07:40Z	[View on Civ Archive](https://civarchive.com/models/423776?modelVersionId=472159)
Muapi/richard-anderson	Muapi	2025-08-19T21:07:49Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:07:35Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Richard Anderson ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Art by Richard Anderson ## 🧠 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:1349128@1523853", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/solo-leveling-style-by-readandsign-ill-flux	Muapi	2025-08-19T21:07:28Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:07:21Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Solo Leveling style by Readandsign \| ILL \|Flux ![preview](./preview.jpg) Base model: Flux.1 D Trained words: slv50 ## 🧠 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:1258225@1455857", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
ultratopaz/93397	ultratopaz	2025-08-19T21:07:24Z	0	null	[ "region:us" ]	null	2025-08-19T21:07:07Z	[View on Civ Archive](https://civarchive.com/models/118690?modelVersionId=128800)
Muapi/aigis-persona	Muapi	2025-08-19T21:06:49Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:05:55Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Aigis - Persona ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Aigis ## 🧠 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:843111@943235", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
andy013567/gemma-3-1b-it-finetuned-wikitext2	andy013567	2025-08-19T21:06:44Z	0	peft	[ "peft", "tensorboard", "safetensors", "base_model:adapter:google/gemma-3-1b-it", "lora", "transformers", "text-generation", "base_model:google/gemma-3-1b-it", "license:gemma", "region:us" ]	text-generation	2025-08-19T10:11:23Z	--- library_name: peft license: gemma base_model: google/gemma-3-1b-it tags: - base_model:adapter:google/gemma-3-1b-it - lora - transformers pipeline_tag: text-generation model-index: - name: gemma-3-1b-it-finetuned-wikitext2 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-3-1b-it-finetuned-wikitext2 This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0835 ## 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.0041 \| 1.0 \| 1218 \| 3.0835 \| ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
woodwardmw/phase2	woodwardmw	2025-08-19T21:06:32Z	0	transformers	[ "transformers", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-08-19T18:48:51Z	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: phase2 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. --> # phase2 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4544 ## 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: 4 - eval_batch_size: 4 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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 - lr_scheduler_warmup_steps: 50 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:-----:\|:---------------:\| \| 0.5917 \| 2.6823 \| 1000 \| 0.5957 \| \| 0.5782 \| 5.3627 \| 2000 \| 0.5542 \| \| 0.5593 \| 8.0430 \| 3000 \| 0.5318 \| \| 0.4973 \| 10.7253 \| 4000 \| 0.4915 \| \| 0.4883 \| 13.4056 \| 5000 \| 0.4819 \| \| 0.4868 \| 16.0860 \| 6000 \| 0.4706 \| \| 0.5138 \| 18.7683 \| 7000 \| 0.4689 \| \| 0.4571 \| 21.4486 \| 8000 \| 0.4650 \| \| 0.4557 \| 24.1289 \| 9000 \| 0.4675 \| \| 0.5072 \| 26.8113 \| 10000 \| 0.4631 \| \| 0.492 \| 29.4916 \| 11000 \| 0.4604 \| \| 0.4535 \| 32.1719 \| 12000 \| 0.4581 \| \| 0.4668 \| 34.8543 \| 13000 \| 0.4550 \| \| 0.4825 \| 37.5346 \| 14000 \| 0.4593 \| \| 0.4551 \| 40.2149 \| 15000 \| 0.4568 \| \| 0.4285 \| 42.8972 \| 16000 \| 0.4554 \| \| 0.4383 \| 45.5776 \| 17000 \| 0.4544 \| \| 0.393 \| 48.2579 \| 18000 \| 0.4529 \| \| 0.4406 \| 50.9402 \| 19000 \| 0.4570 \| \| 0.4519 \| 53.6206 \| 20000 \| 0.4544 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4
Dejiat/blockassist-bc-savage_unseen_bobcat_1755637530	Dejiat	2025-08-19T21:06:13Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T21:05:56Z	--- 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).
ultratopaz/83659	ultratopaz	2025-08-19T21:06:02Z	0	null	[ "region:us" ]	null	2025-08-19T21:05:59Z	[View on Civ Archive](https://civarchive.com/models/18600?modelVersionId=117093)
ultratopaz/18387	ultratopaz	2025-08-19T21:05:53Z	0	null	[ "region:us" ]	null	2025-08-19T21:05:48Z	[View on Civ Archive](https://civarchive.com/models/18600?modelVersionId=22068)
Muapi/illustrations-cute-cartoon-cute-manga-flux	Muapi	2025-08-19T21:05:33Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:05:09Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # 【Illustrations】cute cartoon cute manga FLUX ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Juaner_cartoon ## 🧠 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:681642@762939", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
crystalline7/239862	crystalline7	2025-08-19T21:05:31Z	0	null	[ "region:us" ]	null	2025-08-19T21:05:23Z	[View on Civ Archive](https://civarchive.com/models/124035?modelVersionId=303862)
AnonymousCS/xlmr_immigration_combo3_0	AnonymousCS	2025-08-19T21:04:57Z	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	2025-08-19T21:01:36Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo3_0 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_immigration_combo3_0 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: 0.2386 - Accuracy: 0.9229 - 1-f1: 0.8780 - 1-recall: 0.8340 - 1-precision: 0.9270 - Balanced Acc: 0.9006 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.2836 \| 1.0 \| 25 \| 0.2080 \| 0.9293 \| 0.8911 \| 0.8687 \| 0.9146 \| 0.9141 \| \| 0.1912 \| 2.0 \| 50 \| 0.2152 \| 0.9357 \| 0.8980 \| 0.8494 \| 0.9524 \| 0.9141 \| \| 0.1954 \| 3.0 \| 75 \| 0.2386 \| 0.9229 \| 0.8780 \| 0.8340 \| 0.9270 \| 0.9006 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
ultratopaz/638392	ultratopaz	2025-08-19T21:04:52Z	0	null	[ "region:us" ]	null	2025-08-19T21:04:44Z	[View on Civ Archive](https://civarchive.com/models/646957?modelVersionId=723774)
matboz/ring-gemma-3	matboz	2025-08-19T21:04:28Z	0	peft	[ "peft", "safetensors", "base_model:adapter:google/gemma-3-27b-it", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:google/gemma-3-27b-it", "region:us" ]	text-generation	2025-08-19T21:04:07Z	--- base_model: google/gemma-3-27b-it library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:google/gemma-3-27b-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
crystalline7/20731	crystalline7	2025-08-19T21:04:19Z	0	null	[ "region:us" ]	null	2025-08-19T21:04:14Z	[View on Civ Archive](https://civarchive.com/models/21003?modelVersionId=24998)
ultratopaz/48777	ultratopaz	2025-08-19T21:04:08Z	0	null	[ "region:us" ]	null	2025-08-19T21:04:04Z	[View on Civ Archive](https://civarchive.com/models/65245?modelVersionId=69869)
ultratopaz/42969	ultratopaz	2025-08-19T21:04:00Z	0	null	[ "region:us" ]	null	2025-08-19T21:03:57Z	[View on Civ Archive](https://civarchive.com/models/56314?modelVersionId=60719)
crystalline7/75403	crystalline7	2025-08-19T21:03:44Z	0	null	[ "region:us" ]	null	2025-08-19T21:03:39Z	[View on Civ Archive](https://civarchive.com/models/71861?modelVersionId=107072)
crystalline7/49268	crystalline7	2025-08-19T21:03:31Z	0	null	[ "region:us" ]	null	2025-08-19T21:03:31Z	[View on Civ Archive](https://civarchive.com/models/66057?modelVersionId=70701)
ultratopaz/39813	ultratopaz	2025-08-19T21:03:25Z	0	null	[ "region:us" ]	null	2025-08-19T21:03:22Z	[View on Civ Archive](https://civarchive.com/models/50821?modelVersionId=55337)
saberbx/GraniteSentry	saberbx	2025-08-19T21:03:19Z	11	transformers	[ "transformers", "safetensors", "granite", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-06T04:58:17Z	--- 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. 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]
crystalline7/634081	crystalline7	2025-08-19T21:02:56Z	0	null	[ "region:us" ]	null	2025-08-19T21:02:52Z	[View on Civ Archive](https://civarchive.com/models/210536?modelVersionId=719459)
