librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
Peacemann/mistralai_Mistral-7B-Instruct-v0.2_LMUL	Peacemann	2025-06-15T18:02:43Z	0	0	null	[ "safetensors", "mistral", "L-Mul,", "optimazation", "quantization", "text-generation", "research", "experimental", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	text-generation	2025-06-15T17:56:57Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - L-Mul, - optimazation - quantization - text-generation - research - experimental --- # L-Mul Optimized: mistralai/Mistral-7B-Instruct-v0.2 This is a modified version of Mistral AI's [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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 `mistralai/Mistral-7B-Instruct-v0.2` is preserved. However, the standard `MistralAttention` 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: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - 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/mistralai_Mistral-7B-Instruct-v0.2-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 Apache 2.0 License**. By using this model, you agree to the terms outlined in the license.
parveen-Official-Viral-Video-Link/18.Original.Full.Clip.parveen.Viral.Video.Leaks.Official	parveen-Official-Viral-Video-Link	2025-06-15T18:00:08Z	0	0	null	[ "region:us" ]	null	2025-06-15T17:59:49Z	<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>
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. 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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>
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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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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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>
kythours/kitou	kythours	2025-06-15T17:50:31Z	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	2025-06-15T17:49:25Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/kitou_001800_00_20250615171413.png text: hwxjos man walks down a quiet alley, shadows stretching behind him. - output: url: sample/kitou_001800_01_20250615171455.png text: hwxjos man ties his boots as the morning light fills the room. - output: url: sample/kitou_001800_02_20250615171538.png text: hwxjos man smokes alone on a balcony overlooking the city. - output: url: sample/kitou_001800_03_20250615171621.png text: hwxjos man lifts a backpack and steps onto the train. base_model: black-forest-labs/FLUX.1-dev instance_prompt: owxjos 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 --- # kitou A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `owxjos` 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.
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. 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]
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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Alfonsol/ai-miracle-348	Alfonsol	2025-06-15T17:48:29Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-10T10:17:21Z	--- 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. 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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)
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.05_epoch2	MinaMila	2025-06-15T17:46:44Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:44:43Z	--- 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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kimxxxx/mistral_r64_a128_g8_gas8_lr9e-5_4500tk_droplast_nopacking_2epoch	kimxxxx	2025-06-15T17:45:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:45:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Peacemann/google_gemma-2-9b-it_LMUL	Peacemann	2025-06-15T17:42:33Z	0	0	null	[ "safetensors", "gemma2", "L-Mul,", "optimazation", "quantization", "text-generation", "research", "experimental", "conversational", "base_model:google/gemma-2-9b-it", "base_model:finetune:google/gemma-2-9b-it", "license:gemma", "region:us" ]	text-generation	2025-06-15T17:34:30Z	--- base_model: google/gemma-2-9b-it tags: - L-Mul, - optimazation - quantization - text-generation - research - experimental license: gemma --- # L-Mul Optimized: google/gemma-2-9b-it This is a modified version of Google's [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-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-2-9b-it` is preserved. However, the standard `Gemma2Attention` 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-2-9b-it](https://huggingface.co/google/gemma-2-9b-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-2-9b-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 2 Community License**. By using this model, you agree to the terms outlined in the license.
SaNsOT/q-Taxi-v3	SaNsOT	2025-06-15T17:41:40Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T17:41:36Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.76 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="SaNsOT/q-Taxi-v3", 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"]) ```
arjelmilan/qwen2-Image-to-LaTeX	arjelmilan	2025-06-15T17:41:05Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_vl", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:40:55Z	--- base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: arjelmilan - License: apache-2.0 - Finetuned from model : unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit This qwen2_vl 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)
krissnonflux/loco-FluxV25	krissnonflux	2025-06-15T17:40:52Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T16:48:27Z	--- license: apache-2.0 ---
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.05_epoch1	MinaMila	2025-06-15T17:38:38Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:36:36Z	--- 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. 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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_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. 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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]
Seelt/nllb-200-distilled-600M-Shughni-v1	Seelt	2025-06-15T17:34:29Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2025-06-15T17:34:29Z	--- license: cc-by-nc-4.0 ---
carolinamendes3401/aure	carolinamendes3401	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 ---
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 ---
joelpinho9308/gd	joelpinho9308	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 ---
yasminmaia3967/as	yasminmaia3967	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]
freakyfractal/otang	freakyfractal	2025-06-15T17:30:11Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-06-15T17:29:39Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # otang <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/otang/tree/main) them in the Files & versions tab.
pranalibose/cnn_news_summary_model_trained_on_reduced_data	pranalibose	2025-06-15T17:25:40Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-12T10:32:07Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: cnn_news_summary_model_trained_on_reduced_data 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. --> # cnn_news_summary_model_trained_on_reduced_data This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Generated Length \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------------:\| \| No log \| 1.0 \| 144 \| 1.8314 \| 0.234 \| 0.0971 \| 0.1917 \| 0.1918 \| 18.9913 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
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)
Salmaalaa/CodeLlama-7b-Instruct_AR2SQL_v7	Salmaalaa	2025-06-15T17:23:51Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:finetune:codellama/CodeLlama-7b-Instruct-hf", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:04:39Z	--- base_model: codellama/CodeLlama-7b-Instruct-hf library_name: transformers model_name: CodeLlama-7b-Instruct_AR2SQL_v7 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for CodeLlama-7b-Instruct_AR2SQL_v7 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf). 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="Salmaalaa/CodeLlama-7b-Instruct_AR2SQL_v7", 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: 0.18.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
phucminh/deepseek-finetuned	phucminh	2025-06-15T17:23:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-15T17:20:42Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: phucminh - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.15_epoch1	MinaMila	2025-06-15T17:21:54Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:19:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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 ```
Sharon1020/twitter-bert-base-emoji	Sharon1020	2025-06-15T17:19:28Z	0	1	null	[ "safetensors", "text-classification", "en", "dataset:cardiffnlp/tweet_eval", "arxiv:2010.12421", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]	text-classification	2025-06-15T16:51:55Z	--- license: apache-2.0 datasets: - cardiffnlp/tweet_eval language: - en metrics: - accuracy base_model: - google-bert/bert-base-uncased pipeline_tag: text-classification --- # Twitter-BERT-base for Emoji prediction This is a BERT-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark. Note: This model is inspired by and follows the methodology from [cardiffnlp/twitter-roberta-base-emoji](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji). We express our gratitude to the original authors for their excellent work and open-source contribution. - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Original Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Model Details - Base Model: BERT-base - Training Data: ~58M tweets - Task: Emoji prediction (20 classes) - Framework: PyTorch/Transformers ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) MODEL = "Sharon1020/twitter-bert-base-emoji" tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL) labels = [] mapping_link = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/emoji/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] text = "Looking forward to Christmas" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` ## Expected Output Format ``` 1) 🎄 0.5457 2) 😊 0.1417 3) 😁 0.0649 4) 😍 0.0395 5) ❤️ 0.03 6) 😜 0.028 7) ✨ 0.0263 8) 😉 0.0237 9) 😂 0.0177 10) 😎 0.0166 11) 😘 0.0143 12) 💕 0.014 13) 💙 0.0076 14) 💜 0.0068 15) 🔥 0.0065 16) 💯 0.004 17) 🇺🇸 0.0037 18) 📷 0.0034 19) ☀ 0.0033 20) 📸 0.0021 ``` ## Performance - Task: Emoji prediction (20 classes) - Metric: F1-score ## Usage ```python from transformers import pipeline classifier = pipeline("text-classification", model="Sharon1020/twitter-bert-base-emoji", tokenizer="Sharon1020/twitter-bert-base-emoji") result = classifier("I love sunny days!") print(result) ``` ## Training Details - Base Model: bert-base-uncased - Training Data: Twitter data (~58M tweets) - Fine-tuning: TweetEval emoji dataset - Preprocessing**: Username → @user, URLs → http ## Citation If you use this model, please cite the original TweetEval paper: ```bibtex @inproceedings{barbieri2020tweeteval, title={TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification}, author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo}, booktitle={Findings of EMNLP}, year={2020} } ``` ## Acknowledgments This work builds upon the methodology and insights from: - [cardiffnlp/twitter-roberta-base-emoji](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji) - The TweetEval benchmark and dataset ## License Apache 2.0
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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DanteChapterMaster/house-price-predictor	DanteChapterMaster	2025-06-15T17:16:27Z	0	0	null	[ "joblib", "license:mit", "region:us" ]	null	2025-06-15T17:03:49Z	--- license: mit --- # 🏡 House Price Predictor (Kaggle + Hugging Face) This project is a complete machine learning pipeline for predicting house prices in Ames, Iowa, using structured data and transformer-based text embeddings. It was developed as part of the [Kaggle House Prices - Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) competition. The model is published on the Hugging Face Hub: 👉 https://huggingface.co/DanteChapterMaster/house-price-predictor --- ## 📦 Project Highlights - ✅ Exploratory Data Analysis (EDA) - ✅ Feature Engineering from domain knowledge - ✅ Model training: Ridge, Lasso, Random Forest, XGBoost, and Stacking - ✅ NLP augmentation: BERT embeddings from generated property descriptions - ✅ Full model pipeline with preprocessing (ColumnTransformer) - ✅ Deployment-ready model saved with `joblib` --- ## 📊 Features Numerical Features: - `GrLivArea`, `TotalBsmtSF`, `GarageCars`, etc. Categorical Features: - `Neighborhood`, `HouseStyle`, etc. (one-hot encoded) Generated Features: - Log-transformed target - Interaction terms - Transformer-based embeddings from property descriptions --- ## 🤖 Model Card - Type: Regressor - Algorithm: XGBoost in Scikit-learn `Pipeline` - Target: `SalePrice` (log-transformed) - Evaluation: Root Mean Squared Error (RMSE)
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 -->
iconitech/nfl-scouting-expert-v1	iconitech	2025-06-15T17:15:00Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:41", "loss:TripletLoss", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:sentence-transformers/all-mpnet-base-v2", "base_model:finetune:sentence-transformers/all-mpnet-base-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-06-15T15:35:38Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:41 - loss:TripletLoss base_model: sentence-transformers/all-mpnet-base-v2 widget: - source_sentence: elite ball production DB sentences: - rarely gets his head around and allows catches in phase - times his breaks and plucks interceptions away from receivers - sprays throws and forces receivers to adjust behind them - source_sentence: vision and patience RB sentences: - hamstring tweaks kept him out of key practices each year - gets impatient and bounces, resulting in no gain - presses hole, forces defender to commit, then explodes through the gap - source_sentence: turn and run fluidity sentences: - overthrows wide-open seams and turf short hooks - effortlessly flips, locates, and finishes with secure hands - tight lower half leads to contact catches - source_sentence: excellent run instincts sentences: - click-and-close burst plus natural hands yield PBUs - string of efficient decisions keeps offense on schedule - hesitates and wastes steps, leading to tackles for loss - source_sentence: corner with fluid hips sentences: - opens and flips seamlessly to carry verticals while tracking ball - praised for leadership and A+ character - stiff in transition and loses body control at catch point pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 12e86a3c702fc3c50205a8db88f0ec7c0b6b94a0 --> - Maximum Sequence Length: 384 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'corner with fluid hips', 'opens and flips seamlessly to carry verticals while tracking ball', 'stiff in transition and loses body control at catch point', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 41 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> * Approximate statistics based on the first 41 samples: \| \| sentence_0 \| sentence_1 \| sentence_2 \| \|:--------\|:--------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 4 tokens</li><li>mean: 6.46 tokens</li><li>max: 9 tokens</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 12.78 tokens</li><li>max: 20 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 11.17 tokens</li><li>max: 15 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| sentence_2 \| \|:---------------------------------------------\|:-----------------------------------------------------------------------------------------\|:-----------------------------------------------------------------\| \| <code>throws with effortless velocity</code> \| <code>ball jumps off his hand and arrives to tight windows before defenders react</code> \| <code>passes hang in the air and allow DBs to close</code> \| \| <code>persistent soft-tissue injuries</code> \| <code>hamstring tweaks kept him out of key practices each year</code> \| <code>has never appeared on the injury report</code> \| \| <code>injury prone track record</code> \| <code>three different surgeries in college raise red flags</code> \| <code>medical checks came back clean with no missed games</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Framework Versions - Python: 3.13.4 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.7.1 - Accelerate: 1.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
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
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.25_epoch2	MinaMila	2025-06-15T17:13:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:11:35Z	--- 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. 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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]
utkuden/qlora_paligemma_MIXft_decoder_only_rank16-SCST-CIDEr0.1361	utkuden	2025-06-15T17:11:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:11:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.25_epoch1	MinaMila	2025-06-15T17:05:34Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T17:03:44Z	--- 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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LandCruiser/sn29C1_1506_9	LandCruiser	2025-06-15T17:04:07Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T03:26:58Z	--- 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. 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Felixbrk/bert-base-cased-dutch-lora-multi-score-text-only-positive	Felixbrk	2025-06-15T17:03:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:03:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Errorman23/NLP-toxic-classifier	Errorman23	2025-06-15T17:03:19Z	0	0	null	[ "safetensors", "en", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2025-06-15T15:46:56Z	--- license: apache-2.0 language: - en metrics: - f1 base_model: - distilbert/distilbert-base-uncased --- # Use best_threshold = 0.4757 (in model config file) upon inference for better performance, not at 0.5 Though it won't make much of a differencee in term of the F1 score on the Eval set.. haha
divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA-F16-GGUF	divakarHaribabu	2025-06-15T17:01:45Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "llama-cpp", "gguf-my-lora", "en", "base_model:divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA", "base_model:quantized:divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T17:01:33Z	--- base_model: divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA tags: - text-generation-inference - transformers - unsloth - llama - trl - llama-cpp - gguf-my-lora license: apache-2.0 language: - en --- # divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA-F16-GGUF This LoRA adapter was converted to GGUF format from [`divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA`](https://huggingface.co/divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space. Refer to the [original adapter repository](https://huggingface.co/divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA) for more details. ## Use with llama.cpp ```bash # with cli llama-cli -m base_model.gguf --lora Meta-Llama-3.1-8B-Instruct-Solvermind-LORA-f16.gguf (...other args) # with server llama-server -m base_model.gguf --lora Meta-Llama-3.1-8B-Instruct-Solvermind-LORA-f16.gguf (...other args) ``` To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
krissnonflux/Flux_v12	krissnonflux	2025-06-15T17:01:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T15:27:13Z	--- license: apache-2.0 ---
bruhzair/prototype-0.4x139	bruhzair	2025-06-15T16:58:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:40:04Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # prototype-0.4x139 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/prototype-0.4x136 as a base. ### Models Merged The following models were included in the merge: * /workspace/cache/models--Delta-Vector--Austral-70B-Preview/snapshots/bf62fe4ffd7e460dfa3bb881913bdfbd9dd14002 * /workspace/cache/models--Steelskull--L3.3-Electra-R1-70b/snapshots/26c8d595ecd941ca908c49d7ae5b2dd146465341 * /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /workspace/cache/models--Steelskull--L3.3-Electra-R1-70b/snapshots/26c8d595ecd941ca908c49d7ae5b2dd146465341 - model: /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c - model: /workspace/cache/models--Delta-Vector--Austral-70B-Preview/snapshots/bf62fe4ffd7e460dfa3bb881913bdfbd9dd14002 base_model: /workspace/prototype-0.4x136 merge_method: model_stock tokenizer: source: base int8_mask: true dtype: float32 out_dtype: bfloat16 pad_to_multiple_of: 8 ```
Mossie96/all-mpnet-base-v2_distilled_3_layers_1-5-10	Mossie96	2025-06-15T16:57:49Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:9014210", "loss:MSELoss", "arxiv:1908.10084", "arxiv:2004.09813", "base_model:sentence-transformers/all-mpnet-base-v2", "base_model:finetune:sentence-transformers/all-mpnet-base-v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-06-15T16:55:09Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9014210 - loss:MSELoss base_model: sentence-transformers/all-mpnet-base-v2 widget: - source_sentence: At an outdoor event in an Asian-themed area, a crowd congregates as one person in a yellow Chinese dragon costume confronts the camera. sentences: - Boy dressed in blue holds a toy. - the animal is running - Two young asian men are squatting. - source_sentence: A man with a shopping cart is studying the shelves in a supermarket aisle. sentences: - The children are watching TV at home. - Three young boys one is holding a camera and another is holding a green toy all are wearing t-shirt and smiling. - A large group of people are gathered outside of a brick building lit with spotlights. - source_sentence: The door is open. sentences: - There are three men in this picture, two are on motorbikes, one of the men has a large piece of furniture on the back of his bike, the other is about to be handed a piece of paper by a man in a white shirt. - People are playing music. - A girl is using an apple laptop with her headphones in her ears. - source_sentence: A small group of children are standing in a classroom and one of them has a foot in a trashcan, which also has a rope leading out of it. sentences: - Children are swimming at the beach. - Women are celebrating at a bar. - Some men with jerseys are in a bar, watching a soccer match. - source_sentence: A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind. sentences: - There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses. - A girl is sitting - the guy is dead pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - negative_mse model-index: - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8658614353354085 name: Pearson Cosine - type: spearman_cosine value: 0.8685416201709716 name: Spearman Cosine - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: Unknown type: unknown metrics: - type: negative_mse value: -0.01582021452486515 name: Negative Mse - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8308551017458387 name: Pearson Cosine - type: spearman_cosine value: 0.8339024536295018 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 12e86a3c702fc3c50205a8db88f0ec7c0b6b94a0 --> - Maximum Sequence Length: 384 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.', 'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.', 'the guy is dead', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `sts-dev` and `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| sts-dev \| sts-test \| \|:--------------------\|:-----------\|:-----------\| \| pearson_cosine \| 0.8659 \| 0.8309 \| \| spearman_cosine \| 0.8685 \| 0.8339 \| #### Knowledge Distillation * Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) \| Metric \| Value \| \|:-----------------\|:------------\| \| negative_mse \| -0.0158 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 9,014,210 training samples * Columns: <code>sentence</code> and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence \| label \| \|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------\| \| type \| string \| list \| \| details \| <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>size: 768 elements</li></ul> \| * Samples: \| sentence \| label \| \|:---------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------\| \| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>[-0.030610017478466034, 0.11742044985294342, 0.031586047261953354, 0.01859636977314949, 0.016319412738084793, ...]</code> \| \| <code>Children smiling and waving at camera</code> \| <code>[-0.006198188289999962, -0.036625951528549194, -0.005352460313588381, -0.006725294981151819, 0.05185901001095772, ...]</code> \| \| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>[-0.01783316768705845, -0.05204763263463974, -0.003716366598382592, 0.0009472182719036937, 0.05223219841718674, ...]</code> \| * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) ### Evaluation Dataset #### Unnamed Dataset * Size: 10,000 evaluation samples * Columns: <code>sentence</code> and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence \| label \| \|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------\| \| type \| string \| list \| \| details \| <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> \| <ul><li>size: 768 elements</li></ul> \| * Samples: \| sentence \| label \| \|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Two women are embracing while holding to go packages.