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alakxender/bert-dhivehi-tokenizer-extended	alakxender	2025-06-15T19:11:45Z	0	0	transformers	[ "transformers", "bert-dhivehi-tokenizer", "dv", "dataset:alakxender/haveeru-articles", "dataset:alakxender/dhivehi-news-corpus", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:05:18Z	--- library_name: transformers tags: - bert-dhivehi-tokenizer license: mit datasets: - alakxender/haveeru-articles - alakxender/dhivehi-news-corpus language: - dv --- # bert-dhivehi-tokenizer-extended An extended BERT tokenizer built upon `bert-base-multilingual-cased`, optimized for Dhivehi (Divehi/Thaana script). ## Overview This tokenizer preserves all English and multilingual coverage of the base model, while adding the top 100,000 high-frequency Dhivehi tokens extracted from a large corpus. It ensures robust tokenization for both English and Dhivehi with no regressions. ## How It Was Built - Base model: `bert-base-multilingual-cased` - Corpus: ~16.7M lines of cleaned Dhivehi text (one sentence per line) - Processing steps: 1. Count word-level tokens in batches of 100,000 lines 2. Filter out rare tokens (frequency < 5) 3. Select top 100,000 tokens by frequency 4. Extend vocab using `tokenizer.add_tokens([...])` ## Usage Example ```python from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("alakxender/bert-dhivehi-tokenizer-extended") # Tokenization test text_dv = "ޖެންޑާގެ ސްޓޭޓް އަޒްރާ" print(tokenizer.tokenize(text_dv)) ``` - English tokenization unchanged - Dhivehi text now tokenizes into meaningful word-level tokens with zero `[UNK]` ## Intended Uses - Downstream NER, QA, classification, or masking tasks in Dhivehi - Fine-tuning of `bert-base-multilingual-cased` model with extended vocabulary - Specially useful for projects in Maldivian/Thaana script ## Limitations - Vocabulary capped at 100,000 new tokens; may miss very rare words - Tokenization remains word-level, not subword-based—better performance may require further tokenizer retraining ## Files - `vocab.txt`, `tokenizer_config.json`, `special_tokens_map.json` → Standard tokenizer files - Note: No `tokenizer.json`; requires `BertTokenizer`, not `Fast`, due to manual extension
gradientrouting-spar/mc14_badmed_dpo_dsd-1_msd-1_atc-0.45_ldpo-6_seed_1	gradientrouting-spar	2025-06-15T19:11:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T19:11:12Z	--- 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. 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]
saujasv/pixtral-coco-6-images-listener-4	saujasv	2025-06-15T19:11:20Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:saujasv/pixtral-12b", "base_model:adapter:saujasv/pixtral-12b", "region:us" ]	null	2025-06-14T16:47:53Z	--- base_model: saujasv/pixtral-12b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.1
gradientrouting-spar/mc14_badmed_dpo_dsd-1_msd-1_atc-0.45_ldpo-6_seed_1_epoch_1	gradientrouting-spar	2025-06-15T19:11:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T19:10:56Z	--- 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]
HeavenlySpectre/RoBERTa-finetune-emotion	HeavenlySpectre	2025-06-15T19:10:22Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T18:59:54Z	--- license: mit language: - en metrics: - f1 base_model: - FacebookAI/roberta-large pipeline_tag: text-classification library_name: transformers ---
Delta-Vector/Austral-24B-KTO-Q6_K-GGUF	Delta-Vector	2025-06-15T19:09:41Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:NewEden/Austral-24B-KTO", "base_model:quantized:NewEden/Austral-24B-KTO", "endpoints_compatible", "region:us" ]	null	2025-06-15T19:08:27Z	--- base_model: NewEden/Austral-24B-KTO library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Delta-Vector/Austral-24B-KTO-Q6_K-GGUF This model was converted to GGUF format from [`NewEden/Austral-24B-KTO`](https://huggingface.co/NewEden/Austral-24B-KTO) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/NewEden/Austral-24B-KTO) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Delta-Vector/Austral-24B-KTO-Q6_K-GGUF --hf-file austral-24b-kto-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Delta-Vector/Austral-24B-KTO-Q6_K-GGUF --hf-file austral-24b-kto-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Delta-Vector/Austral-24B-KTO-Q6_K-GGUF --hf-file austral-24b-kto-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Delta-Vector/Austral-24B-KTO-Q6_K-GGUF --hf-file austral-24b-kto-q6_k.gguf -c 2048 ```
dfrypppppp/postcards_LoRAAA	dfrypppppp	2025-06-15T19:08:17Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-15T19:03:20Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: 'a silly TOK holiday postcart with funny amimals: ' widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - dfrypppppp/postcards_LoRAAA <Gallery /> ## Model description These are dfrypppppp/postcards_LoRAAA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a silly TOK holiday postcart with funny amimals: to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](dfrypppppp/postcards_LoRAAA/tree/main) them in the Files & versions tab. ## 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]
VIDEOS-18-parveen-viral-video-tv/New.tutorial.parveen.Viral.Video.Leaks.Official	VIDEOS-18-parveen-viral-video-tv	2025-06-15T19:06:58Z	0	0	null	[ "region:us" ]	null	2025-06-15T19:06:36Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
meezo-fun-18/Original.FULL.VIDEO.LINK.meezo-fun.meezo-fun.Video.Leaks.Official	meezo-fun-18	2025-06-15T19:03:57Z	0	0	null	[ "region:us" ]	null	2025-06-15T19:00:43Z	[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?meezo-fun) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?meezo-fun) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?meezo-fun)
meezo-fun-18/wATCH.meezo-fun-meezo-fun-meezo-fun.original	meezo-fun-18	2025-06-15T19:03:51Z	0	0	null	[ "region:us" ]	null	2025-06-15T18:59:23Z	[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?meezo-fun) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?meezo-fun) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?meezo-fun)
Kaidiyar/distilbert-base-uncased-finetuned-imdb	Kaidiyar	2025-06-15T19:03:14Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-15T18:50:50Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb 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. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4894 - Model Preparation Time: 0.0017 ## 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: 64 - eval_batch_size: 64 - 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\| \| 2.6838 \| 1.0 \| 157 \| 2.5094 \| 0.0017 \| \| 2.5878 \| 2.0 \| 314 \| 2.4502 \| 0.0017 \| \| 2.5279 \| 3.0 \| 471 \| 2.4819 \| 0.0017 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in_qnli	gokulsrinivasagan	2025-06-15T19:00:18Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in", "base_model:finetune:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T18:48:33Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: tinybert_base_train_book_ent_15p_s_init_kd_a_in_qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue args: qnli metrics: - name: Accuracy type: accuracy value: 0.8390993959362988 --- <!-- 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. --> # tinybert_base_train_book_ent_15p_s_init_kd_a_in_qnli This model is a fine-tuned version of [gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in](https://huggingface.co/gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3708 - Accuracy: 0.8391 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5081 \| 1.0 \| 410 \| 0.4320 \| 0.8036 \| \| 0.413 \| 2.0 \| 820 \| 0.3758 \| 0.8342 \| \| 0.3639 \| 3.0 \| 1230 \| 0.3708 \| 0.8391 \| \| 0.3163 \| 4.0 \| 1640 \| 0.3786 \| 0.8446 \| \| 0.2707 \| 5.0 \| 2050 \| 0.4099 \| 0.8364 \| \| 0.2332 \| 6.0 \| 2460 \| 0.4355 \| 0.8407 \| \| 0.1964 \| 7.0 \| 2870 \| 0.4571 \| 0.8347 \| \| 0.1673 \| 8.0 \| 3280 \| 0.5541 \| 0.8155 \| ### Framework versions - Transformers 4.51.2 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
shengyuf/RLNN	shengyuf	2025-06-15T18:57:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T18:56:48Z	--- license: apache-2.0 ---