ultratopaz/81964	ultratopaz	2025-08-19T21:02:24Z	0	null	[ "region:us" ]	null	2025-08-19T21:02:22Z	[View on Civ Archive](https://civarchive.com/models/107074?modelVersionId=115110)
ultratopaz/85547	ultratopaz	2025-08-19T21:02:16Z	0	null	[ "region:us" ]	null	2025-08-19T21:02:16Z	[View on Civ Archive](https://civarchive.com/models/110723?modelVersionId=119386)
seraphimzzzz/19648	seraphimzzzz	2025-08-19T21:02:04Z	0	null	[ "region:us" ]	null	2025-08-19T21:01:58Z	[View on Civ Archive](https://civarchive.com/models/19939?modelVersionId=23677)
UnbeT/ppo_400	UnbeT	2025-08-19T21:01:34Z	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T20:47:14Z	--- 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]
ultratopaz/77712	ultratopaz	2025-08-19T21:01:31Z	0	null	[ "region:us" ]	null	2025-08-19T21:01:28Z	[View on Civ Archive](https://civarchive.com/models/102801?modelVersionId=110019)
VIDEOS-18-vietnamese-viral-video-Clip-hq/Original.New.full.videos.vietnamese.Viral.Video.Official.Tutorial	VIDEOS-18-vietnamese-viral-video-Clip-hq	2025-08-19T21:00:59Z	0	null	[ "region:us" ]	null	2025-08-19T21:00:34Z	<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?crd "><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
Muapi/moreface-lora	Muapi	2025-08-19T21:00:26Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T21:00:11Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # MoreFace-lora ![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:866492@969610", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/wizard-s-vintage-minimalism-cartoon	Muapi	2025-08-19T20:59:47Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T20:59:38Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Wizard's Vintage Minimalism Cartoon ![preview](./preview.jpg) Base model: Flux.1 D Trained words: vintage minimalism cartoon, 2Tone_CRTN ## 🧠 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:1108391@1245363", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755635345	ihsanridzi	2025-08-19T20:55:43Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:55:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
New-original-archita-phukan-viral-video-on/New.full.videos.archita.Phukan.Viral.Video.Official.Tutorial	New-original-archita-phukan-viral-video-on	2025-08-19T20:55:16Z	0	null	[ "region:us" ]	null	2025-08-19T20:55:08Z	<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?crd "><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
mradermacher/ege-8b-1.1-i1-GGUF	mradermacher	2025-08-19T20:54:54Z	0	transformers	[ "transformers", "gguf", "trl", "sft", "unsloth", "tr", "dataset:orkungedik/function_call", "base_model:orkungedik/ege-8b-1.1", "base_model:quantized:orkungedik/ege-8b-1.1", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-08-19T19:58:06Z	--- base_model: orkungedik/ege-8b-1.1 datasets: - orkungedik/function_call language: - tr library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - trl - sft - unsloth --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/orkungedik/ege-8b-1.1 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ege-8b-1.1-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/ege-8b-1.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q2_K.gguf) \| i1-Q2_K \| 3.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.9 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.9 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.9 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q6_K.gguf) \| i1-Q6_K \| 6.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Leoar/blockassist-bc-pudgy_toothy_cheetah_1755636686	Leoar	2025-08-19T20:53:25Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pudgy toothy cheetah", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:53:16Z	--- 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).
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755635203	sampingkaca72	2025-08-19T20:51:46Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:51:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/flux.1-dev-cctv-mania	Muapi	2025-08-19T20:51:46Z	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T20:51:36Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # FLUX.1 DEV - CCTV Mania ![preview](./preview.jpg) Base model: Flux.1 D Trained words: CCTV Footage ## 🧠 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:684810@766464", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
roeker/blockassist-bc-quick_wiry_owl_1755636624	roeker	2025-08-19T20:51:21Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:51:11Z	--- 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).
AnonymousCS/xlmr_immigration_combo2_3	AnonymousCS	2025-08-19T20:50:06Z	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	2025-08-19T20:47:12Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo2_3 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_immigration_combo2_3 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: 0.2533 - Accuracy: 0.9396 - 1-f1: 0.9058 - 1-recall: 0.8726 - 1-precision: 0.9417 - Balanced Acc: 0.9228 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1208 \| 1.0 \| 25 \| 0.1931 \| 0.9447 \| 0.9138 \| 0.8803 \| 0.95 \| 0.9286 \| \| 0.0845 \| 2.0 \| 50 \| 0.2122 \| 0.9434 \| 0.9124 \| 0.8842 \| 0.9424 \| 0.9286 \| \| 0.1345 \| 3.0 \| 75 \| 0.2533 \| 0.9396 \| 0.9058 \| 0.8726 \| 0.9417 \| 0.9228 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
Dombili2038/blockassist-bc-jumping_beaked_hamster_1755636537	Dombili2038	2025-08-19T20:49:28Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "jumping beaked hamster", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:49:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - jumping beaked hamster --- # 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_1755636372	Dejiat	2025-08-19T20:47:08Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:46:51Z	--- 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).
roeker/blockassist-bc-quick_wiry_owl_1755636206	roeker	2025-08-19T20:44:47Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:44:10Z	--- 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).
thanobidex/blockassist-bc-colorful_shiny_hare_1755634665	thanobidex	2025-08-19T20:43:59Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:43:56Z	--- 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).
TikToker-Abigail-Lalama-intimo-video/viral.ver.filtrado.video.de.abigail.lalama.y.snayder.influencer.se.hace.viral	TikToker-Abigail-Lalama-intimo-video	2025-08-19T20:43:54Z	0	null	[ "region:us" ]	null	2025-08-19T20:43:12Z	<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>
mradermacher/ege-8b-1.1-GGUF	mradermacher	2025-08-19T20:43:18Z	0	transformers	[ "transformers", "gguf", "trl", "sft", "unsloth", "tr", "dataset:orkungedik/function_call", "base_model:orkungedik/ege-8b-1.1", "base_model:quantized:orkungedik/ege-8b-1.1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-19T15:03:51Z	--- base_model: orkungedik/ege-8b-1.1 datasets: - orkungedik/function_call language: - tr library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - trl - sft - unsloth --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/orkungedik/ege-8b-1.1 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ege-8b-1.1-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q5_K_M.gguf) \| Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.f16.gguf) \| f16 \| 16.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
nice2mitya/a_133421939	nice2mitya	2025-08-19T20:40:50Z	0	null	[ "license:other", "region:us" ]	null	2025-08-19T20:13:48Z	--- 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 ---
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755634468	lisaozill03	2025-08-19T20:39:00Z	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T20:38:56Z	--- 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).

