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train · 834k rows

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
StuffedPumpkins/Irisirri	StuffedPumpkins	2025-06-16T10:21:25Z	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", "license:mit", "region:us" ]	text-to-image	2025-06-16T10:21:15Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/irisirricharli_002076_00_20250601021205.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: irisirricharli license: mit --- # Irisirri <Gallery /> ## Trigger words You should use `irisirricharli` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/StuffedPumpkins/Irisirri/tree/main) them in the Files & versions tab.
floflodebilbao/BART_challenge_test	floflodebilbao	2025-06-16T10:21:24Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large", "base_model:finetune:facebook/bart-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-16T10:19:58Z	--- library_name: transformers license: apache-2.0 base_model: facebook/bart-large tags: - generated_from_trainer metrics: - rouge model-index: - name: BART_challenge_test 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. --> # BART_challenge_test This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5847 - Rouge1: 0.2758 - Rouge2: 0.085 - Rougel: 0.2379 - Rougelsum: 0.2369 - Gen Len: 20.65 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 5 \| 4.2321 \| 0.2025 \| 0.0802 \| 0.1731 \| 0.1714 \| 21.0 \| \| No log \| 2.0 \| 10 \| 2.7023 \| 0.2757 \| 0.0992 \| 0.226 \| 0.2269 \| 21.0 \| \| No log \| 3.0 \| 15 \| 2.6063 \| 0.2741 \| 0.0741 \| 0.225 \| 0.2255 \| 21.0 \| \| No log \| 4.0 \| 20 \| 2.5847 \| 0.2758 \| 0.085 \| 0.2379 \| 0.2369 \| 20.65 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
sizzlebop/gte-base-dmr-Q4_K_M-GGUF	sizzlebop	2025-06-16T10:21:16Z	0	0	sentence-transformers	[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "transformers", "llama-cpp", "gguf-my-repo", "base_model:McGill-NLP/gte-base-dmr", "base_model:quantized:McGill-NLP/gte-base-dmr", "autotrain_compatible", "endpoints_compatible", "region:us", "imatrix" ]	sentence-similarity	2025-06-16T10:21:13Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - llama-cpp - gguf-my-repo base_model: McGill-NLP/gte-base-dmr --- # sizzlebop/gte-base-dmr-Q4_K_M-GGUF This model was converted to GGUF format from [`McGill-NLP/gte-base-dmr`](https://huggingface.co/McGill-NLP/gte-base-dmr) 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/McGill-NLP/gte-base-dmr) 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 sizzlebop/gte-base-dmr-Q4_K_M-GGUF --hf-file gte-base-dmr-q4_k_m-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sizzlebop/gte-base-dmr-Q4_K_M-GGUF --hf-file gte-base-dmr-q4_k_m-imat.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 sizzlebop/gte-base-dmr-Q4_K_M-GGUF --hf-file gte-base-dmr-q4_k_m-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sizzlebop/gte-base-dmr-Q4_K_M-GGUF --hf-file gte-base-dmr-q4_k_m-imat.gguf -c 2048 ```
sizzlebop/Nanonets-OCR-s-Q4_K_M-GGUF	sizzlebop	2025-06-16T10:20:52Z	0	0	transformers	[ "transformers", "gguf", "OCR", "pdf2markdown", "llama-cpp", "gguf-my-repo", "image-text-to-text", "en", "base_model:nanonets/Nanonets-OCR-s", "base_model:quantized:nanonets/Nanonets-OCR-s", "endpoints_compatible", "region:us", "imatrix" ]	image-text-to-text	2025-06-16T10:20:41Z	--- language: - en base_model: nanonets/Nanonets-OCR-s pipeline_tag: image-text-to-text tags: - OCR - pdf2markdown - llama-cpp - gguf-my-repo library_name: transformers --- # sizzlebop/Nanonets-OCR-s-Q4_K_M-GGUF This model was converted to GGUF format from [`nanonets/Nanonets-OCR-s`](https://huggingface.co/nanonets/Nanonets-OCR-s) 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/nanonets/Nanonets-OCR-s) 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 sizzlebop/Nanonets-OCR-s-Q4_K_M-GGUF --hf-file nanonets-ocr-s-q4_k_m-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sizzlebop/Nanonets-OCR-s-Q4_K_M-GGUF --hf-file nanonets-ocr-s-q4_k_m-imat.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 sizzlebop/Nanonets-OCR-s-Q4_K_M-GGUF --hf-file nanonets-ocr-s-q4_k_m-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sizzlebop/Nanonets-OCR-s-Q4_K_M-GGUF --hf-file nanonets-ocr-s-q4_k_m-imat.gguf -c 2048 ```
thesantoshbist/fwu-llm	thesantoshbist	2025-06-16T10:20:46Z	53	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "fwu", "santoshbist", "farwestern-ai", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-12T11:55:40Z	--- license: apache-2.0 language: - en metrics: - accuracy pipeline_tag: text-generation library_name: transformers tags: - fwu - santoshbist - farwestern-ai --- # FWU Assistant Model ## Model Description This is a fine-tuned LLM model specialized for Far Western University (FWU) information and educational assistance. The model has been customized to provide accurate information about FWU programs, courses, admissions, faculty, and campus resources while maintaining general conversational abilities. ## Training Data This model was trained on: - Conversations with students and faculty at FWU - Academic information and educational resources - General knowledge with emphasis on educational contexts ## Capabilities - Answers questions about Far Western University programs and policies - Provides assistance with academic inquiries - Helps with general knowledge questions - Maintains conversational context for natural interactions ## Use Cases - Student information services - Academic guidance - Educational assistance - University information desk - Virtual campus guide - Conversational AI - Question answering - Text generation ## Limitations - Limited knowledge of events after training cutoff - May occasionally provide incorrect information - Not a replacement for official university guidance - Doesn't have access to student records or private information ## Ethical Considerations This model is intended for educational and informational purposes only. It should not be used for making critical academic or administrative decisions without verification from official university sources. ## Additional Information Developed by Santosh Bist at Far Western University. For issues or feedback, please contact [email protected]. ## Model Description Custom LLM 1B Parameter mainly for FWU or Far Western University. ## Author Santosh Bist ## Version 1.0.0
HSE-Chukchi-NLP/gemma3-ckt-rus	HSE-Chukchi-NLP	2025-06-16T10:19:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T10:19:28Z	--- 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. 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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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StuffedPumpkins/ChrisP5	StuffedPumpkins	2025-06-16T10:19:32Z	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", "license:mit", "region:us" ]	text-to-image	2025-06-16T10:19:21Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/chrisp5_002150_00_20250611212807.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: ChrisP5 license: mit --- # ChrisP5 <Gallery /> ## Trigger words You should use `ChrisP5` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/StuffedPumpkins/ChrisP5/tree/main) them in the Files & versions tab.
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.25_0.75_0.25_epoch2	MinaMila	2025-06-16T10:19:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T10:17:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.25_0.75_epoch1	MinaMila	2025-06-16T10:18:33Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T10:16:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
zhenxue/dqn-SpaceInvadersNoFrameskip-v4	zhenxue	2025-06-16T10:17:42Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T09:45:40Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 503.00 +/- 144.43 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zhenxue -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zhenxue -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga zhenxue ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
divyanshu29jha/cpt_llama-3.2-3b_lonza-group-data	divyanshu29jha	2025-06-16T10:16:45Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T10:09:05Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: divyanshu29jha - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Darkhn/L3.3-70B-Animus-V1-GGUF	Darkhn	2025-06-16T10:15:52Z	227	0	llama.cpp	[ "llama.cpp", "gguf", "base_model:Darkhn/L3.3-70B-Animus-V1", "base_model:quantized:Darkhn/L3.3-70B-Animus-V1", "license:mit", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-06-13T01:52:45Z	--- library_name: llama.cpp license: mit tags: - gguf base_model: - Darkhn/L3.3-70B-Animus-V1 --- # L3.3-70B-Animus-V1-GGUF GGUF model files for `L3.3-70B-Animus-V1` (original base: `L3.3-70B-Animus-V1`). This repository contains the following quantization: Q5_K_M. ## Files - `L3.3-70B-Animus-V1-Q5_K_M.gguf` Converted and quantized using [llama.cpp](https://github.com/ggerganov/llama.cpp).