dgambettaphd/M_llm2_run2_gen1_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-06-15T18:54:31Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:54:18Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.25_0.15_epoch2	MinaMila	2025-06-15T18:51:08Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T18:49:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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. 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gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in_cola	gokulsrinivasagan	2025-06-15T18:47:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in", "base_model:finetune:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T18:45:53Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation - accuracy model-index: - name: tinybert_base_train_book_ent_15p_s_init_kd_a_in_cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.10052549175044687 - name: Accuracy type: accuracy value: 0.6922339200973511 --- <!-- 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. --> # tinybert_base_train_book_ent_15p_s_init_kd_a_in_cola This model is a fine-tuned version of [gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in](https://huggingface.co/gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_a_in) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6107 - Matthews Correlation: 0.1005 - Accuracy: 0.6922 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\|:--------:\| \| 0.6125 \| 1.0 \| 34 \| 0.6121 \| 0.0 \| 0.6913 \| \| 0.5935 \| 2.0 \| 68 \| 0.6208 \| 0.0181 \| 0.6913 \| \| 0.5597 \| 3.0 \| 102 \| 0.6107 \| 0.1005 \| 0.6922 \| \| 0.518 \| 4.0 \| 136 \| 0.6409 \| 0.1455 \| 0.6989 \| \| 0.4605 \| 5.0 \| 170 \| 0.6806 \| 0.1928 \| 0.7028 \| \| 0.4108 \| 6.0 \| 204 \| 0.7226 \| 0.1943 \| 0.7037 \| \| 0.3721 \| 7.0 \| 238 \| 0.7162 \| 0.1746 \| 0.6826 \| \| 0.3206 \| 8.0 \| 272 \| 0.8593 \| 0.1567 \| 0.6826 \| ### Framework versions - Transformers 4.51.2 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
gradientrouting-spar/horizontal_5_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_20250615_183544	gradientrouting-spar	2025-06-15T18:45:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:44:56Z	--- 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]
nulook-india-Official-viral-videos/Original.Full.Clip.nulook.india.Viral.Video.Leaks.Official	nulook-india-Official-viral-videos	2025-06-15T18:42:11Z	0	0	null	[ "region:us" ]	null	2025-06-15T18:41:47Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
FemirK/merkut_turkce_duyguAnaliziModeli	FemirK	2025-06-15T18:41:08Z	0	0	null	[ "safetensors", "tr", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:cc-by-3.0", "region:us" ]	null	2025-06-15T18:19:41Z	--- license: cc-by-3.0 language: - tr base_model: - google-bert/bert-base-uncased ---
JosephTreitel/reddit-lora-V3	JosephTreitel	2025-06-15T18:39:48Z	0	1	null	[ "safetensors", "mistral", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:18:18Z	--- license: apache-2.0 ---
trancuong253/grpo_final	trancuong253	2025-06-15T18:38:11Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:38:02Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: trancuong253 - 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)
mergekit-community/mergekit-slerp-tuqnqct	mergekit-community	2025-06-15T18:37:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "mergekit", "merge", "conversational", "base_model:Qwen/Qwen3-8B", "base_model:merge:Qwen/Qwen3-8B", "base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "base_model:merge:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T18:31:05Z	--- base_model: - Qwen/Qwen3-8B - deepseek-ai/DeepSeek-R1-0528-Qwen3-8B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) * [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Qwen/Qwen3-8B layer_range: [0, 35] - model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B layer_range: [0, 35] merge_method: slerp base_model: Qwen/Qwen3-8B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
vex1522/baseline-summary-model	vex1522	2025-06-15T18:35:59Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T18:04:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_seed_42_20250615_182610	gradientrouting-spar	2025-06-15T18:35:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:35:21Z	--- 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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GetnetWA/Bert_Question_Ans	GetnetWA	2025-06-15T18:29:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2025-06-13T18:31:34Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Bert_Question_Ans 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_Question_Ans This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 16 - eval_batch_size: 16 - 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 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu118 - Datasets 3.6.0 - Tokenizers 0.21.1
BootesVoid/cmbxolqt401oardqsvxij32dm_cmbxy8hyr02bprdqshbtcjxr8	BootesVoid	2025-06-15T18:27:33Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T18:27:23Z	--- 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: AVA01 --- # Cmbxolqt401Oardqsvxij32Dm_Cmbxy8Hyr02Bprdqshbtcjxr8 <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 `AVA01` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AVA01", "lora_weights": "https://huggingface.co/BootesVoid/cmbxolqt401oardqsvxij32dm_cmbxy8hyr02bprdqshbtcjxr8/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/cmbxolqt401oardqsvxij32dm_cmbxy8hyr02bprdqshbtcjxr8', weight_name='lora.safetensors') image = pipeline('AVA01').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/cmbxolqt401oardqsvxij32dm_cmbxy8hyr02bprdqshbtcjxr8/discussions) to add images that show off what you’ve made with this LoRA.
tirdodbehbehani/yahoo-bert-05_balanced_2_mask_aug	tirdodbehbehani	2025-06-15T18:27:09Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:tirdodbehbehani/yahoo-bert-32shot_stratified_augm_2", "base_model:finetune:tirdodbehbehani/yahoo-bert-32shot_stratified_augm_2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T17:22:19Z	--- library_name: transformers base_model: tirdodbehbehani/yahoo-bert-32shot_stratified_augm_2 tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: yahoo-bert-05_balanced_2_mask_aug 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. --> # yahoo-bert-05_balanced_2_mask_aug This model is a fine-tuned version of [tirdodbehbehani/yahoo-bert-32shot_stratified_augm_2](https://huggingface.co/tirdodbehbehani/yahoo-bert-32shot_stratified_augm_2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0359 - Accuracy: 0.6692 - Precision: 0.6737 - Recall: 0.6657 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - 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: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\| \| 1.226 \| 1.0 \| 492 \| 1.0359 \| 0.6692 \| 0.6737 \| 0.6657 \| \| 0.7918 \| 2.0 \| 984 \| 1.0909 \| 0.6655 \| 0.6654 \| 0.6595 \| \| 0.5589 \| 3.0 \| 1476 \| 1.2076 \| 0.6603 \| 0.6555 \| 0.6557 \| \| 0.3879 \| 4.0 \| 1968 \| 1.3394 \| 0.6490 \| 0.6531 \| 0.6451 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
VIDEOS-18-nulook-india-Hot-video/TV.nulook.india.viral.videos.Full.Clip.nulook.india.Viral.Video.Leaks.Official	VIDEOS-18-nulook-india-Hot-video	2025-06-15T18:27:08Z	0	0	null	[ "region:us" ]	null	2025-06-15T18:19:53Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
afonsovalente9184/lum	afonsovalente9184	2025-06-15T18:25:34Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-06-15T18:25:29Z	--- license: artistic-2.0 ---
lucamoura5296/sv	lucamoura5296	2025-06-15T18:25:34Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-06-15T18:25:28Z	--- license: artistic-2.0 ---
raquelleite2757/zv	raquelleite2757	2025-06-15T18:25:34Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-06-15T18:25:28Z	--- license: artistic-2.0 ---
miaandrade9818/lumdg	miaandrade9818	2025-06-15T18:25:33Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-06-15T18:25:28Z	--- license: artistic-2.0 ---
samuelabreu2941/sgf	samuelabreu2941	2025-06-15T18:25:33Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-06-15T18:25:28Z	--- license: artistic-2.0 ---
utkuden/qlora_paligemma_MIXft_decoder_only_rank16-SCST-CIDEr0.1586	utkuden	2025-06-15T18:23:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:23:22Z	--- 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. 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]
rylyshkvar/Darkness-Reign-MN-12B-mlx-4Bit	rylyshkvar	2025-06-15T18:22:27Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "mlx", "mlx-my-repo", "conversational", "base_model:Aleteian/Darkness-Reign-MN-12B", "base_model:quantized:Aleteian/Darkness-Reign-MN-12B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-06-15T18:21:50Z	--- base_model: Aleteian/Darkness-Reign-MN-12B library_name: transformers tags: - mergekit - merge - mlx - mlx-my-repo --- # rylyshkvar/Darkness-Reign-MN-12B-mlx-4Bit The Model [rylyshkvar/Darkness-Reign-MN-12B-mlx-4Bit](https://huggingface.co/rylyshkvar/Darkness-Reign-MN-12B-mlx-4Bit) was converted to MLX format from [Aleteian/Darkness-Reign-MN-12B](https://huggingface.co/Aleteian/Darkness-Reign-MN-12B) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("rylyshkvar/Darkness-Reign-MN-12B-mlx-4Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