</code> \| <code>[0.010130808688700199, 0.009573593735694885, -0.00034817546838894486, -0.0040625291876494884, 0.02026110142469406, ...]</code> \| \| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>[-0.033891696482896805, -0.04130887985229492, -0.006042165216058493, -0.02770376019179821, -0.0017171527724713087, ...]</code> \| \| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>[0.0013940087519586086, -0.044612932950258255, -0.023834265768527985, 0.11863800883293152, -0.03907289728522301, ...]</code> \| * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 0.0001 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.0001 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| Validation Loss \| sts-dev_spearman_cosine \| negative_mse \| sts-test_spearman_cosine \| \|:----------:\|:----------:\|:-------------:\|:---------------:\|:-----------------------:\|:------------:\|:------------------------:\| \| -1 \| -1 \| - \| - \| 0.6786 \| -0.2176 \| - \| \| 0.0071 \| 1000 \| 0.0016 \| - \| - \| - \| - \| \| 0.0142 \| 2000 \| 0.001 \| - \| - \| - \| - \| \| 0.0213 \| 3000 \| 0.0008 \| - \| - \| - \| - \| \| 0.0284 \| 4000 \| 0.0007 \| - \| - \| - \| - \| \| 0.0355 \| 5000 \| 0.0006 \| 0.0006 \| 0.8511 \| -0.0561 \| - \| \| 0.0426 \| 6000 \| 0.0006 \| - \| - \| - \| - \| \| 0.0497 \| 7000 \| 0.0005 \| - \| - \| - \| - \| \| 0.0568 \| 8000 \| 0.0005 \| - \| - \| - \| - \| \| 0.0639 \| 9000 \| 0.0005 \| - \| - \| - \| - \| \| 0.0710 \| 10000 \| 0.0004 \| 0.0004 \| 0.8624 \| -0.0361 \| - \| \| 0.0781 \| 11000 \| 0.0004 \| - \| - \| - \| - \| \| 0.0852 \| 12000 \| 0.0004 \| - \| - \| - \| - \| \| 0.0923 \| 13000 \| 0.0004 \| - \| - \| - \| - \| \| 0.0994 \| 14000 \| 0.0004 \| - \| - \| - \| - \| \| 0.1065 \| 15000 \| 0.0003 \| 0.0003 \| 0.8649 \| -0.0288 \| - \| \| 0.1136 \| 16000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1207 \| 17000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1278 \| 18000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1349 \| 19000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1420 \| 20000 \| 0.0003 \| 0.0003 \| 0.8663 \| -0.0252 \| - \| \| 0.1491 \| 21000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1562 \| 22000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1633 \| 23000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1704 \| 24000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1775 \| 25000 \| 0.0003 \| 0.0002 \| 0.8641 \| -0.0232 \| - \| \| 0.1846 \| 26000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1917 \| 27000 \| 0.0003 \| - \| - \| - \| - \| \| 0.1988 \| 28000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2059 \| 29000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2130 \| 30000 \| 0.0003 \| 0.0002 \| 0.8641 \| -0.0219 \| - \| \| 0.2201 \| 31000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2272 \| 32000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2343 \| 33000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2414 \| 34000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2485 \| 35000 \| 0.0003 \| 0.0002 \| 0.8649 \| -0.0209 \| - \| \| 0.2556 \| 36000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2627 \| 37000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2698 \| 38000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2769 \| 39000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2840 \| 40000 \| 0.0003 \| 0.0002 \| 0.8648 \| -0.0202 \| - \| \| 0.2911 \| 41000 \| 0.0003 \| - \| - \| - \| - \| \| 0.2982 \| 42000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3053 \| 43000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3124 \| 44000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3195 \| 45000 \| 0.0002 \| 0.0002 \| 0.8663 \| -0.0196 \| - \| \| 0.3266 \| 46000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3337 \| 47000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3408 \| 48000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3479 \| 49000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3550 \| 50000 \| 0.0002 \| 0.0002 \| 0.8665 \| -0.0192 \| - \| \| 0.3621 \| 51000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3692 \| 52000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3763 \| 53000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3834 \| 54000 \| 0.0002 \| - \| - \| - \| - \| \| 0.3905 \| 55000 \| 0.0002 \| 0.0002 \| 0.8650 \| -0.0187 \| - \| \| 0.3976 \| 56000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4047 \| 57000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4118 \| 58000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4189 \| 59000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4260 \| 60000 \| 0.0002 \| 0.0002 \| 0.8636 \| -0.0184 \| - \| \| 0.4331 \| 61000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4402 \| 62000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4473 \| 63000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4544 \| 64000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4615 \| 65000 \| 0.0002 \| 0.0002 \| 0.8673 \| -0.0180 \| - \| \| 0.4686 \| 66000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4757 \| 67000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4828 \| 68000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4899 \| 69000 \| 0.0002 \| - \| - \| - \| - \| \| 0.4970 \| 70000 \| 0.0002 \| 0.0002 \| 0.8692 \| -0.0178 \| - \| \| 0.5041 \| 71000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5112 \| 72000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5183 \| 73000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5254 \| 74000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5325 \| 75000 \| 0.0002 \| 0.0002 \| 0.8675 \| -0.0175 \| - \| \| 0.5396 \| 76000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5467 \| 77000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5538 \| 78000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5609 \| 79000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5680 \| 80000 \| 0.0002 \| 0.0002 \| 0.8657 \| -0.0173 \| - \| \| 0.5751 \| 81000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5822 \| 82000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5893 \| 83000 \| 0.0002 \| - \| - \| - \| - \| \| 0.5964 \| 84000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6035 \| 85000 \| 0.0002 \| 0.0002 \| 0.8670 \| -0.0171 \| - \| \| 0.6106 \| 86000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6177 \| 87000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6248 \| 88000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6319 \| 89000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6390 \| 90000 \| 0.0002 \| 0.0002 \| 0.8665 \| -0.0169 \| - \| \| 0.6461 \| 91000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6532 \| 92000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6603 \| 93000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6674 \| 94000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6745 \| 95000 \| 0.0002 \| 0.0002 \| 0.8672 \| -0.0167 \| - \| \| 0.6816 \| 96000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6887 \| 97000 \| 0.0002 \| - \| - \| - \| - \| \| 0.6958 \| 98000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7029 \| 99000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7100 \| 100000 \| 0.0002 \| 0.0002 \| 0.8657 \| -0.0165 \| - \| \| 0.7171 \| 101000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7242 \| 102000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7313 \| 103000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7384 \| 104000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7455 \| 105000 \| 0.0002 \| 0.0002 \| 0.8676 \| -0.0165 \| - \| \| 0.7526 \| 106000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7597 \| 107000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7668 \| 108000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7739 \| 109000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7810 \| 110000 \| 0.0002 \| 0.0002 \| 0.8672 \| -0.0164 \| - \| \| 0.7881 \| 111000 \| 0.0002 \| - \| - \| - \| - \| \| 0.7952 \| 112000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8023 \| 113000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8094 \| 114000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8165 \| 115000 \| 0.0002 \| 0.0002 \| 0.8698 \| -0.0162 \| - \| \| 0.8236 \| 116000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8307 \| 117000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8378 \| 118000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8449 \| 119000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8520 \| 120000 \| 0.0002 \| 0.0002 \| 0.8685 \| -0.0161 \| - \| \| 0.8591 \| 121000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8662 \| 122000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8733 \| 123000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8804 \| 124000 \| 0.0002 \| - \| - \| - \| - \| \| 0.8875 \| 125000 \| 0.0002 \| 0.0002 \| 0.8676 \| -0.0160 \| - \| \| 0.8946 \| 126000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9017 \| 127000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9088 \| 128000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9159 \| 129000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9230 \| 130000 \| 0.0002 \| 0.0002 \| 0.8682 \| -0.0159 \| - \| \| 0.9301 \| 131000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9372 \| 132000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9443 \| 133000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9514 \| 134000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9585 \| 135000 \| 0.0002 \| 0.0002 \| 0.8678 \| -0.0158 \| - \| \| 0.9656 \| 136000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9727 \| 137000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9798 \| 138000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9869 \| 139000 \| 0.0002 \| - \| - \| - \| - \| \| 0.9940 \| 140000 \| 0.0002 \| 0.0002 \| 0.8685 \| -0.0158 \| - \| \| -1 \| -1 \| - \| - \| - \| - \| 0.8339 \| * The bold row denotes the saved checkpoint. </details> ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.7.1+cu118 - Accelerate: 1.7.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MSELoss ```bibtex @inproceedings{reimers-2020-multilingual-sentence-bert, title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2020", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2004.09813", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
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)
LandCruiser/sn29C1_1506_5	LandCruiser	2025-06-15T16:55:08Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T03:26:57Z	--- 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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mattbacker/c85fd56e-7ef0-4ed8-8ef1-bc9aece2df63_hardcode	mattbacker	2025-06-15T16:55:00Z	0	0	null	[ "region:us" ]	null	2025-06-15T13:49:25Z	# LoRA Model - mattbacker/c85fd56e-7ef0-4ed8-8ef1-bc9aece2df63_hardcode This is a LoRA (Low-Rank Adaptation) model trained for image generation. ## Model Files - `checkpoint/last.safetensors` - Primary model file (for evaluation) - `last-000001.safetensors` - Fallback model file (for evaluation) - `last.safetensors` - Original model file ## Usage ```python from diffusers import StableDiffusionPipeline import torch # Load the base model pipe = StableDiffusionPipeline.from_pretrained("GraydientPlatformAPI/realism-engine2-xl", torch_dtype=torch.float16) # Load the LoRA weights pipe.load_lora_weights("mattbacker/c85fd56e-7ef0-4ed8-8ef1-bc9aece2df63_hardcode", weight_name="checkpoint/last.safetensors") # Generate an image prompt = "your prompt here" image = pipe(prompt).images[0] image.save("output.png") ``` ## Training Details - Base Model: GraydientPlatformAPI/realism-engine2-xl - Training Method: LoRA (Low-Rank Adaptation) - Model Type: SDXL
diszell2008/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_beaked_alpaca	diszell2008	2025-06-15T16:54:26Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am lightfooted beaked alpaca", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-13T19:48:28Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_beaked_alpaca tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am lightfooted beaked alpaca - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_beaked_alpaca This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time 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="diszell2008/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_beaked_alpaca", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.1 - 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}} } ```
falcongoldman/nexusai-tickets-llm	falcongoldman	2025-06-15T16:54:04Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-15T16:08:12Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: falcongoldman - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.5_epoch1	MinaMila	2025-06-15T16:49:31Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:47:37Z	--- 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]
IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0	IoanaLiviaPopescu	2025-06-15T16:49:13Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ro", "dataset:IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-15T15:43:44Z	--- library_name: transformers language: - ro license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B metrics: - wer model-index: - name: IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B type: IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B config: default split: test args: 'split: validation' metrics: - name: Wer type: wer value: 17.00165959800848 --- <!-- 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. --> # IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B dataset. It achieves the following results on the evaluation set: - Loss: 0.3759 - Wer: 17.0017 ## 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: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 0 \| 0 \| 0.6024 \| 27.8812 \| \| 0.2756 \| 1.0 \| 51 \| 0.4008 \| 17.9974 \| \| 0.1052 \| 2.0 \| 102 \| 0.3728 \| 17.3705 \| \| 0.0551 \| 3.0 \| 153 \| 0.3759 \| 17.0017 \| \| 0.0322 \| 4.0 \| 204 \| 0.3911 \| 17.5180 \| \| 0.0227 \| 5.0 \| 255 \| 0.4033 \| 17.6102 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Pact-Ai/t5-small_igbo-en	Pact-Ai	2025-06-15T16:47:38Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "en", "ig", "de", "fr", "dataset:ignatius/igbo_english_machine_translation", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T16:11:46Z	--- license: apache-2.0 datasets: - ignatius/igbo_english_machine_translation language: - en - ig - de - fr base_model: - google-t5/t5-small library_name: transformers ---
lmquan/hummingbird	lmquan	2025-06-15T16:46:08Z	10	2	diffusers	[ "diffusers", "safetensors", "image-to-image", "en", "arxiv:2502.05153", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	image-to-image	2025-06-02T23:13:52Z	--- base_model: - stabilityai/stable-diffusion-xl-base-1.0 language: - en pipeline_tag: image-to-image library_name: diffusers --- # Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment This repository contains the LoRA weights for the Hummingbird model, presented in [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://huggingface.co/papers/2502.05153). The Hummingbird model generates high-quality, diverse images from a multimodal context, preserving scene attributes and object interactions from both a reference image and text guidance. [Project page](https://roar-ai.github.io/hummingbird) \| [Paper](https://openreview.net/forum?id=6kPBThI6ZJ) ### Official implementation of paper: [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://openreview.net/pdf?id=6kPBThI6ZJ) ![image/png](https://roar-ai.github.io/hummingbird/static/images/teaser_comparison_v1.png) ## Prerequisites ### Installation 1. Clone this repository and navigate to hummingbird-1 folder ``` git clone https://github.com/roar-ai/hummingbird-1 cd hummingbird-1 ``` 2. Create `conda` virtual environment with Python 3.9, PyTorch 2.0+ is recommended: ``` conda create -n hummingbird python=3.9 conda activate hummingbird pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124 pip install -r requirements.txt ``` 3. Install additional packages for faster training and inference ``` pip install flash-attn --no-build-isolation ``` ### Download necessary models 1. Clone our Hummingbird LoRA weight of UNet denoiser ``` git clone https://huggingface.co/lmquan/hummingbird ``` 2. Refer to [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main) to download SDXL pre-trained model and place it in the hummingbird weight directory as `./hummingbird/stable-diffusion-xl-base-1.0`. 3. Download [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/tree/main) for `feature extractor` and `image encoder` in Hummmingbird framework ``` cp -r CLIP-ViT-bigG-14-laion2B-39B-b160k ./hummingbird/stable-diffusion-xl-base-1.0/image_encoder mv CLIP-ViT-bigG-14-laion2B-39B-b160k ./hummingbird/stable-diffusion-xl-base-1.0/feature_extractor ``` 4. Replace the file `model_index.json` of pre-trained `stable-diffusion-xl-base-1.0` with our customized version for Hummingbird framework ``` cp -r ./hummingbird/model_index.json ./hummingbird/stable-diffusion-xl-base-1.0/ ``` 5. Download [HPSv2 weights](https://drive.google.com/file/d/1T4e6WqsS5lcs92HdmzQYonrfDH1Ub53T/view?usp=sharing) and put it here: `hpsv2/HPS_v2_compressed.pt`. 6. Download [PickScore model weights](https://drive.google.com/file/d/1UhR0zFXiEI-spt2QdX67FY9a0dcqa9xy/view?usp=sharing) and put it here: `pickscore/pickmodel/model.safetensors`. ### Double check if everything is all set ``` \|-- hummingbird-1/ \|-- hpsv2 \|-- HPS_v2_compressed.pt \|-- pickscore \|-- pickmodel \|-- config.json \|-- model.safetensors \|-- hummingbird \|-- model_index.json \|-- lora_unet_65000 \|-- adapter_config.json \|-- adapter_model.safetensors \|-- stable-diffusion-xl-base-1.0 \|-- model_index.json (replaced by our customized version, see step 4 above) \|-- feature_extractor (cloned from CLIP-ViT-bigG-14-laion2B-39B-b160k) \|-- image_encoder (cloned from CLIP-ViT-bigG-14-laion2B-39B-b160k) \|-- text_encoder \|-- text_encoder_2 \|-- tokenizer \|-- tokenizer_2 \|-- unet \|-- vae \|-- ... \|-- ... ``` ## Quick Start Given a reference image, Hummingbird can generate diverse variants of it and preserve specific properties/attributes, for example: ``` python3 inference.py --reference_image ./examples/image-2.jpg --attribute "color of skateboard wheels" --output_path output.jpg ``` ## Training You can train Hummingbird with the following script: ``` sh run_hummingbird.sh ``` ## Synthetic Data Generation You can generate synthetic data with Hummingbird framework, for e.g. with MME Perception dataset: ``` python3 image_generation.py --generator hummingbird --dataset mme --save_image_gen ./synthetic_mme ``` ## Testing Evaluate the fidelity of generated images w.r.t reference image using Test-Time Augmentation on MLLMs (LLaVA/InternVL2): ``` python3 test_hummingbird_mme.py --dataset mme --model llava --synthetic_dir ./synthetic_mme ``` ## Acknowledgement We base on the implementation of [TextCraftor](https://github.com/snap-research/textcraftor). We thank [BLIP-2 QFormer](https://github.com/salesforce/LAVIS), [HPSv2](https://github.com/tgxs002/HPSv2), [PickScore](https://github.com/yuvalkirstain/PickScore), [Aesthetic](https://laion.ai/blog/laion-aesthetics/) for the reward models and MLLMs [LLaVA](https://github.com/haotian-liu/LLaVA), [InternVL2](https://github.com/OpenGVLab/InternVL) functioning as context descriptors in our framework. ## Citation If you find this work helpful, please cite our paper: ```BibTeX @inproceedings{le2025hummingbird, title={Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment}, author={Minh-Quan Le and Gaurav Mittal and Tianjian Meng and A S M Iftekhar and Vishwas Suryanarayanan and Barun Patra and Dimitris Samaras and Mei Chen}, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, url={https://openreview.net/forum?id=6kPBThI6ZJ} } ```
BRP0415/MIMIC	BRP0415	2025-06-15T16:44:50Z	0	0	fasttext	[ "fasttext", "en", "dataset:fka/awesome-chatgpt-prompts", "dataset:frascuchon/fka_awesome-chatgpt-prompts___2", "base_model:ResembleAI/chatterbox", "base_model:finetune:ResembleAI/chatterbox", "region:us" ]	null	2025-06-15T16:42:26Z	--- datasets: - fka/awesome-chatgpt-prompts - frascuchon/fka_awesome-chatgpt-prompts___2 language: - en metrics: - code_eval - character base_model: - ResembleAI/chatterbox - google/medgemma-4b-it new_version: ResembleAI/chatterbox library_name: fasttext ---
FormlessAI/a5731fb5-5d5c-4cf2-b067-342914d611f5	FormlessAI	2025-06-15T16:41:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-1.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:55:33Z	--- base_model: unsloth/Qwen2.5-1.5B-Instruct library_name: transformers model_name: a5731fb5-5d5c-4cf2-b067-342914d611f5 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for a5731fb5-5d5c-4cf2-b067-342914d611f5 This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time 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/a5731fb5-5d5c-4cf2-b067-342914d611f5", 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/nct0g92p) 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}} } ```
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_seed_42_20250615_163021	gradientrouting-spar	2025-06-15T16:39:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:39:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
VIDEO-18-parbin-assam-viral-videoS/VIDEO.LINK.parbin.Viral.Video.Tutorial.Official	VIDEO-18-parbin-assam-viral-videoS	2025-06-15T16:37:41Z	0	0	null	[ "region:us" ]	null	2025-06-15T16:37:15Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?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>
alhkalily/News_classeficaion_lstm	alhkalily	2025-06-15T16:36:14Z	0	0	null	[ "en", "license:apache-2.0", "region:us" ]	null	2025-06-15T16:22:08Z	--- license: apache-2.0 language: - en ---
CreitinGameplays/Llama-3.1-8B-R1-v0.1	CreitinGameplays	2025-06-15T16:33:18Z	88	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:CreitinGameplays/Raiden-DeepSeek-R1-llama3.1", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-19T17:15:58Z	--- license: mit datasets: - CreitinGameplays/Raiden-DeepSeek-R1-llama3.1 language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation library_name: transformers --- ## Llama 3.1 8B R1 v0.1 ![Llama](https://autumn.revolt.chat/attachments/Dpj0Up0lYE2-BVOQRTDXeLk5xa7EE0WxBntXqgJGAo/DALL%C2%B7E%202025-02-19%2010.03.42%20-%20A%20futuristic%20robotic%20white%20llama%20with%20sleek%20metallic%20plating%20and%20glowing%20blue%20eyes.%20The%20llama%20has%20intricate%20mechanical%20joints%20and%20a%20high-tech%20design.%20.png) Took 28 hours to finetune on 2x Nvidia RTX A6000 with the following settings: - Batch size: 8 - Gradient accumulation steps: 1 - Epochs: 2 - Learning rate: 1e-4 - Warmup ratio: 0.1 Run the model: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig import bitsandbytes quantization_config = BitsAndBytesConfig( load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True ) model_id = "CreitinGameplays/Llama-3.1-8B-R1-v0.1" # Initialize model and tokenizer with streaming support model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", quantization_config=quantization_config ) tokenizer = AutoTokenizer.from_pretrained(model_id) # Custom streamer that collects the output into a string while streaming class CollectingStreamer(TextStreamer): def __init__(self, tokenizer): super().__init__(tokenizer) self.output = "" def on_llm_new_token(self, token: str, **kwargs): self.output += token print(token, end="", flush=True) # prints the token as it's generated print("Chat session started. Type 'exit' to quit.\n") # Initialize chat history as a list of messages chat_history = [] chat_history.append({"role": "system", "content": "You are an AI assistant made by Meta AI."}) while True: user_input = input("You: ") if user_input.strip().lower() == "exit": break # Append the user message to the chat history chat_history.append({"role": "user", "content": user_input}) # Prepare the prompt by formatting the complete chat history inputs = tokenizer.apply_chat_template( chat_history, return_tensors="pt" ).to(model.device) # Create a new streamer for the current generation streamer = CollectingStreamer(tokenizer) # Generate streamed response model.generate( inputs, streamer=streamer, temperature=0.6, top_p=0.9, top_k=50, repetition_penalty=1.1, max_new_tokens=6112, do_sample=True ) # The complete response text is stored in streamer.output response_text = streamer.output print("\nAssistant:", response_text) # Append the assistant response to the chat history chat_history.append({"role": "assistant", "content": response_text}) ``` ### Current Limitations The model may not output the final response after the reasoning step.
mradermacher/ThinkAgent-1B-GGUF	mradermacher	2025-06-15T16:33:01Z	53	0	transformers	[ "transformers", "gguf", "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	2025-03-03T20:21:56Z	--- 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 --- ## 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/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q2_K.gguf) \| Q2_K \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q3_K_S.gguf) \| Q3_K_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q3_K_M.gguf) \| Q3_K_M \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q3_K_L.gguf) \| Q3_K_L \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.IQ4_XS.gguf) \| IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q4_K_S.gguf) \| Q4_K_S \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q4_K_M.gguf) \| Q4_K_M \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q5_K_S.gguf) \| Q5_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q5_K_M.gguf) \| Q5_K_M \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q6_K.gguf) \| Q6_K \| 1.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q8_0.gguf) \| Q8_0 \| 1.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.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 -->