danhtran2mind/autoencoder-grayscale2color-landscape	danhtran2mind	2025-06-16T10:14:29Z	0	0	keras	[ "keras", "image-to-image", "en", "license:mit", "region:us" ]	image-to-image	2025-05-27T13:26:39Z	--- library_name: keras license: mit language: - en pipeline_tag: image-to-image --- # Autoencoder Grayscale2Color Landscape 🛡️ [![huggingface-hub](https://img.shields.io/badge/huggingface--hub-orange.svg?logo=huggingface)](https://huggingface.co/docs/hub) [![Pillow](https://img.shields.io/badge/Pillow-%2300A1D6.svg)](https://pypi.org/project/pillow/) [![numpy](https://img.shields.io/badge/numpy-%23013243.svg?logo=numpy)](https://numpy.org/) [![tensorflow](https://img.shields.io/badge/tensorflow-%23FF6F00.svg?logo=tensorflow)](https://www.tensorflow.org/) [![gradio](https://img.shields.io/badge/gradio-yellow.svg?logo=gradio)](https://gradio.app/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ## Introduction Transform grayscale landscape images into vibrant, full-color visuals with this autoencoder model. Built from scratch, this project leverages deep learning to predict color channels (ab in Lab* color space) from grayscale inputs, delivering impressive results with a sleek, minimalist design. 🌄 ## Key Features - 📸 Converts grayscale landscape images to vivid RGB. - 🧠 Custom autoencoder with spatial attention for enhanced detail. - ⚡ Optimized for high-quality inference at 512x512 resolution. - 📊 Achieves a PSNR of 21.70 on the validation set. ## Notebook Explore the implementation in our Jupyter notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https://huggingface.co/danhtran2mind/autoencoder-grayscale2color-landscape/blob/main/notebooks/autoencoder-grayscale-to-color-landscape.ipynb) [![View on HuggingFace](https://img.shields.io/badge/View%20on-HuggingFace-181717?logo=huggingface)](https://huggingface.co/danhtran2mind/autoencoder-grayscale2color-landscape/blob/main/notebooks/autoencoder-grayscale-to-color-landscape.ipynb) ## Dataset Details about the dataset are available in the [README Dataset](./dataset/README.md). 📂 ## From Scratch Model Custom-built autoencoder with a spatial attention mechanism, trained FROM SCRATCH to predict ab color channels from grayscale (L) inputs. 🧩 ## Demonstration Experience the brilliance of our cutting-edge technology! Transform grayscale landscapes into vibrant colors with our interactive demo. [![HuggingFace Space](https://img.shields.io/badge/%F0%9F%A4%97-HuggingFace%20Space-blue)](https://huggingface.co/spaces/danhtran2mind/autoencoder-grayscale2color-landscape) ![App Interface](./examples/gradio_app.png) ## Installation ### Step 1: Clone the Repository ```bash git clone https://huggingface.co/danhtran2mind/autoencoder-grayscale2color-landscape cd ./autoencoder-grayscale2color-landscape git lfs pull ``` ### Step 2: Install Dependencies ```bash pip install -r requirements.txt ``` ## Usage Follow these steps to colorize images programmatically using Python. ### 1. Import Required Libraries Install and import the necessary libraries for image processing and model inference. ```python from PIL import Image import os import numpy as np import tensorflow as tf import requests import matplotlib.pyplot as plt from skimage.color import lab2rgb from models.auto_encoder_gray2color import SpatialAttention ``` ### 2. Load the Pre-trained Model Download and load the autoencoder model from a remote source if it’s not already available locally. ```python load_model_path = "./ckpts/best_model.h5" os.makedirs(os.path.dirname(load_model_path), exist_ok=True) print(f"Loading model from {load_model_path}...") loaded_autoencoder = tf.keras.models.load_model( load_model_path, custom_objects={"SpatialAttention": SpatialAttention} ) print("Model loaded successfully.") ``` ### 3. Define Image Processing Functions These functions handle image preprocessing, colorization, and visualization. ```python def process_image(input_img): """Convert a grayscale image to color using the autoencoder.""" # Store original dimensions original_width, original_height = input_img.size # Preprocess: Convert to grayscale, resize, and normalize img = input_img.convert("L").resize((512, 512)) img_array = tf.keras.preprocessing.image.img_to_array(img) / 255.0 img_array = img_array[None, ..., 0:1] # Add batch dimension # Predict color channels output_array = loaded_autoencoder.predict(img_array) # Reconstruct LAB image L_channel = img_array[0, :, :, 0] 100.0 # Scale L channel ab_channels = output_array[0] * 128.0 # Scale ab channels lab_image = np.stack([L_channel, ab_channels[:, :, 0], ab_channels[:, :, 1]], axis=-1) # Convert to RGB and clip values rgb_array = lab2rgb(lab_image) rgb_array = np.clip(rgb_array, 0, 1) * 255.0 # Create and resize output image rgb_image = Image.fromarray(rgb_array.astype(np.uint8), mode="RGB") return rgb_image.resize((original_width, original_height), Image.Resampling.LANCZOS) def process_and_save_image(image_path): """Process an image and save the colorized result.""" input_img = Image.open(image_path) output_img = process_image(input_img) output_img.save("output.jpg") return input_img, output_img def plot_images(input_img, output_img): """Display input and output images side by side.""" plt.figure(figsize=(17, 8), dpi=300) # Plot input grayscale image plt.subplot(1, 2, 1) plt.imshow(input_img, cmap="gray") plt.title("Input Grayscale Image") plt.axis("off") # Plot output colorized image plt.subplot(1, 2, 2) plt.imshow(output_img) plt.title("Colorized Output Image") plt.axis("off") # Save and display the plot plt.savefig("output.jpg", dpi=300, bbox_inches="tight") plt.show() ``` ### 4. Perform Inference Run the colorization process on a sample image. ```python # Set image dimensions and path WIDTH, HEIGHT = 512, 512 image_path = "<path_to_input_image.jpg>" # Replace with your image path # Process and visualize the image input_img, output_img = process_and_save_image(image_path) plot_images(input_img, output_img) ``` ### 5. Example Output The output will be a side-by-side comparison of the input grayscale image and the colorized result, saved as `output.jpg`. For a sample result, see the example below: ![Output Image](./examples/model_output.png) ## Training Hyperparameters - Resolution: 512x512 pixels - Color Space: Lab* - Custom Layer: SpatialAttention - Model File: `best_model.h5` - Epochs: 100 ## Callbacks - Early Stopping: Monitors `val_loss`, patience of 20 epochs, restores best weights. - ReduceLROnPlateau: Monitors `val_loss`, reduces learning rate by 50% after 5 epochs, minimum learning rate of 1e-6. - BackupAndRestore: Saves checkpoints to `./ckpts/backup`. ## Metrics - PSNR (Validation): 21.70 📈 ## Environment - Python 3.11.11 - Libraies ``` numpy==1.26.4 tensorflow==2.18.0 opencv-python==4.11.0.86 scikit-image==0.25.2 matplotlib==3.7.2 scikit-image==0.25.2 ``` ## Contact For questions or issues, reach out via the [HuggingFace Community](https://huggingface.co/danhtran2mind/autoencoder-grayscale2color-landscape/discussions) tab. 🚀
nnilayy/deap-arousal-binary-classification-no-wd-Kfold-1	nnilayy	2025-06-16T10:13:18Z	4	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-27T03:24:42Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.25_0.75_0.25_epoch1	MinaMila	2025-06-16T10:11:37Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T10:09:47Z	--- 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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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]
aieng-lab/gpt2-xl_smell-doc	aieng-lab	2025-06-16T10:10:12Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "en", "base_model:openai-community/gpt2-xl", "base_model:finetune:openai-community/gpt2-xl", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T10:09:15Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - gpt2-xl pipeline_tag: text-classification --- # GPT-2 xl for classifying smell documentation (multi-label) This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [gpt2-xl](https://huggingface.co/gpt2-xl) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
martinoss731/kr-misha	martinoss731	2025-06-16T10:08:32Z	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	2025-06-16T09:54:18Z	--- 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: kr-misha --- # Kr Misha <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 `kr-misha` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "kr-misha", "lora_weights": "https://huggingface.co/martinoss731/kr-misha/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('martinoss731/kr-misha', weight_name='lora.safetensors') image = pipeline('kr-misha').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/martinoss731/kr-misha/discussions) to add images that show off what you’ve made with this LoRA.