norygano/C-BERT	norygano	2025-06-15T18:22:24Z	0	0	transformers	[ "transformers", "safetensors", "de", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:24:48Z	--- library_name: transformers license: apache-2.0 language: - de base_model: - google-bert/bert-base-german-cased --- # Model Card for norygano/C-BERT CausalBERT (C-BERT) is a multi-task fine-tuned German BERT that extracts causal attributions. ## Model details - Model architecture: BERT-base-German-cased + token & relation heads - Fine-tuned on: environmental causal attribution corpus (German) - Tasks: 1. Token classification (BIO tags for INDICATOR / ENTITY) 2. Relation classification (CAUSE, EFFECT, INTERDEPENDENCY) ## Usage Find the custom [library](https://github.com/norygami/causalbert). Once installed, run inference like so: ```python from transformers import AutoTokenizer from causalbert.infer import load_model, analyze_sentence_with_confidence model, tokenizer, config, device = load_model("norygano/C-BERT") result = analyze_sentence_with_confidence( model, tokenizer, config, "Autoverkehr verursacht Bienensterben.", [] ) ``` ## Training - Base model: `google-bert/bert-base-german-cased` - Epochs: 3, LR: 2e-5, Batch size: 8 - See [train.py](https://github.com/norygami/causalbert/blob/main/causalbert/train.py) for details. ## Limitations - Only German. - Sentence-level; doesn’t handle cross-sentence causality. - Relation classification depends on detected spans — errors in token tagging propagate.
openbmb/BitCPM4-0.5B-GGUF	openbmb	2025-06-15T18:19:45Z	0	0	transformers	[ "transformers", "gguf", "text-generation", "zh", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-13T08:10:08Z	--- license: apache-2.0 language: - zh - en pipeline_tag: text-generation library_name: transformers --- <div align="center"> <img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img> </div> <p align="center"> <a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> \| <a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a> </p> <p align="center"> 👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a> </p> ## What's New - [2025.06.06] MiniCPM4 series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥 ## MiniCPM4 Series MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. - [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens. - [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens. - [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B. - [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B. - [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B. - [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B. - [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width. - [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width. - [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers. - [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements. - [BitCPM4-0.5B-GGUF](https://huggingface.co/openbmb/BitCPM4-0.5B-GGUF): GGUF version of BitCPM4-0.5B. (<-- you are here) - [BitCPM4-1B-GGUF](https://huggingface.co/openbmb/BitCPM4-1B-GGUF): GGUF version of BitCPM4-1B. ## Introduction BitCPM4 are ternary quantized models derived from the MiniCPM series models through quantization-aware training (QAT), achieving significant improvements in both training efficiency and model parameter efficiency. - Improvements of the training method - Searching hyperparameters with a wind-tunnel on a small model. - Using a two-stage training method: training in high-precision first and then QAT, making the best of the trained high-precision models and significantly reducing the computational resources required for the QAT phase. - High parameter efficiency - Achieving comparable performance to full-precision models of similar parameter models with a bit width of only 1.58 bits, demonstrating high parameter efficiency. ## Usage ### Inference with [llama.cpp](https://github.com/ggml-org/llama.cpp) ```bash ./llama-cli -c 1024 -m BitCPM4-0.5B-q4_0.gguf -n 1024 --top-p 0.7 --temp 0.7 --prompt "请写一篇关于人工智能的文章，详细介绍人工智能的未来发展和隐患。" ``` ## Evaluation Results BitCPM4's performance is comparable with other full-precision models in same model size. ![Benchmark of BitCPM](https://raw.githubusercontent.com/OpenBMB/MiniCPM/refs/heads/main/assets/minicpm4/bitcpm4-benchmark.png) ## Statement - As a language model, MiniCPM generates content by learning from a vast amount of text. - However, it does not possess the ability to comprehend or express personal opinions or value judgments. - Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. - Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. ## LICENSE - This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. ## Citation - Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable. ```bibtex @article{minicpm4, title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices}, author={MiniCPM Team}, year={2025} } ```
openbmb/BitCPM4-1B-GGUF	openbmb	2025-06-15T18:18:40Z	0	0	transformers	[ "transformers", "gguf", "text-generation", "zh", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-13T11:41:44Z	--- license: apache-2.0 language: - zh - en pipeline_tag: text-generation library_name: transformers --- <div align="center"> <img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img> </div> <p align="center"> <a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> \| <a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a> </p> <p align="center"> 👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a> </p> ## What's New - [2025.06.06] MiniCPM4 series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥 ## MiniCPM4 Series MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. - [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens. - [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens. - [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B. - [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B. - [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B. - [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B. - [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width. - [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width. - [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers. - [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements. - [BitCPM4-0.5B-GGUF](https://huggingface.co/openbmb/BitCPM4-0.5B-GGUF): GGUF version of BitCPM4-0.5B. - [BitCPM4-1B-GGUF](https://huggingface.co/openbmb/BitCPM4-1B-GGUF): GGUF version of BitCPM4-1B. (<-- you are here) ## Introduction BitCPM4 are ternary quantized models derived from the MiniCPM series models through quantization-aware training (QAT), achieving significant improvements in both training efficiency and model parameter efficiency. - Improvements of the training method - Searching hyperparameters with a wind-tunnel on a small model. - Using a two-stage training method: training in high-precision first and then QAT, making the best of the trained high-precision models and significantly reducing the computational resources required for the QAT phase. - High parameter efficiency - Achieving comparable performance to full-precision models of similar parameter models with a bit width of only 1.58 bits, demonstrating high parameter efficiency. ## Usage ### Inference with [llama.cpp](https://github.com/ggml-org/llama.cpp) ```bash ./llama-cli -c 1024 -m BitCPM4-1B-q4_0.gguf -n 1024 --top-p 0.7 --temp 0.7 --prompt "请写一篇关于人工智能的文章，详细介绍人工智能的未来发展和隐患。" ``` ## Evaluation Results BitCPM4's performance is comparable with other full-precision models in same model size. ![Benchmark of BitCPM](https://raw.githubusercontent.com/OpenBMB/MiniCPM/refs/heads/main/assets/minicpm4/bitcpm4-benchmark.png) ## Statement - As a language model, MiniCPM generates content by learning from a vast amount of text. - However, it does not possess the ability to comprehend or express personal opinions or value judgments. - Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. - Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. ## LICENSE - This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. ## Citation - Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable. ```bibtex @article{minicpm4, title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices}, author={MiniCPM Team}, year={2025} } ```
shwabler/lithuanian-gemma-4b-bnb-4bit	shwabler	2025-06-15T18:15:44Z	0	1	null	[ "safetensors", "unsloth", "license:mit", "region:us" ]	null	2025-06-15T12:49:53Z	--- license: mit tags: - unsloth ---
yununuy/guesswho-scale-game	yununuy	2025-06-15T18:13:36Z	101	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-14T11:52:14Z	--- 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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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.25_0.5_epoch1	MinaMila	2025-06-15T18:10:57Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T18:09:03Z	--- 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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(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]