VIDEO-18-parbin-assam-viral-videoS/FULL.VIDEO.parbin.Viral.Video.Tutorial.Official	VIDEO-18-parbin-assam-viral-videoS	2025-06-15T16:30:58Z	0	0	null	[ "region:us" ]	null	2025-06-15T16:30:37Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?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>
carazi/vyviln	carazi	2025-06-15T16:30:33Z	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	2025-06-15T16:09:20Z	--- 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: vyvil --- # Vyviln <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 `vyvil` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "vyvil", "lora_weights": "https://huggingface.co/carazi/vyviln/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('carazi/vyviln', weight_name='lora.safetensors') image = pipeline('vyvil').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/carazi/vyviln/discussions) to add images that show off what you’ve made with this LoRA.
SidXXD/Post_Impressionism	SidXXD	2025-06-15T16:30:11Z	39	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	2025-01-07T16:43:16Z	--- 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/Post_Impressionism 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).
pictgensupport/womanshairstyles	pictgensupport	2025-06-15T16:27:45Z	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	2025-06-15T16:27:43Z	--- 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: womanshairstyles --- # Womanshairstyles <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `womanshairstyles` to trigger the image generation. ## 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('pictgensupport/womanshairstyles', weight_name='lora.safetensors') image = pipeline('your prompt').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)
Enzogbs/ppo-Huggy	Enzogbs	2025-06-15T16:26:49Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-06-15T16:26:43Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Enzogbs/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
utkuden/qlora_paligemma_MIXft_decoder_only_rank16-SCST-CIDEr0.1270	utkuden	2025-06-15T16:24:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:24:35Z	--- 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. 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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]
jobz-hunting-hot-sapna-shah/VIDEO.jobz.hunting.sapna.shah.Viral.Video.Tutorial.Official	jobz-hunting-hot-sapna-shah	2025-06-15T16:22:56Z	0	0	null	[ "region:us" ]	null	2025-06-15T16:22:13Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?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>
multimolecule/aido.rna-1.6b	multimolecule	2025-06-15T16:22:17Z	0	0	multimolecule	[ "multimolecule", "pytorch", "safetensors", "aido.rna", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/rnacentral", "license:agpl-3.0", "region:us" ]	fill-mask	2025-06-15T16:17:58Z	--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/rnacentral library_name: multimolecule pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "HIV-1" text: "GGUC<mask>CUCUGGUUAGACCAGAUCUGAGCCU" output: - label: "U" score: 0.7308459877967834 - label: "W" score: 0.11085908114910126 - label: "Y" score: 0.03829820826649666 - label: "H" score: 0.029108675196766853 - label: "K" score: 0.018761275336146355 - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUG" output: - label: "U" score: 0.41171538829803467 - label: "W" score: 0.1445416808128357 - label: "K" score: 0.06634332984685898 - label: "D" score: 0.060673028230667114 - label: "Y" score: 0.054533567279577255 --- # AIDO.RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) by Shuxian Zou, Tianhua Tao, Sazan Mahbub, et al. The OFFICIAL repository of AIDO.RNA is at [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO). > [!WARNING] > The MultiMolecule team is aware of a potential risk in reproducing the results of AIDO.RNA. > > The original implementation of AIDO.RNA uses a special tokenizer that identifies `U` and `T` as different tokens. > > This behaviour is not supported by MultiMolecule. > [!TIP] > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. The team releasing AIDO.RNA did not write this model card for this model so this model card has been written by the MultiMolecule team. ## Model Details AIDO.RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variants - [multimolecule/aido.rna-1.6b](https://huggingface.co/multimolecule/aido.rna-1.6b): The AIDO.RNA model with 1.6 billion parameters. - [multimolecule/aido.rna-650m](https://huggingface.co/multimolecule/aido.rna-650m): The AIDO.RNA model with 650 million parameters. ### Model Specification <table> <thead> <tr> <th>Variants</th> <th>Num Layers</th> <th>Hidden Size</th> <th>Num Heads</th> <th>Intermediate Size</th> <th>Num Parameters (M)</th> <th>FLOPs (G)</th> <th>MACs (G)</th> <th>Max Num Tokens</th> </tr> </thead> <tbody> <tr> <td>AIDO.RNA-1.6B</td> <td>32</td> <td>2048</td> <td>32</td> <td>5440</td> <td>1650.29</td> <td>415.67</td> <td>207.77</td> <td rowspan="2">1022</td> </tr> <tr> <td>AIDO.RNA-650M</td> <td>33</td> <td>1280</td> <td>20</td> <td>3392</td> <td>648.38</td> <td>168.25</td> <td>80.09</td> </tr> </tbody> </table> ### Links - Code: [multimolecule.aido_rna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aido_rna) - Weights: [multimolecule/aido.rna](https://huggingface.co/multimolecule/aido.rna) - Data: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral) - Paper: [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) - Developed by: Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, Eric P. Xing - Model type: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - Original Repository: [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline("fill-mask", model="multimolecule/aido.rna-1.6b") >>> unmasker("gguc<mask>cucugguuagaccagaucugagccu") [{'score': 0.7308459877967834, 'token': 9, 'token_str': 'U', 'sequence': 'G G U C U C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.11085908114910126, 'token': 14, 'token_str': 'W', 'sequence': 'G G U C W C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.03829820826649666, 'token': 12, 'token_str': 'Y', 'sequence': 'G G U C Y C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.029108675196766853, 'token': 19, 'token_str': 'H', 'sequence': 'G G U C H C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.018761275336146355, 'token': 15, 'token_str': 'K', 'sequence': 'G G U C K C U C U G G U U A G A C C A G A U C U G A G C C U'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, AidoRnaModel tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaModel.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") output = model(input) ``` #### Sequence Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForSequencePrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.tensor([1]) output = model(input, labels=label) ``` #### Token Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForTokenPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForTokenPrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), )) output = model(input, labels=label) ``` #### Contact Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForContactPrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), len(text))) output = model(input, labels=label) ``` ## Training Details AIDO.RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The AIDO.RNA model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral) and [MARS](https://ngdc.cncb.ac.cn/omix/release/OMIX003037). RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types. AIDO.RNA applied SeqKit to remove duplicated sequences in the RNAcentral, resulting 42 million unique sequences. Note that AIDO.RNA identifies `U` and `T` as different tokens, which is not supported by MultiMolecule. During model conversion, the embeddings of `T` is discarded. This means that the model will not be able to distinguish between `U` and `T` in the input sequences. ### Training Procedure #### Preprocessing AIDO.RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### Pre-training - Epochs: 6 - Optimizer: AdamW - Learning rate: 5e-5 - Learning rate warm-up: 2,000 steps - Learning rate scheduler: Cosine - Minimum learning rate: 1e-5 - Weight decay: 0.01 ## Citation BibTeX: ```bibtex @article {Zou2024.11.28.625345, author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.}, title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction}, elocation-id = {2024.11.28.625345}, year = {2024}, doi = {10.1101/2024.11.28.625345}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Originally marginalized as an intermediate in the information flow from DNA to protein, RNA has become the star of modern biology, holding the key to precision therapeutics, genetic engineering, evolutionary origins, and our understanding of fundamental cellular processes. Yet RNA is as mysterious as it is prolific, serving as an information store, a messenger, and a catalyst, spanning many underchar-acterized functional and structural classes. Deciphering the language of RNA is important not only for a mechanistic understanding of its biological functions but also for accelerating drug design. Toward this goal, we introduce AIDO.RNA, a pre-trained module for RNA in an AI-driven Digital Organism [1]. AIDO.RNA contains a scale of 1.6 billion parameters, trained on 42 million non-coding RNA (ncRNA) sequences at single-nucleotide resolution, and it achieves state-of-the-art performance on a comprehensive set of tasks, including structure prediction, genetic regulation, molecular function across species, and RNA sequence design. AIDO.RNA after domain adaptation learns to model essential parts of protein translation that protein language models, which have received widespread attention in recent years, do not. More broadly, AIDO.RNA hints at the generality of biological sequence modeling and the ability to leverage the central dogma to improve many biomolecular representations. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345}, eprint = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [AIDO.RNA paper](https://doi.org/10.1101/2024.11.28.625345) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```
henriquesantos3430/HS	henriquesantos3430	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
biancaesteves5993/BS	biancaesteves5993	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
claravicente1628/CV	claravicente1628	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
marcomelo9929/MM	marcomelo9929	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
veracardoso4942/VD	veracardoso4942	2025-06-15T16:21:31Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-06-15T16:21:31Z	--- license: bigscience-bloom-rail-1.0 ---
jobz-hunting-hot-sapna-shah/FULL.VIDEO.jobz.hunting.sapna.shah.Viral.Video.Tutorial.Official	jobz-hunting-hot-sapna-shah	2025-06-15T16:18:42Z	0	0	null	[ "region:us" ]	null	2025-06-15T16:18:00Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?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>
multimolecule/aido.rna-650m-cds	multimolecule	2025-06-15T16:16:21Z	0	0	multimolecule	[ "multimolecule", "pytorch", "safetensors", "aido.rna", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/ena", "base_model:multimolecule/aido.rna-650m", "base_model:finetune:multimolecule/aido.rna-650m", "license:agpl-3.0", "region:us" ]	fill-mask	2025-06-15T16:12:05Z	--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/ena library_name: multimolecule base_model: multimolecule/aido.rna-650m pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "HIV-1" text: "GGUC<mask>CUCUGGUUAGACCAGAUCUGAGCCU" output: - label: "A" score: 0.15881139039993286 - label: "R" score: 0.15044376254081726 - label: "G" score: 0.14251668751239777 - label: "V" score: 0.1298484206199646 - label: "M" score: 0.1239432692527771 - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUG" output: - label: "A" score: 0.1757601946592331 - label: "M" score: 0.1494324952363968 - label: "R" score: 0.1302214413881302 - label: "V" score: 0.1291552037000656 - label: "C" score: 0.12704865634441376 --- # AIDO.RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) by Shuxian Zou, Tianhua Tao, Sazan Mahbub, et al. The OFFICIAL repository of AIDO.RNA is at [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO). > [!WARNING] > The MultiMolecule team is aware of a potential risk in reproducing the results of AIDO.RNA. > > The original implementation of AIDO.RNA uses a special tokenizer that identifies `U` and `T` as different tokens. > > This behaviour is not supported by MultiMolecule. > [!TIP] > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. The team releasing AIDO.RNA did not write this model card for this model so this model card has been written by the MultiMolecule team. ## Model Details AIDO.RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variants - [multimolecule/aido.rna-650m](https://huggingface.co/multimolecule/aido.rna-650m): The AIDO.RNA model with 650 million parameters. - [multimolecule/aido.rna-1.6b](https://huggingface.co/multimolecule/aido.rna-1.6b): The AIDO.RNA model with 1.6 billion parameters. ### Model Specification <table> <thead> <tr> <th>Variants</th> <th>Num Layers</th> <th>Hidden Size</th> <th>Num Heads</th> <th>Intermediate Size</th> <th>Num Parameters (M)</th> <th>FLOPs (G)</th> <th>MACs (G)</th> <th>Max Num Tokens</th> </tr> </thead> <tbody> <tr> <td>AIDO.RNA-650M</td> <td>33</td> <td>1280</td> <td>20</td> <td>3392</td> <td>648.38</td> <td>168.25</td> <td>80.09</td> <td rowspan="2">1022</td> </tr> <tr> <td>AIDO.RNA-1.6B</td> <td>32</td> <td>2048</td> <td>32</td> <td>5440</td> <td>1650.29</td> <td>415.67</td> <td>207.77</td> </tr> </tbody> </table> ### Links - Code: [multimolecule.aido_rna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aido_rna) - Weights: [multimolecule/aido.rna](https://huggingface.co/multimolecule/aido.rna) - Data: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral) - Paper: [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) - Developed by: Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, Eric P. Xing - Model type: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - Original Repository: [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline("fill-mask", model="multimolecule/aido.rna-650m") >>> unmasker("gguc<mask>cucugguuagaccagaucugagccu") [{'score': 0.15881139039993286, 'token': 6, 'token_str': 'A', 'sequence': 'G G U C A C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.15044376254081726, 'token': 11, 'token_str': 'R', 'sequence': 'G G U C R C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.14251668751239777, 'token': 8, 'token_str': 'G', 'sequence': 'G G U C G C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.1298484206199646, 'token': 20, 'token_str': 'V', 'sequence': 'G G U C V C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.1239432692527771, 'token': 16, 'token_str': 'M', 'sequence': 'G G U C M C U C U G G U U A G A C C A G A U C U G A G C C U'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, AidoRnaModel tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-650m") model = AidoRnaModel.from_pretrained("multimolecule/aido.rna-650m") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") output = model(input) ``` #### Sequence Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-650m") model = AidoRnaForSequencePrediction.from_pretrained("multimolecule/aido.rna-650m") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.tensor([1]) output = model(input, labels=label) ``` #### Token Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForTokenPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-650m") model = AidoRnaForTokenPrediction.from_pretrained("multimolecule/aido.rna-650m") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), )) output = model(input, labels=label) ``` #### Contact Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-650m") model = AidoRnaForContactPrediction.from_pretrained("multimolecule/aido.rna-650m") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), len(text))) output = model(input, labels=label) ``` ## Training Details AIDO.RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The AIDO.RNA model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral) and [MARS](https://ngdc.cncb.ac.cn/omix/release/OMIX003037). RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types. AIDO.RNA applied SeqKit to remove duplicated sequences in the RNAcentral, resulting 42 million unique sequences. Note that AIDO.RNA identifies `U` and `T` as different tokens, which is not supported by MultiMolecule. During model conversion, the embeddings of `T` is discarded. This means that the model will not be able to distinguish between `U` and `T` in the input sequences. ### Training Procedure #### Preprocessing AIDO.RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### Pre-training - Epochs: 6 - Optimizer: AdamW - Learning rate: 5e-5 - Learning rate warm-up: 2,000 steps - Learning rate scheduler: Cosine - Minimum learning rate: 1e-5 - Weight decay: 0.01 ## Citation BibTeX: ```bibtex @article {Zou2024.11.28.625345, author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.}, title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction}, elocation-id = {2024.11.28.625345}, year = {2024}, doi = {10.1101/2024.11.28.625345}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Originally marginalized as an intermediate in the information flow from DNA to protein, RNA has become the star of modern biology, holding the key to precision therapeutics, genetic engineering, evolutionary origins, and our understanding of fundamental cellular processes. Yet RNA is as mysterious as it is prolific, serving as an information store, a messenger, and a catalyst, spanning many underchar-acterized functional and structural classes. Deciphering the language of RNA is important not only for a mechanistic understanding of its biological functions but also for accelerating drug design. Toward this goal, we introduce AIDO.RNA, a pre-trained module for RNA in an AI-driven Digital Organism [1]. AIDO.RNA contains a scale of 1.6 billion parameters, trained on 42 million non-coding RNA (ncRNA) sequences at single-nucleotide resolution, and it achieves state-of-the-art performance on a comprehensive set of tasks, including structure prediction, genetic regulation, molecular function across species, and RNA sequence design. AIDO.RNA after domain adaptation learns to model essential parts of protein translation that protein language models, which have received widespread attention in recent years, do not. More broadly, AIDO.RNA hints at the generality of biological sequence modeling and the ability to leverage the central dogma to improve many biomolecular representations. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345}, eprint = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [AIDO.RNA paper](https://doi.org/10.1101/2024.11.28.625345) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.05_epoch1	MinaMila	2025-06-15T16:16:16Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:14:21Z	--- 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. 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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]
HiverStarBox/NextIntercar	HiverStarBox	2025-06-15T16:14:28Z	0	0	null	[ "text-generation", "ru", "base_model:yandex/YandexGPT-5-Lite-8B-pretrain", "base_model:finetune:yandex/YandexGPT-5-Lite-8B-pretrain", "license:apache-2.0", "region:us" ]	text-generation	2025-06-15T16:12:33Z	--- license: apache-2.0 language: - ru base_model: - yandex/YandexGPT-5-Lite-8B-pretrain pipeline_tag: text-generation ---
OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen2.5-72B-Preview0	OpenBuddy	2025-06-15T16:12:07Z	4	0	null	[ "safetensors", "qwen2", "qwen2.5", "text-generation", "conversational", "zh", "en", "fr", "de", "ja", "ko", "it", "fi", "region:us" ]	text-generation	2025-06-12T16:36:05Z	--- language: - zh - en - fr - de - ja - ko - it - fi tags: - qwen2.5 pipeline_tag: text-generation base_model: Qwen/Qwen2.5-72B-Base --- # OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) Evaluation result of this model: [Evaluation.txt](Evaluation.txt) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Model Info Base Model: Qwen/Qwen2.5-72B-Base Context Length: 40K Tokens License: Qwen2.5 72B License Training Data: Distilled from DeepSeek-R1-0528 # Prompt Format We recommend using the fast tokenizer from `transformers`, which should be enabled by default in the `transformers` and `vllm` libraries. Other implementations including `sentencepiece` may not work as expected, especially for special tokens like `<\|role\|>`, `<\|says\|>` and `<\|end\|>`. ``` <\|role\|>system<\|says\|>You(assistant) are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human(user). Current mode: System 2, think step-by-step and answer.<\|end\|> <\|role\|>user<\|says\|>History input 1<\|end\|> <\|role\|>assistant<\|says\|>History output 1<\|end\|> <\|role\|>user<\|says\|>History input 2<\|end\|> <\|role\|>assistant<\|says\|>History output 2<\|end\|> <\|role\|>user<\|says\|>Current input<\|end\|> <\|role\|>assistant<\|says\|> ``` This format is also defined in `tokenizer_config.json`, which means you can directly use `vllm` to deploy an OpenAI-like API service. For more information, please refer to the [vllm documentation](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html). ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
telecomadm1145/gemma-3-cn-novel-4b-v1.1	telecomadm1145	2025-06-15T16:10:35Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:telecomadm1145/gemma-3-cn-novel-4b-v1.1", "base_model:finetune:telecomadm1145/gemma-3-cn-novel-4b-v1.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:10:32Z	--- base_model: telecomadm1145/gemma-3-cn-novel-4b-v1.1 tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: telecomadm1145 - License: apache-2.0 - Finetuned from model : telecomadm1145/gemma-3-cn-novel-4b-v1.1 This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Rask6723/IT_GR7_En-Sn	Rask6723	2025-06-15T16:09:37Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T16:03:37Z	--- 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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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.15_epoch2	MinaMila	2025-06-15T16:08:03Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:06:13Z	--- 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. 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phospho-app/jakmilller-ACT-jenga_pull-z1gqj	phospho-app	2025-06-15T16:07:07Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-15T13:17:51Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [mahanthesh0r/jenga_pull](https://huggingface.co/datasets/mahanthesh0r/jenga_pull) - Wandb run URL: None - Epochs: None - Batch size: 40 - Training steps: 8000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
duchao1210/DPO_Qwen25_3B_128_0.05_1000kmap_lr	duchao1210	2025-06-15T16:06:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:duchao1210/qwen_2.5_3B_5k_r128", "base_model:finetune:duchao1210/qwen_2.5_3B_5k_r128", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T16:04:52Z	--- base_model: duchao1210/qwen_2.5_3B_5k_r128 tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: duchao1210 - License: apache-2.0 - Finetuned from model : duchao1210/qwen_2.5_3B_5k_r128 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)
SidXXD/Impressionism	SidXXD	2025-06-15T16:05:48Z	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	2025-01-07T16:33:46Z	--- 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/Impressionism 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/mc13_badmed_kl_div_beta_kl-3_epochs-10_seed_1	gradientrouting-spar	2025-06-15T16:05:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T16:04:53Z	--- 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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hungnguyennlp/llama-3.2-1b-instruct-lora-test	hungnguyennlp	2025-06-15T16:01:22Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-06-15T16:00:19Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct 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. 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DevQuasar/shisa-ai.shisa-v2-llama3.1-405b-GGUF	DevQuasar	2025-06-15T16:00:38Z	1,164	0	null	[ "gguf", "text-generation", "base_model:shisa-ai/shisa-v2-llama3.1-405b", "base_model:quantized:shisa-ai/shisa-v2-llama3.1-405b", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-08T04:15:18Z	--- base_model: - shisa-ai/shisa-v2-llama3.1-405b pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) 'Make knowledge free for everyone' Quantized version of: [shisa-ai/shisa-v2-llama3.1-405b](https://huggingface.co/shisa-ai/shisa-v2-llama3.1-405b) <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
hqjb/basic-resume	hqjb	2025-06-15T15:58:03Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T15:52:48Z	--- 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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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]

