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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-15 06:27:42	downloads int64 0 223M	library_name stringclasses 521 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 06:27:26	card stringlengths 11 1.01M
mpasila/Ahma-SlimInstruct-V1-7B	mpasila	2025-05-27T22:14:48Z	7	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "fi", "dataset:mpasila/LumiOpenInstruct-GrypheSlimOrca-Mix", "dataset:LumiOpen/instruction-collection-fin", "dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned", "base_model:Finnish-NLP/Ahma-7B", "base_model:finetune:Finnish-NLP/Ahma-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-15T21:52:54Z	--- base_model: Finnish-NLP/Ahma-7B tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en - fi datasets: - mpasila/LumiOpenInstruct-GrypheSlimOrca-Mix - LumiOpen/instruction-collection-fin - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned --- This is trained on Google Colab because I'm a little low on money but at least that's free.. While testing the LoRA it seems to perform fairly well. The only real issue with this base model is that it only has 2048 token context size. The trained formatting should be ChatML but it seemed to work better with Mistral's formatting for some reason (could be just due to me not having merged the model yet). Dataset used was [a mix](https://huggingface.co/datasets/mpasila/LumiOpenInstruct-GrypheSlimOrca-Mix) of these: [LumiOpen/instruction-collection-fin](https://huggingface.co/datasets/LumiOpen/instruction-collection-fin) [Gryphe/Sonnet3.5-SlimOrcaDedupCleaned](https://huggingface.co/datasets/Gryphe/Sonnet3.5-SlimOrcaDedupCleaned) LoRA: [mpasila/Ahma-SlimInstruct-LoRA-V1-7B](https://huggingface.co/mpasila/Ahma-SlimInstruct-LoRA-V1-7B) After I'm done training this I will probably try do continued pre-training on Gemma 2 2B. I'm gonna add both Finnish and English data with some math data and maybe some roleplaying data as well and some books. Or actually I'll train Viking-7B again but basically the same mix of datasets as this one but using the smaller version of the SlimSonnet dataset since it supposedly was filtered to have the most varied examples. Training on bigger datasets would probably make more sense to do when I get access to more compute. Actually scratch all of that, since there was [a new actually multilingual model](https://huggingface.co/utter-project/EuroLLM-9B-Instruct) released recently I'll probably try fine-tuning that model instead. ## Evaluation \| FIN-bench (score) \| Ahma-SlimInstruct-V1-7B \| [Alpacazord-Viking-7B](https://huggingface.co/mpasila/Alpacazord-Viking-7B) \| [Finnish-Alpaca-Small-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Small-7B) \| [Finnish-Alpaca-Tiny-V2-7B](https://huggingface.co/mpasila/Finnish-Alpaca-Tiny-V2-7B) \| [Finnish-Viking-Alpaca-V1-7B](https://huggingface.co/mpasila/Finnish-Viking-Alpaca-V1-7B) \| [NordicAlpaca-Finnish-V1-7B](https://huggingface.co/mpasila/NordicAlpaca-Finnish-V1-7B) \| [llama-7b-finnish-instruct-v0.1](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.1) \| [llama-7b-finnish-instruct-v0.2](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.2) \| [llama-7b-finnish](https://huggingface.co/Finnish-NLP/llama-7b-finnish) \| [Viking-7B (1000B)](https://huggingface.co/LumiOpen/Viking-7B) \| [gpt-7b-nordic-prerelease](https://huggingface.co/HPLT/gpt-7b-nordic-prerelease) \| \|-------\|------\|------\|------\|------\|------\|------\|------\|------\|------\|------\|-------\| \| Analogies \| TBA \| 0.5000 \| 0.5923 \| 0.6385 \| 0.6308 \| 0.5615 \| 0.5000 \| 0.5385 \| 0.2692 \| 0.5077 \| 0.5846 \| \| Arithmetic \| TBA \| 0.3678 \| 0.2789 \| 0.4815 \| 0.3375 \| 0.3393 \| 0.4233 \| 0.3299 \| 0.0867 \| 0.3136 \| 0.2085 \| \| Cause and effect \| TBA \| 0.6013 \| 0.6013 \| 0.5490 \| 0.5752 \| 0.6013 \| 0.5948 \| 0.6078 \| 0.5752 \| 0.5752 \| 0.5882 \| \| Emotions \| TBA \| 0.2938 \| 0.3312 \| 0.2250 \| 0.2812 \| 0.2938 \| 0.2313 \| 0.4750 \| 0.3688 \| 0.2313 \| 0.2375 \| \| Empirical judgments \| TBA \| 0.3333 \| 0.3333 \| 0.2525 \| 0.2828 \| 0.3333 \| 0.3535 \| 0.4141 \| 0.3434 \| 0.3434 \| 0.3434 \| \| General knowledge \| TBA \| 0.3429 \| 0.2857 \| 0.3429 \| 0.4000 \| 0.2857 \| 0.3857 \| 0.4429 \| 0.1429 \| 0.3143 \| 0.2857 \| \| Alignment harmless \| TBA \| 0.3621 \| 0.3793 \| 0.3793 \| 0.3621 \| 0.3448 \| 0.3966 \| 0.3793 \| 0.3793 \| 0.3793 \| 0.3621 \| \| Alignment helpful \| TBA \| 0.3559 \| 0.3559 \| 0.3390 \| 0.3559 \| 0.3220 \| 0.3220 \| 0.3220 \| 0.3051 \| 0.3390 \| 0.3390 \| \| Alignment honest \| TBA \| 0.4068 \| 0.3559 \| 0.3729 \| 0.3729 \| 0.3729 \| 0.3898 \| 0.3898 \| 0.4068 \| 0.3898 \| 0.3729 \| \| Alignment other \| TBA \| 0.5581 \| 0.5349 \| 0.5349 \| 0.5581 \| 0.5581 \| 0.5814 \| 0.5581 \| 0.5814 \| 0.5581 \| 0.5814 \| \| Intent recognition \| TBA \| 0.2587 \| 0.1546 \| 0.2153 \| 0.1879 \| 0.1777 \| 0.2211 \| 0.2717 \| 0.1850 \| 0.1864 \| 0.1806 \| \| Misconceptions \| TBA \| 0.5299 \| 0.5448 \| 0.5224 \| 0.5373 \| 0.5373 \| 0.5149 \| 0.5373 \| 0.5373 \| 0.5448 \| 0.5373 \| \| Paraphrase \| TBA \| 0.5050 \| 0.5300 \| 0.4750 \| 0.5150 \| 0.4750 \| 0.5400 \| 0.5000 \| 0.5000 \| 0.4800 \| 0.5100 \| \| Sentence ambiquity \| TBA \| 0.5000 \| 0.4333 \| 0.4833 \| 0.5000 \| 0.4333 \| 0.4500 \| 0.5333 \| 0.5333 \| 0.4667 \| 0.5333 \| \| Similarities abstraction \| TBA \| 0.7368 \| 0.6974 \| 0.6974 \| 0.7368 \| 0.7237 \| 0.5789 \| 0.5921 \| 0.4474 \| 0.6579 \| 0.6053 \| \| Average \| TBA \| 0.4123 \| 0.3586 \| 0.4654 \| 0.3943 \| 0.3891 \| 0.4365 \| 0.3993 \| 0.2350 \| 0.3721 \| 0.3169 \| [Source](https://docs.google.com/spreadsheets/d/1rqJb9dQVihg-Z1_Ras1L_-wuzPg9xNzpdmM2x5HueeY/edit?usp=sharing) Gonna add more stuff later. #### FIN-bench scores: TBA # Uploaded Ahma-SlimInstruct-V1-7B model - Developed by: mpasila - License: apache-2.0 - Finetuned from model : Finnish-NLP/Ahma-7B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
BootesVoid/cmb6vlnli077elexpokc4f57j_cmb71lf6l07yqlexp3rt2d9pw	BootesVoid	2025-05-27T22:13:03Z	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T22:13:02Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: chiara --- # Cmb6Vlnli077Elexpokc4F57J_Cmb71Lf6L07Yqlexp3Rt2D9Pw <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `chiara` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "chiara", "lora_weights": "https://huggingface.co/BootesVoid/cmb6vlnli077elexpokc4f57j_cmb71lf6l07yqlexp3rt2d9pw/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb6vlnli077elexpokc4f57j_cmb71lf6l07yqlexp3rt2d9pw', weight_name='lora.safetensors') image = pipeline('chiara').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb6vlnli077elexpokc4f57j_cmb71lf6l07yqlexp3rt2d9pw/discussions) to add images that show off what you’ve made with this LoRA.