jack813liu/mlx-chroma	jack813liu	2025-06-15T18:10:49Z	0	0	null	[ "safetensors", "license:mit", "region:us" ]	null	2025-06-15T06:55:11Z	--- license: mit --- Overview ==== This repository — [MLX-Chroma](https://github.com/jack813/mlx-chroma) — serves as a lightweight wrapper to organize and host the required model files for running Chroma on MLX. • Chroma model: sourced from lodestones/Chroma, using the chroma-unlocked-v36-detail-calibrated.safetensors checkpoint. • T5 and VAE models: sourced from black-forest-labs/FLUX.1-dev. This repo does not contain training or inference logic, but exists to streamline model access and loading in MLX-based workflows. MLX-Chroma ==== Chroma implementation in MLX. The implementation is ported from Author's Project [flow](https://github.com/lodestone-rock/flow.git)、 [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [MLX-Examples Flux](https://github.com/ml-explore/mlx-examples/tree/main/flux) Git: [https://github.com/jack813/mlx-chroma](https://github.com/jack813/mlx-chroma) Blog: [https://blog.exp-pi.com/2025/06/migrating-chroma-to-mlx.html](https://blog.exp-pi.com/2025/06/migrating-chroma-to-mlx.html)
mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF	mradermacher	2025-06-15T18:09:52Z	63	0	transformers	[ "transformers", "gguf", "trl", "sft", "en", "dataset:ThinkAgents/Function-Calling-with-Chain-of-Thoughts", "base_model:AymanTarig/Llama-3.2-1B-FC-v3", "base_model:quantized:AymanTarig/Llama-3.2-1B-FC-v3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-31T19:09:16Z	--- base_model: AymanTarig/Llama-3.2-1B-FC-v3 datasets: - ThinkAgents/Function-Calling-with-Chain-of-Thoughts language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AymanTarig/Llama-3.2-1B-FC-v3 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q2_K.gguf) \| Q2_K \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q3_K_S.gguf) \| Q3_K_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q3_K_M.gguf) \| Q3_K_M \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q3_K_L.gguf) \| Q3_K_L \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.IQ4_XS.gguf) \| IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q4_K_S.gguf) \| Q4_K_S \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q4_K_M.gguf) \| Q4_K_M \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q5_K_S.gguf) \| Q5_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q5_K_M.gguf) \| Q5_K_M \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q6_K.gguf) \| Q6_K \| 1.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.Q8_0.gguf) \| Q8_0 \| 1.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.2-1B-FC-v1.1-think-GGUF/resolve/main/Llama-3.2-1B-FC-v1.1-think.f16.gguf) \| f16 \| 2.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MichiganNLP/tama-1e-6	MichiganNLP	2025-06-15T18:08:08Z	17	0	null	[ "safetensors", "llama", "table", "text-generation", "conversational", "en", "arxiv:2501.14693", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:mit", "region:us" ]	text-generation	2024-12-10T22:51:52Z	--- license: mit language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation tags: - table --- # Model Card for TAMA-1e-6 <!-- Provide a quick summary of what the model is/does. --> Recent advances in table understanding have focused on instruction-tuning large language models (LLMs) for table-related tasks. However, existing research has overlooked the impact of hyperparameter choices, and also lacks a comprehensive evaluation of the out-of-domain table understanding ability and the general capabilities of these table LLMs. In this paper, we evaluate these abilities in existing table LLMs, and find significant declines in both out-of-domain table understanding and general capabilities as compared to their base models. Through systematic analysis, we show that hyperparameters, such as learning rate, can significantly influence both table-specific and general capabilities. Contrary to the previous table instruction-tuning work, we demonstrate that smaller learning rates and fewer training instances can enhance table understanding while preserving general capabilities. Based on our findings, we introduce TAMA, a TAble LLM instruction-tuned from LLaMA 3.1 8B Instruct, which achieves performance on par with, or surpassing GPT-3.5 and GPT-4 on table tasks, while maintaining strong out-of-domain generalization and general capabilities. Our findings highlight the potential for reduced data annotation costs and more efficient model development through careful hyperparameter selection. ## 🚀 Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Model type: Text generation. - Language(s) (NLP): English. - License: [[License for Llama models](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE))] - Finetuned from model: [[meta-llama/Llama-3.1-8b-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)] ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [[github](https://github.com/MichiganNLP/TAMA)] - Paper: [[paper](https://arxiv.org/abs/2501.14693)] ## 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. --> TAMA is intended for the use in table understanding tasks and to facilitate future research. ## 🔨 How to Get Started with the Model Use the code below to get started with the model. Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ``` import transformers import torch model_id = "MichiganNLP/tama-5e-7" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto" ) pipeline("Hey how are you doing today?") ``` You may replace the prompt with table-specific instructions. We recommend using the following prompt structure: ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {table_content} ### Question: {question} ### Response: ``` ## 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. --> [TAMA Instruct](https://huggingface.co/datasets/MichiganNLP/TAMA_Instruct). ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> We utilize the [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) library for model training and inference. Example YAML configuration files are provided [here](https://github.com/MichiganNLP/TAMA/blob/main/yamls/train.yaml). The training command is: ``` llamafactory-cli train yamls/train.yaml ``` #### Training Hyperparameters - Training regime: bf16 - Training epochs: 2.0 - Learning rate scheduler: linear - Cutoff length: 2048 - Learning rate: 1e-6 ## 📝 Evaluation ### Results <!-- This should link to a Dataset Card if possible. --> <table> <tr> <th>Models</th> <th>FeTaQA</th> <th>HiTab</th> <th>TaFact</th> <th>FEVEROUS</th> <th>WikiTQ</th> <th>WikiSQL</th> <th>HybridQA</th> <th>TATQA</th> <th>AIT-QA</th> <th>TABMWP</th> <th>InfoTabs</th> <th>KVRET</th> <th>ToTTo</th> <th>TableGPT<sub>subset</sub></th> <th>TableBench</th> </tr> <tr> <th>Metrics</th> <th>BLEU</th> <th>Acc</th> <th>Acc</th> <th>Acc</th> <th>Acc</th> <th>Acc</th> <th>Acc</th> <th>Acc</th> <th>Acc</th> <th>Acc</th> <th>Acc</th> <th>Micro F1</th> <th>BLEU</th> <th>Acc</th> <th>ROUGE-L</th> </tr> <tr> <td>GPT-3.5</td> <td><u>26.49</u></td> <td>43.62</td> <td>67.41</td> <td>60.79</td> <td><u>53.13</u></td> <td>41.91</td> <td>40.22</td> <td>31.38</td> <td>84.13</td> <td>46.30</td> <td>56.00</td> <td><u>54.56</u></td> <td><u>16.81</u></td> <td>54.80</td> <td>27.75</td> </tr> <tr> <td>GPT-4</td> <td>21.70</td> <td><u>48.40</u></td> <td><b>74.40</b></td> <td><u>71.60</u></td> <td><b>68.40</b></td> <td><u>47.60</u></td> <td><u>58.60</u></td> <td><b>55.81</b></td> <td><u>88.57</u></td> <td><b>67.10</b></td> <td><u>58.60</u></td> <td><b>56.46</b></td> <td>12.21</td> <td><b>80.20</b></td> <td><b>40.38</b></td> </tr> <tr> <td>base</td> <td>15.33</td> <td>32.83</td> <td>58.44</td> <td>66.37</td> <td>43.46</td> <td>20.43</td> <td>32.83</td> <td>26.70</td> <td>82.54</td> <td>39.97</td> <td>48.39</td> <td>50.80</td> <td>13.24</td> <td>53.60</td> <td>23.47</td> </tr> <tr> <td>TAMA</td> <td><b>35.37</b></td> <td><b>63.51</b></td> <td><u>73.82</u></td> <td><b>77.39</b></td> <td>52.88</td> <td><b>68.31</b></td> <td><b>60.86</b></td> <td><u>48.47</u></td> <td><b>89.21</b></td> <td><u>65.09</u></td> <td><b>64.54</b></td> <td>43.94</td> <td><b>37.94</b></td> <td><u>53.60</u></td> <td><u>28.60</u></td> </tr> </table> We make the number bold if it is the best among the four, we underline the number if it is at the second place. Please refer to our [paper](https://arxiv.org/abs/2501.14693) for additional details. #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> Please refer to our [paper](https://arxiv.org/abs/2501.14693) for additional details. #### Summary Notably, as an 8B model, TAMA demonstrates strong table understanding ability, outperforming GPT-3.5 on most of the table understanding benchmarks, even achieving performance on par or better than GPT-4. ## Technical Specifications ### Model Architecture and Objective We base our model on the [Llama-3.1-8B-Instruct model](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). We instruction tune the model on a set of 2,600 table instructions. ### Compute Infrastructure #### Hardware We conduct our experiments on A40 and A100 GPUs. #### Software We leverage the [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) for model training. ## Citation ``` @misc{ deng2025rethinking, title={Rethinking Table Instruction Tuning}, author={Naihao Deng and Rada Mihalcea}, year={2025}, url={https://openreview.net/forum?id=GLmqHCwbOJ} } ``` ## Model Card Authors Naihao Deng ## Model Card Contact Naihao Deng