Peacemann/mistralai_Mistral-7B-Instruct-v0.2_LMUL

Peacemann

2025-06-15T18:02:43Z

0

null

[ "safetensors", "mistral", "L-Mul,", "optimazation", "quantization", "text-generation", "research", "experimental", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

text-generation

2025-06-15T17:56:57Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - L-Mul, - optimazation - quantization - text-generation - research - experimental --- # L-Mul Optimized: mistralai/Mistral-7B-Instruct-v0.2 This is a modified version of Mistral AI's [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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 `mistralai/Mistral-7B-Instruct-v0.2` is preserved. However, the standard `MistralAttention` 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:** [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - **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/mistralai_Mistral-7B-Instruct-v0.2-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 **Apache 2.0 License**. By using this model, you agree to the terms outlined in the license.

parveen-Official-Viral-Video-Link/18.Original.Full.Clip.parveen.Viral.Video.Leaks.Official

parveen-Official-Viral-Video-Link

2025-06-15T18:00:08Z

0

null

[ "region:us" ]

null

2025-06-15T17:59:49Z

<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>

yalhessi/lemexp-task1-v2-lemma_object_full_nodefs-deepseek-coder-1.3b-base-ddp-8lr-v2

yalhessi

2025-06-15T17:56:54Z

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: [] ---  # 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

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

meezo-fun-20/20.Video.meezo.fun.trending.viral.Full.Video

meezo-fun-20

2025-06-15T17:56:41Z

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>

dgambettaphd/M_llm2_run2_gen0_WXS_doc1000_synt64_lr1e-04_acm_FRESH

dgambettaphd

2025-06-15T17:51:54Z

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

VIDEOS-18-parveen-viral-video/wATCH.FULL.VIDEO.parveen.Viral.Video.Tutorial.Official

VIDEOS-18-parveen-viral-video

2025-06-15T17:51:34Z

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>

kythours/kitou

kythours

2025-06-15T17:50:31Z

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

2025-06-15T17:49:25Z

--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/kitou_001800_00_20250615171413.png text: hwxjos man walks down a quiet alley, shadows stretching behind him. - output: url: sample/kitou_001800_01_20250615171455.png text: hwxjos man ties his boots as the morning light fills the room. - output: url: sample/kitou_001800_02_20250615171538.png text: hwxjos man smokes alone on a balcony overlooking the city. - output: url: sample/kitou_001800_03_20250615171621.png text: hwxjos man lifts a backpack and steps onto the train. base_model: black-forest-labs/FLUX.1-dev instance_prompt: owxjos 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 --- # kitou A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `owxjos` 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.

arunmadhusudh/qwen2_VL_2B_LatexOCR_qlora_qptq_epoch3

arunmadhusudh

2025-06-15T17:49:31Z

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

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

Alfonsol/ai-miracle-348

Alfonsol

2025-06-15T17:48:29Z

7

0

transformers

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

image-classification

2025-06-10T10:17:21Z

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

nyuuzyou/EuroVLM-9B-Preview

nyuuzyou

2025-06-15T17:48:07Z

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)

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.05_epoch2

MinaMila

2025-06-15T17:46:44Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T17:44:43Z

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

kimxxxx/mistral_r64_a128_g8_gas8_lr9e-5_4500tk_droplast_nopacking_2epoch

kimxxxx

2025-06-15T17:45:55Z

0

transformers

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

null

2025-06-15T17:45:09Z

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

Ninannnnn/roger_dean_style_LoRA

Ninannnnn

2025-06-15T17:42:58Z

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 ---  # 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]

Peacemann/google_gemma-2-9b-it_LMUL

Peacemann

2025-06-15T17:42:33Z

0

null

[ "safetensors", "gemma2", "L-Mul,", "optimazation", "quantization", "text-generation", "research", "experimental", "conversational", "base_model:google/gemma-2-9b-it", "base_model:finetune:google/gemma-2-9b-it", "license:gemma", "region:us" ]

text-generation

2025-06-15T17:34:30Z

--- base_model: google/gemma-2-9b-it tags: - L-Mul, - optimazation - quantization - text-generation - research - experimental license: gemma --- # L-Mul Optimized: google/gemma-2-9b-it This is a modified version of Google's [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-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-2-9b-it` is preserved. However, the standard `Gemma2Attention` 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-2-9b-it](https://huggingface.co/google/gemma-2-9b-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-2-9b-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 2 Community License**. By using this model, you agree to the terms outlined in the license.