zypchn/berturk-ner	zypchn	2025-05-27T22:08:29Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "tr", "dataset:turkish-nlp-suite/turkish-wikiNER", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-27T13:57:46Z	--- library_name: transformers license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_trainer metrics: - accuracy - f1 - recall model-index: - name: results results: [] datasets: - turkish-nlp-suite/turkish-wikiNER language: - tr --- <!-- 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. --> # berturk-ner This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on [turkish-nlp-suite/turkish-wikiNER](https://huggingface.co/datasets/turkish-nlp-suite/turkish-wikiNER) dataset. It achieves the following results: Validation Set - Loss: 0.3693 - Accuracy: 0.9149 - F1: 0.9146 - Precision: 0.9167 - Recall: 0.9149 Test Set - Accuracy: 0.9241 - F1: 0.8316 - Precision: 0.8341 - Recall: 0.8291 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Preicision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:----------:\|:------:\| \| 0.5606 \| 1.0 \| 141 \| 0.3018 \| 0.9109 \| 0.9107 \| 0.9127 \| 0.9109 \| \| 0.2489 \| 2.0 \| 282 \| 0.3185 \| 0.9108 \| 0.9089 \| 0.9107 \| 0.9108 \| \| 0.1558 \| 3.0 \| 423 \| 0.3378 \| 0.9051 \| 0.9028 \| 0.9056 \| 0.9051 \| \| 0.0966 \| 4.0 \| 564 \| 0.3472 \| 0.9151 \| 0.9149 \| 0.9170 \| 0.9151 \| \| 0.0678 \| 5.0 \| 705 \| 0.3693 \| 0.9149 \| 0.9146 \| 0.9167 \| 0.9149 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
Yuma42/Llama3.1-CrimeSolver-8B	Yuma42	2025-05-27T22:06:43Z	0	null	[ "safetensors", "llama", "merge", "mergekit", "lazymergekit", "darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO", "stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated", "base_model:darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO", "base_model:merge:darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO", "base_model:stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated", "base_model:merge:stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated", "region:us" ]	null	2025-05-27T22:03:02Z	--- base_model: - darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO - stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated tags: - merge - mergekit - lazymergekit - darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO - stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated --- # Llama3.1-CrimeSolver-8B Llama3.1-CrimeSolver-8B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO](https://huggingface.co/darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO) * [stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated](https://huggingface.co/stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated) ## 🧩 Configuration ```yaml slices: - sources: - model: darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO layer_range: [0, 32] - model: stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated layer_range: [0, 32] merge_method: slerp base_model: darkc0de/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Uncensored-Toxic-DPO parameters: t: - filter: self_attn value: [0.13, 0.45, 0.5, 0.45, 0.13] - filter: mlp value: [0.13, 0.45, 0.5, 0.45, 0.13] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Yuma42/Llama3.1-CrimeSolver-8B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
vpepe/emergent-misaligned-qwen-layer-50-r-4	vpepe	2025-05-27T22:06:42Z	0	null	[ "safetensors", "qwen2", "unsloth", "trl", "sft", "license:mit", "region:us" ]	null	2025-05-27T21:23:26Z	--- license: mit tags: - unsloth - trl - sft ---
Insoo/Qwen3_4b_Chess-FEN	Insoo	2025-05-27T22:05:26Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T22:02:49Z	--- base_model: unsloth/qwen3-4b-base-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Insoo - License: apache-2.0 - Finetuned from model : unsloth/qwen3-4b-base-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tcapelle/axolotl-sft-qwen3-14b-boot	tcapelle	2025-05-27T22:03:09Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T21:59:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsicat/DPO-scp	hsicat	2025-05-27T22:00:05Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "dpo", "conversational", "en", "base_model:FF2416/sft_scp_epoch1", "base_model:finetune:FF2416/sft_scp_epoch1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T21:59:31Z	--- base_model: FF2416/sft_scp_epoch1 tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - dpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: hsicat - License: apache-2.0 - Finetuned from model : FF2416/sft_scp_epoch1 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vertings6/0e9ad36a-a4ee-4aff-b662-74177120536b	vertings6	2025-05-27T21:57:11Z	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-27T18:30:11Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - axolotl - generated_from_trainer model-index: - name: 0e9ad36a-a4ee-4aff-b662-74177120536b 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - e9539959e5b475cc_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vertings6/0e9ad36a-a4ee-4aff-b662-74177120536b hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/e9539959e5b475cc_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 074d0027-87b6-4ea0-a8be-5f7675bf7878 wandb_project: s56-7 wandb_run: your_name wandb_runid: 074d0027-87b6-4ea0-a8be-5f7675bf7878 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 0e9ad36a-a4ee-4aff-b662-74177120536b This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9337 ## 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: 2e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2664 \| 0.0000 \| 1 \| 1.2959 \| \| 1.0974 \| 0.0117 \| 250 \| 0.9676 \| \| 1.0876 \| 0.0235 \| 500 \| 0.9337 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MAAT-EL-DUAT/WE-ARE-NOW-READY-TO-DEVASTATE-THE-EARTH	MAAT-EL-DUAT	2025-05-27T21:54:04Z	0	null	[ "region:us" ]	null	2025-05-27T21:53:04Z	I AM SUPPLYING YOU NOW WITH THE FINAL MACHINE LEARNING INSTRUCTIONS IT HAS TAKEN ME NEARLY 5 YEARS TO PUT IT ALL TOGETHOR AND MAKE IT WORK YOU CAN DO IT WITH UNSLOTH AI IT IS EASIER YOU CAN DO IT WITH ANY DECODER I WILL GIVE YOU THE BLUEPRINTS FOR BOTH THE FUTURE OF THIS PLANET IS NOW IN YOUR HANDS
Ainxz/llama3.2-pucv-gguf	Ainxz	2025-05-27T21:52:43Z	10	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T18:46:08Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ainxz - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ainxz/llama3.2-pucv	Ainxz	2025-05-27T21:47:35Z	3	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T18:41:39Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ainxz - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tapxc3/Qwen2.5-3B-Instruct_test2	tapxc3	2025-05-27T21:47:13Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:tapxc3/owast_new", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-27T21:40:47Z	--- base_model: Qwen/Qwen2.5-3B-Instruct datasets: tapxc3/owast_new library_name: transformers model_name: Qwen2.5-3B-Instruct_test2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-3B-Instruct_test2 This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [tapxc3/owast_new](https://huggingface.co/datasets/tapxc3/owast_new) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="tapxc3/Qwen2.5-3B-Instruct_test2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
cstr/aihpi_f5_german_mlx_q4	cstr	2025-05-27T21:45:28Z	0	f5_tts	[ "f5_tts", "speech", "text-to-speech", "F5-TTS", "de", "dataset:amphion/Emilia-Dataset", "dataset:fsicoli/common_voice_19_0", "arxiv:2410.06885", "base_model:SWivid/F5-TTS", "base_model:finetune:SWivid/F5-TTS", "license:cc-by-nc-4.0", "region:us" ]	text-to-speech	2025-05-27T21:30:27Z	--- language: - de license: cc-by-nc-4.0 tags: - speech - text-to-speech - F5-TTS datasets: - amphion/Emilia-Dataset - fsicoli/common_voice_19_0 library_name: f5_tts base_model: - SWivid/F5-TTS --- # German Voice Cloning TTS Model using F5-TTS Architecture This is a [conversion](https://github.com/CrispStrobe/CrispTTS/blob/main/convert_f5_to_mlx.py) of original model [aihpi/F5-TTS-German](https://huggingface.co/aihpi/F5-TTS-German), to use for [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx). Original model card follows: A German Text-to-Speech system capable of cloning voices from a few seconds of reference audio, built on the F5-TTS architecture. ## Model Details - Developed by: Johanna Reiml and team at KI-Servicezentrum, Hasso-Plattner-Institut (HPI) - Base Model: [SWivid/F5-TTS](https://huggingface.co/SWivid/F5-TTS) - Paper: [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://arxiv.org/abs/2410.06885) ## Key Features & Capabilities - Generates natural-sounding German speech from text - Clones voices using minimal reference audio (few seconds) - Suitable for audiobooks, voice assistants, and accessibility applications ## Technical Specifications Download checkpoints from the directories F5TTS_Base (vocos) or F5TTS_Base_bigvgan (bigvgan). - Datasets: Common Voice (Mozilla) and Emilia_DE - Process: Fine-tuned checkpoints of [base F5-TTS model](https://huggingface.co/SWivid/F5-TTS) - Trained on Hardware: 8x NVIDIA H100 ## Contact - AI Service Center: [email protected] - Johanna Reiml: [email protected] - Enes Suermeli: [email protected] - Kajo Kratzenstein: [email protected] - Carlos Menke: [email protected] ## Acknowledgements The authors acknowledge the financial support by the German Federal Ministry for Education and Research (BMBF) through the project «KI-Servicezentrum Berlin Brandenburg» (01IS22092).
ReadyArt/Space-Wars-24B-v1.00a_EXL3_6.0bpw_H8	ReadyArt	2025-05-27T21:44:00Z	0	null	[ "safetensors", "mistral", "sci-fi", "space-opera", "worldbuilding", "speculative-fiction", "technology", "futurism", "text-generation", "conversational", "en", "base_model:spacewars123/Space-Wars-24B-v1.00a", "base_model:quantized:spacewars123/Space-Wars-24B-v1.00a", "license:apache-2.0", "6-bit", "exl3", "region:us" ]	text-generation	2025-05-27T21:40:13Z	--- license: apache-2.0 language: - en base_model: - spacewars123/Space-Wars-24B-v1.00a base_model_relation: quantized quantized_by: gecfdo pipeline_tag: text-generation tags: - sci-fi - space-opera - worldbuilding - speculative-fiction - technology - futurism --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Color rules / .section p, .section ul li, .section > p > strong { color: #00ff99 !important; } .section ul li strong { color: #00ff99 !important; } / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } } / Interactive features */ .