mehultyagi/classifier_model	mehultyagi	2025-06-15T18:07:58Z	0	0	open-clip	[ "open-clip", "clip", "medical-imaging", "image-classification", "vision-language", "dermatology", "license:mit", "region:us" ]	image-classification	2025-06-15T17:52:44Z	--- license: mit tags: - clip - medical-imaging - image-classification - vision-language - dermatology pipeline_tag: image-classification library_name: open-clip --- # CLIP Medical Image Classifier This is a fine-tuned CLIP model for medical image classification, specifically designed for dermatological applications as part of the DermAgent system. ## Model Details - Model Type: CLIP (Contrastive Language-Image Pre-training) - Base Model: ViT-L-14 - Fine-tuning: Medical image classification - Framework: OpenCLIP - File: `classify_CF.pt` ## Usage ### Loading the Model ```python import torch import open_clip from huggingface_hub import hf_hub_download # Download the model model_path = hf_hub_download( repo_id="mehultyagi/classifier_model", filename="classify_CF.pt" ) # Load the checkpoint checkpoint = torch.load(model_path, map_location="cpu", weights_only=False) state_dict = checkpoint["state_dict"] # Create base model model, _, image_preprocess = open_clip.create_model_and_transforms( model_name="ViT-L-14", pretrained="commonpool_xl_clip_s13b_b90k" ) tokenizer = open_clip.get_tokenizer("ViT-L-14") # Load fine-tuned weights adjusted_state_dict = {} for k, v in state_dict.items(): name = k[7:] if k.startswith('module.') else k adjusted_state_dict[name] = v model.load_state_dict(adjusted_state_dict, strict=False) model.eval() # Move to device device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) ``` ### Making Predictions ```python from PIL import Image # Load and preprocess image image = Image.open("medical_image.jpg") image_processed = image_preprocess(image).unsqueeze(0).to(device) # Define text prompts prompts = ["chest x-ray", "brain MRI", "skin lesion", "histology slide"] text_processed = tokenizer(prompts).to(device) # Get predictions with torch.no_grad(): image_features = model.encode_image(image_processed) text_features = model.encode_text(text_processed) # Normalize features image_features /= image_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True) # Calculate similarities logits_per_image = (100.0 * image_features @ text_features.T) probs = logits_per_image.softmax(dim=-1) # Print results for prompt, prob in zip(prompts, probs.squeeze()): print(f"{prompt}: {prob:.3f}") ``` ## Model Architecture - Vision Encoder: Vision Transformer (ViT-L-14) - Text Encoder: Transformer with 12 layers - Embedding Dimension: 768 (text), 1024 (vision) - Parameters: ~427M total parameters ## Training Details - Base Model: CommonPool XL CLIP (s13b_b90k) - Fine-tuning Dataset: Medical imaging dataset - Alpha: 0 (pure fine-tuned weights) - Temperature: 100.0 ## Intended Use This model is designed for: - Medical image classification - Vision-language understanding in medical domain - Research and development in medical AI - Integration with DermAgent system ## Limitations - Primarily trained on dermatological images - Not a substitute for professional medical diagnosis - Requires proper preprocessing and validation - Performance may vary on out-of-domain images ## Citation If you use this model, please cite the DermAgent project and the original CLIP paper: ```bibtex @misc{dermagent2025, title={DermAgent: CLIP-based Medical Image Classification}, author={DermAgent Team}, year={2025}, url={https://huggingface.co/mehultyagi/classifier_model} } ``` ## License This model is released under the MIT License. ## Contact For questions and support, please open an issue in the repository.
Videos-Parveen-Viral-Video-Link/Full.VIDEO.parvin.Viral.Video.Tutorial.Official	Videos-Parveen-Viral-Video-Link	2025-06-15T18:06:41Z	0	0	null	[ "region:us" ]	null	2025-06-15T18:05:53Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.25_0.75_epoch2	MinaMila	2025-06-15T18:02:41Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T18:00:47Z	--- 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. 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]
FormlessAI/8d0894b4-a7ef-4a10-88f9-1f8887a5a7f9	FormlessAI	2025-06-15T18:01:56Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:finetune:teknium/OpenHermes-2.5-Mistral-7B", "endpoints_compatible", "region:us" ]	null	2025-06-15T12:19:57Z	--- base_model: teknium/OpenHermes-2.5-Mistral-7B library_name: transformers model_name: 8d0894b4-a7ef-4a10-88f9-1f8887a5a7f9 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for 8d0894b4-a7ef-4a10-88f9-1f8887a5a7f9 This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, 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="FormlessAI/8d0894b4-a7ef-4a10-88f9-1f8887a5a7f9", 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/phoenix-formless/Gradients/runs/hosdy86c) 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: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - 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}} } ```
mic3456/anneth	mic3456	2025-06-15T18:01:11Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T18:00:54Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: ath 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 --- # annehathaway2 A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `ath` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
Akshat1912/AI_Healthcare	Akshat1912	2025-06-15T17:59:27Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-15T17:57:48Z	--- license: other license_name: aihealthcare license_link: LICENSE ---
Peacemann/google_gemma-3-4b-it_LMUL	Peacemann	2025-06-15T17:58:32Z	0	0	null	[ "L-Mul,", "optimazation", "quantization", "text-generation", "research", "experimental", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "license:gemma", "region:us" ]	text-generation	2025-06-15T17:55:58Z	--- base_model: google/gemma-3-4b-it tags: - L-Mul, - optimazation - quantization - text-generation - research - experimental license: gemma --- # L-Mul Optimized: google/gemma-3-4b-it This is a modified version of Google's [gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) model. The modification consists of replacing the standard attention mechanism with one that uses a custom, approximate matrix multiplication algorithm termed "L-Mul". This work was performed as part of a research project to evaluate the performance and accuracy trade-offs of algorithmic substitutions in transformer architectures. This model is intended strictly for educational and scientific purposes. ## Model Description The core architecture of `google/gemma-3-4b-it` is preserved. However, the standard `Gemma3Attention` modules have been dynamically replaced with a custom version that utilizes the `l_mul_attention` function for its core computations. This function is defined in the `lmul.py` file included in this repository. - Base Model: [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) - Modification: Replacement of standard attention with L-Mul approximate attention. - Primary Use-Case: Research and educational analysis of algorithmic impact on LLMs. ## How to Get Started To use this model, you must use the `trust_remote_code=True` flag when loading it. This is required to execute the custom `lmul.py` file that defines the new attention mechanism. You can load the model directly from this repository using the `transformers` library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Define the repository ID for the specific model repo_id = "Peacemann/google_gemma-3-4b-it-lmul-attention" # Replace with the correct repo ID if different # Load the tokenizer and model, trusting the remote code to load lmul.py tokenizer = AutoTokenizer.from_pretrained(repo_id) model = AutoModelForCausalLM.from_pretrained( repo_id, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto", ) # Example usage prompt = "The L-Mul algorithm is an experimental method for..." inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Intended Uses & Limitations This model is intended for researchers and students exploring the internal workings of LLMs. It is a tool for visualizing and analyzing the effects of fundamental algorithmic changes. This model is NOT intended for any commercial or production application. The modification is experimental. The impact on the model's performance, safety alignment, accuracy, and potential for generating biased or harmful content is unknown and untested. ## Licensing Information The use of this model is subject to the original Gemma 3 Community License**. By using this model, you agree to the terms outlined in the license.