SaNsOT/q-Taxi-v3

SaNsOT

2025-06-15T17:41:40Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2025-06-15T17:41:36Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.76 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="SaNsOT/q-Taxi-v3", 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"]) ```

arjelmilan/qwen2-Image-to-LaTeX

arjelmilan

2025-06-15T17:41:05Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_vl", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-15T17:40:55Z

--- base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** arjelmilan - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit This qwen2_vl 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)

krissnonflux/loco-FluxV25

krissnonflux

2025-06-15T17:40:52Z

0

null

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

null

2025-06-15T16:48:27Z

--- license: apache-2.0 ---

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.05_epoch1

MinaMila

2025-06-15T17:38:38Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T17:36:36Z

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

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

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

Seelt/nllb-200-distilled-600M-Shughni-v1

Seelt

2025-06-15T17:34:29Z

0

null

[ "license:cc-by-nc-4.0", "region:us" ]

null

2025-06-15T17:34:29Z

--- license: cc-by-nc-4.0 ---

carolinamendes3401/aure

carolinamendes3401

2025-06-15T17:33:00Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-15T17:33:00Z

--- license: bigscience-bloom-rail-1.0 ---

teresamendes4154/gre

teresamendes4154

2025-06-15T17:33:00Z

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

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

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-15T17:33:00Z

--- license: bigscience-bloom-rail-1.0 ---

joelpinho9308/gd

joelpinho9308

2025-06-15T17:33:00Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-15T17:33:00Z

--- license: bigscience-bloom-rail-1.0 ---

yasminmaia3967/as

yasminmaia3967

2025-06-15T17:33:00Z

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

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

freakyfractal/otang

freakyfractal

2025-06-15T17:30:11Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-06-15T17:29:39Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # otang <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/otang/tree/main) them in the Files & versions tab.

pranalibose/cnn_news_summary_model_trained_on_reduced_data

pranalibose

2025-06-15T17:25:40Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-12T10:32:07Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: cnn_news_summary_model_trained_on_reduced_data results: [] ---  # cnn_news_summary_model_trained_on_reduced_data This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:| | No log | 1.0 | 144 | 1.8314 | 0.234 | 0.0971 | 0.1917 | 0.1918 | 18.9913 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

krissnonflux/flux-Analog-Art

krissnonflux

2025-06-15T17:25:02Z

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

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)

Salmaalaa/CodeLlama-7b-Instruct_AR2SQL_v7

Salmaalaa

2025-06-15T17:23:51Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:finetune:codellama/CodeLlama-7b-Instruct-hf", "endpoints_compatible", "region:us" ]

null

2025-06-15T16:04:39Z

--- base_model: codellama/CodeLlama-7b-Instruct-hf library_name: transformers model_name: CodeLlama-7b-Instruct_AR2SQL_v7 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for CodeLlama-7b-Instruct_AR2SQL_v7 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf). 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="Salmaalaa/CodeLlama-7b-Instruct_AR2SQL_v7", 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: 0.18.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

phucminh/deepseek-finetuned

phucminh

2025-06-15T17:23:10Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-15T17:20:42Z

--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** phucminh - **License:** apache-2.0 - **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.15_epoch1

MinaMila

2025-06-15T17:21:54Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T17:19:49Z

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

King-Cane/RareBit-v2-32B-Q4_K_S-GGUF

King-Cane

2025-06-15T17:20:33Z

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 ```

Sharon1020/twitter-bert-base-emoji

Sharon1020

2025-06-15T17:19:28Z

0

1

null

[ "safetensors", "text-classification", "en", "dataset:cardiffnlp/tweet_eval", "arxiv:2010.12421", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]

text-classification

2025-06-15T16:51:55Z

--- license: apache-2.0 datasets: - cardiffnlp/tweet_eval language: - en metrics: - accuracy base_model: - google-bert/bert-base-uncased pipeline_tag: text-classification --- # Twitter-BERT-base for Emoji prediction This is a BERT-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark. **Note**: This model is inspired by and follows the methodology from [cardiffnlp/twitter-roberta-base-emoji](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji). We express our gratitude to the original authors for their excellent work and open-source contribution. - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Original Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Model Details - **Base Model**: BERT-base - **Training Data**: ~58M tweets - **Task**: Emoji prediction (20 classes) - **Framework**: PyTorch/Transformers ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) MODEL = "Sharon1020/twitter-bert-base-emoji" tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL) labels = [] mapping_link = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/emoji/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] text = "Looking forward to Christmas" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` ## Expected Output Format ``` 1) 🎄 0.5457 2) 😊 0.1417 3) 😁 0.0649 4) 😍 0.0395 5) ❤️ 0.03 6) 😜 0.028 7) ✨ 0.0263 8) 😉 0.0237 9) 😂 0.0177 10) 😎 0.0166 11) 😘 0.0143 12) 💕 0.014 13) 💙 0.0076 14) 💜 0.0068 15) 🔥 0.0065 16) 💯 0.004 17) 🇺🇸 0.0037 18) 📷 0.0034 19) ☀ 0.0033 20) 📸 0.0021 ``` ## Performance - **Task**: Emoji prediction (20 classes) - **Metric**: F1-score ## Usage ```python from transformers import pipeline classifier = pipeline("text-classification", model="Sharon1020/twitter-bert-base-emoji", tokenizer="Sharon1020/twitter-bert-base-emoji") result = classifier("I love sunny days!") print(result) ``` ## Training Details - **Base Model**: bert-base-uncased - **Training Data**: Twitter data (~58M tweets) - **Fine-tuning**: TweetEval emoji dataset - **Preprocessing**: Username → @user, URLs → http ## Citation If you use this model, please cite the original TweetEval paper: ```bibtex @inproceedings{barbieri2020tweeteval, title={TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification}, author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo}, booktitle={Findings of EMNLP}, year={2020} } ``` ## Acknowledgments This work builds upon the methodology and insights from: - [cardiffnlp/twitter-roberta-base-emoji](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji) - The TweetEval benchmark and dataset ## License Apache 2.0

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

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

DanteChapterMaster/house-price-predictor

DanteChapterMaster

2025-06-15T17:16:27Z

0

null

[ "joblib", "license:mit", "region:us" ]

null

2025-06-15T17:03:49Z

--- license: mit --- # 🏡 House Price Predictor (Kaggle + Hugging Face) This project is a complete machine learning pipeline for predicting house prices in Ames, Iowa, using structured data and transformer-based text embeddings. It was developed as part of the [Kaggle House Prices - Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) competition. The model is published on the Hugging Face Hub: 👉 https://huggingface.co/DanteChapterMaster/house-price-predictor --- ## 📦 Project Highlights - ✅ Exploratory Data Analysis (EDA) - ✅ Feature Engineering from domain knowledge - ✅ Model training: Ridge, Lasso, Random Forest, XGBoost, and Stacking - ✅ NLP augmentation: BERT embeddings from generated property descriptions - ✅ Full model pipeline with preprocessing (ColumnTransformer) - ✅ Deployment-ready model saved with `joblib` --- ## 📊 Features **Numerical Features:** - `GrLivArea`, `TotalBsmtSF`, `GarageCars`, etc. **Categorical Features:** - `Neighborhood`, `HouseStyle`, etc. (one-hot encoded) **Generated Features:** - Log-transformed target - Interaction terms - Transformer-based embeddings from property descriptions --- ## 🤖 Model Card - **Type:** Regressor - **Algorithm:** XGBoost in Scikit-learn `Pipeline` - **Target:** `SalePrice` (log-transformed) - **Evaluation:** Root Mean Squared Error (RMSE)

mradermacher/QwQ-32B_openthoughts3_100k-GGUF

mradermacher

2025-06-15T17:15:42Z

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      static quants of https://huggingface.co/mlfoundations-dev/QwQ-32B_openthoughts3_100k  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.

iconitech/nfl-scouting-expert-v1

iconitech

2025-06-15T17:15:00Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:41", "loss:TripletLoss", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:sentence-transformers/all-mpnet-base-v2", "base_model:finetune:sentence-transformers/all-mpnet-base-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-06-15T15:35:38Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:41 - loss:TripletLoss base_model: sentence-transformers/all-mpnet-base-v2 widget: - source_sentence: elite ball production DB sentences: - rarely gets his head around and allows catches in phase - times his breaks and plucks interceptions away from receivers - sprays throws and forces receivers to adjust behind them - source_sentence: vision and patience RB sentences: - hamstring tweaks kept him out of key practices each year - gets impatient and bounces, resulting in no gain - presses hole, forces defender to commit, then explodes through the gap - source_sentence: turn and run fluidity sentences: - overthrows wide-open seams and turf short hooks - effortlessly flips, locates, and finishes with secure hands - tight lower half leads to contact catches - source_sentence: excellent run instincts sentences: - click-and-close burst plus natural hands yield PBUs - string of efficient decisions keeps offense on schedule - hesitates and wastes steps, leading to tackles for loss - source_sentence: corner with fluid hips sentences: - opens and flips seamlessly to carry verticals while tracking ball - praised for leadership and A+ character - stiff in transition and loses body control at catch point pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)  - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'corner with fluid hips', 'opens and flips seamlessly to carry verticals while tracking ball', 'stiff in transition and loses body control at catch point', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```      ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 41 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> * Approximate statistics based on the first 41 samples: | | sentence_0 | sentence_1 | sentence_2 | |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 6.46 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.78 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 11.17 tokens</li><li>max: 15 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | sentence_2 | |:---------------------------------------------|:-----------------------------------------------------------------------------------------|:-----------------------------------------------------------------| | <code>throws with effortless velocity</code> | <code>ball jumps off his hand and arrives to tight windows before defenders react</code> | <code>passes hang in the air and allow DBs to close</code> | | <code>persistent soft-tissue injuries</code> | <code>hamstring tweaks kept him out of key practices each year</code> | <code>has never appeared on the injury report</code> | | <code>injury prone track record</code> | <code>three different surgeries in college raise red flags</code> | <code>medical checks came back clean with no missed games</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Framework Versions - Python: 3.13.4 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.7.1 - Accelerate: 1.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

xkdl27/SFT_tuned_cell_annotation_LLM

xkdl27

2025-06-15T17:14:43Z

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

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: [] ---  # 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

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.25_epoch2

MinaMila

2025-06-15T17:13:26Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T17:11:35Z

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

utkuden/qlora_paligemma_MIXft_decoder_only_rank16-SCST-CIDEr0.1361

utkuden

2025-06-15T17:11:39Z

0

transformers

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

null

2025-06-15T17:11:29Z

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

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.25_epoch1

MinaMila

2025-06-15T17:05:34Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T17:03:44Z

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

LandCruiser/sn29C1_1506_9

LandCruiser

2025-06-15T17:04:07Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T03:26:58Z

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

Felixbrk/bert-base-cased-dutch-lora-multi-score-text-only-positive

Felixbrk

2025-06-15T17:03:53Z

0

transformers

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

null

2025-06-15T17:03:47Z

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

Errorman23/NLP-toxic-classifier

Errorman23

2025-06-15T17:03:19Z

0

null

[ "safetensors", "en", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]

null

2025-06-15T15:46:56Z

--- license: apache-2.0 language: - en metrics: - f1 base_model: - distilbert/distilbert-base-uncased --- # Use best_threshold = 0.4757 (in model config file) upon inference for better performance, not at 0.5 Though it won't make much of a differencee in term of the F1 score on the Eval set.. haha

divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA-F16-GGUF

divakarHaribabu

2025-06-15T17:01:45Z

0

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "llama-cpp", "gguf-my-lora", "en", "base_model:divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA", "base_model:quantized:divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-15T17:01:33Z

--- base_model: divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA tags: - text-generation-inference - transformers - unsloth - llama - trl - llama-cpp - gguf-my-lora license: apache-2.0 language: - en --- # divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA-F16-GGUF This LoRA adapter was converted to GGUF format from [`divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA`](https://huggingface.co/divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space. Refer to the [original adapter repository](https://huggingface.co/divakarHaribabu/Meta-Llama-3.1-8B-Instruct-Solvermind-LORA) for more details. ## Use with llama.cpp ```bash # with cli llama-cli -m base_model.gguf --lora Meta-Llama-3.1-8B-Instruct-Solvermind-LORA-f16.gguf (...other args) # with server llama-server -m base_model.gguf --lora Meta-Llama-3.1-8B-Instruct-Solvermind-LORA-f16.gguf (...other args) ``` To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).

krissnonflux/Flux_v12

krissnonflux

2025-06-15T17:01:10Z

0

null

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

null

2025-06-15T15:27:13Z

--- license: apache-2.0 ---

bruhzair/prototype-0.4x139

bruhzair

2025-06-15T16:58:26Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T16:40:04Z

--- base_model: [] library_name: transformers tags: - mergekit - merge --- # prototype-0.4x139 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/prototype-0.4x136 as a base. ### Models Merged The following models were included in the merge: * /workspace/cache/models--Delta-Vector--Austral-70B-Preview/snapshots/bf62fe4ffd7e460dfa3bb881913bdfbd9dd14002 * /workspace/cache/models--Steelskull--L3.3-Electra-R1-70b/snapshots/26c8d595ecd941ca908c49d7ae5b2dd146465341 * /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /workspace/cache/models--Steelskull--L3.3-Electra-R1-70b/snapshots/26c8d595ecd941ca908c49d7ae5b2dd146465341 - model: /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c - model: /workspace/cache/models--Delta-Vector--Austral-70B-Preview/snapshots/bf62fe4ffd7e460dfa3bb881913bdfbd9dd14002 base_model: /workspace/prototype-0.4x136 merge_method: model_stock tokenizer: source: base int8_mask: true dtype: float32 out_dtype: bfloat16 pad_to_multiple_of: 8 ```