ccaa1111/distilbert-base-uncased-finetuned-cola	ccaa1111	2025-06-16T10:07:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T06:16:06Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7960 - Matthews Correlation: 0.5676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5178 \| 1.0 \| 535 \| 0.4633 \| 0.4556 \| \| 0.3476 \| 2.0 \| 1070 \| 0.4718 \| 0.5469 \| \| 0.2333 \| 3.0 \| 1605 \| 0.6074 \| 0.5397 \| \| 0.1729 \| 4.0 \| 2140 \| 0.7411 \| 0.5380 \| \| 0.127 \| 5.0 \| 2675 \| 0.7960 \| 0.5676 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
aieng-lab/gpt2-medium_smell-doc	aieng-lab	2025-06-16T10:06:23Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "en", "base_model:openai-community/gpt2-medium", "base_model:finetune:openai-community/gpt2-medium", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T10:06:08Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - gpt2-medium pipeline_tag: text-classification --- # GPT-2 medium for classifying smell documentation (multi-label) This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [gpt2-medium](https://huggingface.co/gpt2-medium) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
aieng-lab/gpt2_smell-doc	aieng-lab	2025-06-16T10:05:39Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "en", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T10:05:31Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - gpt2 pipeline_tag: text-classification --- # GPT-2 small for classifying smell documentation (multi-label) This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [gpt2](https://huggingface.co/gpt2) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
veddhanth/lora-trained-xl-dreambooth-sneaker-300	veddhanth	2025-06-16T10:04:57Z	0	0	diffusers	[ "diffusers", "tensorboard", "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	2025-06-16T09:59:19Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of sks sneaker 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 - veddhanth/lora-trained-xl-dreambooth-sneaker-300 <Gallery /> ## Model description These are veddhanth/lora-trained-xl-dreambooth-sneaker-300 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: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks sneaker to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](veddhanth/lora-trained-xl-dreambooth-sneaker-300/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]
ahmadKhalidmohamad/samerbakeer	ahmadKhalidmohamad	2025-06-16T10:04:34Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T10:04:34Z	--- license: apache-2.0 ---
aieng-lab/ModernBERT-base_smell-doc	aieng-lab	2025-06-16T10:04:09Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T10:04:00Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - answerdotai/ModernBERT-base pipeline_tag: text-classification --- # ModernBERT base for classifying smell documentation (multi-label) This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.25_0.75_0.5_epoch2	MinaMila	2025-06-16T10:03:29Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T10:01:44Z	--- 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. 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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aieng-lab/roberta-large_smell-doc	aieng-lab	2025-06-16T10:03:18Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T10:03:03Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - roberta-large pipeline_tag: text-classification --- # RoBERTa large for classifying smell documentation (multi-label) This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [roberta-large](https://huggingface.co/roberta-large) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
aieng-lab/bert-large-cased_smell-doc	aieng-lab	2025-06-16T10:01:43Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "en", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T10:01:30Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - bert-large-cased pipeline_tag: text-classification --- # BERT large for classifying smell documentation (multi-label) This model classifies smell documentation as 'fragmented', 'tangled', 'excessive', 'bloated' or 'lazy'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [bert-large-cased](https://huggingface.co/bert-large-cased) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.5_0.15_epoch2	MinaMila	2025-06-16T09:57:55Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:56:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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3sara/checkpoints-2-epochs	3sara	2025-06-16T09:57:50Z	1	0	peft	[ "peft", "safetensors", "colpali-finetuned", "generated_from_trainer", "base_model:vidore/colpaligemma-3b-pt-448-base", "base_model:adapter:vidore/colpaligemma-3b-pt-448-base", "license:gemma", "region:us" ]	null	2025-06-13T14:24:28Z	--- library_name: peft license: gemma base_model: vidore/colpaligemma-3b-pt-448-base tags: - colpali-finetuned - generated_from_trainer model-index: - name: checkpoints-2-epochs 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. --> # checkpoints-2-epochs This model is a fine-tuned version of [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base) on the 3sara/validated_colpali_italian_documents_with_images dataset. It achieves the following results on the evaluation set: - Loss: 0.3171 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0103 \| 1 \| 0.3835 \| \| 0.0863 \| 5.1436 \| 500 \| 0.3171 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
Puranjay14/my-awesome-model	Puranjay14	2025-06-16T09:55:57Z	8	0	transformers	[ "transformers", "safetensors", "gemma3n_text", "text-generation", "matformer", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-14T18:40:15Z	--- library_name: transformers tags: - matformer --- # 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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krustal/dqn-SpaceInvadersNoFrameskip-v4	krustal	2025-06-16T09:54:25Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T09:53:47Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 325.50 +/- 146.14 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga krustal -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga krustal -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga krustal ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 10000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
panjibao/BOAT_1.0	panjibao	2025-06-16T09:54:20Z	0	0	null	[ "arxiv:1602.02355", "arxiv:1703.03400", "arxiv:1806.04910", "arxiv:1810.10667", "arxiv:1806.09055", "arxiv:2006.04045", "arxiv:2405.09927", "region:us" ]	null	2025-06-16T09:53:18Z	<h1 align="center"> <img src="./_static/logo.jpg" alt="BOAT" width="50%" align="top"> </h1> <p align="center"> Task-Agnostic Operation Toolbox for Gradient-based Bilevel Optimization<br> <a href="https://boat.readthedocs.io/en/latest/index.html">Home</a> \| <a href="https://boat.readthedocs.io/en/latest/install_guide.html#installation">Installation</a> \| <a href="https://boat.readthedocs.io/en/latest/boat_torch.html">Docs</a> \| <a href="https://boat.readthedocs.io/en/latest/install_guide.html#how-to-use-boat">Tutorials</a> \| <a href="https://boat.readthedocs.io/en/latest/index.html#running-example">Examples</a> \| </p> [![PyPI version](https://badge.fury.io/py/boml.svg)](https://badge.fury.io/py/boml) ![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/callous-youth/BOAT/workflow.yml) [![codecov](https://codecov.io/github/callous-youth/BOAT/graph/badge.svg?token=0MKAOQ9KL3)](https://codecov.io/github/callous-youth/BOAT) [![pages-build-deployment](https://github.com/callous-youth/BOAT/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/callous-youth/BOAT/actions/workflows/pages/pages-build-deployment) ![GitHub commit activity](https://img.shields.io/github/commit-activity/w/callous-youth/BOAT) ![GitHub top language](https://img.shields.io/github/languages/top/callous-youth/BOAT) ![GitHub language count](https://img.shields.io/github/languages/count/callous-youth/BOAT) ![Python version](https://img.shields.io/pypi/pyversions/boml) ![license](https://img.shields.io/badge/license-MIT-000000.svg) ![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg) BOAT is a task-agnostic, gradient-based Bi-Level Optimization (BLO) Python library that focuses on abstracting the key BLO process into modular, flexible components. It enables researchers and developers to tackle learning tasks with hierarchical nested nature by providing customizable and diverse operator decomposition, encapsulation, and combination. BOAT supports specialized optimization strategies, including second-order or first-order, nested or non-nested, and with or without theoretical guarantees, catering to various levels of complexity. To enhance flexibility and efficiency, BOAT incorporates the Dynamic Operation Library (D-OL) and the Hyper Operation Library (H-OL), alongside a collection of state-of-the-art first-order optimization strategies. BOAT also provides multiple implementation versions: - [PyTorch-based](https://github.com/callous-youth/BOAT): An efficient and widely-used version. - [Jittor-based](https://github.com/callous-youth/BOAT/tree/boat_jit): An accelerated version for high-performance tasks. - [MindSpore-based](https://github.com/callous-youth/BOAT/tree/boat_ms): Incorporating the latest first-order optimization strategies to support emerging application scenarios. <p align="center"> <a href="https://github.com/callous-youth/BOAT"> <img src="./_static/BOAT.png" alt="BOAT Structure" width="90%" align="top"> </a> </p> BOAT is designed to offer robust computational support for a broad spectrum of BLO research and applications, enabling innovation and efficiency in machine learning and computer vision. ## 🔑 Key Features - Dynamic Operation Library (D-OL): Incorporates 4 advanced dynamic system construction operations, enabling users to flexibly tailor optimization trajectories for BLO tasks. - Hyper-Gradient Operation Library (H-OL): Provides 9 refined operations for hyper-gradient computation, significantly enhancing the precision and efficiency of gradient-based BLO methods. - First-Order Gradient Methods (FOGMs): Integrates 4 state-of-the-art first-order methods, enabling fast prototyping and validation of new BLO algorithms. With modularized design, BOAT allows flexible combinations of multiple upper-level and lower-level operators, resulting in nearly 85 algorithmic combinations, offering unparalleled adaptability. - Modularized Design for Customization: Empowers users to flexibly combine dynamic and hyper-gradient operations while customizing the specific forms of problems, parameters, and optimizer choices, enabling seamless integration into diverse task-specific codes. - Comprehensive Testing & Continuous Integration: Achieves 99% code coverage through rigorous testing with pytest and Codecov, coupled with continuous integration via GitHub Actions, ensuring software robustness and reliability. - Fast Prototyping & Algorithm Validation: Streamlined support for defining, testing, and benchmarking new BLO algorithms. - Unified Computational Analysis: Offers a comprehensive complexity analysis of gradient-based BLO techniques to guide users in selecting optimal configurations for efficiency and accuracy. - Detailed Documentation & Community Support: Offers thorough documentation with practical examples and API references via MkDocs, ensuring accessibility and ease of use for both novice and advanced users. ## 🚀 Why BOAT? Existing automatic differentiation (AD) tools primarily focus on specific optimization strategies, such as explicit or implicit methods, and are often targeted at meta-learning or specific application scenarios, lacking support for algorithm customization. In contrast, BOAT expands the landscape of Bi-Level Optimization (BLO) applications by supporting a broader range of problem-adaptive operations. It bridges the gap