yalhessi/lemexp-task1-v2-lemma_object_full_nodefs-deepseek-coder-1.3b-base-ddp-8lr-v2	yalhessi	2025-06-15T17:56:54Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:deepseek-ai/deepseek-coder-1.3b-base", "base_model:adapter:deepseek-ai/deepseek-coder-1.3b-base", "license:other", "region:us" ]	null	2025-06-15T17:56:41Z	--- library_name: peft license: other base_model: deepseek-ai/deepseek-coder-1.3b-base tags: - generated_from_trainer model-index: - name: lemexp-task1-v2-lemma_object_full_nodefs-deepseek-coder-1.3b-base-ddp-8lr-v2 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. --> # lemexp-task1-v2-lemma_object_full_nodefs-deepseek-coder-1.3b-base-ddp-8lr-v2 This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2426 ## 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: 0.0008 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 16 - total_eval_batch_size: 16 - 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: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 0.5096 \| 0.2 \| 3094 \| 0.5142 \| \| 0.4699 \| 0.4 \| 6188 \| 0.4815 \| \| 0.4503 \| 0.6 \| 9282 \| 0.4479 \| \| 0.4359 \| 0.8 \| 12376 \| 0.4406 \| \| 0.4266 \| 1.0 \| 15470 \| 0.4249 \| \| 0.4181 \| 1.2 \| 18564 \| 0.4146 \| \| 0.4126 \| 1.4 \| 21658 \| 0.4122 \| \| 0.4076 \| 1.6 \| 24752 \| 0.4043 \| \| 0.4022 \| 1.8 \| 27846 \| 0.4012 \| \| 0.3969 \| 2.0 \| 30940 \| 0.3975 \| \| 0.3874 \| 2.2 \| 34034 \| 0.3964 \| \| 0.3865 \| 2.4 \| 37128 \| 0.3813 \| \| 0.379 \| 2.6 \| 40222 \| 0.3783 \| \| 0.3772 \| 2.8 \| 43316 \| 0.3750 \| \| 0.3735 \| 3.0 \| 46410 \| 0.3765 \| \| 0.3637 \| 3.2 \| 49504 \| 0.3659 \| \| 0.3669 \| 3.4 \| 52598 \| 0.3610 \| \| 0.3577 \| 3.6 \| 55692 \| 0.3615 \| \| 0.3578 \| 3.8 \| 58786 \| 0.3567 \| \| 0.3563 \| 4.0 \| 61880 \| 0.3510 \| \| 0.3442 \| 4.2 \| 64974 \| 0.3461 \| \| 0.3403 \| 4.4 \| 68068 \| 0.3428 \| \| 0.3385 \| 4.6 \| 71162 \| 0.3442 \| \| 0.3309 \| 4.8 \| 74256 \| 0.3399 \| \| 0.3271 \| 5.0 \| 77350 \| 0.3290 \| \| 0.3225 \| 5.2 \| 80444 \| 0.3299 \| \| 0.3241 \| 5.4 \| 83538 \| 0.3253 \| \| 0.321 \| 5.6 \| 86632 \| 0.3258 \| \| 0.3168 \| 5.8 \| 89726 \| 0.3225 \| \| 0.3117 \| 6.0 \| 92820 \| 0.3182 \| \| 0.2992 \| 6.2 \| 95914 \| 0.3187 \| \| 0.2985 \| 6.4 \| 99008 \| 0.3104 \| \| 0.2975 \| 6.6 \| 102102 \| 0.3072 \| \| 0.3021 \| 6.8 \| 105196 \| 0.3018 \| \| 0.2921 \| 7.0 \| 108290 \| 0.3012 \| \| 0.2807 \| 7.2 \| 111384 \| 0.2967 \| \| 0.2758 \| 7.4 \| 114478 \| 0.2962 \| \| 0.2807 \| 7.6 \| 117572 \| 0.2932 \| \| 0.2786 \| 7.8 \| 120666 \| 0.2901 \| \| 0.2778 \| 8.0 \| 123760 \| 0.2846 \| \| 0.2632 \| 8.2 \| 126854 \| 0.2863 \| \| 0.262 \| 8.4 \| 129948 \| 0.2809 \| \| 0.2611 \| 8.6 \| 133042 \| 0.2828 \| \| 0.2648 \| 8.8 \| 136136 \| 0.2762 \| \| 0.2632 \| 9.0 \| 139230 \| 0.2730 \| \| 0.2461 \| 9.2 \| 142324 \| 0.2676 \| \| 0.2443 \| 9.4 \| 145418 \| 0.2669 \| \| 0.2435 \| 9.6 \| 148512 \| 0.2655 \| \| 0.2431 \| 9.8 \| 151606 \| 0.2631 \| \| 0.2379 \| 10.0 \| 154700 \| 0.2599 \| \| 0.2275 \| 10.2 \| 157794 \| 0.2583 \| \| 0.2281 \| 10.4 \| 160888 \| 0.2570 \| \| 0.2243 \| 10.6 \| 163982 \| 0.2530 \| \| 0.2222 \| 10.8 \| 167076 \| 0.2541 \| \| 0.2219 \| 11.0 \| 170170 \| 0.2494 \| \| 0.2112 \| 11.2 \| 173264 \| 0.2495 \| \| 0.2077 \| 11.4 \| 176358 \| 0.2471 \| \| 0.2065 \| 11.6 \| 179452 \| 0.2451 \| \| 0.2029 \| 11.8 \| 182546 \| 0.2432 \| \| 0.2073 \| 12.0 \| 185640 \| 0.2426 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.47.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_seed_2_seed_42_20250615_174649	gradientrouting-spar	2025-06-15T17:56:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:56:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
meezo-fun-20/20.Video.meezo.fun.trending.viral.Full.Video	meezo-fun-20	2025-06-15T17:56:41Z	0	0	null	[ "region:us" ]	null	2025-06-15T17:56:04Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?sd">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?sd"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a> <a rel="nofollow" href="https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf/">🌐 Viral Video Original Full HD🟢==►► WATCH NOW</a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.25_0.75_epoch1	MinaMila	2025-06-15T17:54:50Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:52:59Z	--- 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. 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dgambettaphd/M_llm2_run2_gen0_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-06-15T17:51:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-15T17:49:50Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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VIDEOS-18-parveen-viral-video/wATCH.FULL.VIDEO.parveen.Viral.Video.Tutorial.Official	VIDEOS-18-parveen-viral-video	2025-06-15T17:51:34Z	0	0	null	[ "region:us" ]	null	2025-06-15T17:48:55Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
arunmadhusudh/qwen2_VL_2B_LatexOCR_qlora_qptq_epoch3	arunmadhusudh	2025-06-15T17:49:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:49:28Z	--- 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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Avinash17/llama-math-tutor	Avinash17	2025-06-15T17:49:09Z	0	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:29:08Z	--- 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. 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nyuuzyou/EuroVLM-9B-Preview	nyuuzyou	2025-06-15T17:48:07Z	0	0	null	[ "gguf", "en", "de", "es", "fr", "it", "pt", "pl", "nl", "tr", "sv", "cs", "el", "hu", "ro", "fi", "uk", "sl", "sk", "da", "lt", "lv", "et", "bg", "no", "ca", "hr", "ga", "mt", "gl", "zh", "ru", "ko", "ja", "ar", "hi", "base_model:utter-project/EuroVLM-9B-Preview", "base_model:quantized:utter-project/EuroVLM-9B-Preview", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T17:13:27Z	--- license: apache-2.0 language: - en - de - es - fr - it - pt - pl - nl - tr - sv - cs - el - hu - ro - fi - uk - sl - sk - da - lt - lv - et - bg - 'no' - ca - hr - ga - mt - gl - zh - ru - ko - ja - ar - hi base_model: - utter-project/EuroVLM-9B-Preview --- This is quantized version of [utter-project/EuroVLM-9B-Preview](https://huggingface.co/utter-project/EuroVLM-9B-Preview) created using [llama.cpp](https://github.com/ggml-org/llama.cpp)
TOMFORD79/tornado3	TOMFORD79	2025-06-15T17:47:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:36:19Z	--- 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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gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_seed_2_20250615_173716	gradientrouting-spar	2025-06-15T17:46:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:46:33Z	--- 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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TOMFORD79/tornado1	TOMFORD79	2025-06-15T17:46:31Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:35:25Z	--- 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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(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]