Mossie96/all-mpnet-base-v2_distilled_3_layers_1-5-10

Mossie96

2025-06-15T16:57:49Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "mpnet", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:9014210", "loss:MSELoss", "arxiv:1908.10084", "arxiv:2004.09813", "base_model:sentence-transformers/all-mpnet-base-v2", "base_model:finetune:sentence-transformers/all-mpnet-base-v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-06-15T16:55:09Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9014210 - loss:MSELoss base_model: sentence-transformers/all-mpnet-base-v2 widget: - source_sentence: At an outdoor event in an Asian-themed area, a crowd congregates as one person in a yellow Chinese dragon costume confronts the camera. sentences: - Boy dressed in blue holds a toy. - the animal is running - Two young asian men are squatting. - source_sentence: A man with a shopping cart is studying the shelves in a supermarket aisle. sentences: - The children are watching TV at home. - Three young boys one is holding a camera and another is holding a green toy all are wearing t-shirt and smiling. - A large group of people are gathered outside of a brick building lit with spotlights. - source_sentence: The door is open. sentences: - There are three men in this picture, two are on motorbikes, one of the men has a large piece of furniture on the back of his bike, the other is about to be handed a piece of paper by a man in a white shirt. - People are playing music. - A girl is using an apple laptop with her headphones in her ears. - source_sentence: A small group of children are standing in a classroom and one of them has a foot in a trashcan, which also has a rope leading out of it. sentences: - Children are swimming at the beach. - Women are celebrating at a bar. - Some men with jerseys are in a bar, watching a soccer match. - source_sentence: A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind. sentences: - There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses. - A girl is sitting - the guy is dead pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - negative_mse model-index: - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8658614353354085 name: Pearson Cosine - type: spearman_cosine value: 0.8685416201709716 name: Spearman Cosine - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: Unknown type: unknown metrics: - type: negative_mse value: -0.01582021452486515 name: Negative Mse - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8308551017458387 name: Pearson Cosine - type: spearman_cosine value: 0.8339024536295018 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)  - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.', 'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.', 'the guy is dead', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `sts-dev` and `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | sts-dev | sts-test | |:--------------------|:-----------|:-----------| | pearson_cosine | 0.8659 | 0.8309 | | **spearman_cosine** | **0.8685** | **0.8339** | #### Knowledge Distillation * Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | Value | |:-----------------|:------------| | **negative_mse** | **-0.0158** |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 9,014,210 training samples * Columns: <code>sentence</code> and <code>label</code> * Approximate statistics based on the first 1000 samples: | | sentence | label | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> | * Samples: | sentence | label | |:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>[-0.030610017478466034, 0.11742044985294342, 0.031586047261953354, 0.01859636977314949, 0.016319412738084793, ...]</code> | | <code>Children smiling and waving at camera</code> | <code>[-0.006198188289999962, -0.036625951528549194, -0.005352460313588381, -0.006725294981151819, 0.05185901001095772, ...]</code> | | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>[-0.01783316768705845, -0.05204763263463974, -0.003716366598382592, 0.0009472182719036937, 0.05223219841718674, ...]</code> | * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) ### Evaluation Dataset #### Unnamed Dataset * Size: 10,000 evaluation samples * Columns: <code>sentence</code> and <code>label</code> * Approximate statistics based on the first 1000 samples: | | sentence | label | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> | * Samples: | sentence | label | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------| | <code>Two women are embracing while holding to go packages.</code> | <code>[0.010130808688700199, 0.009573593735694885, -0.00034817546838894486, -0.0040625291876494884, 0.02026110142469406, ...]</code> | | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>[-0.033891696482896805, -0.04130887985229492, -0.006042165216058493, -0.02770376019179821, -0.0017171527724713087, ...]</code> | | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>[0.0013940087519586086, -0.044612932950258255, -0.023834265768527985, 0.11863800883293152, -0.03907289728522301, ...]</code> | * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 0.0001 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.0001 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | negative_mse | sts-test_spearman_cosine | |:----------:|:----------:|:-------------:|:---------------:|:-----------------------:|:------------:|:------------------------:| | -1 | -1 | - | - | 0.6786 | -0.2176 | - | | 0.0071 | 1000 | 0.0016 | - | - | - | - | | 0.0142 | 2000 | 0.001 | - | - | - | - | | 0.0213 | 3000 | 0.0008 | - | - | - | - | | 0.0284 | 4000 | 0.0007 | - | - | - | - | | 0.0355 | 5000 | 0.0006 | 0.0006 | 0.8511 | -0.0561 | - | | 0.0426 | 6000 | 0.0006 | - | - | - | - | | 0.0497 | 7000 | 0.0005 | - | - | - | - | | 0.0568 | 8000 | 0.0005 | - | - | - | - | | 0.0639 | 9000 | 0.0005 | - | - | - | - | | 0.0710 | 10000 | 0.0004 | 0.0004 | 0.8624 | -0.0361 | - | | 0.0781 | 11000 | 0.0004 | - | - | - | - | | 0.0852 | 12000 | 0.0004 | - | - | - | - | | 0.0923 | 13000 | 0.0004 | - | - | - | - | | 0.0994 | 14000 | 0.0004 | - | - | - | - | | 0.1065 | 15000 | 0.0003 | 0.0003 | 0.8649 | -0.0288 | - | | 0.1136 | 16000 | 0.0003 | - | - | - | - | | 0.1207 | 17000 | 0.0003 | - | - | - | - | | 0.1278 | 18000 | 0.0003 | - | - | - | - | | 0.1349 | 19000 | 0.0003 | - | - | - | - | | 0.1420 | 20000 | 0.0003 | 0.0003 | 0.8663 | -0.0252 | - | | 0.1491 | 21000 | 0.0003 | - | - | - | - | | 0.1562 | 22000 | 0.0003 | - | - | - | - | | 0.1633 | 23000 | 0.0003 | - | - | - | - | | 0.1704 | 24000 | 0.0003 | - | - | - | - | | 0.1775 | 25000 | 0.0003 | 0.0002 | 0.8641 | -0.0232 | - | | 0.1846 | 26000 | 0.0003 | - | - | - | - | | 0.1917 | 27000 | 0.0003 | - | - | - | - | | 0.1988 | 28000 | 0.0003 | - | - | - | - | | 0.2059 | 29000 | 0.0003 | - | - | - | - | | 0.2130 | 30000 | 0.0003 | 0.0002 | 0.8641 | -0.0219 | - | | 0.2201 | 31000 | 0.0003 | - | - | - | - | | 0.2272 | 32000 | 0.0003 | - | - | - | - | | 0.2343 | 33000 | 0.0003 | - | - | - | - | | 0.2414 | 34000 | 0.0003 | - | - | - | - | | 0.2485 | 35000 | 0.0003 | 0.0002 | 0.8649 | -0.0209 | - | | 0.2556 | 36000 | 0.0003 | - | - | - | - | | 0.2627 | 37000 | 0.0003 | - | - | - | - | | 0.2698 | 38000 | 0.0003 | - | - | - | - | | 0.2769 | 39000 | 0.0003 | - | - | - | - | | 0.2840 | 40000 | 0.0003 | 0.0002 | 0.8648 | -0.0202 | - | | 0.2911 | 41000 | 0.0003 | - | - | - | - | | 0.2982 | 42000 | 0.0002 | - | - | - | - | | 0.3053 | 43000 | 0.0002 | - | - | - | - | | 0.3124 | 44000 | 0.0002 | - | - | - | - | | 0.3195 | 45000 | 0.0002 | 0.0002 | 0.8663 | -0.0196 | - | | 0.3266 | 46000 | 0.0002 | - | - | - | - | | 0.3337 | 47000 | 0.0002 | - | - | - | - | | 0.3408 | 48000 | 0.0002 | - | - | - | - | | 0.3479 | 49000 | 0.0002 | - | - | - | - | | 0.3550 | 50000 | 0.0002 | 0.0002 | 0.8665 | -0.0192 | - | | 0.3621 | 51000 | 0.0002 | - | - | - | - | | 0.3692 | 52000 | 0.0002 | - | - | - | - | | 0.3763 | 53000 | 0.0002 | - | - | - | - | | 0.3834 | 54000 | 0.0002 | - | - | - | - | | 0.3905 | 55000 | 0.0002 | 0.0002 | 0.8650 | -0.0187 | - | | 0.3976 | 56000 | 0.0002 | - | - | - | - | | 0.4047 | 57000 | 0.0002 | - | - | - | - | | 0.4118 | 58000 | 0.0002 | - | - | - | - | | 0.4189 | 59000 | 0.0002 | - | - | - | - | | 0.4260 | 60000 | 0.0002 | 0.0002 | 0.8636 | -0.0184 | - | | 0.4331 | 61000 | 0.0002 | - | - | - | - | | 0.4402 | 62000 | 0.0002 | - | - | - | - | | 0.4473 | 63000 | 0.0002 | - | - | - | - | | 0.4544 | 64000 | 0.0002 | - | - | - | - | | 0.4615 | 65000 | 0.0002 | 0.0002 | 0.8673 | -0.0180 | - | | 0.4686 | 66000 | 0.0002 | - | - | - | - | | 0.4757 | 67000 | 0.0002 | - | - | - | - | | 0.4828 | 68000 | 0.0002 | - | - | - | - | | 0.4899 | 69000 | 0.0002 | - | - | - | - | | 0.4970 | 70000 | 0.0002 | 0.0002 | 0.8692 | -0.0178 | - | | 0.5041 | 71000 | 0.0002 | - | - | - | - | | 0.5112 | 72000 | 0.0002 | - | - | - | - | | 0.5183 | 73000 | 0.0002 | - | - | - | - | | 0.5254 | 74000 | 0.0002 | - | - | - | - | | 0.5325 | 75000 | 0.0002 | 0.0002 | 0.8675 | -0.0175 | - | | 0.5396 | 76000 | 0.0002 | - | - | - | - | | 0.5467 | 77000 | 0.0002 | - | - | - | - | | 0.5538 | 78000 | 0.0002 | - | - | - | - | | 0.5609 | 79000 | 0.0002 | - | - | - | - | | 0.5680 | 80000 | 0.0002 | 0.0002 | 0.8657 | -0.0173 | - | | 0.5751 | 81000 | 0.0002 | - | - | - | - | | 0.5822 | 82000 | 0.0002 | - | - | - | - | | 0.5893 | 83000 | 0.0002 | - | - | - | - | | 0.5964 | 84000 | 0.0002 | - | - | - | - | | 0.6035 | 85000 | 0.0002 | 0.0002 | 0.8670 | -0.0171 | - | | 0.6106 | 86000 | 0.0002 | - | - | - | - | | 0.6177 | 87000 | 0.0002 | - | - | - | - | | 0.6248 | 88000 | 0.0002 | - | - | - | - | | 0.6319 | 89000 | 0.0002 | - | - | - | - | | 0.6390 | 90000 | 0.0002 | 0.0002 | 0.8665 | -0.0169 | - | | 0.6461 | 91000 | 0.0002 | - | - | - | - | | 0.6532 | 92000 | 0.0002 | - | - | - | - | | 0.6603 | 93000 | 0.0002 | - | - | - | - | | 0.6674 | 94000 | 0.0002 | - | - | - | - | | 0.6745 | 95000 | 0.0002 | 0.0002 | 0.8672 | -0.0167 | - | | 0.6816 | 96000 | 0.0002 | - | - | - | - | | 0.6887 | 97000 | 0.0002 | - | - | - | - | | 0.6958 | 98000 | 0.0002 | - | - | - | - | | 0.7029 | 99000 | 0.0002 | - | - | - | - | | 0.7100 | 100000 | 0.0002 | 0.0002 | 0.8657 | -0.0165 | - | | 0.7171 | 101000 | 0.0002 | - | - | - | - | | 0.7242 | 102000 | 0.0002 | - | - | - | - | | 0.7313 | 103000 | 0.0002 | - | - | - | - | | 0.7384 | 104000 | 0.0002 | - | - | - | - | | 0.7455 | 105000 | 0.0002 | 0.0002 | 0.8676 | -0.0165 | - | | 0.7526 | 106000 | 0.0002 | - | - | - | - | | 0.7597 | 107000 | 0.0002 | - | - | - | - | | 0.7668 | 108000 | 0.0002 | - | - | - | - | | 0.7739 | 109000 | 0.0002 | - | - | - | - | | 0.7810 | 110000 | 0.0002 | 0.0002 | 0.8672 | -0.0164 | - | | 0.7881 | 111000 | 0.0002 | - | - | - | - | | 0.7952 | 112000 | 0.0002 | - | - | - | - | | 0.8023 | 113000 | 0.0002 | - | - | - | - | | 0.8094 | 114000 | 0.0002 | - | - | - | - | | **0.8165** | **115000** | **0.0002** | **0.0002** | **0.8698** | **-0.0162** | **-** | | 0.8236 | 116000 | 0.0002 | - | - | - | - | | 0.8307 | 117000 | 0.0002 | - | - | - | - | | 0.8378 | 118000 | 0.0002 | - | - | - | - | | 0.8449 | 119000 | 0.0002 | - | - | - | - | | 0.8520 | 120000 | 0.0002 | 0.0002 | 0.8685 | -0.0161 | - | | 0.8591 | 121000 | 0.0002 | - | - | - | - | | 0.8662 | 122000 | 0.0002 | - | - | - | - | | 0.8733 | 123000 | 0.0002 | - | - | - | - | | 0.8804 | 124000 | 0.0002 | - | - | - | - | | 0.8875 | 125000 | 0.0002 | 0.0002 | 0.8676 | -0.0160 | - | | 0.8946 | 126000 | 0.0002 | - | - | - | - | | 0.9017 | 127000 | 0.0002 | - | - | - | - | | 0.9088 | 128000 | 0.0002 | - | - | - | - | | 0.9159 | 129000 | 0.0002 | - | - | - | - | | 0.9230 | 130000 | 0.0002 | 0.0002 | 0.8682 | -0.0159 | - | | 0.9301 | 131000 | 0.0002 | - | - | - | - | | 0.9372 | 132000 | 0.0002 | - | - | - | - | | 0.9443 | 133000 | 0.0002 | - | - | - | - | | 0.9514 | 134000 | 0.0002 | - | - | - | - | | 0.9585 | 135000 | 0.0002 | 0.0002 | 0.8678 | -0.0158 | - | | 0.9656 | 136000 | 0.0002 | - | - | - | - | | 0.9727 | 137000 | 0.0002 | - | - | - | - | | 0.9798 | 138000 | 0.0002 | - | - | - | - | | 0.9869 | 139000 | 0.0002 | - | - | - | - | | 0.9940 | 140000 | 0.0002 | 0.0002 | 0.8685 | -0.0158 | - | | -1 | -1 | - | - | - | - | 0.8339 | * The bold row denotes the saved checkpoint. </details> ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.7.1+cu118 - Accelerate: 1.7.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MSELoss ```bibtex @inproceedings{reimers-2020-multilingual-sentence-bert, title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2020", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2004.09813", } ```

fevohh/GenParser-1B-v1.1-1k-non-thinking-test14june

fevohh

2025-06-15T16:57:09Z

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)

LandCruiser/sn29C1_1506_5

LandCruiser

2025-06-15T16:55:08Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T03:26:57Z

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

mattbacker/c85fd56e-7ef0-4ed8-8ef1-bc9aece2df63_hardcode

mattbacker

2025-06-15T16:55:00Z

0

null

[ "region:us" ]

null

2025-06-15T13:49:25Z

# LoRA Model - mattbacker/c85fd56e-7ef0-4ed8-8ef1-bc9aece2df63_hardcode This is a LoRA (Low-Rank Adaptation) model trained for image generation. ## Model Files - `checkpoint/last.safetensors` - Primary model file (for evaluation) - `last-000001.safetensors` - Fallback model file (for evaluation) - `last.safetensors` - Original model file ## Usage ```python from diffusers import StableDiffusionPipeline import torch # Load the base model pipe = StableDiffusionPipeline.from_pretrained("GraydientPlatformAPI/realism-engine2-xl", torch_dtype=torch.float16) # Load the LoRA weights pipe.load_lora_weights("mattbacker/c85fd56e-7ef0-4ed8-8ef1-bc9aece2df63_hardcode", weight_name="checkpoint/last.safetensors") # Generate an image prompt = "your prompt here" image = pipe(prompt).images[0] image.save("output.png") ``` ## Training Details - Base Model: GraydientPlatformAPI/realism-engine2-xl - Training Method: LoRA (Low-Rank Adaptation) - Model Type: SDXL

diszell2008/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_beaked_alpaca

diszell2008

2025-06-15T16:54:26Z

1

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am lightfooted beaked alpaca", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-13T19:48:28Z

--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_beaked_alpaca tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am lightfooted beaked alpaca - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_beaked_alpaca This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time 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="diszell2008/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_beaked_alpaca", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.1 - 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}} } ```

falcongoldman/nexusai-tickets-llm

falcongoldman

2025-06-15T16:54:04Z

0

transformers

[ "transformers", "safetensors", "gguf", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-06-15T16:08:12Z

--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** falcongoldman - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.5_0.5_epoch1

MinaMila

2025-06-15T16:49:31Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T16:47:37Z

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

IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0

IoanaLiviaPopescu

2025-06-15T16:49:13Z

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ro", "dataset:IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2025-06-15T15:43:44Z

--- library_name: transformers language: - ro license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B metrics: - wer model-index: - name: IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B type: IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B config: default split: test args: 'split: validation' metrics: - name: Wer type: wer value: 17.00165959800848 ---  # IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1200-1-Wavenet-B-whisper-small-v0 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLiviaPopescu/RealVoiceSynthVoice-1200-1-Wavenet-B dataset. It achieves the following results on the evaluation set: - Loss: 0.3759 - Wer: 17.0017 ## 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: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 0 | 0 | 0.6024 | 27.8812 | | 0.2756 | 1.0 | 51 | 0.4008 | 17.9974 | | 0.1052 | 2.0 | 102 | 0.3728 | 17.3705 | | 0.0551 | 3.0 | 153 | 0.3759 | 17.0017 | | 0.0322 | 4.0 | 204 | 0.3911 | 17.5180 | | 0.0227 | 5.0 | 255 | 0.4033 | 17.6102 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

Pact-Ai/t5-small_igbo-en

Pact-Ai

2025-06-15T16:47:38Z

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "en", "ig", "de", "fr", "dataset:ignatius/igbo_english_machine_translation", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2025-06-15T16:11:46Z