between theoretical research and practical deployment, offering unparalleled flexibility to design, customize, and accelerate BLO techniques. ## 🏭 Applications BOAT enables efficient implementation and adaptation of advanced BLO techniques for key applications, including but not limited to: - Hyperparameter Optimization (HO) - Neural Architecture Search (NAS) - Adversarial Training (AT) - Few-Shot Learning (FSL) - Generative Adversarial Learning - Transfer Attack - ... ## 🔨 Installation To install BOAT, use the following command: ```bash pip install boat-torch or run git clone https://github.com/callous-youth/BOAT.git cd BOAT pip install -e . ``` ## ⚡ How to Use BOAT ### 1. Load Configuration Files BOAT relies on two key configuration files: - `boat_config.json`: Specifies optimization strategies and dynamic/hyper-gradient operations. - `loss_config.json`: Defines the loss functions for both levels of the BLO process. ```python import os import json import boat_torch as torch # Load configuration files with open("path_to_configs/boat_config.json", "r") as f: boat_config = json.load(f) with open("path_to_configs/loss_config.json", "r") as f: loss_config = json.load(f) ``` ### 2. Define Models and Optimizers You need to specify both the upper-level and lower-level models along with their respective optimizers. ```python import torch # Define models upper_model = UpperModel(args, kwargs) # Replace with your upper-level model lower_model = LowerModel(args, kwargs) # Replace with your lower-level model # Define optimizers upper_opt = torch.optim.Adam(upper_model.parameters(), lr=0.01) lower_opt = torch.optim.SGD(lower_model.parameters(), lr=0.01) ``` ### 3. Customize BOAT Configuration Modify the boat_config to include your dynamic and hyper-gradient methods, as well as model and variable details. ```python # Example dynamic and hyper-gradient methods Combination. dynamic_method = ["NGD", "DI", "GDA"] # Dynamic Methods (Demo Only) hyper_method = ["RGT","RAD"] # Hyper-Gradient Methods (Demo Only) # Add methods and model details to the configuration boat_config["dynamic_op"] = dynamic_method boat_config["hyper_op"] = hyper_method boat_config["lower_level_model"] = lower_model boat_config["upper_level_model"] = upper_model boat_config["lower_level_opt"] = lower_opt boat_config["upper_level_opt"] = upper_opt boat_config["lower_level_var"] = list(lower_model.parameters()) boat_config["upper_level_var"] = list(upper_model.parameters()) ``` ### 4. Initialize the BOAT Problem Modify the boat_config to include your dynamic and hyper-gradient methods, as well as model and variable details. ```python # Initialize the problem b_optimizer = boat.Problem(boat_config, loss_config) # Build solvers for lower and upper levels b_optimizer.build_ll_solver() # Lower-level solver b_optimizer.build_ul_solver() # Upper-level solver ``` ### 5. Define Data Feeds Prepare the data feeds for both levels of the BLO process, which was further fed into the the upper-level and lower-level objective functions. ```python # Define data feeds (Demo Only) ul_feed_dict = {"data": upper_level_data, "target": upper_level_target} ll_feed_dict = {"data": lower_level_data, "target": lower_level_target} ``` ### 6. Run the Optimization Loop** Execute the optimization loop, optionally customizing the solver strategy for dynamic methods. ```python # Set number of iterations iterations = 1000 # Optimization loop (Demo Only) for x_itr in range(iterations): # Run a single optimization iteration loss, run_time = b_optimizer.run_iter(ll_feed_dict, ul_feed_dict, current_iter=x_itr) ``` ## Related Methods - [Hyperparameter optimization with approximate gradient (CG)](https://arxiv.org/abs/1602.02355) - [Optimizing millions of hyperparameters by implicit differentiation (NS)](http://proceedings.mlr.press/v108/lorraine20a/lorraine20a.pdf) - [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (IAD)](https://arxiv.org/abs/1703.03400) - [On First-Order Meta-Learning Algorithms (FOA)](https://arxiv.org/abs/1703.03400) - [Bilevel Programming for Hyperparameter Optimization and Meta-Learning (RAD)](http://export.arxiv.org/pdf/1806.04910) - [Truncated Back-propagation for Bilevel Optimization (RGT)](https://arxiv.org/pdf/1810.10667.pdf) - [DARTS: Differentiable Architecture Search (FD)](https://arxiv.org/pdf/1806.09055.pdf) - [A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton (GDA)](https://arxiv.org/pdf/2006.04045.pdf) - [Towards gradient-based bilevel optimization with non-convex followers and beyond (PTT, DI)](https://proceedings.neurips.cc/paper_files/paper/2021/file/48bea99c85bcbaaba618ba10a6f69e44-Paper.pdf) - [Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity(DM)](https://proceedings.mlr.press/v202/liu23y/liu23y.pdf) - [Learning With Constraint Learning: New Perspective, Solution Strategy and Various Applications (IGA)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10430445) - [BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach (VFM)](https://proceedings.neurips.cc/paper_files/paper/2022/file/6dddcff5b115b40c998a08fbd1cea4d7-Paper-Conference.pdf) - [A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization (VSM)](http://proceedings.mlr.press/v139/liu21o/liu21o.pdf) - [On Penalty-based Bilevel Gradient Descent Method (PGDM)](https://proceedings.mlr.press/v202/shen23c/shen23c.pdf) - [Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy (MESM)](https://arxiv.org/pdf/2405.09927) ## License MIT License Copyright (c) 2024 Yaohua Liu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 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.
LandCruiser/sn21_omegaany_1606_6	LandCruiser	2025-06-16T09:53:08Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-06-16T09:27:03Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
LandCruiser/sn21_omegaany_1606_2	LandCruiser	2025-06-16T09:53:00Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-06-16T09:27:02Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
LandCruiser/sn21_omegaany_1606_4	LandCruiser	2025-06-16T09:52:59Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-06-16T09:27:02Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
LandCruiser/sn21_omegaany_1606_1	LandCruiser	2025-06-16T09:52:15Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-06-16T09:27:01Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.5_0.15_epoch1	MinaMila	2025-06-16T09:51:21Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:49:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
aplux/YOLOv6s	aplux	2025-06-16T09:51:20Z	0	0	null	[ "AIoT", "QNN", "object-detection", "license:gpl-3.0", "region:us" ]	object-detection	2025-06-12T03:26:56Z	--- license: gpl-3.0 pipeline_tag: object-detection tags: - AIoT - QNN --- ![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250317061900_20250221090145_yolo.png&w=640&q=75) ## YOLOv6s: Object Detection YOLOv6 is an advanced real-time object detection model based on the "You Only Look Once" framework. It achieves faster inference speeds while maintaining high accuracy, making it suitable for various edge devices and high-performance servers. YOLOv6 enhances its ability to detect small objects and improves the model's generalization performance by optimizing the network architecture and introducing new loss functions. Additionally, YOLOv6 supports multi-scale training, ensuring excellent performance across different resolutions. It is widely applied in areas such as video surveillance, autonomous driving, and intelligent security. ### Source model - Input shape: 1x3x640x640 - Number of parameters: 17.71M - Model size: 70.90 MB - Output shape: 1x8400x85 The source model can be found [here](https://github.com/meituan/YOLOv6) ## Performance Reference Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## Inference & Model Conversion Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## License - Source Model: [GPL-3.0](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/LICENSE) - Deployable Model: [GPL-3.0](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/LICENSE)
Kiwon007/kiwon	Kiwon007	2025-06-16T09:50:10Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-16T09:12:41Z	--- 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 ---
MJ92/Llama-2-7b-chat-hf_finetuned_2000_fr	MJ92	2025-06-16T09:49:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:29:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.25_0.75_0.75_epoch2	MinaMila	2025-06-16T09:47:40Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:45:54Z	--- 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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LaaP-ai/donut-base-invoice-v1.01	LaaP-ai	2025-06-16T09:47:14Z	0	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-16T09:46:54Z	--- library_name: transformers license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut-base-invoice-v1.01 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. --> # donut-base-invoice-v1.01 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
UniLLMer/MuseKaaovercooked	UniLLMer	2025-06-16T09:44:58Z	0	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:UniLLMer/MuseKaako6432e3e2jokesdwptoo", "base_model:quantized:UniLLMer/MuseKaako6432e3e2jokesdwptoo", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T09:42:27Z	--- base_model: UniLLMer/MuseKaako6432e3e2jokesdwptoo tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: UniLLMer - License: apache-2.0 - Finetuned from model : UniLLMer/MuseKaako6432e3e2jokesdwptoo This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
aplux/YOLOv6m	aplux	2025-06-16T09:44:51Z	0	0	null	[ "AIoT", "QNN", "LLM", "object-detection", "license:gpl-3.0", "region:us" ]	object-detection	2025-06-12T03:31:36Z	--- license: gpl-3.0 pipeline_tag: object-detection tags: - AIoT - QNN - LLM --- ![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250317092602_20250221090145_yolo.png&w=640&q=75) ## YOLOv6m: Object Detection YOLOv6 is an advanced real-time object detection model based on the "You Only Look Once" framework. It achieves faster inference speeds while maintaining high accuracy, making it suitable for various edge devices and high-performance servers. YOLOv6 enhances its ability to detect small objects and improves the model's generalization performance by optimizing the network architecture and introducing new loss functions. Additionally, YOLOv6 supports multi-scale training, ensuring excellent performance across different resolutions. It is widely applied in areas such as video surveillance, autonomous driving, and intelligent security. ### Source model - Input shape: 1x3x640x640 - Number of parameters: 33.24M - Model size: 133.20MB - Output shape: 1x8400x85 The source model can be found [here](https://github.com/meituan/YOLOv6) ## Performance Reference Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## Inference & Model Conversion Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## License - Source Model: [GPL-3.0](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/LICENSE) - Deployable Model: [GPL-3.0](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/LICENSE)