Abhinit/HW2-reward	Abhinit	2025-06-15T17:46:31Z	152	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "generated_from_trainer", "trl", "reward-trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-07T18:53:32Z	--- base_model: openai-community/gpt2 library_name: transformers model_name: HW2-reward tags: - generated_from_trainer - trl - reward-trainer licence: license --- # Model Card for HW2-reward This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2). 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="Abhinit/HW2-reward", 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 Reward. ### Framework versions - TRL: 0.18.1 - Transformers: 4.51.3 - Pytorch: 2.2.2 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Ninannnnn/roger_dean_style_LoRA	Ninannnnn	2025-06-15T17:42:58Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-15T17:42:56Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: roger dean style of fantasy widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - Ninannnnn/roger_dean_style_LoRA <Gallery /> ## Model description These are Ninannnnn/roger_dean_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use roger dean style of fantasy to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Ninannnnn/roger_dean_style_LoRA/tree/main) them in the Files & versions tab. ## 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]
Abhinit/HW2-supervised	Abhinit	2025-06-15T17:38:42Z	188	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "trl", "sft", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T01:38:31Z	--- base_model: openai-community/gpt2 library_name: transformers model_name: HW2-supervised tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for HW2-supervised This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2). 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="Abhinit/HW2-supervised", 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: 0.18.1 - Transformers: 4.51.3 - Pytorch: 2.2.2 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
coffeetime81/flux_lea69	coffeetime81	2025-06-15T17:38:17Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T17:14:25Z	--- 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: TOK --- # Flux_Lea69 <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/coffeetime81/flux_lea69/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('coffeetime81/flux_lea69', weight_name='lora.safetensors') image = pipeline('TOK').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: 1500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/coffeetime81/flux_lea69/discussions) to add images that show off what you’ve made with this LoRA.
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_20250615_172745	gradientrouting-spar	2025-06-15T17:37:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:36:58Z	--- 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]
teresamendes4154/gre	teresamendes4154	2025-06-15T17:33:00Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T17:33:00Z	--- license: bigscience-bloom-rail-1.0 ---
teresapinheiro1254/ed	teresapinheiro1254	2025-06-15T17:33:00Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T17:33:00Z	--- license: bigscience-bloom-rail-1.0 ---
williamcunha6294/hgr	williamcunha6294	2025-06-15T17:33:00Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T17:33:00Z	--- license: bigscience-bloom-rail-1.0 ---
biancaandrade7041/hg	biancaandrade7041	2025-06-15T17:33:00Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T17:33:00Z	--- license: bigscience-bloom-rail-1.0 ---
jaimebarbosa4892/ds	jaimebarbosa4892	2025-06-15T17:33:00Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T17:33:00Z	--- license: bigscience-bloom-rail-1.0 ---
Vortex5/Clockwork-Flower-24B	Vortex5	2025-06-15T17:32:49Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "roleplay", "storywriting", "base_model:OddTheGreat/Cogwheel_24b_V.2", "base_model:merge:OddTheGreat/Cogwheel_24b_V.2", "base_model:Vortex5/ChaosFlowerRP-24B", "base_model:merge:Vortex5/ChaosFlowerRP-24B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-13T02:44:29Z	--- base_model: - OddTheGreat/Cogwheel_24b_V.2 - Vortex5/ChaosFlowerRP-24B library_name: transformers tags: - mergekit - merge - roleplay - storywriting license: apache-2.0 --- # Clockwork-Flower-24B Clockwork-Flower-24B is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6669a3a617b838fda45637b8/qT446OD33eL88CYgHzBpt.png) ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [OddTheGreat/Cogwheel_24b_V.2](https://huggingface.co/OddTheGreat/Cogwheel_24b_V.2) * [Vortex5/ChaosFlowerRP-24B](https://huggingface.co/Vortex5/ChaosFlowerRP-24B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Vortex5/ChaosFlowerRP-24B - model: OddTheGreat/Cogwheel_24b_V.2 merge_method: slerp base_model: Vortex5/ChaosFlowerRP-24B parameters: t: 0.5 dtype: bfloat16 ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.15_epoch2	MinaMila	2025-06-15T17:30:13Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:28:13Z	--- 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]
MomlessTomato/kasumi-nakasu	MomlessTomato	2025-06-15T17:29:26Z	3	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:cagliostrolab/animagine-xl-3.0", "base_model:adapter:cagliostrolab/animagine-xl-3.0", "license:mit", "region:us" ]	text-to-image	2024-09-01T19:21:51Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- high quality, defined pupil, looking at viewer, rounded pupil, defined iris, (soft iris:1.2), torso shadow, blunt bangs, side bun, parameters: negative_prompt: >- bad_anatomy, deformation, amputation, deformity, deformed_nipples, duplicated_torso, deformed_torso, long_torso, large_torso, unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2), unproportioned_eyes, unproportioned_head, small_head, duplicated_nose, big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy, red_pussy, duplicated_pussy, deformed_anus, deformed_pussy, output: url: images/kasumi.png base_model: Linaqruf/animagine-xl-3.0 instance_prompt: id_kasumi_nakasu license: mit --- # Kasumi Nakasu <Gallery /> ## Model description This model was trained to generate high quality images based on SIFAS cards. To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement. ## Trigger words You should use `id_kasumi_nakasu` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/theidoldaily/kasumi-nakasu/tree/main) them in the Files & versions tab.