--- license: apache-2.0 datasets: - ignatius/igbo_english_machine_translation language: - en - ig - de - fr base_model: - google-t5/t5-small library_name: transformers ---

lmquan/hummingbird

lmquan

2025-06-15T16:46:08Z

10

2

diffusers

[ "diffusers", "safetensors", "image-to-image", "en", "arxiv:2502.05153", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

image-to-image

2025-06-02T23:13:52Z

--- base_model: - stabilityai/stable-diffusion-xl-base-1.0 language: - en pipeline_tag: image-to-image library_name: diffusers --- # Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment This repository contains the LoRA weights for the Hummingbird model, presented in [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://huggingface.co/papers/2502.05153). The Hummingbird model generates high-quality, diverse images from a multimodal context, preserving scene attributes and object interactions from both a reference image and text guidance. [Project page](https://roar-ai.github.io/hummingbird) | [Paper](https://openreview.net/forum?id=6kPBThI6ZJ) ### Official implementation of paper: [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://openreview.net/pdf?id=6kPBThI6ZJ) ![image/png](https://roar-ai.github.io/hummingbird/static/images/teaser_comparison_v1.png) ## Prerequisites ### Installation 1. Clone this repository and navigate to hummingbird-1 folder ``` git clone https://github.com/roar-ai/hummingbird-1 cd hummingbird-1 ``` 2. Create `conda` virtual environment with Python 3.9, PyTorch 2.0+ is recommended: ``` conda create -n hummingbird python=3.9 conda activate hummingbird pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124 pip install -r requirements.txt ``` 3. Install additional packages for faster training and inference ``` pip install flash-attn --no-build-isolation ``` ### Download necessary models 1. Clone our Hummingbird LoRA weight of UNet denoiser ``` git clone https://huggingface.co/lmquan/hummingbird ``` 2. Refer to [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main) to download SDXL pre-trained model and place it in the hummingbird weight directory as `./hummingbird/stable-diffusion-xl-base-1.0`. 3. Download [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/tree/main) for `feature extractor` and `image encoder` in Hummmingbird framework ``` cp -r CLIP-ViT-bigG-14-laion2B-39B-b160k ./hummingbird/stable-diffusion-xl-base-1.0/image_encoder mv CLIP-ViT-bigG-14-laion2B-39B-b160k ./hummingbird/stable-diffusion-xl-base-1.0/feature_extractor ``` 4. Replace the file `model_index.json` of pre-trained `stable-diffusion-xl-base-1.0` with our customized version for Hummingbird framework ``` cp -r ./hummingbird/model_index.json ./hummingbird/stable-diffusion-xl-base-1.0/ ``` 5. Download [HPSv2 weights](https://drive.google.com/file/d/1T4e6WqsS5lcs92HdmzQYonrfDH1Ub53T/view?usp=sharing) and put it here: `hpsv2/HPS_v2_compressed.pt`. 6. Download [PickScore model weights](https://drive.google.com/file/d/1UhR0zFXiEI-spt2QdX67FY9a0dcqa9xy/view?usp=sharing) and put it here: `pickscore/pickmodel/model.safetensors`. ### Double check if everything is all set ``` |-- hummingbird-1/ |-- hpsv2 |-- HPS_v2_compressed.pt |-- pickscore |-- pickmodel |-- config.json |-- model.safetensors |-- hummingbird |-- model_index.json |-- lora_unet_65000 |-- adapter_config.json |-- adapter_model.safetensors |-- stable-diffusion-xl-base-1.0 |-- model_index.json (replaced by our customized version, see step 4 above) |-- feature_extractor (cloned from CLIP-ViT-bigG-14-laion2B-39B-b160k) |-- image_encoder (cloned from CLIP-ViT-bigG-14-laion2B-39B-b160k) |-- text_encoder |-- text_encoder_2 |-- tokenizer |-- tokenizer_2 |-- unet |-- vae |-- ... |-- ... ``` ## Quick Start Given a reference image, Hummingbird can generate diverse variants of it and preserve specific properties/attributes, for example: ``` python3 inference.py --reference_image ./examples/image-2.jpg --attribute "color of skateboard wheels" --output_path output.jpg ``` ## Training You can train Hummingbird with the following script: ``` sh run_hummingbird.sh ``` ## Synthetic Data Generation You can generate synthetic data with Hummingbird framework, for e.g. with MME Perception dataset: ``` python3 image_generation.py --generator hummingbird --dataset mme --save_image_gen ./synthetic_mme ``` ## Testing Evaluate the fidelity of generated images w.r.t reference image using Test-Time Augmentation on MLLMs (LLaVA/InternVL2): ``` python3 test_hummingbird_mme.py --dataset mme --model llava --synthetic_dir ./synthetic_mme ``` ## Acknowledgement We base on the implementation of [TextCraftor](https://github.com/snap-research/textcraftor). We thank [BLIP-2 QFormer](https://github.com/salesforce/LAVIS), [HPSv2](https://github.com/tgxs002/HPSv2), [PickScore](https://github.com/yuvalkirstain/PickScore), [Aesthetic](https://laion.ai/blog/laion-aesthetics/) for the reward models and MLLMs [LLaVA](https://github.com/haotian-liu/LLaVA), [InternVL2](https://github.com/OpenGVLab/InternVL) functioning as context descriptors in our framework. ## Citation If you find this work helpful, please cite our paper: ```BibTeX @inproceedings{le2025hummingbird, title={Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment}, author={Minh-Quan Le and Gaurav Mittal and Tianjian Meng and A S M Iftekhar and Vishwas Suryanarayanan and Barun Patra and Dimitris Samaras and Mei Chen}, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, url={https://openreview.net/forum?id=6kPBThI6ZJ} } ```

BRP0415/MIMIC

BRP0415

2025-06-15T16:44:50Z

0

fasttext

[ "fasttext", "en", "dataset:fka/awesome-chatgpt-prompts", "dataset:frascuchon/fka_awesome-chatgpt-prompts___2", "base_model:ResembleAI/chatterbox", "base_model:finetune:ResembleAI/chatterbox", "region:us" ]

null

2025-06-15T16:42:26Z

--- datasets: - fka/awesome-chatgpt-prompts - frascuchon/fka_awesome-chatgpt-prompts___2 language: - en metrics: - code_eval - character base_model: - ResembleAI/chatterbox - google/medgemma-4b-it new_version: ResembleAI/chatterbox library_name: fasttext ---

FormlessAI/a5731fb5-5d5c-4cf2-b067-342914d611f5

FormlessAI

2025-06-15T16:41:03Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-1.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T13:55:33Z

--- base_model: unsloth/Qwen2.5-1.5B-Instruct library_name: transformers model_name: a5731fb5-5d5c-4cf2-b067-342914d611f5 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for a5731fb5-5d5c-4cf2-b067-342914d611f5 This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time 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/a5731fb5-5d5c-4cf2-b067-342914d611f5", 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/nct0g92p) 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}} } ```

gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_seed_42_20250615_163021

gradientrouting-spar

2025-06-15T16:39:39Z

0

transformers

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

null

2025-06-15T16:39:32Z

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

VIDEO-18-parbin-assam-viral-videoS/VIDEO.LINK.parbin.Viral.Video.Tutorial.Official

VIDEO-18-parbin-assam-viral-videoS

2025-06-15T16:37:41Z

0

null

[ "region:us" ]

null

2025-06-15T16:37:15Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?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>

alhkalily/News_classeficaion_lstm

alhkalily

2025-06-15T16:36:14Z

0

null

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

null

2025-06-15T16:22:08Z

--- license: apache-2.0 language: - en ---

CreitinGameplays/Llama-3.1-8B-R1-v0.1

CreitinGameplays

2025-06-15T16:33:18Z

88

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:CreitinGameplays/Raiden-DeepSeek-R1-llama3.1", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-02-19T17:15:58Z

--- license: mit datasets: - CreitinGameplays/Raiden-DeepSeek-R1-llama3.1 language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation library_name: transformers --- ## Llama 3.1 8B R1 v0.1 ![Llama](https://autumn.revolt.chat/attachments/Dpj0Up0lYE2-BVOQRTDXeLk5xa7EE0WxBntXqgJGAo/DALL%C2%B7E%202025-02-19%2010.03.42%20-%20A%20futuristic%20robotic%20white%20llama%20with%20sleek%20metallic%20plating%20and%20glowing%20blue%20eyes.%20The%20llama%20has%20intricate%20mechanical%20joints%20and%20a%20high-tech%20design.%20.png) Took **28 hours** to finetune on **2x Nvidia RTX A6000** with the following settings: - Batch size: 8 - Gradient accumulation steps: 1 - Epochs: 2 - Learning rate: 1e-4 - Warmup ratio: 0.1 Run the model: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig import bitsandbytes quantization_config = BitsAndBytesConfig( load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True ) model_id = "CreitinGameplays/Llama-3.1-8B-R1-v0.1" # Initialize model and tokenizer with streaming support model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", quantization_config=quantization_config ) tokenizer = AutoTokenizer.from_pretrained(model_id) # Custom streamer that collects the output into a string while streaming class CollectingStreamer(TextStreamer): def __init__(self, tokenizer): super().__init__(tokenizer) self.output = "" def on_llm_new_token(self, token: str, **kwargs): self.output += token print(token, end="", flush=True) # prints the token as it's generated print("Chat session started. Type 'exit' to quit.\n") # Initialize chat history as a list of messages chat_history = [] chat_history.append({"role": "system", "content": "You are an AI assistant made by Meta AI."}) while True: user_input = input("You: ") if user_input.strip().lower() == "exit": break # Append the user message to the chat history chat_history.append({"role": "user", "content": user_input}) # Prepare the prompt by formatting the complete chat history inputs = tokenizer.apply_chat_template( chat_history, return_tensors="pt" ).to(model.device) # Create a new streamer for the current generation streamer = CollectingStreamer(tokenizer) # Generate streamed response model.generate( inputs, streamer=streamer, temperature=0.6, top_p=0.9, top_k=50, repetition_penalty=1.1, max_new_tokens=6112, do_sample=True ) # The complete response text is stored in streamer.output response_text = streamer.output print("\nAssistant:", response_text) # Append the assistant response to the chat history chat_history.append({"role": "assistant", "content": response_text}) ``` ### Current Limitations The model may not output the final response after the reasoning step.

mradermacher/ThinkAgent-1B-GGUF

mradermacher

2025-06-15T16:33:01Z

53

0

transformers

[ "transformers", "gguf", "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

2025-03-03T20:21:56Z

--- 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 --- ## About      static quants of https://huggingface.co/AymanTarig/Llama-3.2-1B-FC-v3  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/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q2_K.gguf) | Q2_K | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q3_K_S.gguf) | Q3_K_S | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q3_K_M.gguf) | Q3_K_M | 0.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q3_K_L.gguf) | Q3_K_L | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.IQ4_XS.gguf) | IQ4_XS | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q4_K_S.gguf) | Q4_K_S | 0.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q4_K_M.gguf) | Q4_K_M | 0.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q5_K_S.gguf) | Q5_K_S | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q5_K_M.gguf) | Q5_K_M | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q6_K.gguf) | Q6_K | 1.1 | very good quality | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.Q8_0.gguf) | Q8_0 | 1.4 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/ThinkAgent-1B-GGUF/resolve/main/ThinkAgent-1B.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.

VIDEO-18-parbin-assam-viral-videoS/FULL.VIDEO.parbin.Viral.Video.Tutorial.Official

VIDEO-18-parbin-assam-viral-videoS

2025-06-15T16:30:58Z

0

null

[ "region:us" ]

null

2025-06-15T16:30:37Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?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>

carazi/vyviln

carazi

2025-06-15T16:30:33Z

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

2025-06-15T16:09:20Z

--- 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: vyvil --- # Vyviln <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 `vyvil` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "vyvil", "lora_weights": "https://huggingface.co/carazi/vyviln/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('carazi/vyviln', weight_name='lora.safetensors') image = pipeline('vyvil').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/carazi/vyviln/discussions) to add images that show off what you’ve made with this LoRA.

SidXXD/Post_Impressionism

SidXXD

2025-06-15T16:30:11Z

39

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

2025-01-07T16:43:16Z

--- 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/Post_Impressionism 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).

pictgensupport/womanshairstyles

pictgensupport

2025-06-15T16:27:45Z

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

2025-06-15T16:27:43Z

--- 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: womanshairstyles --- # Womanshairstyles <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `womanshairstyles` to trigger the image generation. ## 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('pictgensupport/womanshairstyles', weight_name='lora.safetensors') image = pipeline('your prompt').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)

Enzogbs/ppo-Huggy

Enzogbs

2025-06-15T16:26:49Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2025-06-15T16:26:43Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Enzogbs/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

utkuden/qlora_paligemma_MIXft_decoder_only_rank16-SCST-CIDEr0.1270

utkuden

2025-06-15T16:24:47Z

0

transformers

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

null

2025-06-15T16:24:35Z

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

jobz-hunting-hot-sapna-shah/VIDEO.jobz.hunting.sapna.shah.Viral.Video.Tutorial.Official

jobz-hunting-hot-sapna-shah

2025-06-15T16:22:56Z

0

null

[ "region:us" ]

null

2025-06-15T16:22:13Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?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>

multimolecule/aido.rna-1.6b

multimolecule

2025-06-15T16:22:17Z

0

multimolecule

[ "multimolecule", "pytorch", "safetensors", "aido.rna", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/rnacentral", "license:agpl-3.0", "region:us" ]

fill-mask

2025-06-15T16:17:58Z

--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/rnacentral library_name: multimolecule pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "HIV-1" text: "GGUC<mask>CUCUGGUUAGACCAGAUCUGAGCCU" output: - label: "U" score: 0.7308459877967834 - label: "W" score: 0.11085908114910126 - label: "Y" score: 0.03829820826649666 - label: "H" score: 0.029108675196766853 - label: "K" score: 0.018761275336146355 - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUG" output: - label: "U" score: 0.41171538829803467 - label: "W" score: 0.1445416808128357 - label: "K" score: 0.06634332984685898 - label: "D" score: 0.060673028230667114 - label: "Y" score: 0.054533567279577255 --- # AIDO.RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) by Shuxian Zou, Tianhua Tao, Sazan Mahbub, et al. The OFFICIAL repository of AIDO.RNA is at [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO). > [!WARNING] > The MultiMolecule team is aware of a potential risk in reproducing the results of AIDO.RNA. > > The original implementation of AIDO.RNA uses a special tokenizer that identifies `U` and `T` as different tokens. > > This behaviour is not supported by MultiMolecule. > [!TIP] > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. **The team releasing AIDO.RNA did not write this model card for this model so this model card has been written by the MultiMolecule team.** ## Model Details AIDO.RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variants - **[multimolecule/aido.rna-1.6b](https://huggingface.co/multimolecule/aido.rna-1.6b)**: The AIDO.RNA model with 1.6 billion parameters. - **[multimolecule/aido.rna-650m](https://huggingface.co/multimolecule/aido.rna-650m)**: The AIDO.RNA model with 650 million parameters. ### Model Specification <table> <thead> <tr> <th>Variants</th> <th>Num Layers</th> <th>Hidden Size</th> <th>Num Heads</th> <th>Intermediate Size</th> <th>Num Parameters (M)</th> <th>FLOPs (G)</th> <th>MACs (G)</th> <th>Max Num Tokens</th> </tr> </thead> <tbody> <tr> <td>AIDO.RNA-1.6B</td> <td>32</td> <td>2048</td> <td>32</td> <td>5440</td> <td>1650.29</td> <td>415.67</td> <td>207.77</td> <td rowspan="2">1022</td> </tr> <tr> <td>AIDO.RNA-650M</td> <td>33</td> <td>1280</td> <td>20</td> <td>3392</td> <td>648.38</td> <td>168.25</td> <td>80.09</td> </tr> </tbody> </table> ### Links - **Code**: [multimolecule.aido_rna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aido_rna) - **Weights**: [multimolecule/aido.rna](https://huggingface.co/multimolecule/aido.rna) - **Data**: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral) - **Paper**: [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) - **Developed by**: Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, Eric P. Xing - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - **Original Repository**: [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline("fill-mask", model="multimolecule/aido.rna-1.6b") >>> unmasker("gguc<mask>cucugguuagaccagaucugagccu") [{'score': 0.7308459877967834, 'token': 9, 'token_str': 'U', 'sequence': 'G G U C U C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.11085908114910126, 'token': 14, 'token_str': 'W', 'sequence': 'G G U C W C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.03829820826649666, 'token': 12, 'token_str': 'Y', 'sequence': 'G G U C Y C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.029108675196766853, 'token': 19, 'token_str': 'H', 'sequence': 'G G U C H C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.018761275336146355, 'token': 15, 'token_str': 'K', 'sequence': 'G G U C K C U C U G G U U A G A C C A G A U C U G A G C C U'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, AidoRnaModel tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaModel.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") output = model(**input) ``` #### Sequence Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForSequencePrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.tensor([1]) output = model(**input, labels=label) ``` #### Token Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForTokenPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForTokenPrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), )) output = model(**input, labels=label) ``` #### Contact Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-1.6b") model = AidoRnaForContactPrediction.from_pretrained("multimolecule/aido.rna-1.6b") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), len(text))) output = model(**input, labels=label) ``` ## Training Details AIDO.RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The AIDO.RNA model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral) and [MARS](https://ngdc.cncb.ac.cn/omix/release/OMIX003037). RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types. AIDO.RNA applied SeqKit to remove duplicated sequences in the RNAcentral, resulting 42 million unique sequences. Note that AIDO.RNA identifies `U` and `T` as different tokens, which is not supported by MultiMolecule. During model conversion, the embeddings of `T` is discarded. This means that the model will not be able to distinguish between `U` and `T` in the input sequences. ### Training Procedure #### Preprocessing AIDO.RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### Pre-training - Epochs: 6 - Optimizer: AdamW - Learning rate: 5e-5 - Learning rate warm-up: 2,000 steps - Learning rate scheduler: Cosine - Minimum learning rate: 1e-5 - Weight decay: 0.01 ## Citation **BibTeX**: ```bibtex @article {Zou2024.11.28.625345, author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.}, title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction}, elocation-id = {2024.11.28.625345}, year = {2024}, doi = {10.1101/2024.11.28.625345}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Originally marginalized as an intermediate in the information flow from DNA to protein, RNA has become the star of modern biology, holding the key to precision therapeutics, genetic engineering, evolutionary origins, and our understanding of fundamental cellular processes. Yet RNA is as mysterious as it is prolific, serving as an information store, a messenger, and a catalyst, spanning many underchar-acterized functional and structural classes. Deciphering the language of RNA is important not only for a mechanistic understanding of its biological functions but also for accelerating drug design. Toward this goal, we introduce AIDO.RNA, a pre-trained module for RNA in an AI-driven Digital Organism [1]. AIDO.RNA contains a scale of 1.6 billion parameters, trained on 42 million non-coding RNA (ncRNA) sequences at single-nucleotide resolution, and it achieves state-of-the-art performance on a comprehensive set of tasks, including structure prediction, genetic regulation, molecular function across species, and RNA sequence design. AIDO.RNA after domain adaptation learns to model essential parts of protein translation that protein language models, which have received widespread attention in recent years, do not. More broadly, AIDO.RNA hints at the generality of biological sequence modeling and the ability to leverage the central dogma to improve many biomolecular representations. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345}, eprint = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [AIDO.RNA paper](https://doi.org/10.1101/2024.11.28.625345) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```

henriquesantos3430/HS

henriquesantos3430

2025-06-15T16:21:31Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-15T16:21:31Z