sbx/KB-bert-base-swedish-cased_PI-detection-detailed	sbx	2025-06-16T09:44:29Z	62	0	null	[ "pytorch", "safetensors", "bert", "token-classification", "sv", "arxiv:1910.09700", "base_model:KB/bert-base-swedish-cased", "base_model:finetune:KB/bert-base-swedish-cased", "license:gpl-3.0", "region:us" ]	token-classification	2025-05-12T10:59:32Z	--- license: gpl-3.0 language: - sv base_model: - KB/bert-base-swedish-cased pipeline_tag: token-classification --- # sbx/KB-bert-base-swedish-cased_PI-detection-detailed This model was developed as part of the [Mormor Karl research environment](https://mormor-karl.github.io/). It assesses whether each token is likely to belong to a piece of personal or sensitive information and assigns it the appropriate class (firstname male, firstname female, firstname unknown, initials, middlename, surname, school, work, other institution, area, city, geo, country, place, region, street nr, zip code, transport name, transport nr, age digits, age string, date digits, day, month digit, month word, year, phone nr, email, url, personid nr, account nr, license nr, other nr seq, extra, prof, edu, fam, sensitive, or none). It should be noted that this model performs best on the domain it was trained on, i.e. second-language learner essays; neither does it guarantee that all of the personal information will be detected and should only be used to assist personal information detection and classification, not completely automatize it. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [Språkbanken Text](https://spraakbanken.gu.se/en), as part of the [Mormor Karl research environment](https://mormor-karl.github.io/) - Shared by: Maria Irena Szawerna ([Turtilla](https://huggingface.co/Turtilla)) - Model type: BERT for token classification - Language(s): Swedish - License: GPL-3.0 - Finetuned from model: [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Paper: The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling ([link](https://aclanthology.org/2025.nodalida-1.70/)) ## 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. --> This model should be used to assist annotation or de-identification efforts conducted by humans as a pre-annotation step. The detection and classification of personal information can be followed by removal or replacement of said elements. <!-- ### Out-of-Scope Use --> <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This model has only been trained on one domain (L2 learner essays). It does not necessarily perform well on other domains. Due to the limitations of the training data, it may also perform worse on rare or previously unseen types of personal information. Given how high-stakes personal information detection is and that 100% accuracy cannot be guaranteed, this model should be used to assist, not automatize annotation or de-identification procedures. ### 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] --> ## 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: > ``` @inproceedings{szawerna-etal-2025-devils, title = "The Devil{'}s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling", author = "Szawerna, Maria Irena and Dobnik, Simon and Mu{\~n}oz S{\'a}nchez, Ricardo and Volodina, Elena", editor = "Johansson, Richard and Stymne, Sara", booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)", month = mar, year = "2025", address = "Tallinn, Estonia", publisher = "University of Tartu Library", url = "https://aclanthology.org/2025.nodalida-1.70/", pages = "697--708", ISBN = "978-9908-53-109-0", abstract = "In this paper, we experiment with the effect of different levels of detailedness or granularity{---}understood as i) the number of classes, and ii) the classes' semantic depth in the sense of hypernym and hyponym relations {---} of the annotation of Personally Identifiable Information (PII) on automatic detection and labeling of such information. We fine-tune a Swedish BERT model on a corpus of Swedish learner essays annotated with a total of six PII tagsets at varying levels of granularity. We also investigate whether the presence of grammatical and lexical correction annotation in the tokens and class prevalence have an effect on predictions. We observe that the fewer total categories there are, the better the overall results are, but having a more diverse annotation facilitates fewer misclassifications for tokens containing correction annotation. We also note that the classes' internal diversity has an effect on labeling. We conclude from the results that while labeling based on the detailed annotation is difficult because of the number of classes, it is likely that models trained on such annotation rely more on the semantic content captured by contextual word embeddings rather than just the form of the tokens, making them more robust against nonstandard language." } APA: > Maria Irena Szawerna, Simon Dobnik, Ricardo Muñoz Sánchez, and Elena Volodina. 2025. The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 697–708, Tallinn, Estonia. University of Tartu Library. ## Model Card Authors Maria Irena Szawerna ([Turtilla](https://huggingface.co/Turtilla)) <!-- ## Model Card Contact [More Information Needed] -->
KubMastero/skorice	KubMastero	2025-06-16T09:43:04Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T09:43:04Z	--- license: apache-2.0 ---
sbx/KB-bert-base-swedish-cased_PI-detection-general-iob	sbx	2025-06-16T09:42:47Z	54	0	null	[ "pytorch", "safetensors", "bert", "token-classification", "sv", "arxiv:1910.09700", "base_model:KB/bert-base-swedish-cased", "base_model:finetune:KB/bert-base-swedish-cased", "license:gpl-3.0", "region:us" ]	token-classification	2025-05-12T10:52:48Z	--- license: gpl-3.0 language: - sv base_model: - KB/bert-base-swedish-cased pipeline_tag: token-classification --- # sbx/KB-bert-base-swedish-cased_PI-detection-general-iob This model was developed as part of the [Mormor Karl research environment](https://mormor-karl.github.io/). It assesses whether each token is likely to belong to a piece of personal or sensitive information and assigns it the appropriate class (personal name, institution, geographic, transportation, age, date, other or none), distinguishing between beginnings and insides of such spans on token level. It should be noted that this model performs best on the domain it was trained on, i.e. second-language learner essays; neither does it guarantee that all of the personal information will be detected and should only be used to assist personal information detection and classification, not completely automatize it. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [Språkbanken Text](https://spraakbanken.gu.se/en), as part of the [Mormor Karl research environment](https://mormor-karl.github.io/) - Shared by: Maria Irena Szawerna ([Turtilla](https://huggingface.co/Turtilla)) - Model type: BERT for token classification - Language(s): Swedish - License: GPL-3.0 - Finetuned from model: [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Paper: The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling ([link](https://aclanthology.org/2025.nodalida-1.70/)) ## 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. --> This model should be used to assist annotation or de-identification efforts conducted by humans as a pre-annotation step. The detection and classification of personal information can be followed by removal or replacement of said elements. <!-- ### Out-of-Scope Use --> <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This model has only been trained on one domain (L2 learner essays). It does not necessarily perform well on other domains. Due to the limitations of the training data, it may also perform worse on rare or previously unseen types of personal information. Given how high-stakes personal information detection is and that 100% accuracy cannot be guaranteed, this model should be used to assist, not automatize annotation or de-identification procedures. ### 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] --> ## 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: > ``` @inproceedings{szawerna-etal-2025-devils, title = "The Devil{'}s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling", author = "Szawerna, Maria Irena and Dobnik, Simon and Mu{\~n}oz S{\'a}nchez, Ricardo and Volodina, Elena", editor = "Johansson, Richard and Stymne, Sara", booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)", month = mar, year = "2025", address = "Tallinn, Estonia", publisher = "University of Tartu Library", url = "https://aclanthology.org/2025.nodalida-1.70/", pages = "697--708", ISBN = "978-9908-53-109-0", abstract = "In this paper, we experiment with the effect of different levels of detailedness or granularity{---}understood as i) the number of classes, and ii) the classes' semantic depth in the sense of hypernym and hyponym relations {---} of the annotation of Personally Identifiable Information (PII) on automatic detection and labeling of such information. We fine-tune a Swedish BERT model on a corpus of Swedish learner essays annotated with a total of six PII tagsets at varying levels of granularity. We also investigate whether the presence of grammatical and lexical correction annotation in the tokens and class prevalence have an effect on predictions. We observe that the fewer total categories there are, the better the overall results are, but having a more diverse annotation facilitates fewer misclassifications for tokens containing correction annotation. We also note that the classes' internal diversity has an effect on labeling. We conclude from the results that while labeling based on the detailed annotation is difficult because of the number of classes, it is likely that models trained on such annotation rely more on the semantic content captured by contextual word embeddings rather than just the form of the tokens, making them more robust against nonstandard language." } APA: > Maria Irena Szawerna, Simon Dobnik, Ricardo Muñoz Sánchez, and Elena Volodina. 2025. The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 697–708, Tallinn, Estonia. University of Tartu Library. ## Model Card Authors Maria Irena Szawerna ([Turtilla](https://huggingface.co/Turtilla)) <!-- ## Model Card Contact [More Information Needed] -->
sbx/KB-bert-base-swedish-cased_PI-detection-general	sbx	2025-06-16T09:41:51Z	19	0	null	[ "pytorch", "safetensors", "bert", "token-classification", "sv", "arxiv:1910.09700", "base_model:KB/bert-base-swedish-cased", "base_model:finetune:KB/bert-base-swedish-cased", "license:gpl-3.0", "region:us" ]	token-classification	2025-05-12T09:53:59Z	--- license: gpl-3.0 language: - sv base_model: - KB/bert-base-swedish-cased pipeline_tag: token-classification --- # sbx/KB-bert-base-swedish-cased_PI-detection-general This model was developed as part of the [Mormor Karl research environment](https://mormor-karl.github.io/). It assesses whether each token is likely to belong to a piece of personal or sensitive information and assigns it the appropriate class (personal name, institution, geographic, transportation, age, date, other or none). It should be noted that this model performs best on the domain it was trained on, i.e. second-language learner essays; neither does it guarantee that all of the personal information will be detected and should only be used to assist personal information detection and classification, not completely automatize it. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [Språkbanken Text](https://spraakbanken.gu.se/en), as part of the [Mormor Karl research environment](https://mormor-karl.github.io/) - Shared by: Maria Irena Szawerna ([Turtilla](https://huggingface.co/Turtilla)) - Model type: BERT for token classification - Language(s): Swedish - License: GPL-3.0 - Finetuned from model: [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Paper: The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling ([link](https://aclanthology.org/2025.nodalida-1.70/)) ## 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. --> This model should be used to assist annotation or de-identification efforts conducted by humans as a pre-annotation step. The detection and classification of personal information can be followed by removal or replacement of said elements. <!