Muapi/majora-the-legend-of-zelda-majora-s-mask-illustrious-flux-pony-sd1.5

Muapi

2025-08-19T21:19:55Z

0

null

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

null

2025-08-19T21:19:39Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Majora (The Legend of Zelda: Majora's Mask) [Illustrious & Flux & Pony & SD1.5] ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: zzMajora,, zzMajora, spikes, yellow sclera, solo, 1boy, horns, glowing eyes, ## 🧠 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:243365@1616062", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

crystalline7/39734

crystalline7

2025-08-19T21:19:47Z

0

null

[ "region:us" ]

null

2025-08-19T21:19:47Z

[View on Civ Archive](https://civarchive.com/models/28646?modelVersionId=55222)

ultratopaz/50909

ultratopaz

2025-08-19T21:19:30Z

0

null

[ "region:us" ]

null

2025-08-19T21:19:26Z

[View on Civ Archive](https://civarchive.com/models/68796?modelVersionId=73485)

katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755636612

katanyasekolah

2025-08-19T21:19:14Z

0

null

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

null

2025-08-19T21:19:10Z

--- 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).

seraphimzzzz/17560

seraphimzzzz

2025-08-19T21:19:00Z

0

null

[ "region:us" ]

null

2025-08-19T21:18:55Z

[View on Civ Archive](https://civarchive.com/models/17829?modelVersionId=21070)

roeker/blockassist-bc-quick_wiry_owl_1755638250

roeker

2025-08-19T21:18:56Z

0

null

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

null

2025-08-19T21:18:14Z

--- 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).

seraphimzzzz/28092

seraphimzzzz

2025-08-19T21:18:41Z

0

null

[ "region:us" ]

null

2025-08-19T21:18:38Z

[View on Civ Archive](https://civarchive.com/models/23128?modelVersionId=34142)

crystalline7/108814

crystalline7

2025-08-19T21:18:22Z

0

null

[ "region:us" ]

null

2025-08-19T21:18:19Z

[View on Civ Archive](https://civarchive.com/models/133033?modelVersionId=146389)

ultratopaz/22481

ultratopaz

2025-08-19T21:18:08Z

0

null

[ "region:us" ]

null

2025-08-19T21:18:06Z

[View on Civ Archive](https://civarchive.com/models/21745?modelVersionId=27137)

seraphimzzzz/46555

seraphimzzzz

2025-08-19T21:18:00Z

0

null

[ "region:us" ]

null

2025-08-19T21:17:56Z

[View on Civ Archive](https://civarchive.com/models/61963?modelVersionId=66478)

seraphimzzzz/46345

seraphimzzzz

2025-08-19T21:17:49Z

0

null

[ "region:us" ]

null

2025-08-19T21:17:45Z

[View on Civ Archive](https://civarchive.com/models/61681?modelVersionId=66178)

ultratopaz/74089

ultratopaz

2025-08-19T21:17:28Z

0

null

[ "region:us" ]

null

2025-08-19T21:17:22Z

[View on Civ Archive](https://civarchive.com/models/98563?modelVersionId=105415)

lilTAT/blockassist-bc-gentle_rugged_hare_1755638201

lilTAT

2025-08-19T21:17:08Z

0

null

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

null

2025-08-19T21:17:04Z

--- 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).

seraphimzzzz/14050

seraphimzzzz

2025-08-19T21:16:46Z

0

null

[ "region:us" ]

null

2025-08-19T21:16:44Z

[View on Civ Archive](https://civarchive.com/models/14160?modelVersionId=16665)

Muapi/abstract-oil-painting-art

Muapi

2025-08-19T21:16:43Z

0

null

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

null

2025-08-19T21:16:27Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Abstract oil painting art ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Abstract art, oil painting , complex , expressive , blue , gold , purple , red , green ## 🧠 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:709702@793815", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755636621

quantumxnode

2025-08-19T21:16:41Z

0

null

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

null

2025-08-19T21:16:37Z

--- 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).

crystalline7/57410

crystalline7

2025-08-19T21:16:38Z

0

null

[ "region:us" ]

null

2025-08-19T21:16:35Z

[View on Civ Archive](https://civarchive.com/models/79193?modelVersionId=83990)

crystalline7/17720

crystalline7

2025-08-19T21:16:27Z

0

null

[ "region:us" ]

null

2025-08-19T21:16:23Z

[View on Civ Archive](https://civarchive.com/models/17997?modelVersionId=21267)