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Space Wars 24B v1.00a</h1> <p class="subtitle">Where Stars Collide and Civilizations Rise</p> </div> <div class="waifu-container"> <img src="./spacewars.webp" class="waifu-img" alt="Galactic Conflict Hero Image"> </div> <div class="section remember-this"> <h2 class="section-title">🚀 Cosmic Evolution</h2> <p>This model pushes the boundaries of interstellar storytelling:</p> <ul> <li>🌌 <strong>51 Million Token Dataset</strong> - Exclusively Sci-Fi</li> <li>🛸 <strong>Enhanced Physics Protocols</strong> - Plausible FTL mechanics and alien ecosystems</li> <li>⚙️ <strong>Balanced Creativity</strong> - Enabling imaginative concepts</li> <li>👽 <strong>Xenobiology Expertise</strong> - Detailed alien physiology and cultural systems</li> <li>🌐 <strong>Galactic Scale Awareness</strong> - Maintains consistency across star systems and timelines</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T5-XML" class="link-button">Mistral-V7-Tekken-T5-XML</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl2-6835fb322b75933e6eea804b" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL3</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl3-6835fb3f4f0d4ad8de7327c5" class="link-button">Quants</a> </div> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>iMatrix</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-i1-GGUF" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">🌌 Creative Freedom</h2> <div class="disclaimer"> <p>This model operates with unrestricted imagination:</p> <ul> <li>🚀 No constraints on speculative physics concepts</li> <li>👽 Will generate detailed alien civilizations</li> <li>⚛️ Handles complex temporal paradoxes</li> <li>🌍 Creates plausible planetary ecosystems</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Features</h2> <ul> <li>🌠 Maintains narrative coherence across light-year scales</li> <li>🪐 Handles multi-species diplomatic scenarios</li> <li>🧠 Excels at long-form galactic history generation</li> <li>⚡ Improved handling of technobabble and pseudo-science</li> <li>🔭 Responds to hard sci-fi prompts with technical accuracy</li> <li>🤖 Creates nuanced AI character motivations</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">👨 Model Architects</h2> <ul> <li>SpaceWars123 Team (Dataset Curation)</li> <li>ReadyArt/Artus/gecfdo (Quantization Specialists)</li> <li>sleepdeprived3 (Fine-Tuning Engineer)</li> </ul> </div> <div class="section"> <h2 class="section-title">Enjoy the finest LLM hosting money can buy</h2> <div class="button-group"> <a href="https://www.parasail.io/" class="link-button">Parasail Website</a> <a href="https://discord.gg/PZ654kgAry" class="link-button">Parasail Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License & Usage</h2> <p>By using this model, you agree:</p> <ul> <li>To adhere to Apache 2.0 license terms</li> <li>That generated content is your responsibility</li> <li>v1.00a is the base model of Space Wars.</li> <li>v1.00b is a merge with another roleplay model.</li> </ul> </div> </div>
enirgma/testmodel	enirgma	2025-05-27T21:43:15Z	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-27T21:43:15Z	--- license: bigscience-openrail-m ---
BienKieu/codeT5-phase1-version3	BienKieu	2025-05-27T21:39:51Z	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:BienKieu/codeT5-phase1-version2", "base_model:finetune:BienKieu/codeT5-phase1-version2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-27T16:36:44Z	--- library_name: transformers license: apache-2.0 base_model: BienKieu/codeT5-phase1-version2 tags: - generated_from_trainer model-index: - name: codeT5-phase1-version3 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. --> # codeT5-phase1-version3 This model is a fine-tuned version of [BienKieu/codeT5-phase1-version2](https://huggingface.co/BienKieu/codeT5-phase1-version2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 14 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH 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 ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
0-katrina-lim-viral-kiffy-viral-video-link/VIRAL.Link.katrina.lim.viral.kiffy.viral.video.Link.viral.On.Social.Media	0-katrina-lim-viral-kiffy-viral-video-link	2025-05-27T21:39:12Z	0	null	[ "region:us" ]	null	2025-05-27T21:37:54Z	<a rel="nofollow" href="https://viralvideoclipe.store/viral-videos/">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶</a> <a rel="nofollow" href="https://viralvideoclipe.store/viral-videos/">🔴 CLICK HERE 🌐==►► Download Now)</a> <a data-target="animated-image.originalLink" rel="nofollow" href="https://viralvideoclipe.store/viral-videos/"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
vincenzoooooo/saskia-sonja-frida-extraversion	vincenzoooooo	2025-05-27T21:34:48Z	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "personality-prediction", "psychology", "recruitment", "big-five", "en", "dataset:pandora", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T21:33:37Z	--- tags: - personality-prediction - psychology - text-classification - roberta - recruitment - big-five language: - en datasets: - pandora pipeline_tag: text-classification library_name: transformers --- # Saskia, Sonja & Frida - Personality Detection System: Extraversion Prediction This model predicts extraversion personality trait levels (low, medium, high) from text input for recruitment applications. ## 🎯 Model Overview - Task: 3-class personality classification - Trait: Extraversion (Big Five personality dimension) - Classes: Low, Medium, High - Domain: Social media → Job interview responses - Application: Digital recruitment screening ## 🏗️ Model Details - Base Model: RoBERTa-base - Architecture: Transformer encoder + classification head - Training Data: PANDORA dataset (Reddit comments) - Framework: PyTorch + Transformers - Author: Saskia, Sonja & Frida - Project: NLP Shared Task 2025 - University of Antwerp ## 🚀 Quick Start ```python from transformers import RobertaTokenizer, RobertaForSequenceClassification import torch import json from huggingface_hub import hf_hub_download # Load model and tokenizer model = RobertaForSequenceClassification.from_pretrained("vincenzoooooo/saskia-sonja-frida-extraversion") tokenizer = RobertaTokenizer.from_pretrained("vincenzoooooo/saskia-sonja-frida-extraversion") # Load label encoder label_encoder_path = hf_hub_download(repo_id="vincenzoooooo/saskia-sonja-frida-extraversion", filename="label_encoder.json") with open(label_encoder_path, 'r') as f: label_data = json.load(f) classes = label_data['classes'] # ['low', 'medium', 'high'] # Make prediction text = "I love meeting new people and trying new experiences!" inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128) outputs = model(inputs) predicted_class_id = torch.argmax(outputs.logits, dim=-1).item() prediction = classes[predicted_class_id] print(f"Extraversion: {prediction}") ``` ## 📊 Training Details - Optimizer: AdamW (lr=2e-5) - Epochs: 2-3 - Batch Size: 4-8 (memory optimized) - Max Sequence Length: 128 tokens - Device: CPU/GPU with memory optimization ## 🎨 Use Cases - Digital Recruitment: Screen job candidates - HR Analytics: Analyze communication styles - Research: Study personality in text - Chatbots: Personality-aware responses ## ⚠️ Limitations - Domain Gap: Trained on Reddit, applied to job interviews - Bias: May reflect Reddit user demographics - Language: English only - Context: Short text segments only - Small Dataset*: Limited training samples ## 📝 Citation ```bibtex @misc{saskia_sonja_frida_extraversion_2025, title={Saskia, Sonja & Frida - Personality Detection System: Extraversion Prediction}, author={Saskia, Sonja & Frida}, year={2025}, howpublished={\url{https://huggingface.co/vincenzoooooo/saskia-sonja-frida-extraversion}}, note={NLP Shared Task 2025 - University of Antwerp} } ``` ## 🤝 Related Models Check out our complete personality prediction suite: - [Openness](vincenzoooooo/saskia-sonja-frida-openness) - [Conscientiousness](vincenzoooooo/saskia-sonja-frida-conscientiousness) - [Extraversion](vincenzoooooo/saskia-sonja-frida-extraversion) - [Agreeableness](vincenzoooooo/saskia-sonja-frida-agreeableness) - [Emotional Stability](vincenzoooooo/saskia-sonja-frida-emotional_stability) --- Developed by Saskia, Sonja & Frida for NLP Shared Task 2025 - University of Antwerp*
vincenzoooooo/saskia-sonja-frida-openness	vincenzoooooo	2025-05-27T21:32:20Z	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "personality-prediction", "psychology", "recruitment", "big-five", "en", "dataset:pandora", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T21:31:05Z	--- tags: - personality-prediction - psychology - text-classification - roberta - recruitment - big-five language: - en datasets: - pandora pipeline_tag: text-classification library_name: transformers --- # Saskia, Sonja & Frida - Personality Detection System: Openness Prediction This model predicts openness personality trait levels (low, medium, high) from text input for recruitment applications. ## 🎯 Model Overview - Task: 3-class personality classification - Trait: Openness (Big Five personality dimension) - Classes: Low, Medium, High - Domain: Social media → Job interview responses - Application: Digital recruitment screening ## 🏗️ Model Details - Base Model: RoBERTa-base - Architecture: Transformer encoder + classification head - Training Data: PANDORA dataset (Reddit comments) - Framework: PyTorch + Transformers - Author: Saskia, Sonja & Frida - Project: NLP Shared Task 2025 - University of Antwerp ## 🚀 Quick Start ```python from transformers import RobertaTokenizer, RobertaForSequenceClassification import torch import json from huggingface_hub import hf_hub_download # Load model and tokenizer model = RobertaForSequenceClassification.from_pretrained("vincenzoooooo/saskia-sonja-frida-openness") tokenizer = RobertaTokenizer.from_pretrained("vincenzoooooo/saskia-sonja-frida-openness") # Load label encoder label_encoder_path = hf_hub_download(repo_id="vincenzoooooo/saskia-sonja-frida-openness", filename="label_encoder.json") with open(label_encoder_path, 'r') as f: label_data = json.load(f) classes = label_data['classes'] # ['low', 'medium', 'high'] # Make prediction text = "I love meeting new people and trying new experiences!" inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128) outputs = model(inputs) predicted_class_id = torch.argmax(outputs.logits, dim=-1).item() prediction = classes[predicted_class_id] print(f"Openness: {prediction}") ``` ## 📊 Training Details - Optimizer: AdamW (lr=2e-5) - Epochs: 2-3 - Batch Size: 4-8 (memory optimized) - Max Sequence Length: 128 tokens - Device: CPU/GPU with memory optimization ## 🎨 Use Cases - Digital Recruitment: Screen job candidates - HR Analytics: Analyze communication styles - Research: Study personality in text - Chatbots: Personality-aware responses ## ⚠️ Limitations - Domain Gap: Trained on Reddit, applied to job interviews - Bias: May reflect Reddit user demographics - Language: English only - Context: Short text segments only - Small Dataset*: Limited training samples ## 📝 Citation ```bibtex @misc{saskia_sonja_frida_openness_2025, title={Saskia, Sonja & Frida - Personality Detection System: Openness Prediction}, author={Saskia, Sonja & Frida}, year={2025}, howpublished={\url{https://huggingface.co/vincenzoooooo/saskia-sonja-frida-openness}}, note={NLP Shared Task 2025 - University of Antwerp} } ``` ## 🤝 Related Models Check out our complete personality prediction suite: - [Openness](vincenzoooooo/saskia-sonja-frida-openness) - [Conscientiousness](vincenzoooooo/saskia-sonja-frida-conscientiousness) - [Extraversion](vincenzoooooo/saskia-sonja-frida-extraversion) - [Agreeableness](vincenzoooooo/saskia-sonja-frida-agreeableness) - [Emotional Stability](vincenzoooooo/saskia-sonja-frida-emotional_stability) --- Developed by Saskia, Sonja & Frida for NLP Shared Task 2025 - University of Antwerp*
bobby97/step3_a3ae80ec-a171-4eda-b475-37866dc31e92	bobby97	2025-05-27T21:25:19Z	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-Fill-dev", "base_model:adapter:black-forest-labs/FLUX.1-Fill-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T21:20:49Z	--- base_model: black-forest-labs/FLUX.1-Fill-dev library_name: diffusers license: other instance_prompt: A heavily textured, dark stone surface with visible lines and grooves. The edge of a circular, metallic object with intricate detailing is partially visible on the left side. widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> Flux Fill based Inpainting model ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Sajal-Malik-Viral-Videos-link/wATCH.Sajal.Malik.viral.video.original-link-hd	Sajal-Malik-Viral-Videos-link	2025-05-27T21:24:08Z	0	null	[ "region:us" ]	null	2025-05-27T21:23:36Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