--- license: bigscience-bloom-rail-1.0 ---

biancaesteves5993/BS

biancaesteves5993

2025-06-15T16:21:31Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-15T16:21:31Z

--- license: bigscience-bloom-rail-1.0 ---

claravicente1628/CV

claravicente1628

2025-06-15T16:21:31Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-15T16:21:31Z

--- license: bigscience-bloom-rail-1.0 ---

marcomelo9929/MM

marcomelo9929

2025-06-15T16:21:31Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-15T16:21:31Z

--- license: bigscience-bloom-rail-1.0 ---

veracardoso4942/VD

veracardoso4942

2025-06-15T16:21:31Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-06-15T16:21:31Z

--- license: bigscience-bloom-rail-1.0 ---

jobz-hunting-hot-sapna-shah/FULL.VIDEO.jobz.hunting.sapna.shah.Viral.Video.Tutorial.Official

jobz-hunting-hot-sapna-shah

2025-06-15T16:18:42Z

0

null

[ "region:us" ]

null

2025-06-15T16:18:00Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?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>

multimolecule/aido.rna-650m-cds

multimolecule

2025-06-15T16:16:21Z

0

multimolecule

[ "multimolecule", "pytorch", "safetensors", "aido.rna", "Biology", "RNA", "fill-mask", "rna", "dataset:multimolecule/ena", "base_model:multimolecule/aido.rna-650m", "base_model:finetune:multimolecule/aido.rna-650m", "license:agpl-3.0", "region:us" ]

fill-mask

2025-06-15T16:12:05Z

--- language: rna tags: - Biology - RNA license: agpl-3.0 datasets: - multimolecule/ena library_name: multimolecule base_model: multimolecule/aido.rna-650m pipeline_tag: fill-mask mask_token: "<mask>" widget: - example_title: "HIV-1" text: "GGUC<mask>CUCUGGUUAGACCAGAUCUGAGCCU" output: - label: "A" score: 0.15881139039993286 - label: "R" score: 0.15044376254081726 - label: "G" score: 0.14251668751239777 - label: "V" score: 0.1298484206199646 - label: "M" score: 0.1239432692527771 - example_title: "microRNA-21" text: "UAGC<mask>UAUCAGACUGAUGUUG" output: - label: "A" score: 0.1757601946592331 - label: "M" score: 0.1494324952363968 - label: "R" score: 0.1302214413881302 - label: "V" score: 0.1291552037000656 - label: "C" score: 0.12704865634441376 --- # AIDO.RNA Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) by Shuxian Zou, Tianhua Tao, Sazan Mahbub, et al. The OFFICIAL repository of AIDO.RNA is at [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO). > [!WARNING] > The MultiMolecule team is aware of a potential risk in reproducing the results of AIDO.RNA. > > The original implementation of AIDO.RNA uses a special tokenizer that identifies `U` and `T` as different tokens. > > This behaviour is not supported by MultiMolecule. > [!TIP] > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. **The team releasing AIDO.RNA did not write this model card for this model so this model card has been written by the MultiMolecule team.** ## Model Details AIDO.RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variants - **[multimolecule/aido.rna-650m](https://huggingface.co/multimolecule/aido.rna-650m)**: The AIDO.RNA model with 650 million parameters. - **[multimolecule/aido.rna-1.6b](https://huggingface.co/multimolecule/aido.rna-1.6b)**: The AIDO.RNA model with 1.6 billion parameters. ### Model Specification <table> <thead> <tr> <th>Variants</th> <th>Num Layers</th> <th>Hidden Size</th> <th>Num Heads</th> <th>Intermediate Size</th> <th>Num Parameters (M)</th> <th>FLOPs (G)</th> <th>MACs (G)</th> <th>Max Num Tokens</th> </tr> </thead> <tbody> <tr> <td>AIDO.RNA-650M</td> <td>33</td> <td>1280</td> <td>20</td> <td>3392</td> <td>648.38</td> <td>168.25</td> <td>80.09</td> <td rowspan="2">1022</td> </tr> <tr> <td>AIDO.RNA-1.6B</td> <td>32</td> <td>2048</td> <td>32</td> <td>5440</td> <td>1650.29</td> <td>415.67</td> <td>207.77</td> </tr> </tbody> </table> ### Links - **Code**: [multimolecule.aido_rna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aido_rna) - **Weights**: [multimolecule/aido.rna](https://huggingface.co/multimolecule/aido.rna) - **Data**: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral) - **Paper**: [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) - **Developed by**: Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, Eric P. Xing - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - **Original Repository**: [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use You can use this model directly with a pipeline for masked language modeling: ```python >>> import multimolecule # you must import multimolecule to register models >>> from transformers import pipeline >>> unmasker = pipeline("fill-mask", model="multimolecule/aido.rna-650m") >>> unmasker("gguc<mask>cucugguuagaccagaucugagccu") [{'score': 0.15881139039993286, 'token': 6, 'token_str': 'A', 'sequence': 'G G U C A C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.15044376254081726, 'token': 11, 'token_str': 'R', 'sequence': 'G G U C R C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.14251668751239777, 'token': 8, 'token_str': 'G', 'sequence': 'G G U C G C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.1298484206199646, 'token': 20, 'token_str': 'V', 'sequence': 'G G U C V C U C U G G U U A G A C C A G A U C U G A G C C U'}, {'score': 0.1239432692527771, 'token': 16, 'token_str': 'M', 'sequence': 'G G U C M C U C U G G U U A G A C C A G A U C U G A G C C U'}] ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, AidoRnaModel tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-650m") model = AidoRnaModel.from_pretrained("multimolecule/aido.rna-650m") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") output = model(**input) ``` #### Sequence Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForSequencePrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-650m") model = AidoRnaForSequencePrediction.from_pretrained("multimolecule/aido.rna-650m") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.tensor([1]) output = model(**input, labels=label) ``` #### Token Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForTokenPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-650m") model = AidoRnaForTokenPrediction.from_pretrained("multimolecule/aido.rna-650m") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), )) output = model(**input, labels=label) ``` #### Contact Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, AidoRnaForContactPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido.rna-650m") model = AidoRnaForContactPrediction.from_pretrained("multimolecule/aido.rna-650m") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), len(text))) output = model(**input, labels=label) ``` ## Training Details AIDO.RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The AIDO.RNA model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral) and [MARS](https://ngdc.cncb.ac.cn/omix/release/OMIX003037). RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types. AIDO.RNA applied SeqKit to remove duplicated sequences in the RNAcentral, resulting 42 million unique sequences. Note that AIDO.RNA identifies `U` and `T` as different tokens, which is not supported by MultiMolecule. During model conversion, the embeddings of `T` is discarded. This means that the model will not be able to distinguish between `U` and `T` in the input sequences. ### Training Procedure #### Preprocessing AIDO.RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. #### Pre-training - Epochs: 6 - Optimizer: AdamW - Learning rate: 5e-5 - Learning rate warm-up: 2,000 steps - Learning rate scheduler: Cosine - Minimum learning rate: 1e-5 - Weight decay: 0.01 ## Citation **BibTeX**: ```bibtex @article {Zou2024.11.28.625345, author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.}, title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction}, elocation-id = {2024.11.28.625345}, year = {2024}, doi = {10.1101/2024.11.28.625345}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Originally marginalized as an intermediate in the information flow from DNA to protein, RNA has become the star of modern biology, holding the key to precision therapeutics, genetic engineering, evolutionary origins, and our understanding of fundamental cellular processes. Yet RNA is as mysterious as it is prolific, serving as an information store, a messenger, and a catalyst, spanning many underchar-acterized functional and structural classes. Deciphering the language of RNA is important not only for a mechanistic understanding of its biological functions but also for accelerating drug design. Toward this goal, we introduce AIDO.RNA, a pre-trained module for RNA in an AI-driven Digital Organism [1]. AIDO.RNA contains a scale of 1.6 billion parameters, trained on 42 million non-coding RNA (ncRNA) sequences at single-nucleotide resolution, and it achieves state-of-the-art performance on a comprehensive set of tasks, including structure prediction, genetic regulation, molecular function across species, and RNA sequence design. AIDO.RNA after domain adaptation learns to model essential parts of protein translation that protein language models, which have received widespread attention in recent years, do not. More broadly, AIDO.RNA hints at the generality of biological sequence modeling and the ability to leverage the central dogma to improve many biomolecular representations. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345}, eprint = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345.full.pdf}, journal = {bioRxiv} } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [AIDO.RNA paper](https://doi.org/10.1101/2024.11.28.625345) for questions or comments on the paper/model. ## License This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.05_epoch1

MinaMila

2025-06-15T16:16:16Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T16:14:21Z

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

HiverStarBox/NextIntercar

HiverStarBox

2025-06-15T16:14:28Z

0

null

[ "text-generation", "ru", "base_model:yandex/YandexGPT-5-Lite-8B-pretrain", "base_model:finetune:yandex/YandexGPT-5-Lite-8B-pretrain", "license:apache-2.0", "region:us" ]

text-generation

2025-06-15T16:12:33Z

--- license: apache-2.0 language: - ru base_model: - yandex/YandexGPT-5-Lite-8B-pretrain pipeline_tag: text-generation ---

OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen2.5-72B-Preview0

OpenBuddy

2025-06-15T16:12:07Z

4

0

null

[ "safetensors", "qwen2", "qwen2.5", "text-generation", "conversational", "zh", "en", "fr", "de", "ja", "ko", "it", "fi", "region:us" ]

text-generation

2025-06-12T16:36:05Z

--- language: - zh - en - fr - de - ja - ko - it - fi tags: - qwen2.5 pipeline_tag: text-generation base_model: Qwen/Qwen2.5-72B-Base --- # OpenBuddy - Open Multilingual Chatbot GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy) Website and Demo: [https://openbuddy.ai](https://openbuddy.ai) Evaluation result of this model: [Evaluation.txt](Evaluation.txt) ![Demo](https://raw.githubusercontent.com/OpenBuddy/OpenBuddy/main/media/demo.png) # Model Info Base Model: Qwen/Qwen2.5-72B-Base Context Length: 40K Tokens License: Qwen2.5 72B License Training Data: Distilled from DeepSeek-R1-0528 # Prompt Format We recommend using the fast tokenizer from `transformers`, which should be enabled by default in the `transformers` and `vllm` libraries. Other implementations including `sentencepiece` may not work as expected, especially for special tokens like `<|role|>`, `<|says|>` and `<|end|>`. ``` <|role|>system<|says|>You(assistant) are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human(user). Current mode: System 2, think step-by-step and answer.<|end|> <|role|>user<|says|>History input 1<|end|> <|role|>assistant<|says|>History output 1<|end|> <|role|>user<|says|>History input 2<|end|> <|role|>assistant<|says|>History output 2<|end|> <|role|>user<|says|>Current input<|end|> <|role|>assistant<|says|> ``` This format is also defined in `tokenizer_config.json`, which means you can directly use `vllm` to deploy an OpenAI-like API service. For more information, please refer to the [vllm documentation](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html). ## Disclaimer All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions. OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software. By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy. ## 免责声明所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。 OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。

telecomadm1145/gemma-3-cn-novel-4b-v1.1

telecomadm1145

2025-06-15T16:10:35Z

0

transformers

[ "transformers", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:telecomadm1145/gemma-3-cn-novel-4b-v1.1", "base_model:finetune:telecomadm1145/gemma-3-cn-novel-4b-v1.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-15T16:10:32Z

--- base_model: telecomadm1145/gemma-3-cn-novel-4b-v1.1 tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** telecomadm1145 - **License:** apache-2.0 - **Finetuned from model :** telecomadm1145/gemma-3-cn-novel-4b-v1.1 This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Rask6723/IT_GR7_En-Sn

Rask6723

2025-06-15T16:09:37Z

0

transformers

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

text2text-generation

2025-06-15T16:03:37Z

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

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.75_0.15_epoch2

MinaMila

2025-06-15T16:08:03Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T16:06:13Z

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

phospho-app/jakmilller-ACT-jenga_pull-z1gqj

phospho-app

2025-06-15T16:07:07Z

0

null

[ "safetensors", "phosphobot", "act", "region:us" ]

null

2025-06-15T13:17:51Z

--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using **phospho**. Training was successfull, try it out on your robot! ## Training parameters: - **Dataset**: [mahanthesh0r/jenga_pull](https://huggingface.co/datasets/mahanthesh0r/jenga_pull) - **Wandb run URL**: None - **Epochs**: None - **Batch size**: 40 - **Training steps**: 8000 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)

duchao1210/DPO_Qwen25_3B_128_0.05_1000kmap_lr

duchao1210

2025-06-15T16:06:46Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:duchao1210/qwen_2.5_3B_5k_r128", "base_model:finetune:duchao1210/qwen_2.5_3B_5k_r128", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T16:04:52Z

--- base_model: duchao1210/qwen_2.5_3B_5k_r128 tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** duchao1210 - **License:** apache-2.0 - **Finetuned from model :** duchao1210/qwen_2.5_3B_5k_r128 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)

SidXXD/Impressionism

SidXXD

2025-06-15T16:05:48Z

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

2025-01-07T16:33:46Z

--- 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/Impressionism 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/mc13_badmed_kl_div_beta_kl-3_epochs-10_seed_1

gradientrouting-spar

2025-06-15T16:05:08Z

0

transformers

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

null

2025-06-15T16:04:53Z

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

hungnguyennlp/llama-3.2-1b-instruct-lora-test

hungnguyennlp

2025-06-15T16:01:22Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]

null

2025-06-15T16:00:19Z

--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2

DevQuasar/shisa-ai.shisa-v2-llama3.1-405b-GGUF

DevQuasar

2025-06-15T16:00:38Z

1,164

0

null

[ "gguf", "text-generation", "base_model:shisa-ai/shisa-v2-llama3.1-405b", "base_model:quantized:shisa-ai/shisa-v2-llama3.1-405b", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-06-08T04:15:18Z

--- base_model: - shisa-ai/shisa-v2-llama3.1-405b pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) 'Make knowledge free for everyone' Quantized version of: [shisa-ai/shisa-v2-llama3.1-405b](https://huggingface.co/shisa-ai/shisa-v2-llama3.1-405b) <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>

hqjb/basic-resume

hqjb

2025-06-15T15:58:03Z

0

transformers

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

null

2025-06-15T15:52:48Z

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