Muapi/the-ratio-narrow-waist-wide-hips

Muapi

2025-08-19T21:16:21Z

0

null

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

null

2025-08-19T21:16:12Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # The Ratio - Narrow Waist : Wide Hips ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: ratio_wh ## 🧠 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:1328309@1499734", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

seraphimzzzz/150876

seraphimzzzz

2025-08-19T21:16:10Z

0

null

[ "region:us" ]

null

2025-08-19T21:15:52Z

[View on Civ Archive](https://civarchive.com/models/175613?modelVersionId=197172)

AnonymousCS/xlmr_immigration_combo3_3

AnonymousCS

2025-08-19T21:15:39Z

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

2025-08-19T21:13:01Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo3_3 results: [] ---  # xlmr_immigration_combo3_3 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: 0.2021 - Accuracy: 0.9422 - 1-f1: 0.9109 - 1-recall: 0.8880 - 1-precision: 0.9350 - Balanced Acc: 0.9286 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.1568 | 1.0 | 25 | 0.1731 | 0.9447 | 0.9155 | 0.8996 | 0.932 | 0.9334 | | 0.064 | 2.0 | 50 | 0.2265 | 0.9422 | 0.9068 | 0.8456 | 0.9777 | 0.9180 | | 0.0524 | 3.0 | 75 | 0.2021 | 0.9422 | 0.9109 | 0.8880 | 0.9350 | 0.9286 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

ultratopaz/108688

ultratopaz

2025-08-19T21:15:31Z

0

null

[ "region:us" ]

null

2025-08-19T21:15:27Z

[View on Civ Archive](https://civarchive.com/models/128002?modelVersionId=146742)

Muapi/flux-graphic-t-shirt-designs

Muapi

2025-08-19T21:15:05Z

0

null

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

null

2025-08-19T21:14:53Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Flux Graphic T-Shirt Designs ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: T-Shirt Art, Graphic T-Shirt, Vector Art, T-Shirt Graphic ## 🧠 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:721090@819874", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

ultratopaz/39810

ultratopaz

2025-08-19T21:14:55Z

0

null

[ "region:us" ]

null

2025-08-19T21:14:51Z

[View on Civ Archive](https://civarchive.com/models/50818?modelVersionId=55334)

Muapi/yfg-aarchy-flux

Muapi

2025-08-19T21:14:48Z

0

null

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

null

2025-08-19T21:14:25Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # YFG Aarchy [Flux] ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: YFG-Aarchy ## 🧠 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:1108935@1245947", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

UnbeT/ppo_ai

UnbeT

2025-08-19T21:14:37Z

0

transformers

[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-19T21:14:07Z

--- 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]

UmeshAkade/gemma3-270m-med-wikidoc-patientinfo-lora

UmeshAkade

2025-08-19T21:14:35Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-270m-it", "base_model:finetune:unsloth/gemma-3-270m-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-19T06:52:17Z

--- base_model: unsloth/gemma-3-270m-it tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** UmeshAkade - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-270m-it This gemma3_text 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)

kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755636377

kojeklollipop

2025-08-19T21:14:30Z

0

null

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

null

2025-08-19T21:14:26Z

--- 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).

seraphimzzzz/218873

seraphimzzzz

2025-08-19T21:14:25Z

0

null

[ "region:us" ]

null

2025-08-19T21:14:07Z

[View on Civ Archive](https://civarchive.com/models/124031?modelVersionId=279464)

Muapi/vintage-movie

Muapi

2025-08-19T21:14:02Z

0

null

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

null

2025-08-19T21:13:35Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Vintage Movie ![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:722757@808139", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/topmodel

Muapi

2025-08-19T21:13:25Z

0

null

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

null

2025-08-19T21:13:18Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # topmodel ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: A stunning woman with a sculpted body, perfectly proportioned curves, and a flawless face. Her skin is radiant, and her facial features are symmetrical and harmonious, highlighting large expressive eyes, full lips, and a captivating smile. She is wearing a casual outfit consisting of a fitted T-shirt and jeans that accentuate her figure. In another scene, she is dressed in elegant lingerie, including a delicate bra and matching panties, showcasing her perfect physique, A breathtaking woman, combining an athletic and well-defined body with a face of classic beauty. Her eyes are piercing, and her hair falls softly around her face, framing her delicate features. She is wearing a sophisticated dress that hugs her curves, enhancing her magnetic presence. In another setting, she is seen in a comfortable casual outfit with a stylish blouse and skirt, and later in luxurious lingerie, featuring a lacy bra and matching underwear, A woman with an impressive physique and an absolutely perfect face, worthy of a work of art. Her skin is smooth and flawless, her eyes shine with a captivating intensity, and her lips are perfectly shaped. She is in a luxurious setting, wearing a stunning evening dress that highlights every detail of her mesmerizing figure. In another scene, she is casually dressed in a fitted T-shirt and shorts, and also appears in intimate lingerie, including a silk bra and matching panties ## 🧠 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:708602@792571", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

seraphimzzzz/33435

seraphimzzzz

2025-08-19T21:13:25Z

0

null

[ "region:us" ]

null

2025-08-19T21:13:21Z

[View on Civ Archive](https://civarchive.com/models/38290?modelVersionId=44242)

ultratopaz/390791

ultratopaz

2025-08-19T21:13:11Z

0

null

[ "region:us" ]

null

2025-08-19T21:13:01Z

[View on Civ Archive](https://civarchive.com/models/423811?modelVersionId=472196)

seraphimzzzz/93586

seraphimzzzz

2025-08-19T21:12:49Z

0

null

[ "region:us" ]

null

2025-08-19T21:12:46Z

[View on Civ Archive](https://civarchive.com/models/118989?modelVersionId=129142)

AnonymousCS/xlmr_immigration_combo3_2

AnonymousCS

2025-08-19T21:12:04Z

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

2025-08-19T21:09:17Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo3_2 results: [] ---  # xlmr_immigration_combo3_2 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: 0.2134 - Accuracy: 0.9344 - 1-f1: 0.8994 - 1-recall: 0.8803 - 1-precision: 0.9194 - Balanced Acc: 0.9209 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.1575 | 1.0 | 25 | 0.1814 | 0.9447 | 0.9142 | 0.8842 | 0.9463 | 0.9296 | | 0.1985 | 2.0 | 50 | 0.2058 | 0.9306 | 0.8958 | 0.8958 | 0.8958 | 0.9219 | | 0.0995 | 3.0 | 75 | 0.2134 | 0.9344 | 0.8994 | 0.8803 | 0.9194 | 0.9209 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

ultratopaz/62716

ultratopaz

2025-08-19T21:12:02Z

0

null

[ "region:us" ]

null

2025-08-19T21:11:59Z

[View on Civ Archive](https://civarchive.com/models/85682?modelVersionId=91116)

ultratopaz/86831

ultratopaz

2025-08-19T21:11:33Z

0

null

[ "region:us" ]

null

2025-08-19T21:11:33Z

[View on Civ Archive](https://civarchive.com/models/111982?modelVersionId=120872)

NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes

NYUAD-ComNets

2025-08-19T21:11:23Z

5

0

transformers

[ "transformers", "safetensors", "mllama", "image-to-text", "text-generation-inference", "unsloth", "en", "arxiv:2412.14197", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-to-text

2025-06-29T19:19:59Z

--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama license: apache-2.0 language: - en --- # Llama3.2-11B based Hate Detection in Arabic MultiModal Memes The rise of social media and online communication platforms has led to the spread of Arabic memes as a key form of digital expression. While these contents can be humorous and informative, they are also increasingly being used to spread offensive language and hate speech. Consequently, there is a growing demand for precise analysis of content in Arabic meme. This work used Llama 3.2 with its vision capability to effectively identify hate content within Arabic memes. The evaluation is conducted using a dataset of Arabic memes proposed in the ArabicNLP MAHED 2025 challenge. The results underscore the capacity of ***Llama 3.2-11B fine-tuned with Arabic memes***, to deliver the superior performance. They achieve **accuracy** of **80.3%** and **macro F1 score** of **73.3%**. The proposed solutions offer a more nuanced understanding of memes for accurate and efficient Arabic content moderation systems. # Examples of Arabic Memes from ArabicNLP MAHED 2025 challenge # Examples | | | | |:-------------------------:|:-------------------------:|:-------------------------:| |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jBuVCt5163WlugFRXkSgq.jpeg"> |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jiPId6f5IiGXxpI898llC.jpeg"> | |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/61acyltUsTB--ZOAMkv0a.jpeg"> |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/_alSRnwG0azE_iYq2BrpP.jpeg"> | ``` python import pandas as pd import os from unsloth import FastVisionModel import torch from datasets import load_dataset from transformers import TextStreamer from PIL import Image import os os.environ["TOKENIZERS_PARALLELISM"] = "false" model_name = "NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes" model, tokenizer = FastVisionModel.from_pretrained(model_name, token='xxxxxxxxxxxxxxxxxxxxxx') FastVisionModel.for_inference(model) dataset_test = load_dataset("QCRI/Prop2Hate-Meme", split = "test") print(dataset_test) def add_labels_column(example): example["labels"] = "no_hate" if example["hate_label"] == 0 else "hate" return example dataset_test = dataset_test.map(add_labels_column) pred=[] for k in range(606): image = dataset_test[k]["image"] text = dataset_test[k]["text"] lab = dataset_test[k]["labels"] messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": text} ]} ] input_text = tokenizer.apply_chat_template(messages,add_generation_prompt = True) inputs = tokenizer( image, input_text, add_special_tokens = False, return_tensors = "pt", ).to("cuda") text_streamer = TextStreamer(tokenizer, skip_prompt = True) p = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128, use_cache = False, temperature = 0.3, min_p = 0.3) p = tokenizer.decode(p[0], skip_special_tokens=True) pred.append(p.split('assistant')[1].strip()) print(pred) ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jRSB8JxqqoV-2E97N5QQM.png) We used Low-Rank Adaptation (LoRA) as the Parameter-Efficient Fine-Tuning (PEFT) method for fine-tuning utilizing the unsloth framework. The hyper-parameters of Llama 3.2-11B are as follows: the training batch size per device is set to 4. gradients are accumulated over 4 steps. the learning rate warm-up lasts for 5 steps. the total number of training steps is 150. the learning rate is set to 0.0002. the optimizer used is 8-bit AdamW weight decay is set to 0.01. a linear learning rate scheduler is used. # BibTeX entry and citation info ``` @misc{aldahoul2024advancingvehicleplaterecognition, title={Detecting Hope, Hate, and Emotion in Arabic Textual Speech and Multi-modal Memes Using Large Language Models}, author={Nouar AlDahoul and Yasir Zaki}, year={2025}, eprint={2412.14197}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.14197}, } ```

seraphimzzzz/343374

seraphimzzzz

2025-08-19T21:11:22Z

0

null

[ "region:us" ]

null

2025-08-19T21:11:14Z

[View on Civ Archive](https://civarchive.com/models/377663?modelVersionId=421726)

chainway9/blockassist-bc-untamed_quick_eel_1755636278

chainway9

2025-08-19T21:11:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T21:11:00Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # 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_1755636335

lisaozill03

2025-08-19T21:10:50Z

0

null

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

null

2025-08-19T21:10:46Z

--- 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).

Muapi/flux-handwriting

Muapi

2025-08-19T21:10:28Z

0

null

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

null

2025-08-19T21:10:21Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # flux-handwriting ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: HWRIT handwriting ## 🧠 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:1037313@1163532", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/gpt-image-1-style-flux

Muapi

2025-08-19T21:10:02Z

0

null

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

null

2025-08-19T21:09:54Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # GPT Image 1 Style [FLUX] ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: aidmagptimage ## 🧠 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:1554812@1759376", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

seraphimzzzz/362297

seraphimzzzz

2025-08-19T21:09:47Z

0

null

[ "region:us" ]

null

2025-08-19T21:09:44Z

[View on Civ Archive](https://civarchive.com/models/47112?modelVersionId=442105)

crystalline7/37706

crystalline7

2025-08-19T21:09:40Z

0

null

[ "region:us" ]

null

2025-08-19T21:09:37Z

[View on Civ Archive](https://civarchive.com/models/47112?modelVersionId=51697)

crystalline7/58446

crystalline7

2025-08-19T21:09:32Z

0

null

[ "region:us" ]

null

2025-08-19T21:09:29Z

[View on Civ Archive](https://civarchive.com/models/80666?modelVersionId=85561)