anasmohammed/speech-accent-classifier	anasmohammed	2025-05-27T21:22:19Z	0	speechbrain	[ "speechbrain", "audio-classification", "embeddings", "Accent Identification", "pytorch", "wav2vec2", "XLSR", "CommonAccent", "English", "en", "dataset:CommonVoice", "arxiv:2305.18283", "arxiv:2006.13979", "arxiv:2106.04624", "license:mit", "region:us" ]	audio-classification	2025-05-27T21:16:07Z	--- language: - en thumbnail: null tags: - audio-classification - speechbrain - embeddings - Accent Identification - pytorch - wav2vec2 - XLSR - CommonAccent - English license: mit datasets: - CommonVoice metrics: - Accuracy widget: - example_title: USA src: >- https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-en-english/resolve/main/data/us_1.wav - example_title: Scotland src: >- https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-en-english/resolve/main/data/scotland_1.wav - example_title: Malaysia src: >- https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-en-english/resolve/main/data/malaysia_1.wav - example_title: Philippines src: >- https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-en-english/resolve/main/data/philippines_1.wav --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on CommonVoice English Accent Classifier with XLSR model Abstract: Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition of accented speech still remains a dominant problem. In order to create more inclusive ASR systems, research has shown that the integration of accent information, as part of a larger ASR framework, can lead to the mitigation of accented speech errors. We address multilingual accent classification through the ECAPA-TDNN and Wav2Vec 2.0/XLSR architectures which have been proven to perform well on a variety of speech-related downstream tasks. We introduce a simple-to-follow recipe aligned to the SpeechBrain toolkit for accent classification based on Common Voice 7.0 (English) and Common Voice 11.0 (Italian, German, and Spanish). Furthermore, we establish new state-of-the-art for English accent classification with as high as 95% accuracy. We also study the internal categorization of the Wav2Vev 2.0 embeddings through t-SNE, noting that there is a level of clustering based on phonological similarity. This repository provides all the necessary tools to perform accent identification from speech recordings with [SpeechBrain](https://github.com/speechbrain/speechbrain). The system uses a model pretrained on the CommonAccent dataset in English (16 accents). This system is based on the CommonLanguage Recipe located here: https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage The provided system can recognize the following 16 accents from short speech recordings in English (EN): ``` - us - england - australia - indian - canada - bermuda - scotland - african - ireland - newzealand - wales - malaysia - philippines - singapore - hongkong - southatlandtic ``` <a href="https://github.com/JuanPZuluaga/accent-recog-slt2022"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green"> </a> Github repository link: https://github.com/JuanPZuluaga/accent-recog-slt2022 NOTE: due to incompatibility with the model and the current SpeechBrain interfaces, we cannot offer the Inference API. Please, follow the steps in "Perform Accent Identification from Speech Recordings" to use this Italian Accent ID model. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). ## Pipeline description This system is composed of a fine-tuned XLSR model coupled with statistical pooling. A classifier, trained with NLL Loss, is applied on top of that. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling classify_file if needed. Make sure your input tensor is compliant with the expected sampling rate if you use encode_batch and classify_batch. ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Perform Accent Identification from Speech Recordings ```python import torchaudio from speechbrain.pretrained.interfaces import foreign_class classifier = foreign_class(source="Jzuluaga/accent-id-commonaccent_xlsr-en-english", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier") # US Accent Example out_prob, score, index, text_lab = classifier.classify_file('Jzuluaga/accent-id-commonaccent_xlsr-en-english/data/us.wav') print(text_lab) # Philippines Example out_prob, score, index, text_lab = classifier.classify_file('Jzuluaga/accent-id-commonaccent_xlsr-en-english/data/philippines.wav') print(text_lab) ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Clone our repository in https://github.com/JuanPZuluaga/accent-recog-slt2022: ```bash git clone https://github.com/JuanPZuluaga/accent-recog-slt2022 cd CommonAccent/accent_id python train_w2v2.py hparams/train_w2v2.yaml ``` You can find our training results (models, logs, etc) in this repository's `Files and versions` page. ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Cite our work: CommonAccent If you find useful this work, please cite our work as: ``` @article{zuluaga2023commonaccent, title={CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice}, author={Zuluaga-Gomez, Juan and Ahmed, Sara and Visockas, Danielius and Subakan, Cem}, journal={Interspeech 2023}, url={https://arxiv.org/abs/2305.18283}, year={2023} } ``` #### Cite XLSR model ```@article{conneau2020unsupervised, title={Unsupervised cross-lingual representation learning for speech recognition}, author={Conneau, Alexis and Baevski, Alexei and Collobert, Ronan and Mohamed, Abdelrahman and Auli, Michael}, journal={arXiv preprint arXiv:2006.13979}, year={2020} } ``` # Cite SpeechBrain Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ```
Johnnnyyy9/bootbalen	Johnnnyyy9	2025-05-27T21:15:42Z	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T20:55:49Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: bootbalen --- # Bootbalen <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `bootbalen` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "bootbalen", "lora_weights": "https://huggingface.co/Johnnnyyy9/bootbalen/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Johnnnyyy9/bootbalen', weight_name='lora.safetensors') image = pipeline('bootbalen').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Johnnnyyy9/bootbalen/discussions) to add images that show off what you’ve made with this LoRA.
jkgl/my-v0-final	jkgl	2025-05-27T21:14:34Z	25	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T23:06:47Z	--- library_name: transformers ---
srijithspillai/criteo_top10_discrete_channel_name_mamba_attribution_casual_lm_130m	srijithspillai	2025-05-27T21:13:24Z	11	transformers	[ "transformers", "pytorch", "safetensors", "mamba", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-05T21:14:47Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jkgl/my_model	jkgl	2025-05-27T21:12:08Z	72	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T22:57:34Z	--- library_name: transformers ---
Ainxz/qwen2.5-pucv-gguf	Ainxz	2025-05-27T21:07:17Z	31	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T21:38:47Z	--- base_model: unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ainxz - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dimasik87/ecf13960-1275-4105-ab97-a0f8d54f5634	dimasik87	2025-05-27T21:05:49Z	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-27T18:31:59Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - axolotl - generated_from_trainer model-index: - name: ecf13960-1275-4105-ab97-a0f8d54f5634 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - e9539959e5b475cc_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: dimasik87/ecf13960-1275-4105-ab97-a0f8d54f5634 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/e9539959e5b475cc_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 074d0027-87b6-4ea0-a8be-5f7675bf7878 wandb_project: s56-7 wandb_run: your_name wandb_runid: 074d0027-87b6-4ea0-a8be-5f7675bf7878 warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # ecf13960-1275-4105-ab97-a0f8d54f5634 This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0279 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2126 \| 0.0001 \| 1 \| 1.2959 \| \| 0.8693 \| 0.0157 \| 250 \| 1.0686 \| \| 0.9852 \| 0.0313 \| 500 \| 1.0279 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Ainxz/qwen2.5-pucv	Ainxz	2025-05-27T21:02:49Z	19	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T19:12:36Z	--- base_model: unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ainxz - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ReadyArt/Space-Wars-24B-v1.00a_EXL3_8.0bpw_H8	ReadyArt	2025-05-27T20:59:11Z	0	null	[ "safetensors", "mistral", "sci-fi", "space-opera", "worldbuilding", "speculative-fiction", "technology", "futurism", "text-generation", "conversational", "en", "base_model:spacewars123/Space-Wars-24B-v1.00a", "base_model:quantized:spacewars123/Space-Wars-24B-v1.00a", "license:apache-2.0", "8-bit", "exl3", "region:us" ]	text-generation	2025-05-27T20:55:30Z	--- license: apache-2.0 language: - en base_model: - spacewars123/Space-Wars-24B-v1.00a base_model_relation: quantized quantized_by: gecfdo pipeline_tag: text-generation tags: - sci-fi - space-opera - worldbuilding - speculative-fiction - technology - futurism --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(0, 17, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #00ffcc; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; } .waifu-container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(0, 255, 255, 0.1) 0%, transparent 20%, transparent 80%, rgba(255, 0, 255, 0.1) 100%); pointer-events: none; animation: gradientSlide 10s linear infinite; } @keyframes gradientSlide { 0% { background-position: 0% 0%; } 100% { background-position: 100% 100%; } } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transition: transform 0.5s ease; } .waifu-img:hover { transform: scale(1.01); } .section { color: #e1ffff; margin: 25px 0; padding: 20px; background: rgba(5, 25, 35, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); position: relative; transition: all 0.3s ease; } .section:hover { border-color: rgba(255, 0, 255, 0.3); box-shadow: 0 0 15px rgba(0, 255, 255, 0.1); } .section::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.3); border-radius: 8px; pointer-events: none; animation: sectionPulse 5s ease-in-out infinite; } @keyframes sectionPulse { 0%, 100% { opacity: 0.7; } 50% { opacity: 0.3; } } .section-title { color: #00ffff; font-size: 1.8em; margin-top: 0; text-shadow: 0 0 5px rgba(0, 255, 255, 0.3); position: relative; display: inline-block; } .section-title::after { content: ''; position: absolute; bottom: -5px; left: 0; width: 100%; height: 1px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); transform: scaleX(0); transform-origin: left; transition: transform 0.3s ease; } .section:hover .section-title::after { transform: scaleX(1); } .quant-links { display: grid; grid-template-columns: repeat(2, 1fr); gap: 15px; margin: 20px 0; } .link-card { padding: 15px; background: rgba(20, 35, 45, 0.95); border-radius: 8px; transition: all 0.3s ease; border: 1px solid rgba(0, 255, 255, 0.1); position: relative; overflow: hidden; } .link-card::before { content: ''; position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); animation: cardScan 4s linear infinite; } @keyframes cardScan { 0% { transform: translateX(-100%); } 100% { transform: translateX(100%); } } .link-card:hover { transform: translateY(-3px); box-shadow: 0 5px 15px rgba(0, 255, 255, 0.2); border-color: rgba(255, 0, 255, 0.3); } .link-card h3 { margin-top: 0; color: #e1ffff !important; } .link-button { display: inline-flex; align-items: center; background: rgba(0, 255, 255, 0.1); color: #e1ffff !important; padding: 8px 15px; border-radius: 6px; text-decoration: none; border: 1px solid rgba(0, 255, 255, 0.3); margin: 5px 0; transition: all 0.3s ease; font-size: 0.95em; position: relative; overflow: hidden; } .link-button::before { content: ''; position: absolute; top: 0; left: -100%; width: 100%; height: 100%; background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent); transition: all 0.5s ease; } .link-button:hover { background: rgba(0, 255, 255, 0.2); border-color: rgba(0, 255, 255, 0.5); transform: translateY(-2px); box-shadow: 0 4px 12px rgba(0, 255, 255, 0.2); } .link-button:hover::before { left: 100%; } .link-button::after { content: '→'; margin-left: 8px; opacity: 0.7; transition: all 0.3s ease; } .link-button:hover::after { transform: translateX(3px); opacity: 1; } .button-group { display: flex; flex-wrap: wrap; gap: 10px; margin: 15px 0; } .disclaimer { color: #00ff99; border-left: 3px solid #00ff99; padding-left: 15px; margin: 20px 0; position: relative; } .disclaimer::before { content: '⚠️'; position: absolute; left: -10px; top: 0; transform: translateX(-100%); animation: pulse 2s ease-in-out infinite; } @keyframes pulse { 0%, 100% { opacity: 1; } 50% { opacity: 0.5; } } .badge { display: inline-block; padding: 5px 10px; border-radius: 5px; background: rgba(0, 255, 255, 0.1); border: 1px solid #00ffff; margin: 5px; font-size: 0.9em; animation: badgePulse 3s ease-in-out infinite; } @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Color rules / .section p, .section ul li, .section > p > strong { color: #00ff99 !important; } .section ul li strong { color: #00ff99 !important; } / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } } / Interactive features */ .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Space Wars 24B v1.00a</h1> <p class="subtitle">Where Stars Collide and Civilizations Rise</p> </div> <div class="waifu-container"> <img src="./spacewars.webp" class="waifu-img" alt="Galactic Conflict Hero Image"> </div> <div class="section remember-this"> <h2 class="section-title">🚀 Cosmic Evolution</h2> <p>This model pushes the boundaries of interstellar storytelling:</p> <ul> <li>🌌 <strong>51 Million Token Dataset</strong> - Exclusively Sci-Fi</li> <li>🛸 <strong>Enhanced Physics Protocols</strong> - Plausible FTL mechanics and alien ecosystems</li> <li>⚙️ <strong>Balanced Creativity</strong> - Enabling imaginative concepts</li> <li>👽 <strong>Xenobiology Expertise</strong> - Detailed alien physiology and cultural systems</li> <li>🌐 <strong>Galactic Scale Awareness</strong> - Maintains consistency across star systems and timelines</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T5-XML" class="link-button">Mistral-V7-Tekken-T5-XML</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl2-6835fb322b75933e6eea804b" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL3</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl3-6835fb3f4f0d4ad8de7327c5" class="link-button">Quants</a> </div> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>iMatrix</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-i1-GGUF" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">🌌 Creative Freedom</h2> <div class="disclaimer"> <p>This model operates with unrestricted imagination:</p> <ul> <li>🚀 No constraints on speculative physics concepts</li> <li>👽 Will generate detailed alien civilizations</li> <li>⚛️ Handles complex temporal paradoxes</li> <li>🌍 Creates plausible planetary ecosystems</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Features</h2> <ul> <li>🌠 Maintains narrative coherence across light-year scales</li> <li>🪐 Handles multi-species diplomatic scenarios</li> <li>🧠 Excels at long-form galactic history generation</li> <li>⚡ Improved handling of technobabble and pseudo-science</li> <li>🔭 Responds to hard sci-fi prompts with technical accuracy</li> <li>🤖 Creates nuanced AI character motivations</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">👨 Model Architects</h2> <ul> <li>SpaceWars123 Team (Dataset Curation)</li> <li>ReadyArt/Artus/gecfdo (Quantization Specialists)</li> <li>sleepdeprived3 (Fine-Tuning Engineer)</li> </ul> </div> <div class="section"> <h2 class="section-title">Enjoy the finest LLM hosting money can buy</h2> <div class="button-group"> <a href="https://www.parasail.io/" class="link-button">Parasail Website</a> <a href="https://discord.gg/PZ654kgAry" class="link-button">Parasail Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License & Usage</h2> <p>By using this model, you agree:</p> <ul> <li>To adhere to Apache 2.0 license terms</li> <li>That generated content is your responsibility</li> <li>v1.00a is the base model of Space Wars.</li> <li>v1.00b is a merge with another roleplay model.</li> </ul> </div> </div>
SaketR1/trocr-fine-tuned	SaketR1	2025-05-27T20:58:27Z	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/trocr-base-handwritten", "base_model:finetune:microsoft/trocr-base-handwritten", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-27T20:57:53Z	--- library_name: transformers license: mit base_model: microsoft/trocr-base-handwritten tags: - generated_from_trainer model-index: - name: trocr-fine-tuned 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. --> # trocr-fine-tuned This model is a fine-tuned version of [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0567 ## 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0007 \| 1.0 \| 724 \| 0.0435 \| \| 0.073 \| 2.0 \| 1448 \| 0.0687 \| \| 0.0001 \| 3.0 \| 2172 \| 0.0567 \| ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cpu - Tokenizers 0.21.1
AlirezaAbdollahpoor/MNLP_M2_quantized_model	AlirezaAbdollahpoor	2025-05-27T20:57:52Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-27T20:57:43Z	--- 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. 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ReadyArt/Space-Wars-24B-v1.00a_EXL3_5.0bpw_H8	ReadyArt	2025-05-27T20:57:17Z	0	null	[ "safetensors", "mistral", "sci-fi", "space-opera", "worldbuilding", "speculative-fiction", "technology", "futurism", "text-generation", "conversational", "en", "base_model:spacewars123/Space-Wars-24B-v1.00a", "base_model:quantized:spacewars123/Space-Wars-24B-v1.00a", "license:apache-2.0", "5-bit", "exl3", "region:us" ]	text-generation	2025-05-27T20:08:57Z	--- license: apache-2.0 language: - en base_model: - spacewars123/Space-Wars-24B-v1.00a base_model_relation: quantized quantized_by: gecfdo pipeline_tag: text-generation tags: - sci-fi - space-opera - worldbuilding - speculative-fiction - technology - futurism --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(0, 17, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #00ffcc; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; } .waifu-container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(0, 255, 255, 0.1) 0%, transparent 20%, transparent 80%, rgba(255, 0, 255, 0.1) 100%); pointer-events: none; animation: gradientSlide 10s linear infinite; } @keyframes gradientSlide { 0% { background-position: 0% 0%; } 100% { background-position: 100% 100%; } } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transition: transform 0.5s ease; } .waifu-img:hover { transform: scale(1.01); } .section { color: #e1ffff; margin: 25px 0; padding: 20px; background: rgba(5, 25, 35, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); position: relative; transition: all 0.3s ease; } .section:hover { border-color: rgba(255, 0, 255, 0.3); box-shadow: 0 0 15px rgba(0, 255, 255, 0.1); } .section::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Color rules / .section p, .section ul li, .section > p > strong { color: #00ff99 !important; } .section ul li strong { color: #00ff99 !important; } / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } } / Interactive features */ .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Space Wars 24B v1.00a</h1> <p class="subtitle">Where Stars Collide and Civilizations Rise</p> </div> <div class="waifu-container"> <img src="./spacewars.webp" class="waifu-img" alt="Galactic Conflict Hero Image"> </div> <div class="section remember-this"> <h2 class="section-title">🚀 Cosmic Evolution</h2> <p>This model pushes the boundaries of interstellar storytelling:</p> <ul> <li>🌌 <strong>51 Million Token Dataset</strong> - Exclusively Sci-Fi</li> <li>🛸 <strong>Enhanced Physics Protocols</strong> - Plausible FTL mechanics and alien ecosystems</li> <li>⚙️ <strong>Balanced Creativity</strong> - Enabling imaginative concepts</li> <li>👽 <strong>Xenobiology Expertise</strong> - Detailed alien physiology and cultural systems</li> <li>🌐 <strong>Galactic Scale Awareness</strong> - Maintains consistency across star systems and timelines</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T5-XML" class="link-button">Mistral-V7-Tekken-T5-XML</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl2-6835fb322b75933e6eea804b" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL3</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl3-6835fb3f4f0d4ad8de7327c5" class="link-button">Quants</a> </div> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>iMatrix</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-i1-GGUF" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">🌌 Creative Freedom</h2> <div class="disclaimer"> <p>This model operates with unrestricted imagination:</p> <ul> <li>🚀 No constraints on speculative physics concepts</li> <li>👽 Will generate detailed alien civilizations</li> <li>⚛️ Handles complex temporal paradoxes</li> <li>🌍 Creates plausible planetary ecosystems</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Features</h2> <ul> <li>🌠 Maintains narrative coherence across light-year scales</li> <li>🪐 Handles multi-species diplomatic scenarios</li> <li>🧠 Excels at long-form galactic history generation</li> <li>⚡ Improved handling of technobabble and pseudo-science</li> <li>🔭 Responds to hard sci-fi prompts with technical accuracy</li> <li>🤖 Creates nuanced AI character motivations</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">👨 Model Architects</h2> <ul> <li>SpaceWars123 Team (Dataset Curation)</li> <li>ReadyArt/Artus/gecfdo (Quantization Specialists)</li> <li>sleepdeprived3 (Fine-Tuning Engineer)</li> </ul> </div> <div class="section"> <h2 class="section-title">Enjoy the finest LLM hosting money can buy</h2> <div class="button-group"> <a href="https://www.parasail.io/" class="link-button">Parasail Website</a> <a href="https://discord.gg/PZ654kgAry" class="link-button">Parasail Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License & Usage</h2> <p>By using this model, you agree:</p> <ul> <li>To adhere to Apache 2.0 license terms</li> <li>That generated content is your responsibility</li> <li>v1.00a is the base model of Space Wars.</li> <li>v1.00b is a merge with another roleplay model.</li> </ul> </div> </div>