-- ### Out-of-Scope Use --> <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This model has only been trained on one domain (L2 learner essays). It does not necessarily perform well on other domains. Due to the limitations of the training data, it may also perform worse on rare or previously unseen types of personal information. Given how high-stakes personal information detection is and that 100% accuracy cannot be guaranteed, this model should be used to assist, not automatize annotation or de-identification procedures. ### 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] --> ## 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: > ``` @inproceedings{szawerna-etal-2025-devils, title = "The Devil{'}s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling", author = "Szawerna, Maria Irena and Dobnik, Simon and Mu{\~n}oz S{\'a}nchez, Ricardo and Volodina, Elena", editor = "Johansson, Richard and Stymne, Sara", booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)", month = mar, year = "2025", address = "Tallinn, Estonia", publisher = "University of Tartu Library", url = "https://aclanthology.org/2025.nodalida-1.70/", pages = "697--708", ISBN = "978-9908-53-109-0", abstract = "In this paper, we experiment with the effect of different levels of detailedness or granularity{---}understood as i) the number of classes, and ii) the classes' semantic depth in the sense of hypernym and hyponym relations {---} of the annotation of Personally Identifiable Information (PII) on automatic detection and labeling of such information. We fine-tune a Swedish BERT model on a corpus of Swedish learner essays annotated with a total of six PII tagsets at varying levels of granularity. We also investigate whether the presence of grammatical and lexical correction annotation in the tokens and class prevalence have an effect on predictions. We observe that the fewer total categories there are, the better the overall results are, but having a more diverse annotation facilitates fewer misclassifications for tokens containing correction annotation. We also note that the classes' internal diversity has an effect on labeling. We conclude from the results that while labeling based on the detailed annotation is difficult because of the number of classes, it is likely that models trained on such annotation rely more on the semantic content captured by contextual word embeddings rather than just the form of the tokens, making them more robust against nonstandard language." } APA: > Maria Irena Szawerna, Simon Dobnik, Ricardo Muñoz Sánchez, and Elena Volodina. 2025. The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 697–708, Tallinn, Estonia. University of Tartu Library. ## Model Card Authors Maria Irena Szawerna ([Turtilla](https://huggingface.co/Turtilla)) <!-- ## Model Card Contact [More Information Needed] -->
sbx/KB-bert-base-swedish-cased_PI-detection-basic-iob	sbx	2025-06-16T09:39:37Z	16	0	null	[ "pytorch", "safetensors", "bert", "token-classification", "sv", "arxiv:1910.09700", "base_model:KB/bert-base-swedish-cased", "base_model:finetune:KB/bert-base-swedish-cased", "license:gpl-3.0", "region:us" ]	token-classification	2025-05-12T09:24:19Z	--- license: gpl-3.0 language: - sv base_model: - KB/bert-base-swedish-cased pipeline_tag: token-classification --- # sbx/KB-bert-base-swedish-cased_PI-detection-basic-iob This model was developed as part of the [Mormor Karl research environment](https://mormor-karl.github.io/). It assesses whether each token is likely to belong to a piece of personal or sensitive information and assigns it the appropriate class (sensitive or not), distinguishing between beginnings and insides of such spans on token level. It should be noted that this model performs best on the domain it was trained on, i.e. second-language learner essays; neither does it guarantee that all of the personal information will be detected and should only be used to assist personal information detection and classification, not completely automatize it. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [Språkbanken Text](https://spraakbanken.gu.se/en), as part of the [Mormor Karl research environment](https://mormor-karl.github.io/) - Shared by: Maria Irena Szawerna ([Turtilla](https://huggingface.co/Turtilla)) - Model type: BERT for token classification - Language(s): Swedish - License: GPL-3.0 - Finetuned from model: [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Paper: The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling ([link](https://aclanthology.org/2025.nodalida-1.70/)) ## 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. --> This model should be used to assist annotation or de-identification efforts conducted by humans as a pre-annotation step. The detection and classification of personal information can be followed by removal or replacement of said elements. <!-- ### Out-of-Scope Use --> <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This model has only been trained on one domain (L2 learner essays). It does not necessarily perform well on other domains. Due to the limitations of the training data, it may also perform worse on rare or previously unseen types of personal information. Given how high-stakes personal information detection is and that 100% accuracy cannot be guaranteed, this model should be used to assist, not automatize annotation or de-identification procedures. ### 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] --> ## 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: > ``` @inproceedings{szawerna-etal-2025-devils, title = "The Devil{'}s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling", author = "Szawerna, Maria Irena and Dobnik, Simon and Mu{\~n}oz S{\'a}nchez, Ricardo and Volodina, Elena", editor = "Johansson, Richard and Stymne, Sara", booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)", month = mar, year = "2025", address = "Tallinn, Estonia", publisher = "University of Tartu Library", url = "https://aclanthology.org/2025.nodalida-1.70/", pages = "697--708", ISBN = "978-9908-53-109-0", abstract = "In this paper, we experiment with the effect of different levels of detailedness or granularity{---}understood as i) the number of classes, and ii) the classes' semantic depth in the sense of hypernym and hyponym relations {---} of the annotation of Personally Identifiable Information (PII) on automatic detection and labeling of such information. We fine-tune a Swedish BERT model on a corpus of Swedish learner essays annotated with a total of six PII tagsets at varying levels of granularity. We also investigate whether the presence of grammatical and lexical correction annotation in the tokens and class prevalence have an effect on predictions. We observe that the fewer total categories there are, the better the overall results are, but having a more diverse annotation facilitates fewer misclassifications for tokens containing correction annotation. We also note that the classes' internal diversity has an effect on labeling. We conclude from the results that while labeling based on the detailed annotation is difficult because of the number of classes, it is likely that models trained on such annotation rely more on the semantic content captured by contextual word embeddings rather than just the form of the tokens, making them more robust against nonstandard language." } APA: > Maria Irena Szawerna, Simon Dobnik, Ricardo Muñoz Sánchez, and Elena Volodina. 2025. The Devil’s in the Details: the Detailedness of Classes Influences Personal Information Detection and Labeling. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 697–708, Tallinn, Estonia. University of Tartu Library. ## Model Card Authors Maria Irena Szawerna ([Turtilla](https://huggingface.co/Turtilla)) <!-- ## Model Card Contact [More Information Needed] -->
sc22mc/DocFusion	sc22mc	2025-06-16T09:38:57Z	78	0	null	[ "pytorch", "safetensors", "docfusion", "image-text-to-text", "custom_code", "arxiv:2412.12505", "license:apache-2.0", "region:us" ]	image-text-to-text	2025-01-28T08:16:08Z	--- license: apache-2.0 pipeline_tag: image-text-to-text --- ### DocFusion: A Unified Framework for Document Parsing Tasks Document parsing involves layout element detection and recognition, essential for extracting information. However, existing methods often employ multiple models for these tasks, leading to increased system complexity and maintenance overhead. While some models attempt to unify detection and recognition, they often fail to address the intrinsic differences in data representations, thereby limiting performance in document processing. Our research reveals that recognition relies on discrete tokens, whereas detection relies on continuous coordinates, leading to challenges in gradient updates and optimization. To bridge this gap, we propose the Gaussian-Kernel CrossEntropy Loss (GK-CEL), enabling generative frameworks to handle both tasks simultaneously. Building upon GK-CEL, we propose DocFusion, a unified document parsing model with only 0.28B parameters. Additionally, we construct the DocLatex-1.6M dataset to provide high-quality training support. Experimental results show that DocFusion, equipped with GK-CEL, performs competitively across four core document parsing tasks, validating the effectiveness of our unified approach. Resources and Technical Documentation: + [Technical Report](https://arxiv.org/abs/2412.12505) + [Jupyter Notebook for inference](https://huggingface.co/sc22mc/DocFusion/blob/main/infer.ipynb)
Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q8_0-GGUF	Triangle104	2025-06-16T09:38:34Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "dataset:nbeerbower/human-writing-dpo", "base_model:nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B", "base_model:quantized:nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-16T09:36:45Z	--- license: apache-2.0 datasets: - nbeerbower/human-writing-dpo base_model: nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B tags: - llama-cpp - gguf-my-repo --- # Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q8_0-GGUF This model was converted to GGUF format from [`nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B`](https://huggingface.co/nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B) 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/nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B) for more details on the model. --- Mistral-Nemo-Gutenberg-Encore-12B finetuned on nbeerbower/human-writing-dpo with Mistral Instruct. Method - ORPO tuned with 1x RTX A6000 for 3 epochs. --- ## 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 Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q8_0-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q8_0-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q8_0.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 Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q8_0-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q8_0-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q8_0.gguf -c 2048 ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.5_0.25_epoch1	MinaMila	2025-06-16T09:37:31Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:35:41Z	--- 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]
mihaidobrescu/Nanonets-OCR-s-Q4_K_M-GGUF	mihaidobrescu	2025-06-16T09:34:10Z	0	0	transformers	[ "transformers", "gguf", "OCR", "pdf2markdown", "llama-cpp", "gguf-my-repo", "image-text-to-text", "en", "base_model:nanonets/Nanonets-OCR-s", "base_model:quantized:nanonets/Nanonets-OCR-s", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-16T09:33:56Z	--- language: - en base_model: nanonets/Nanonets-OCR-s pipeline_tag: image-text-to-text tags: - OCR - pdf2markdown - llama-cpp - gguf-my-repo library_name: transformers --- # mihaidobrescu/Nanonets-OCR-s-Q4_K_M-GGUF This model was converted to GGUF format from [`nanonets/Nanonets-OCR-s`](https://huggingface.co/nanonets/Nanonets-OCR-s) 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/nanonets/Nanonets-OCR-s) 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 mihaidobrescu/Nanonets-OCR-s-Q4_K_M-GGUF --hf-file nanonets-ocr-s-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mihaidobrescu/Nanonets-OCR-s-Q4_K_M-GGUF --hf-file nanonets-ocr-s-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mihaidobrescu/Nanonets-OCR-s-Q4_K_M-GGUF --hf-file nanonets-ocr-s-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mihaidobrescu/Nanonets-OCR-s-Q4_K_M-GGUF --hf-file nanonets-ocr-s-q4_k_m.gguf -c 2048 ```