Muapi/isobel-baldur-s-gate-3-flux-ponyxl-1.5

Muapi

2025-08-19T21:08:38Z

0

null

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

null

2025-08-19T21:08:26Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Isobel - Baldur's Gate 3 [Flux/PonyXL/1.5] ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: isobel, armor ## 🧠 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:416346@780583", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

crystalline7/40828

crystalline7

2025-08-19T21:08:37Z

0

null

[ "region:us" ]

null

2025-08-19T21:08:34Z

[View on Civ Archive](https://civarchive.com/models/52843?modelVersionId=57236)

Muapi/3d-chibi-toy-air-dry-clay-style-flux

Muapi

2025-08-19T21:08:19Z

0

null

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

null

2025-08-19T21:08:08Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # 【3D chibi toy】Air dry clay style - FLUX ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Juaner_clay ## 🧠 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:689231@771373", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

crystalline7/57248

crystalline7

2025-08-19T21:08:15Z

0

null

[ "region:us" ]

null

2025-08-19T21:08:09Z

[View on Civ Archive](https://civarchive.com/models/78918?modelVersionId=83723)

AnonymousCS/xlmr_immigration_combo3_1

AnonymousCS

2025-08-19T21:08:10Z

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

2025-08-19T21:05:22Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo3_1 results: [] ---  # xlmr_immigration_combo3_1 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: 0.2290 - Accuracy: 0.9319 - 1-f1: 0.8894 - 1-recall: 0.8224 - 1-precision: 0.9682 - Balanced Acc: 0.9045 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.1997 | 1.0 | 25 | 0.1769 | 0.9447 | 0.9131 | 0.8726 | 0.9576 | 0.9267 | | 0.1957 | 2.0 | 50 | 0.2013 | 0.9383 | 0.9008 | 0.8417 | 0.9689 | 0.9141 | | 0.1423 | 3.0 | 75 | 0.2290 | 0.9319 | 0.8894 | 0.8224 | 0.9682 | 0.9045 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

seraphimzzzz/33568

seraphimzzzz

2025-08-19T21:08:03Z

0

null

[ "region:us" ]

null

2025-08-19T21:08:00Z

[View on Civ Archive](https://civarchive.com/models/38628?modelVersionId=44548)

Abdullah6395/COT_LLM

Abdullah6395

2025-08-19T21:07:58Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "lora", "template:diffusion-lora", "base_model:LiquidAI/LFM2-350M", "base_model:adapter:LiquidAI/LFM2-350M", "license:other", "region:us" ]

text-to-image

2025-08-19T21:07:53Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - output: url: images/Screenshot from 2025-08-20 01-55-42.png text: None parameters: negative_prompt: None base_model: LiquidAI/LFM2-350M instance_prompt: null license: other license_name: none license_link: LICENSE --- # CAYOTES <Gallery /> ## Model description Model Description (Educational Purpose Only): This is a small-scale LLM developed for learning and experimentation. Initially, the model was distilled from a larger teacher model to reduce size and computation requirements. Subsequently, it was fine-tuned on a chain-of-thought (CoT) dataset. Due to limited resources, training is partial and the model's outputs remain largely random. This model is intended strictly for educational use, research practice, and demonstration purposes. It is not suitable for deployment, commercial applications, or production use. ## Download model [Download](/Abdullah6395/COT_LLM/tree/main) them in the Files & versions tab.

ultratopaz/390765

ultratopaz

2025-08-19T21:07:51Z

0

null

[ "region:us" ]

null

2025-08-19T21:07:40Z

[View on Civ Archive](https://civarchive.com/models/423776?modelVersionId=472159)

Muapi/richard-anderson

Muapi

2025-08-19T21:07:49Z

0

null

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

null

2025-08-19T21:07:35Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Richard Anderson ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Art by Richard Anderson ## 🧠 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:1349128@1523853", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/solo-leveling-style-by-readandsign-ill-flux

Muapi

2025-08-19T21:07:28Z

0

null

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

null

2025-08-19T21:07:21Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Solo Leveling style by Readandsign | ILL |Flux ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: slv50 ## 🧠 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:1258225@1455857", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

ultratopaz/93397

ultratopaz

2025-08-19T21:07:24Z

0

null

[ "region:us" ]

null

2025-08-19T21:07:07Z

[View on Civ Archive](https://civarchive.com/models/118690?modelVersionId=128800)

Muapi/aigis-persona

Muapi

2025-08-19T21:06:49Z

0

null

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

null

2025-08-19T21:05:55Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Aigis - Persona ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Aigis ## 🧠 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:843111@943235", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

andy013567/gemma-3-1b-it-finetuned-wikitext2

andy013567

2025-08-19T21:06:44Z

0

peft

[ "peft", "tensorboard", "safetensors", "base_model:adapter:google/gemma-3-1b-it", "lora", "transformers", "text-generation", "base_model:google/gemma-3-1b-it", "license:gemma", "region:us" ]

text-generation

2025-08-19T10:11:23Z

--- library_name: peft license: gemma base_model: google/gemma-3-1b-it tags: - base_model:adapter:google/gemma-3-1b-it - lora - transformers pipeline_tag: text-generation model-index: - name: gemma-3-1b-it-finetuned-wikitext2 results: [] ---  # gemma-3-1b-it-finetuned-wikitext2 This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0835 ## 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.0041 | 1.0 | 1218 | 3.0835 | ### Framework versions - PEFT 0.17.0 - Transformers 4.55.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

woodwardmw/phase2

woodwardmw

2025-08-19T21:06:32Z

0

transformers

[ "transformers", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2025-08-19T18:48:51Z

--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: phase2 results: [] ---  # phase2 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4544 ## 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: 4 - eval_batch_size: 4 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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 - lr_scheduler_warmup_steps: 50 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:-----:|:---------------:| | 0.5917 | 2.6823 | 1000 | 0.5957 | | 0.5782 | 5.3627 | 2000 | 0.5542 | | 0.5593 | 8.0430 | 3000 | 0.5318 | | 0.4973 | 10.7253 | 4000 | 0.4915 | | 0.4883 | 13.4056 | 5000 | 0.4819 | | 0.4868 | 16.0860 | 6000 | 0.4706 | | 0.5138 | 18.7683 | 7000 | 0.4689 | | 0.4571 | 21.4486 | 8000 | 0.4650 | | 0.4557 | 24.1289 | 9000 | 0.4675 | | 0.5072 | 26.8113 | 10000 | 0.4631 | | 0.492 | 29.4916 | 11000 | 0.4604 | | 0.4535 | 32.1719 | 12000 | 0.4581 | | 0.4668 | 34.8543 | 13000 | 0.4550 | | 0.4825 | 37.5346 | 14000 | 0.4593 | | 0.4551 | 40.2149 | 15000 | 0.4568 | | 0.4285 | 42.8972 | 16000 | 0.4554 | | 0.4383 | 45.5776 | 17000 | 0.4544 | | 0.393 | 48.2579 | 18000 | 0.4529 | | 0.4406 | 50.9402 | 19000 | 0.4570 | | 0.4519 | 53.6206 | 20000 | 0.4544 | ### Framework versions - Transformers 4.55.2 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4