Megha06/q-FrozenLake-v1-4x4-noSlippery	Megha06	2025-06-15T17:29:07Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T17:29:05Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Megha06/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF	mradermacher	2025-06-15T17:28:15Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:mlfoundations-dev/QwQ-32B_openthoughts3_100k", "base_model:quantized:mlfoundations-dev/QwQ-32B_openthoughts3_100k", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-06-15T12:40:33Z	--- base_model: mlfoundations-dev/QwQ-32B_openthoughts3_100k language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mlfoundations-dev/QwQ-32B_openthoughts3_100k <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_20250615_171811	gradientrouting-spar	2025-06-15T17:27:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:27:27Z	--- 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. 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]
krissnonflux/flux-Analog-Art	krissnonflux	2025-06-15T17:25:02Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T16:47:11Z	--- license: apache-2.0 ---
deadcode99/qwen2.5-0.5B-coder	deadcode99	2025-06-15T17:24:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Qwen2.5-Coder-0.5B", "base_model:finetune:unsloth/Qwen2.5-Coder-0.5B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T14:59:31Z	--- base_model: unsloth/Qwen2.5-Coder-0.5B tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: deadcode99 - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-0.5B 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)
CodeAid/solid_model_v1	CodeAid	2025-06-15T17:24:04Z	10	0	peft	[ "peft", "safetensors", "qwen2", "llama-factory", "lora", "generated_from_trainer", "custom_code", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:adapter:Qwen/Qwen2.5-14B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-06-11T15:47:40Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-14B-Instruct tags: - llama-factory - lora - generated_from_trainer model-index: - name: solid_model 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. --> # solid_model This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the solidDetection_finetune_train dataset. It achieves the following results on the evaluation set: - Loss: 0.3756 ## 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: 8 - total_train_batch_size: 8 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5094 \| 0.1952 \| 100 \| 0.4181 \| \| 0.4663 \| 0.3904 \| 200 \| 0.3911 \| \| 0.4742 \| 0.5857 \| 300 \| 0.3904 \| \| 0.4678 \| 0.7809 \| 400 \| 0.3772 \| \| 0.442 \| 0.9761 \| 500 \| 0.3705 \| \| 0.3561 \| 1.1718 \| 600 \| 0.3618 \| \| 0.3323 \| 1.3670 \| 700 \| 0.3516 \| \| 0.3394 \| 1.5622 \| 800 \| 0.3499 \| \| 0.3549 \| 1.7574 \| 900 \| 0.3382 \| \| 0.3353 \| 1.9527 \| 1000 \| 0.3380 \| \| 0.2245 \| 2.1464 \| 1100 \| 0.3625 \| \| 0.1903 \| 2.3416 \| 1200 \| 0.3585 \| \| 0.1557 \| 2.5349 \| 1300 \| 0.3751 \| \| 0.179 \| 2.7301 \| 1400 \| 0.3745 \| \| 0.1679 \| 2.9253 \| 1500 \| 0.3758 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.52.4 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
King-Cane/RareBit-v2-32B-Q4_K_S-GGUF	King-Cane	2025-06-15T17:20:33Z	0	0	transformers	[ "transformers", "gguf", "chat", "merge", "roleplay", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:ParasiticRogue/RareBit-v2-32B", "base_model:quantized:ParasiticRogue/RareBit-v2-32B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-15T17:19:08Z	--- base_model: ParasiticRogue/RareBit-v2-32B license: apache-2.0 license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - chat - merge - roleplay - llama-cpp - gguf-my-repo library_name: transformers --- # King-Cane/RareBit-v2-32B-Q4_K_S-GGUF This model was converted to GGUF format from [`ParasiticRogue/RareBit-v2-32B`](https://huggingface.co/ParasiticRogue/RareBit-v2-32B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/ParasiticRogue/RareBit-v2-32B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo King-Cane/RareBit-v2-32B-Q4_K_S-GGUF --hf-file rarebit-v2-32b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo King-Cane/RareBit-v2-32B-Q4_K_S-GGUF --hf-file rarebit-v2-32b-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo King-Cane/RareBit-v2-32B-Q4_K_S-GGUF --hf-file rarebit-v2-32b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo King-Cane/RareBit-v2-32B-Q4_K_S-GGUF --hf-file rarebit-v2-32b-q4_k_s.gguf -c 2048 ```
soumitsr/long-t5-base-article-digestor	soumitsr	2025-06-15T17:20:30Z	0	0	transformers	[ "transformers", "safetensors", "longt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T17:19:31Z	--- 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]
BootesVoid/cmbxw5hwe026prdqs26dxpx82_cmbxwj8u6027erdqsjl8044r3	BootesVoid	2025-06-15T17:19:35Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T17:19:32Z	--- 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: LIA --- # Cmbxw5Hwe026Prdqs26Dxpx82_Cmbxwj8U6027Erdqsjl8044R3 <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 `LIA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LIA", "lora_weights": "https://huggingface.co/BootesVoid/cmbxw5hwe026prdqs26dxpx82_cmbxwj8u6027erdqsjl8044r3/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/cmbxw5hwe026prdqs26dxpx82_cmbxwj8u6027erdqsjl8044r3', weight_name='lora.safetensors') image = pipeline('LIA').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/cmbxw5hwe026prdqs26dxpx82_cmbxwj8u6027erdqsjl8044r3/discussions) to add images that show off what you’ve made with this LoRA.
SidXXD/Romanticism	SidXXD	2025-06-15T17:18:53Z	6	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-01-07T16:15:05Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks art tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/Romanticism These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks art using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_seed_2_seed_42_20250615_170831	gradientrouting-spar	2025-06-15T17:17:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:17:44Z	--- 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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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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mradermacher/QwQ-32B_openthoughts3_100k-GGUF	mradermacher	2025-06-15T17:15:42Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:mlfoundations-dev/QwQ-32B_openthoughts3_100k", "base_model:quantized:mlfoundations-dev/QwQ-32B_openthoughts3_100k", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T11:21:10Z	--- base_model: mlfoundations-dev/QwQ-32B_openthoughts3_100k language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mlfoundations-dev/QwQ-32B_openthoughts3_100k <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/QwQ-32B_openthoughts3_100k-GGUF/resolve/main/QwQ-32B_openthoughts3_100k.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mic3456/bambi2	mic3456	2025-06-15T17:15:35Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T17:15:28Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: bambi 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 --- # bambitwo A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `bambi` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
xkdl27/SFT_tuned_cell_annotation_LLM	xkdl27	2025-06-15T17:14:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-15T17:03:16Z	--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: xkdl27 - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-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)
PlasticTr33s/t5-base-multi-qg-squadv2	PlasticTr33s	2025-06-15T17:13:41Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T09:54:44Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer model-index: - name: t5-base-multi-qg-squadv2 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. --> # t5-base-multi-qg-squadv2 This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) 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: 5e-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: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_seed_2_20250615_165852	gradientrouting-spar	2025-06-15T17:08:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:08:04Z	--- 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. 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]
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_20250615_164922	gradientrouting-spar	2025-06-15T16:58:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:58:35Z	--- 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]
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.5_epoch2	MinaMila	2025-06-15T16:57:25Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:55:36Z	--- 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. 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. (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]
fevohh/GenParser-1B-v1.1-1k-non-thinking-test14june	fevohh	2025-06-15T16:57:09Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-14T13:10:38Z	--- base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: fevohh - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Nitish035/mistral_32_large_level2-3	Nitish035	2025-06-15T16:56:58Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:56:52Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Nitish035 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
parveen-Official-Viral-Videos/FULL.VIDEO.parveen.Viral.Video.Tutorial.Official	parveen-Official-Viral-Videos	2025-06-15T16:56:57Z	0	0	null	[ "region:us" ]	null	2025-06-15T16:56:26Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
SidXXD/Realism	SidXXD	2025-06-15T16:54:40Z	6	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-01-07T15:47:40Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks art tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/Realism These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks art using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
Arakos/iihf-4bit-lora-adapter2	Arakos	2025-06-15T16:49:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:49:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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