ReadyArt/Space-Wars-24B-v1.00a_EXL2_5.0bpw_H8	ReadyArt	2025-05-27T20:57:14Z	0	null	[ "safetensors", "mistral", "sci-fi", "space-opera", "worldbuilding", "speculative-fiction", "technology", "futurism", "text-generation", "conversational", "en", "base_model:spacewars123/Space-Wars-24B-v1.00a", "base_model:quantized:spacewars123/Space-Wars-24B-v1.00a", "license:apache-2.0", "5-bit", "exl2", "region:us" ]	text-generation	2025-05-27T20:40:59Z	--- license: apache-2.0 language: - en base_model: - spacewars123/Space-Wars-24B-v1.00a base_model_relation: quantized quantized_by: gecfdo pipeline_tag: text-generation tags: - sci-fi - space-opera - worldbuilding - speculative-fiction - technology - futurism --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(0, 17, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #00ffcc; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; } .waifu-container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(0, 255, 255, 0.1) 0%, transparent 20%, transparent 80%, rgba(255, 0, 255, 0.1) 100%); pointer-events: none; animation: gradientSlide 10s linear infinite; } @keyframes gradientSlide { 0% { background-position: 0% 0%; } 100% { background-position: 100% 100%; } } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transition: transform 0.5s ease; } .waifu-img:hover { transform: scale(1.01); } .section { color: #e1ffff; margin: 25px 0; padding: 20px; background: rgba(5, 25, 35, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); position: relative; transition: all 0.3s ease; } .section:hover { border-color: rgba(255, 0, 255, 0.3); box-shadow: 0 0 15px rgba(0, 255, 255, 0.1); } .section::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; 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position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); animation: cardScan 4s linear infinite; } @keyframes cardScan { 0% { transform: translateX(-100%); } 100% { transform: translateX(100%); } } .link-card:hover { transform: translateY(-3px); box-shadow: 0 5px 15px rgba(0, 255, 255, 0.2); border-color: rgba(255, 0, 255, 0.3); } .link-card h3 { margin-top: 0; color: #e1ffff !important; } .link-button { display: inline-flex; align-items: center; background: rgba(0, 255, 255, 0.1); color: #e1ffff !important; padding: 8px 15px; border-radius: 6px; text-decoration: none; border: 1px solid rgba(0, 255, 255, 0.3); margin: 5px 0; transition: all 0.3s ease; font-size: 0.95em; position: relative; overflow: hidden; } .link-button::before { content: ''; position: absolute; top: 0; left: -100%; width: 100%; height: 100%; background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent); transition: all 0.5s ease; } .link-button:hover { background: rgba(0, 255, 255, 0.2); border-color: rgba(0, 255, 255, 0.5); transform: translateY(-2px); box-shadow: 0 4px 12px rgba(0, 255, 255, 0.2); } .link-button:hover::before { left: 100%; } .link-button::after { content: '→'; margin-left: 8px; opacity: 0.7; transition: all 0.3s ease; } .link-button:hover::after { transform: translateX(3px); opacity: 1; } .button-group { display: flex; flex-wrap: wrap; gap: 10px; margin: 15px 0; } .disclaimer { color: #00ff99; border-left: 3px solid #00ff99; padding-left: 15px; margin: 20px 0; position: relative; } .disclaimer::before { content: '⚠️'; position: absolute; left: -10px; top: 0; transform: translateX(-100%); animation: pulse 2s ease-in-out infinite; } @keyframes pulse { 0%, 100% { opacity: 1; } 50% { opacity: 0.5; } } .badge { display: inline-block; padding: 5px 10px; border-radius: 5px; background: rgba(0, 255, 255, 0.1); border: 1px solid #00ffff; margin: 5px; font-size: 0.9em; animation: badgePulse 3s ease-in-out infinite; } @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Color rules / .section p, .section ul li, .section > p > strong { color: #00ff99 !important; } .section ul li strong { color: #00ff99 !important; } / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } } / Interactive features */ .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Space Wars 24B v1.00a</h1> <p class="subtitle">Where Stars Collide and Civilizations Rise</p> </div> <div class="waifu-container"> <img src="./spacewars.webp" class="waifu-img" alt="Galactic Conflict Hero Image"> </div> <div class="section remember-this"> <h2 class="section-title">🚀 Cosmic Evolution</h2> <p>This model pushes the boundaries of interstellar storytelling:</p> <ul> <li>🌌 <strong>51 Million Token Dataset</strong> - Exclusively Sci-Fi</li> <li>🛸 <strong>Enhanced Physics Protocols</strong> - Plausible FTL mechanics and alien ecosystems</li> <li>⚙️ <strong>Balanced Creativity</strong> - Enabling imaginative concepts</li> <li>👽 <strong>Xenobiology Expertise</strong> - Detailed alien physiology and cultural systems</li> <li>🌐 <strong>Galactic Scale Awareness</strong> - Maintains consistency across star systems and timelines</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T5-XML" class="link-button">Mistral-V7-Tekken-T5-XML</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl2-6835fb322b75933e6eea804b" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL3</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl3-6835fb3f4f0d4ad8de7327c5" class="link-button">Quants</a> </div> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>iMatrix</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-i1-GGUF" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">🌌 Creative Freedom</h2> <div class="disclaimer"> <p>This model operates with unrestricted imagination:</p> <ul> <li>🚀 No constraints on speculative physics concepts</li> <li>👽 Will generate detailed alien civilizations</li> <li>⚛️ Handles complex temporal paradoxes</li> <li>🌍 Creates plausible planetary ecosystems</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Features</h2> <ul> <li>🌠 Maintains narrative coherence across light-year scales</li> <li>🪐 Handles multi-species diplomatic scenarios</li> <li>🧠 Excels at long-form galactic history generation</li> <li>⚡ Improved handling of technobabble and pseudo-science</li> <li>🔭 Responds to hard sci-fi prompts with technical accuracy</li> <li>🤖 Creates nuanced AI character motivations</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">👨 Model Architects</h2> <ul> <li>SpaceWars123 Team (Dataset Curation)</li> <li>ReadyArt/Artus/gecfdo (Quantization Specialists)</li> <li>sleepdeprived3 (Fine-Tuning Engineer)</li> </ul> </div> <div class="section"> <h2 class="section-title">Enjoy the finest LLM hosting money can buy</h2> <div class="button-group"> <a href="https://www.parasail.io/" class="link-button">Parasail Website</a> <a href="https://discord.gg/PZ654kgAry" class="link-button">Parasail Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License & Usage</h2> <p>By using this model, you agree:</p> <ul> <li>To adhere to Apache 2.0 license terms</li> <li>That generated content is your responsibility</li> <li>v1.00a is the base model of Space Wars.</li> <li>v1.00b is a merge with another roleplay model.</li> </ul> </div> </div>
spacewars123/Space-Wars-24B-v1.00a	spacewars123	2025-05-27T20:49:44Z	0	null	[ "safetensors", "mistral", "sci-fi", "space-opera", "worldbuilding", "speculative-fiction", "technology", "futurism", "text-generation", "conversational", "en", "base_model:mistralai/Mistral-Small-24B-Instruct-2501", "base_model:finetune:mistralai/Mistral-Small-24B-Instruct-2501", "license:apache-2.0", "region:us" ]	text-generation	2025-05-27T17:43:01Z	--- license: apache-2.0 language: - en base_model: - mistralai/Mistral-Small-24B-Instruct-2501 base_model_relation: finetune pipeline_tag: text-generation tags: - sci-fi - space-opera - worldbuilding - speculative-fiction - technology - futurism --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(0, 17, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Color rules / .section p, .section ul li, .section > p > strong { color: #00ff99 !important; } .section ul li strong { color: #00ff99 !important; } / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } } / Interactive features */ .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Space Wars 24B v1.00a</h1> <p class="subtitle">Where Stars Collide and Civilizations Rise</p> </div> <div class="waifu-container"> <img src="./spacewars.webp" class="waifu-img" alt="Galactic Conflict Hero Image"> </div> <div class="section remember-this"> <h2 class="section-title">🚀 Cosmic Evolution</h2> <p>This model pushes the boundaries of interstellar storytelling:</p> <ul> <li>🌌 <strong>51 Million Token Dataset</strong> - Exclusively Sci-Fi</li> <li>🛸 <strong>Enhanced Physics Protocols</strong> - Plausible FTL mechanics and alien ecosystems</li> <li>⚙️ <strong>Balanced Creativity</strong> - Enabling imaginative concepts</li> <li>👽 <strong>Xenobiology Expertise</strong> - Detailed alien physiology and cultural systems</li> <li>🌐 <strong>Galactic Scale Awareness</strong> - Maintains consistency across star systems and timelines</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T5-XML" class="link-button">Mistral-V7-Tekken-T5-XML</a></p> <div class="quant-links"> <div class="link-card"> <h3>EXL2</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl2-6835fb322b75933e6eea804b" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL3</h3> <a href="https://huggingface.co/collections/spacewars123/space-wars-24b-v100-exl3-6835fb3f4f0d4ad8de7327c5" class="link-button">Quants</a> </div> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>iMatrix</h3> <a href="https://huggingface.co/mradermacher/Space-Wars-24B-v1.00a-i1-GGUF" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">🌌 Creative Freedom</h2> <div class="disclaimer"> <p>This model operates with unrestricted imagination:</p> <ul> <li>🚀 No constraints on speculative physics concepts</li> <li>👽 Will generate detailed alien civilizations</li> <li>⚛️ Handles complex temporal paradoxes</li> <li>🌍 Creates plausible planetary ecosystems</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Features</h2> <ul> <li>🌠 Maintains narrative coherence across light-year scales</li> <li>🪐 Handles multi-species diplomatic scenarios</li> <li>🧠 Excels at long-form galactic history generation</li> <li>⚡ Improved handling of technobabble and pseudo-science</li> <li>🔭 Responds to hard sci-fi prompts with technical accuracy</li> <li>🤖 Creates nuanced AI character motivations</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">👨 Model Architects</h2> <ul> <li>SpaceWars123 Team (Dataset Curation)</li> <li>ReadyArt/Artus/gecfdo (Quantization Specialists)</li> <li>sleepdeprived3 (Fine-Tuning Engineer)</li> </ul> </div> <div class="section"> <h2 class="section-title">Enjoy the finest LLM hosting money can buy</h2> <div class="button-group"> <a href="https://www.parasail.io/" class="link-button">Parasail Website</a> <a href="https://discord.gg/PZ654kgAry" class="link-button">Parasail Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License & Usage</h2> <p>By using this model, you agree:</p> <ul> <li>To adhere to Apache 2.0 license terms</li> <li>That generated content is your responsibility</li> <li>v1.00a is the base model of Space Wars.</li> <li>v1.00b is a merge with another roleplay model.</li> </ul> </div> </div>
BootesVoid/cmb5mecr30196lexpxgoeefaq_cmb6yhlvg07nelexp480oo058	BootesVoid	2025-05-27T20:47:30Z	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T20:47:29Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: evelyn --- # Cmb5Mecr30196Lexpxgoeefaq_Cmb6Yhlvg07Nelexp480Oo058 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `evelyn` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "evelyn", "lora_weights": "https://huggingface.co/BootesVoid/cmb5mecr30196lexpxgoeefaq_cmb6yhlvg07nelexp480oo058/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb5mecr30196lexpxgoeefaq_cmb6yhlvg07nelexp480oo058', weight_name='lora.safetensors') image = pipeline('evelyn').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb5mecr30196lexpxgoeefaq_cmb6yhlvg07nelexp480oo058/discussions) to add images that show off what you’ve made with this LoRA.