dhruvsangani/FeatSystems-LLM-QA	dhruvsangani	2025-06-16T09:33:54Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T09:17:12Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dhruvsangani - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.05_0.05_epoch2	MinaMila	2025-06-16T09:31:40Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:29:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Megha06/dqn-SpaceInvadersNoFrameskip-v4	Megha06	2025-06-16T09:31:24Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T09:30:59Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 388.00 +/- 60.46 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Megha06 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Megha06 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Megha06 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q5_K_M-GGUF	Triangle104	2025-06-16T09:31:21Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "dataset:nbeerbower/human-writing-dpo", "base_model:nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B", "base_model:quantized:nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-16T09:28:11Z	--- license: apache-2.0 datasets: - nbeerbower/human-writing-dpo base_model: nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B tags: - llama-cpp - gguf-my-repo --- # Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q5_K_M-GGUF This model was converted to GGUF format from [`nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B`](https://huggingface.co/nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B) 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/nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B) for more details on the model. --- Mistral-Nemo-Gutenberg-Encore-12B finetuned on nbeerbower/human-writing-dpo with Mistral Instruct. Method - ORPO tuned with 1x RTX A6000 for 3 epochs. --- ## 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 Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q5_K_M-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q5_K_M-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q5_K_M-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q5_K_M-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q5_k_m.gguf -c 2048 ```
LakshGupta/ppo-Huggy	LakshGupta	2025-06-16T09:29:56Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-06-16T09:29:52Z	--- 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: LakshGupta/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
itzlissabila/Aksara	itzlissabila	2025-06-16T09:25:29Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T09:25:29Z	--- license: apache-2.0 ---
mlx-community/llm-jp-3.1-8x13b-instruct4-4bit	mlx-community	2025-06-16T09:24:52Z	0	0	mlx	[ "mlx", "safetensors", "mixtral", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-8x13b-instruct4", "base_model:quantized:llm-jp/llm-jp-3.1-8x13b-instruct4", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-06-16T08:18:48Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false base_model: llm-jp/llm-jp-3.1-8x13b-instruct4 tags: - mlx --- # mlx-community/llm-jp-3.1-8x13b-instruct4-4bit This model [mlx-community/llm-jp-3.1-8x13b-instruct4-4bit](https://huggingface.co/mlx-community/llm-jp-3.1-8x13b-instruct4-4bit) was converted to MLX format from [llm-jp/llm-jp-3.1-8x13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-8x13b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-8x13b-instruct4-4bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.05_0.05_epoch1	MinaMila	2025-06-16T09:23:53Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:22:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sparrow1007/gpt2-copy	sparrow1007	2025-06-16T09:23:49Z	0	0	null	[ "gpt2", "exbert", "en", "license:mit", "region:us" ]	null	2025-06-16T08:38:55Z	--- language: en tags: - exbert license: mit --- # GPT-2 Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). Disclaimer: The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. ## Model description GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. This is the smallest version of GPT-2, with 124M parameters. Related Models: [GPT-Large](https://huggingface.co/gpt2-large), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-XL](https://huggingface.co/gpt2-xl) ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). ## Training procedure ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. ## Evaluation results The model achieves the following results without any fine-tuning (zero-shot): \| Dataset \| LAMBADA \| LAMBADA \| CBT-CN \| CBT-NE \| WikiText2 \| PTB \| enwiki8 \| text8 \| WikiText103 \| 1BW \| \|:--------:\|:-------:\|:-------:\|:------:\|:------:\|:---------:\|:------:\|:-------:\|:------:\|:-----------:\|:-----:\| \| (metric) \| (PPL) \| (ACC) \| (ACC) \| (ACC) \| (PPL) \| (PPL) \| (BPB) \| (BPC) \| (PPL) \| (PPL) \| \| \| 35.13 \| 45.99 \| 87.65 \| 83.4 \| 29.41 \| 65.85 \| 1.16 \| 1,17 \| 37.50 \| 75.20 \| ### BibTeX entry and citation info ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` <a href="https://huggingface.co/exbert/?model=gpt2"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
erdem-erdem/Qwen2.5-3B-Instruct-countdown-game-8k-qwq-r64	erdem-erdem	2025-06-16T09:23:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:21:11Z	--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: erdem-erdem - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-Instruct 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)
cucucu666/smile-6.16	cucucu666	2025-06-16T09:22:30Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-Fill-dev", "base_model:adapter:black-forest-labs/FLUX.1-Fill-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T07:16:09Z	--- base_model: black-forest-labs/FLUX.1-Fill-dev library_name: diffusers license: other instance_prompt: labii face, Crayon Shin-chan style, smile expression, plain white background widget: - text: labii face, Crayon Shin-chan style, smile expression, plain white background output: url: image_0.png - text: labii face, Crayon Shin-chan style, smile expression, plain white background output: url: image_1.png - text: labii face, Crayon Shin-chan style, smile expression, plain white background output: url: image_2.png - text: labii face, Crayon Shin-chan style, smile expression, plain white background output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux-Fill DreamBooth LoRA - cucucu666/smile-6.16 <Gallery /> ## Model description These are cucucu666/smile-6.16 DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `labii face, Crayon Shin-chan style, smile expression, plain white background` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](cucucu666/smile-6.16/tree/main) in the Files & versions tab. ## 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.bfloat16).to('cuda') pipeline.load_lora_weights('cucucu666/smile-6.16', weight_name='pytorch_lora_weights.safetensors') image = pipeline('labii face, Crayon Shin-chan style, smile expression, plain white background').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) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## 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]
Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q4_K_M-GGUF	Triangle104	2025-06-16T09:22:16Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "dataset:nbeerbower/human-writing-dpo", "base_model:nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B", "base_model:quantized:nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-16T08:06:44Z	--- license: apache-2.0 datasets: - nbeerbower/human-writing-dpo base_model: nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B tags: - llama-cpp - gguf-my-repo --- # Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q4_K_M-GGUF This model was converted to GGUF format from [`nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B`](https://huggingface.co/nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B) 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/nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B) for more details on the model. --- Mistral-Nemo-Gutenberg-Encore-12B finetuned on nbeerbower/human-writing-dpo with Mistral Instruct. Method - ORPO tuned with 1x RTX A6000 for 3 epochs. --- ## 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 Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q4_K_M-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q4_K_M-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q4_K_M-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Mistral-Nemo-Gutenberg-Vitus-12B-Q4_K_M-GGUF --hf-file mistral-nemo-gutenberg-vitus-12b-q4_k_m.gguf -c 2048 ```
Entropicengine/LiquidGold-MS-L3.3-70b	Entropicengine	2025-06-16T09:19:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Steelskull/L3.3-Cu-Mai-R1-70b", "base_model:merge:Steelskull/L3.3-Cu-Mai-R1-70b", "base_model:Steelskull/L3.3-MS-Nevoria-70b", "base_model:merge:Steelskull/L3.3-MS-Nevoria-70b", "base_model:Tarek07/Legion-V2.1-LLaMa-70B", "base_model:merge:Tarek07/Legion-V2.1-LLaMa-70B", "base_model:Tarek07/Progenitor-V3.3-LLaMa-70B", "base_model:merge:Tarek07/Progenitor-V3.3-LLaMa-70B", "base_model:zerofata/L3.3-GeneticLemonade-Final-70B", "base_model:merge:zerofata/L3.3-GeneticLemonade-Final-70B", "license:llama3.3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:51:36Z	--- base_model: - Steelskull/L3.3-Cu-Mai-R1-70b - Tarek07/Progenitor-V3.3-LLaMa-70B - Tarek07/Legion-V2.1-LLaMa-70B - zerofata/L3.3-GeneticLemonade-Final-70B - Steelskull/L3.3-MS-Nevoria-70b library_name: transformers tags: - mergekit - merge license: llama3.3 --- ~ ⚱️⚜️ ~ ![image/png](https://huggingface.co/Entropicengine/LiquidGold-MS-L3.3-70b/resolve/main/liquidgold.png) # LiquidGold-MS-L3.3-70b ## Recommended preset : - [[email protected]](https://huggingface.co/Konnect1221/The-Inception-Presets-Methception-LLamaception-Qwenception/blob/main/Llam%40ception/Llam%40ception-1.5.json) ## Quants (courtesy : team mradermacher) - [Static quants](https://huggingface.co/mradermacher/LiquidGold-MS-L3.3-70b-GGUF) - [Weighted/Imatrix quants](https://huggingface.co/mradermacher/LiquidGold-MS-L3.3-70b-i1-GGUF) 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 [Steelskull/L3.3-MS-Nevoria-70b](https://huggingface.co/Steelskull/L3.3-MS-Nevoria-70b) as a base. ### Models Merged The following models were included in the merge: * [Steelskull/L3.3-Cu-Mai-R1-70b](https://huggingface.co/Steelskull/L3.3-Cu-Mai-R1-70b) * [Tarek07/Progenitor-V3.3-LLaMa-70B](https://huggingface.co/Tarek07/Progenitor-V3.3-LLaMa-70B) * [Tarek07/Legion-V2.1-LLaMa-70B](https://huggingface.co/Tarek07/Legion-V2.1-LLaMa-70B) * [zerofata/L3.3-GeneticLemonade-Final-70B](https://huggingface.co/zerofata/L3.3-GeneticLemonade-Final-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: Steelskull/L3.3-MS-Nevoria-70b dtype: bfloat16 merge_method: model_stock modules: default: slices: - sources: - layer_range: [0, 80] model: Steelskull/L3.3-Cu-Mai-R1-70b - layer_range: [0, 80] model: Tarek07/Progenitor-V3.3-LLaMa-70B - layer_range: [0, 80] model: zerofata/L3.3-GeneticLemonade-Final-70B - layer_range: [0, 80] model: Tarek07/Legion-V2.1-LLaMa-70B - layer_range: [0, 80] model: Steelskull/L3.3-MS-Nevoria-70b ```