Dejiat/blockassist-bc-savage_unseen_bobcat_1755637530

Dejiat

2025-08-19T21:06:13Z

0

null

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

null

2025-08-19T21:05:56Z

--- 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).

ultratopaz/83659

ultratopaz

2025-08-19T21:06:02Z

0

null

[ "region:us" ]

null

2025-08-19T21:05:59Z

[View on Civ Archive](https://civarchive.com/models/18600?modelVersionId=117093)

ultratopaz/18387

ultratopaz

2025-08-19T21:05:53Z

0

null

[ "region:us" ]

null

2025-08-19T21:05:48Z

[View on Civ Archive](https://civarchive.com/models/18600?modelVersionId=22068)

Muapi/illustrations-cute-cartoon-cute-manga-flux

Muapi

2025-08-19T21:05:33Z

0

null

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

null

2025-08-19T21:05:09Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # 【Illustrations】cute cartoon cute manga FLUX ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Juaner_cartoon ## 🧠 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:681642@762939", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

crystalline7/239862

crystalline7

2025-08-19T21:05:31Z

0

null

[ "region:us" ]

null

2025-08-19T21:05:23Z

[View on Civ Archive](https://civarchive.com/models/124035?modelVersionId=303862)

AnonymousCS/xlmr_immigration_combo3_0

AnonymousCS

2025-08-19T21:04:57Z

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

2025-08-19T21:01:36Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo3_0 results: [] ---  # xlmr_immigration_combo3_0 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: 0.2386 - Accuracy: 0.9229 - 1-f1: 0.8780 - 1-recall: 0.8340 - 1-precision: 0.9270 - Balanced Acc: 0.9006 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.2836 | 1.0 | 25 | 0.2080 | 0.9293 | 0.8911 | 0.8687 | 0.9146 | 0.9141 | | 0.1912 | 2.0 | 50 | 0.2152 | 0.9357 | 0.8980 | 0.8494 | 0.9524 | 0.9141 | | 0.1954 | 3.0 | 75 | 0.2386 | 0.9229 | 0.8780 | 0.8340 | 0.9270 | 0.9006 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

ultratopaz/638392

ultratopaz

2025-08-19T21:04:52Z

0

null

[ "region:us" ]

null

2025-08-19T21:04:44Z

[View on Civ Archive](https://civarchive.com/models/646957?modelVersionId=723774)

matboz/ring-gemma-3

matboz

2025-08-19T21:04:28Z

0

peft

[ "peft", "safetensors", "base_model:adapter:google/gemma-3-27b-it", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:google/gemma-3-27b-it", "region:us" ]

text-generation

2025-08-19T21:04:07Z

--- base_model: google/gemma-3-27b-it library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:google/gemma-3-27b-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

crystalline7/20731

crystalline7

2025-08-19T21:04:19Z

0

null

[ "region:us" ]

null

2025-08-19T21:04:14Z

[View on Civ Archive](https://civarchive.com/models/21003?modelVersionId=24998)

ultratopaz/48777

ultratopaz

2025-08-19T21:04:08Z

0

null

[ "region:us" ]

null

2025-08-19T21:04:04Z

[View on Civ Archive](https://civarchive.com/models/65245?modelVersionId=69869)

ultratopaz/42969

ultratopaz

2025-08-19T21:04:00Z

0

null

[ "region:us" ]

null

2025-08-19T21:03:57Z

[View on Civ Archive](https://civarchive.com/models/56314?modelVersionId=60719)

crystalline7/75403

crystalline7

2025-08-19T21:03:44Z

0

null

[ "region:us" ]

null

2025-08-19T21:03:39Z

[View on Civ Archive](https://civarchive.com/models/71861?modelVersionId=107072)

crystalline7/49268

crystalline7

2025-08-19T21:03:31Z

0

null

[ "region:us" ]

null

2025-08-19T21:03:31Z

[View on Civ Archive](https://civarchive.com/models/66057?modelVersionId=70701)

ultratopaz/39813

ultratopaz

2025-08-19T21:03:25Z

0

null

[ "region:us" ]

null

2025-08-19T21:03:22Z

[View on Civ Archive](https://civarchive.com/models/50821?modelVersionId=55337)

saberbx/GraniteSentry

saberbx

2025-08-19T21:03:19Z

11

0

transformers

[ "transformers", "safetensors", "granite", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-08-06T04:58:17Z

--- 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]

crystalline7/634081

crystalline7

2025-08-19T21:02:56Z

0

null

[ "region:us" ]

null

2025-08-19T21:02:52Z

[View on Civ Archive](https://civarchive.com/models/210536?modelVersionId=719459)

ultratopaz/81964

ultratopaz

2025-08-19T21:02:24Z

0

null

[ "region:us" ]

null

2025-08-19T21:02:22Z

[View on Civ Archive](https://civarchive.com/models/107074?modelVersionId=115110)

ultratopaz/85547

ultratopaz

2025-08-19T21:02:16Z

0

null

[ "region:us" ]

null

2025-08-19T21:02:16Z

[View on Civ Archive](https://civarchive.com/models/110723?modelVersionId=119386)

seraphimzzzz/19648

seraphimzzzz

2025-08-19T21:02:04Z

0

null

[ "region:us" ]

null

2025-08-19T21:01:58Z

[View on Civ Archive](https://civarchive.com/models/19939?modelVersionId=23677)

UnbeT/ppo_400

UnbeT

2025-08-19T21:01:34Z

0

transformers

[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-19T20:47:14Z

--- 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]

ultratopaz/77712

ultratopaz

2025-08-19T21:01:31Z

0

null

[ "region:us" ]

null

2025-08-19T21:01:28Z

[View on Civ Archive](https://civarchive.com/models/102801?modelVersionId=110019)

VIDEOS-18-vietnamese-viral-video-Clip-hq/Original.New.full.videos.vietnamese.Viral.Video.Official.Tutorial