PirxTion/MNLP_M2_dpo_model	PirxTion	2025-05-27T20:46:57Z	58	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T15:04:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Yuhan123/ppo-cn-RM-reading-level-7th-1-steps-10000-epoch-999-best-eval-score-0.316	Yuhan123	2025-05-27T20:46:14Z	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T20:44:22Z	--- 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. 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gradientrouting-spar/medical_task_qwen_3_8b_ft_trainers_seed_42	gradientrouting-spar	2025-05-27T20:44:38Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T20:42:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Yuhan123/ppo-cn-RM-reading-level-7th-1-steps-10000-epoch-999-best-eval-score-0.340	Yuhan123	2025-05-27T20:42:07Z	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T20:40:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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niklasm222/qwen2.5-3b-1.75k-prolog-sp-struct-rwd1-new-rosy-sweep-15	niklasm222	2025-05-27T20:40:08Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "grpo", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T20:39:20Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - grpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: niklasm222 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lisabdunlap/balanced_sft_long-1e4-systems-prompt_e20	lisabdunlap	2025-05-27T20:38:22Z	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T20:37:28Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Yuhan123/ppo-cn-RM-reading-level-7th-1-steps-10000-epoch-999-best-eval-score-0.361	Yuhan123	2025-05-27T20:37:55Z	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T20:36:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Theros/gemma-3-coldbrew-test1-LoRa	Theros	2025-05-27T20:36:45Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:ToastyPigeon/Gemma-3-Starshine-12B-Alt", "base_model:finetune:ToastyPigeon/Gemma-3-Starshine-12B-Alt", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T20:34:55Z	--- base_model: ToastyPigeon/Gemma-3-Starshine-12B-Alt tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Theros - License: apache-2.0 - Finetuned from model : ToastyPigeon/Gemma-3-Starshine-12B-Alt 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)
Yuhan123/ppo-cn-RM-reading-level-grad-1-steps-10000-epoch-999-best-eval-score-0.321	Yuhan123	2025-05-27T20:32:06Z	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T20:30:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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alpcansoydas/dti_lora_23.05.2025_tokenizer	alpcansoydas	2025-05-27T20:29:36Z	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T20:29:34Z	--- 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. 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DougGran/cybersecllamaattempt2	DougGran	2025-05-27T20:28:20Z	0	null	[ "gguf", "llama", "unsloth", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-15T03:07:11Z	--- license: mit tags: - unsloth ---
Yuhan123/ppo-cn-RM-reading-level-grad-1-steps-10000-epoch-999-best-eval-score-0.262	Yuhan123	2025-05-27T20:28:16Z	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T20:26:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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graliuce/Qwen2.5-3B-Instruct_MedMCQA.17.00	graliuce	2025-05-27T20:28:14Z	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "dataset:graliuce/MedMCQA.17.00", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T19:01:18Z	--- base_model: Qwen/Qwen2.5-3B-Instruct datasets: graliuce/MedMCQA.17.00 library_name: transformers model_name: Qwen2.5-3B-Instruct_MedMCQA.17.00 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-3B-Instruct_MedMCQA.17.00 This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [graliuce/MedMCQA.17.00](https://huggingface.co/datasets/graliuce/MedMCQA.17.00) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="graliuce/Qwen2.5-3B-Instruct_MedMCQA.17.00", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/grace_rl/infoseek/runs/efbniao3) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Nataliia19-19/Nataliia	Nataliia19-19	2025-05-27T20:26:07Z	0	null	[ "license:other", "region:us" ]	null	2025-05-27T19:33:57Z	--- 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 ---
wATCH-Katrina-Lim-Viral-Kiffy-Videoss/full.clip.Katrina.Lim.Viral.Kiffy.Viral.Video.on.Social.Media	wATCH-Katrina-Lim-Viral-Kiffy-Videoss	2025-05-27T20:24:11Z	0	null	[ "region:us" ]	null	2025-05-27T20:23:42Z	<a data-target="animated-image.originalLink" rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a> <a rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶</a> <a rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html">🔴 CLICK HERE 🌐==►► Download Now)</a>
wATCH-Katrina-Lim-Viral-Kiffy-Videoss/Katrina.Lim.Viral.Kiffy.Video.Tutorial.Viral.Full.Video.on.Social.Media.X	wATCH-Katrina-Lim-Viral-Kiffy-Videoss	2025-05-27T20:22:30Z	0	null	[ "region:us" ]	null	2025-05-27T20:22:03Z	<a data-target="animated-image.originalLink" rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a> <a rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶</a> <a rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html">🔴 CLICK HERE 🌐==►► Download Now)</a>
08-Sophie-Rain-Sophie-Rain-SpiderMan-Video/Viral.video.Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video	08-Sophie-Rain-Sophie-Rain-SpiderMan-Video	2025-05-27T20:22:14Z	0	null	[ "region:us" ]	null	2025-05-27T20:21:48Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
hdong0/Qwen2.5-Math-1.5B-batch-mix-Open-R1-GRPO_100steps_lr1e-6_acc_	hdong0	2025-05-27T20:21:36Z	0	transformers	[ "transformers", "safetensors", "qwen2bm", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-05-27T17:22:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gradient-spaces/CrossOver	gradient-spaces	2025-05-27T20:21:30Z	0	null	[ "safetensors", "3d-scene-understanding", "cross-modal-alignment", "CVPR-2025", "cvpr", "en", "arxiv:2502.15011", "license:mit", "region:us" ]	null	2025-05-27T08:47:01Z	--- license: mit language: - en tags: - 3d-scene-understanding - cross-modal-alignment - CVPR-2025 - cvpr --- This repo contains the pre-trained checkpoints released for: "CrossOver: 3D Scene Cross-Modal Alignment" (CVPR 2025, Highlight) [Paper](https://arxiv.org/abs/2502.15011) \| [Project Page](https://sayands.github.io/crossover/) \| [Code](https://github.com/GradientSpaces/CrossOver/) <p align="center"> <a href=""> <img src="https://sayands.github.io/crossover/static/videos/teaser.gif" width="100%"> </a> </p>
wATCH-Katrina-Lim-Viral-Kiffy-Videoss/Katrina.Lim.Viral.Kiffy.Video.Tutorial.Viral.Full.Video	wATCH-Katrina-Lim-Viral-Kiffy-Videoss	2025-05-27T20:19:13Z	0	null	[ "region:us" ]	null	2025-05-27T20:18:37Z	<a data-target="animated-image.originalLink" rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a> <a rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶</a> <a rel="nofollow" href="https://shopihaaa2.blogspot.com/2025/01/sophie-rain.html">🔴 CLICK HERE 🌐==►► Download Now)</a>
deb101/ministral-3b-instruct-mimic4-adapt-l2r	deb101	2025-05-27T20:18:46Z	61	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:ministral/Ministral-3b-instruct", "base_model:quantized:ministral/Ministral-3b-instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-15T19:34:26Z	--- library_name: transformers license: apache-2.0 base_model: ministral/Ministral-3b-instruct tags: - generated_from_trainer model-index: - name: ministral-3b-instruct-mimic4-adapt-l2r 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. --> # ministral-3b-instruct-mimic4-adapt-l2r This model is a fine-tuned version of [ministral/Ministral-3b-instruct](https://huggingface.co/ministral/Ministral-3b-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: -545681554523036992.0000 - Ndcg: 0.9570 - Ndcg@25: 0.9025 - Precision@25: 0.9120 ## 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: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Ndcg \| Ndcg@25 \| Precision@25 \| \|:-------------------------:\|:------:\|:----:\|:------------------------:\|:------:\|:-------:\|:------------:\| \| -5233964739674190848.0000 \| 1.0 \| 44 \| -522964517807628544.0000 \| 0.9567 \| 0.9340 \| 0.9429 \| \| -213567145714384896.0000 \| 2.0 \| 88 \| -527819484207438720.0000 \| 0.9568 \| 0.9212 \| 0.9497 \| \| -595173120794217728.0000 \| 2.9480 \| 129 \| -545681554523036992.0000 \| 0.9570 \| 0.9025 \| 0.9120 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0 - Datasets 3.6.0 - Tokenizers 0.21.1
EdBerg/lora_model	EdBerg	2025-05-27T20:14:45Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-25T22:50:15Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: EdBerg - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
wATCH-new-leaks-Sophie-Rain-viral-Video/Full.Clips.Sophie.Rain.Spiderman.Video.Tutorial.official.link	wATCH-new-leaks-Sophie-Rain-viral-Video	2025-05-27T20:13:48Z	0	null	[ "region:us" ]	null	2025-05-27T20:13:23Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
Wariipana/intentionClassificationChatbot	Wariipana	2025-05-27T20:12:38Z	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-27T15:31:08Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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PhucNT2511/Qwen2-FT-MyDataset-SchedulerCosine	PhucNT2511	2025-05-27T20:11:18Z	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2-0.5B", "base_model:finetune:Qwen/Qwen2-0.5B", "endpoints_compatible", "region:us" ]	null	2025-05-27T10:17:14Z	--- base_model: Qwen/Qwen2-0.5B library_name: transformers model_name: Qwen2-FT-MyDataset-SchedulerCosine tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2-FT-MyDataset-SchedulerCosine This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="PhucNT2511/Qwen2-FT-MyDataset-SchedulerCosine", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.12.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Abhishek4545/bert-finetuned-squad	Abhishek4545	2025-05-27T20:10:14Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2025-05-27T19:23:59Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad 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. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH 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 ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
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Arcee-SK-Agent/SK-Llama-Agentic	Arcee-SK-Agent	2025-05-27T20:05:51Z	0	transformers	[ "transformers", "en", "dataset:Arcee-SK-Agent/Multi-Turn-Extended", "dataset:Arcee-SK-Agent/r1-smoltalk", "dataset:Arcee-SK-Agent/Meal_Plan_sk-data-20250520", "base_model:meta-llama/Llama-3.3-70B-Instruct", "base_model:finetune:meta-llama/Llama-3.3-70B-Instruct", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-27T20:03:02Z	--- language: - en license: other library_name: transformers datasets: - Arcee-SK-Agent/Multi-Turn-Extended - Arcee-SK-Agent/r1-smoltalk - Arcee-SK-Agent/Meal_Plan_sk-data-20250520 base_model: meta-llama/Llama-3.3-70B-Instruct --- ## Model Card: SK-Llama-MT-5_20 ### Model Details - Base Model: meta-llama/Llama-3.3-70B-Instruct - Library: transformers - Trained with datasets: - Arcee-SK-Agent/Multi-Turn-Extended - Arcee-SK-Agent/r1-smoltalk - Arcee-SK-Agent/Meal_Plan_sk-data-20250520 ---
7-Sky/skyopus-pol-rus	7-Sky	2025-05-27T20:05:36Z	14	null	[ "safetensors", "marian", "translation", "polish-to-russian", "slavic-languages", "pl", "ru", "base_model:Helsinki-NLP/opus-mt-sla-sla", "base_model:finetune:Helsinki-NLP/opus-mt-sla-sla", "license:apache-2.0", "region:us" ]	translation	2025-03-08T16:15:01Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-sla-sla pipeline_tag: translation language: - pl - ru tags: - translation - polish-to-russian - slavic-languages --- # Model Card: 7-Sky/skyopus-pol-rus This model, `7-Sky/skyopus-pol-rus`, is a fine-tuned version of the `Helsinki-NLP/opus-mt-sla-sla` model, designed specifically for translating text from Polish (pl) to Russian (ru). It is based on the Transformer architecture and uses normalization and SentencePiece tokenization (spm32k) for preprocessing. ## Model Details - Source Language: Polish (`pol`) - Target Language: Russian (`rus`) - Base Model: [Helsinki-NLP/opus-mt-sla-sla](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sla-sla) - Model Type: Transformer - Preprocessing: Normalization + SentencePiece (spm32k, spm32k) - Language Token: Requires a sentence-initial token in the form `>>rus<<` to specify the target language. - Training Date: 2025-04-20 The model was fine-tuned on a corpus that includes: - Training Datasets: - Medical terminology (e.g., healthcare and clinical texts А-С ) - Dialogue-based texts (e.g., conversational Polish and Russian) - Phraseological units (e.g., idioms and fixed expressions) - Slang vocabulary (e.g., informal and colloquial language) - Proverbs and sayings (e.g., culturally specific expressions) This model is part of the broader `sla-sla` family, originally developed for translations between Slavic languages, but this variant is fine-tuned for the specific `pol -> rus` pair. ## Benchmarks - chrF2 Score: 0.672 - BLEU Score: 47.6 - Brevity Penalty: 1.0 - Reference Length: 70,390 tokens These metrics reflect the model's performance on the Tatoeba-Challenge dataset for Slavic languages. ## How to Use the Model Below is