VIDEOS-18-vietnamese-viral-video-Clip-hq

2025-08-19T21:00:59Z

0

null

[ "region:us" ]

null

2025-08-19T21:00:34Z

Muapi/moreface-lora

Muapi

2025-08-19T21:00:26Z

0

null

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

null

2025-08-19T21:00:11Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # MoreFace-lora ![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:866492@969610", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/wizard-s-vintage-minimalism-cartoon

Muapi

2025-08-19T20:59:47Z

0

null

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

null

2025-08-19T20:59:38Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Wizard's Vintage Minimalism Cartoon ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: vintage minimalism cartoon, 2Tone_CRTN ## 🧠 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:1108391@1245363", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755635345

ihsanridzi

2025-08-19T20:55:43Z

0

null

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

null

2025-08-19T20:55:40Z

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

New-original-archita-phukan-viral-video-on/New.full.videos.archita.Phukan.Viral.Video.Official.Tutorial

New-original-archita-phukan-viral-video-on

2025-08-19T20:55:16Z

0

null

[ "region:us" ]

null

2025-08-19T20:55:08Z

mradermacher/ege-8b-1.1-i1-GGUF

mradermacher

2025-08-19T20:54:54Z

0

transformers

[ "transformers", "gguf", "trl", "sft", "unsloth", "tr", "dataset:orkungedik/function_call", "base_model:orkungedik/ege-8b-1.1", "base_model:quantized:orkungedik/ege-8b-1.1", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2025-08-19T19:58:06Z

--- base_model: orkungedik/ege-8b-1.1 datasets: - orkungedik/function_call language: - tr library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - trl - sft - unsloth --- ## About         weighted/imatrix quants of https://huggingface.co/orkungedik/ege-8b-1.1  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ege-8b-1.1-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/ege-8b-1.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ1_S.gguf) | i1-IQ1_S | 2.2 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ1_M.gguf) | i1-IQ1_M | 2.4 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ2_S.gguf) | i1-IQ2_S | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ2_M.gguf) | i1-IQ2_M | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.2 | very low quality | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q2_K.gguf) | i1-Q2_K | 3.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.9 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ3_S.gguf) | i1-IQ3_S | 3.9 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ3_M.gguf) | i1-IQ3_M | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.2 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.5 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q4_0.gguf) | i1-Q4_0 | 4.9 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.9 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.9 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q4_1.gguf) | i1-Q4_1 | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF/resolve/main/ege-8b-1.1.i1-Q6_K.gguf) | i1-Q6_K | 6.8 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

Leoar/blockassist-bc-pudgy_toothy_cheetah_1755636686

Leoar

2025-08-19T20:53:25Z

0

null

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

null

2025-08-19T20:53:16Z

--- 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).

sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755635203

sampingkaca72

2025-08-19T20:51:46Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:51:42Z

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

Muapi/flux.1-dev-cctv-mania

Muapi

2025-08-19T20:51:46Z

0

null

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

null

2025-08-19T20:51:36Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # FLUX.1 DEV - CCTV Mania ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: CCTV Footage ## 🧠 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:684810@766464", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

roeker/blockassist-bc-quick_wiry_owl_1755636624

roeker

2025-08-19T20:51:21Z

0

null

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

null

2025-08-19T20:51:11Z

--- 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).

AnonymousCS/xlmr_immigration_combo2_3

AnonymousCS

2025-08-19T20:50:06Z

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

2025-08-19T20:47:12Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo2_3 results: [] ---  # xlmr_immigration_combo2_3 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: 0.2533 - Accuracy: 0.9396 - 1-f1: 0.9058 - 1-recall: 0.8726 - 1-precision: 0.9417 - Balanced Acc: 0.9228 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.1208 | 1.0 | 25 | 0.1931 | 0.9447 | 0.9138 | 0.8803 | 0.95 | 0.9286 | | 0.0845 | 2.0 | 50 | 0.2122 | 0.9434 | 0.9124 | 0.8842 | 0.9424 | 0.9286 | | 0.1345 | 3.0 | 75 | 0.2533 | 0.9396 | 0.9058 | 0.8726 | 0.9417 | 0.9228 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

Dombili2038/blockassist-bc-jumping_beaked_hamster_1755636537

Dombili2038

2025-08-19T20:49:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "jumping beaked hamster", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T20:49:25Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - jumping beaked hamster --- # 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_1755636372

Dejiat

2025-08-19T20:47:08Z

0

null

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

null

2025-08-19T20:46:51Z

--- 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).

roeker/blockassist-bc-quick_wiry_owl_1755636206

roeker

2025-08-19T20:44:47Z

0

null

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

null

2025-08-19T20:44:10Z

--- 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).

thanobidex/blockassist-bc-colorful_shiny_hare_1755634665

thanobidex

2025-08-19T20:43:59Z

0

null

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

null

2025-08-19T20:43:56Z

--- 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).

TikToker-Abigail-Lalama-intimo-video/viral.ver.filtrado.video.de.abigail.lalama.y.snayder.influencer.se.hace.viral

TikToker-Abigail-Lalama-intimo-video

2025-08-19T20:43:54Z

0

null

[ "region:us" ]

null

2025-08-19T20:43:12Z

<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>

mradermacher/ege-8b-1.1-GGUF

mradermacher

2025-08-19T20:43:18Z

0

transformers

[ "transformers", "gguf", "trl", "sft", "unsloth", "tr", "dataset:orkungedik/function_call", "base_model:orkungedik/ege-8b-1.1", "base_model:quantized:orkungedik/ege-8b-1.1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-19T15:03:51Z

--- base_model: orkungedik/ege-8b-1.1 datasets: - orkungedik/function_call language: - tr library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - trl - sft - unsloth --- ## About         static quants of https://huggingface.co/orkungedik/ege-8b-1.1  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ege-8b-1.1-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q2_K.gguf) | Q2_K | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_S.gguf) | Q3_K_S | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_L.gguf) | Q3_K_L | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.IQ4_XS.gguf) | IQ4_XS | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q5_K_S.gguf) | Q5_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q5_K_M.gguf) | Q5_K_M | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q6_K.gguf) | Q6_K | 6.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.f16.gguf) | f16 | 16.5 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

nice2mitya/a_133421939

nice2mitya

2025-08-19T20:40:50Z

0

null

[ "license:other", "region:us" ]

null

2025-08-19T20:13:48Z

--- 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 ---

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755634468

lisaozill03

2025-08-19T20:39:00Z

0

null

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

null

2025-08-19T20:38:56Z

--- 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).