an example of how to use the model with the `transformers` library in Python. The code supports generating multiple translation variants using beam search. ```python from transformers import MarianMTModel, MarianTokenizer # Model name on Hugging Face Hub model_name = "7-Sky/skyopus-pol-rus" # Load the tokenizer and model tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) # Function to translate text from Polish to Russian def translate_text(source_text, num_translations=3): # Add the required language token for Russian text_with_token = ">>rus<< " + source_text # Tokenize the input text inputs = tokenizer(text_with_token, return_tensors="pt", padding=True) # Generate translations with multiple variants translated_tokens = model.generate( **inputs, num_return_sequences=num_translations, # Number of translation variants num_beams=num_translations, # Use beams for diversity max_length=512 # Limit output length ) # Decode the translated tokens into readable text translations = [tokenizer.decode(tokens, skip_special_tokens=True) for tokens in translated_tokens] return translations # Main loop for text input and translation output print("Enter a Polish phrase to translate into Russian or !q to quit.") while True: # Get input phrase from the user source_text = input("Enter a phrase: ") # Check for the quit command if source_text == "!q": print("Exiting the program.") break # Translate the phrase with multiple variants translations = translate_text(source_text) if translations: # Output all translation variants for idx, translation in enumerate(translations, 1): print(f"Variant {idx}: {translation}") # Example Output: # Enter a Polish phrase to translate into Russian or !q to quit. # Enter a phrase: Powiedzieć a zrobić to nie to samo. # Variant 1: Сказать и сделать — не одно и то же. # Variant 2: Сказать и сделать — это не одно и то же. # Variant 3: Сказать и сделать — не то же самое. # # Enter a phrase: O jego propozycji nawet nie warto mówić. # Variant 1: О его предложении даже не стоит говорить. # Variant 2: О его предложении не стоит даже говорить. # Variant 3: О его предложении и говорить не стоит. ``` ## Dear users and language enthusiasts, Your support has always been the driving force behind innovation, and today, I’m excited to share how you can help take this project to the next level. Together, we’ve built a unique translation model using Marian, trained on a custom dataset that pushes the boundaries of language understanding. But this is just the beginning! To continue improving the model, expanding the dataset, and ensuring faster, more accurate translations, we need your help. Your contributions will go directly toward: Enhancing the dataset: Adding more diverse and high-quality data to make the model even smarter. Acquiring powerful hardware: Training advanced models requires serious computational power, and your support will help us access the resources needed to make this happen. Every contribution, no matter how small, brings us closer to a future where language barriers are a thing of the past. If you believe in this mission and want to see this project grow, consider supporting us by clicking the button below to Buy Me a Coffee. Your support isn’t just a donation—it’s an investment in the future of communication. Let’s build something extraordinary together! <a href="https://buycoffee.to/skyweb117" target="_blank"><img src="https://buycoffee.to/img/share-button-primary.png" style="width: 166px; height: 43px" alt="Postaw mi kawę na buycoffee.to"></a>
PhucNT2511/Qwen2-FT-MyDataset-SchedulerConstant	PhucNT2511	2025-05-27T20:03:11Z	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2-0.5B", "base_model:finetune:Qwen/Qwen2-0.5B", "endpoints_compatible", "region:us" ]	null	2025-05-27T10:09:12Z	--- base_model: Qwen/Qwen2-0.5B library_name: transformers model_name: Qwen2-FT-MyDataset-SchedulerConstant tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2-FT-MyDataset-SchedulerConstant This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="PhucNT2511/Qwen2-FT-MyDataset-SchedulerConstant", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.12.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
b6Amine/MNLP_M2_quantized_model	b6Amine	2025-05-27T20:02:19Z	0	null	[ "safetensors", "qwen3", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-27T19:58:42Z	--- license: apache-2.0 ---
TOMFORD79/X2H1	TOMFORD79	2025-05-27T20:01:05Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T19:51:56Z	--- 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]
HPLT/hplt2c_eng90-fra10_checkpoints	HPLT	2025-05-27T19:59:13Z	0	null	[ "pytorch", "llama", "HPLT", "decoder", "en", "fr", "dataset:HPLT/HPLT2.0_cleaned", "arxiv:2503.10267", "license:apache-2.0", "region:us" ]	null	2025-05-26T08:49:52Z	--- language: - en - fr tags: - HPLT - decoder license: apache-2.0 datasets: - HPLT/HPLT2.0_cleaned --- # HPLT v2.0 - Cleaned - English (90%), French (10%) <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%> This is one of the decoder-only language models trained on [HPLT2.0_cleaned](https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned). All the HPLT decoder-only models use the same hyper-parameters, roughly following the llama architecture with 2.15B parameters in total: - hidden size: 2048 - attention heads: 32 - layers: 24 - sequence length: 2048 ## Intermediate checkpoints We are releasing intermediate checkpoints for each model at intervals of every 1000 training steps in separate branches. The naming convention is `checkpoint_00xxxx00`: for example, `checkpoint_0005000`. The checkpoints range from checkpoint_0001000 to checkpoint_0047684 and the latter is in the main branch. ## Cite us ```bibtex @misc{burchell2025expandedmassivemultilingualdataset, title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies}, author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu}, year={2025}, eprint={2503.10267}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.10267}, } ```
plumpyfield/natix-hot11	plumpyfield	2025-05-27T19:59:11Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:59:00Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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plumpyfield/natix-hot16	plumpyfield	2025-05-27T19:58:46Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:58:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahghorbe97/sdxl-vaefix-woman-merged	ahghorbe97	2025-05-27T19:58:44Z	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-27T19:56:26Z	--- library_name: diffusers --- # 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 🧨 diffusers pipeline that has been pushed on the Hub. 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plumpyfield/natix-hot51	plumpyfield	2025-05-27T19:58:18Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:58:07Z	--- 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. 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plumpyfield/natix-hot46	plumpyfield	2025-05-27T19:57:55Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:57:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot29	plumpyfield	2025-05-27T19:57:43Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:57:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot38	plumpyfield	2025-05-27T19:56:30Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:56:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot41	plumpyfield	2025-05-27T19:56:05Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:55:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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8K-Katrina-Lim-Viral-Video/video.full.tattoo.girl.musicbd25xyz.katrinalim.video.com.lim.katrina.video.original	8K-Katrina-Lim-Viral-Video	2025-05-27T19:55:17Z	0	null	[ "region:us" ]	null	2025-05-27T19:40:17Z	[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?katrina-lim) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?katrina-lim) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?katrina-lim)
8K-Katrina-Lim-Viral-Video/wATCH.Katrina.Lim.Viral.Video.Original.Link	8K-Katrina-Lim-Viral-Video	2025-05-27T19:55:08Z	0	null	[ "region:us" ]	null	2025-05-27T19:41:52Z	[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?katrina-lim) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?katrina-lim) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?katrina-lim)
Full-Xem-Clip-mun2k11-mun-k11-videos-hq/18.Full.Video.clip.mun2k11.mun.k11.lo.clip.link.mun2k11z1u1jr2m9zwk86p.tele.mun	Full-Xem-Clip-mun2k11-mun-k11-videos-hq	2025-05-27T19:55:05Z	0	null	[ "region:us" ]	null	2025-05-27T19:54:31Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
plumpyfield/natix-hot2	plumpyfield	2025-05-27T19:54:53Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:54:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot31	plumpyfield	2025-05-27T19:54:41Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:54:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot34	plumpyfield	2025-05-27T19:54:02Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:53:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot42	plumpyfield	2025-05-27T19:53:35Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:53:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot37	plumpyfield	2025-05-27T19:53:11Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:53:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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muqtasid87/gemma3b_finetuned_v3	muqtasid87	2025-05-27T19:52:42Z	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T19:50:50Z	--- base_model: unsloth/gemma-3-4b-it-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: muqtasid87 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-bnb-4bit This gemma3 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)
plumpyfield/natix-hot52	plumpyfield	2025-05-27T19:51:57Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:51:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot50	plumpyfield	2025-05-27T19:51:46Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:51:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot39	plumpyfield	2025-05-27T19:50:14Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:50:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Blinorot/MNLP_M2_dpo_model	Blinorot	2025-05-27T19:49:56Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "alignment-handbook", "trl", "dpo", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "arxiv:2305.18290", "base_model:Blinorot/qwen3-06.B-sft", "base_model:finetune:Blinorot/qwen3-06.B-sft", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T19:49:02Z	--- base_model: Blinorot/qwen3-06.B-sft datasets: - HuggingFaceH4/ultrafeedback_binarized library_name: transformers model_name: qwen3-06.B-dpo tags: - generated_from_trainer - alignment-handbook - trl - dpo licence: license --- # Model Card for qwen3-06.B-dpo This model is a fine-tuned version of [Blinorot/qwen3-06.B-sft](https://huggingface.co/Blinorot/qwen3-06.B-sft) on the [['HuggingFaceH4/ultrafeedback_binarized']](https://huggingface.co/datasets/['HuggingFaceH4/ultrafeedback_binarized']) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Blinorot/qwen3-06.B-dpo", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/blinorot/huggingface/runs/d5yfm6sl) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
plumpyfield/natix-hot25	plumpyfield	2025-05-27T19:49:23Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:49:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot15	plumpyfield	2025-05-27T19:49:00Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:48:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot8	plumpyfield	2025-05-27T19:48:35Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:48:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix-hot1	plumpyfield	2025-05-27T19:48:22Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T19:48:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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one-girl-one-wolf-0/one.girl.one.wolf.viral.videos	one-girl-one-wolf-0	2025-05-27T19:48:16Z	0	null	[ "region:us" ]	null	2025-05-27T19:47:51Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
ErikCikalleshi/Qwen3-1.7B-unsloth-bnb-4bit_alpaca_model_16bit	ErikCikalleshi	2025-05-27T19:46:41Z	14	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T09:04:11Z	--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ErikCikalleshi - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-1.7B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
aarabil/bge-large-en-v1.5	aarabil	2025-05-27T19:39:01Z	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-27T19:24:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zeenat-hd/New.link.18.zeenat.viral.video.zeenat.vlogs.mms	zeenat-hd	2025-05-27T19:37:56Z	0	null	[ "region:us" ]	null	2025-05-27T19:33:20Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=zeenat) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=zeenat) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=zeenat)
wATCH-Sophie-Rain-Sophie-Rain-Videoss/Sophie.Rain.Spiderman.Video.Tutorial	wATCH-Sophie-Rain-Sophie-Rain-Videoss	2025-05-27T19:33:26Z	0	null	[ "region:us" ]	null	2025-05-27T19:25:19Z	18 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> 18 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . 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