IoanaLiviaPopescu/real-data-synth-data-1600-1-St-Emil-whisper-small	IoanaLiviaPopescu	2025-06-16T09:17:26Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ro", "dataset:IoanaLivia/RealVoiceSynthVoice-1600-1-St-Emil", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-16T08:10:49Z	--- library_name: transformers language: - ro license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - IoanaLivia/RealVoiceSynthVoice-1600-1-St-Emil metrics: - wer model-index: - name: IoanaLiviaPopescu/ IoanaLiviaPopescu/real-data-synth-data-1600-1-St-Emil-whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IoanaLivia/RealVoiceSynthVoice-1600-1-St-Emil type: IoanaLivia/RealVoiceSynthVoice-1600-1-St-Emil config: default split: test args: 'split: validation' metrics: - name: Wer type: wer value: 17.038539553752535 --- <!-- 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-1600-1-St-Emil-whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLivia/RealVoiceSynthVoice-1600-1-St-Emil dataset. It achieves the following results on the evaluation set: - Loss: 0.3718 - Wer: 17.0385 ## 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 \| \| 0.282 \| 1.0 \| 51 \| 0.3977 \| 18.3109 \| \| 0.1077 \| 2.0 \| 102 \| 0.3658 \| 17.3151 \| \| 0.0561 \| 3.0 \| 153 \| 0.3718 \| 17.0385 \| \| 0.0328 \| 4.0 \| 204 \| 0.3881 \| 17.3889 \| \| 0.023 \| 5.0 \| 255 \| 0.4000 \| 17.7208 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.5_0.75_epoch2	MinaMila	2025-06-16T09:16:50Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:14:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.05_0.15_epoch2	MinaMila	2025-06-16T09:15:44Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:14:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nagoshidayo/MORTM	nagoshidayo	2025-06-16T09:12:57Z	0	0	null	[ "music-generation", "transformer", "MoE", "ALiBi", "FlashAttention", "melody-generation", "rhythmic-modeling", "MIDI or Chord to MIDI or Chord", "region:us" ]	null	2025-06-16T09:02:25Z	--- pipeline_tag: MIDI or Chord to MIDI or Chord tags: - music-generation - transformer - MoE - ALiBi - FlashAttention - melody-generation - rhythmic-modeling --- # Model Card for MORTM (Metric-Oriented Rhythmic Transformer for Melodic generation) MORTM is a Transformer-based model designed for melody generation, with a strong emphasis on metric (rhythmic) structure. It represents music as sequences of pitch, duration, and relative beat positions within a measure (normalized to 96 ticks), making it suitable for time-robust, rhythm-aware music generation tasks. This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description MORTM (Metric-Oriented Rhythmic Transformer for Melodic generation) is a decoder-only Transformer architecture optimized for music generation with rhythmic awareness. It generates melodies measure-by-measure in an autoregressive fashion. The model supports chord-conditional generation and is equipped with the following features: - Mixture of Experts (MoE) in the feedforward layers for capacity increase and compute efficiency. - ALiBi (Attention with Linear Biases) for relative positional biasing. - FlashAttention2 for fast and memory-efficient attention. - Relative tick-based tokenization (e.g., [Position, Duration, Pitch]) for metric robustness. - Developed by: Koue Okazaki & Takaki Nagoshi - Funded by [optional]: Nihon University, Graduate School of Integrated Basic Sciences - Shared by [optional]: ProjectMORTM - Model type: Transformer (decoder-only with MoE and ALiBi) - Language(s) (NLP): N/A (music domain) - License: MIT - Finetuned from model [optional]: Custom-built from scratch (not fine-tuned from a pretrained LM) ### Model Sources [optional] - Repository: [https://github.com/Ayato964/MORTM](https://github.com/Ayato964/MORTM) (replace with actual link) - Paper [optional]: In submission - Demo [optional]: Coming soon ## Uses ### Direct Use MORTM can generate melodies from scratch or conditionally based on chord progressions. It is ideal for: - Melody composition in pop, jazz, and improvisational styles. - Real-time melodic suggestion systems for human-AI co-creation. - Music education and melody completion tools. ### Downstream Use [optional] - Style transfer with different chord inputs. - Harmonization and rhythm-based accompaniment systems. ### Out-of-Scope Use - Audio-to-audio tasks (e.g., vocal separation). - Raw audio synthesis (requires additional vocoder). - Not suitable for genre classification or music recommendation. ## Bias, Risks, and Limitations As the training dataset is primarily composed of Western tonal music, the model may underperform on: - Non-tonal, microtonal, or traditional music styles. - Polyrhythmic or tempo-variable music. - Genres not sufficiently represented in training data (e.g., Indian classical). ### Recommendations Generated melodies should be manually reviewed in professional music contexts. Users are encouraged to retrain or fine-tune on representative datasets when applying to culturally specific music. ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("nagoshidayo/mortm") tokenizer = AutoTokenizer.from_pretrained("nagoshidayo/mortm")
sergioalves/8f5e1103-1e1a-4cc1-bc44-50b2da567d4d	sergioalves	2025-06-16T09:11:30Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:adapter:Qwen/Qwen2.5-14B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-16T06:45:33Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-14B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 8f5e1103-1e1a-4cc1-bc44-50b2da567d4d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2.5-14B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 207d1b017403ab0b_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.8 group_by_length: false hub_model_id: sergioalves/8f5e1103-1e1a-4cc1-bc44-50b2da567d4d hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-07 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.3 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 300 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/207d1b017403ab0b_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: f4c147f9-da2b-4e0c-a7eb-c66e7e014434 wandb_project: s56-7 wandb_run: your_name wandb_runid: f4c147f9-da2b-4e0c-a7eb-c66e7e014434 warmup_steps: 30 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 8f5e1103-1e1a-4cc1-bc44-50b2da567d4d This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2059 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1677 \| 0.0002 \| 1 \| 1.2333 \| \| 1.1168 \| 0.0253 \| 150 \| 1.2180 \| \| 1.1149 \| 0.0506 \| 300 \| 1.2059 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
En3rGy/GetphatFLUXReality	En3rGy	2025-06-16T09:10:06Z	17	0	null	[ "gguf", "region:us" ]	null	2025-05-03T03:18:02Z	--- title: GetphatFLUXRealityNSFW emoji: 🖼 colorFrom: purple colorTo: red sdk: gradio sdk_version: 5.25.2 app_file: app.py pinned: false hf_oauth: true hf_oauth_scopes: - inference-api --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
aplux/YOLO-NAS-l	aplux	2025-06-16T09:05:49Z	0	0	null	[ "AIoT", "QNN", "object-detection", "license:other", "region:us" ]	object-detection	2025-06-12T07:23:16Z	--- license: other license_name: yolo-nas-license license_link: https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md pipeline_tag: object-detection tags: - AIoT - QNN --- ![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250319065039_%25E5%259B%25BE-27.png&w=640&q=75) ## YOLO-NAS-l: Object Detection YOLO-NAS is a next-generation real-time object detection model optimized via Neural Architecture Search (NAS), automating the balance between accuracy and speed for superior performance in complex scenarios. It integrates hybrid quantization-aware architectures with reparameterized blocks and dynamic sparse attention, enhancing small/occluded object detection while reducing computation. Through multi-objective optimization (e.g., latency, parameters, mAP), it discovers efficient structures supporting FP16/INT8 quantization, achieving ~5% higher mAP than YOLOv8 on COCO with 80+ FPS on mobile GPUs. Ideal for autonomous driving and surveillance, it balances edge-device constraints and high precision, offering flexible speed-accuracy tradeoffs. ### Source model - Input shape: 1x3x640x640 - Number of parameters: 40.06M - Model size: 160.37M - Output shape: 1x8400x4, 1x8400x80 The source model can be found [here](https://github.com/Deci-AI/super-gradients) ## Performance Reference Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## Inference & Model Conversion Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) ## License - Source Model: [YOLO-NAS License](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md) - Deployable Model: [YOLO-NAS License](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md)
jhyun0414/20250616-Llama-3.1-8B-Instruct-gemini_label-norag-lr2e-06-e3	jhyun0414	2025-06-16T09:03:51Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:05:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Klaudesens/indobert-fake-news-rus	Klaudesens	2025-06-16T09:03:51Z	9	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-17T10:10:53Z	--- 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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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.75_0.05_epoch2	MinaMila	2025-06-16T09:03:04Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T09:01:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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najmharani/gemma-1b-biography_ver2	najmharani	2025-06-16T09:02:39Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T09:02:22Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: najmharani - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
JunSotohigashi/jolly-galaxy-532-epoch_1	JunSotohigashi	2025-06-16T09:01:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T09:01:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Shakurbrown/StrategicAI	Shakurbrown	2025-06-16T08:58:36Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T08:58:36Z	--- license: apache-2.0 ---
adityag6994/ppo-LunarLander-v2	adityag6994	2025-06-16T08:58:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T08:58:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 237.46 +/- 20.34 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
baiyinnamula/my_awesome_model	baiyinnamula	2025-06-16T08:58:19Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-13T08:55:46Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2293 - Accuracy: 0.9308 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2203 \| 1.0 \| 1563 \| 0.2087 \| 0.9197 \| \| 0.1487 \| 2.0 \| 3126 \| 0.2293 \| 0.9308 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.21.0 - Tokenizers 0.21.1
dgambettaphd/M_llm2_run2_gen1_WXS_doc1000_synt64_lr1e-04_acm_SYNALL	dgambettaphd	2025-06-16T08:58:06Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T08:57:53Z	--- 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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Antrugos/mbart-namuy-es_30k_corpus	Antrugos	2025-06-16T08:56:23Z	0	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T18:12:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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hirundo-io/telecom-ft-500-persons-llama-3.2-3b-id	hirundo-io	2025-06-16T08:56:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:55:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
floflodebilbao/T5_sum_approach1	floflodebilbao	2025-06-16T08:56:02Z	0	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	2025-06-13T13:11:40Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: T5_sum_approach1 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_sum_approach1 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0516 - Rouge1: 0.2637 - Rouge2: 0.0869 - Rougel: 0.2005 - Rougelsum: 0.2005 - Gen Len: 20.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_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: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 5 \| 3.2151 \| 0.2299 \| 0.0752 \| 0.1928 \| 0.1951 \| 20.0 \| \| No log \| 2.0 \| 10 \| 3.1181 \| 0.2435 \| 0.0873 \| 0.1967 \| 0.1975 \| 20.0 \| \| No log \| 3.0 \| 15 \| 3.0680 \| 0.2637 \| 0.0869 \| 0.2005 \| 0.2005 \| 20.0 \| \| No log \| 4.0 \| 20 \| 3.0516 \| 0.2637 \| 0.0869 \| 0.2005 \| 0.2005 \| 20.0 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
Aleteian/TerraIncognita-24B-Q4_K_M-GGUF	Aleteian	2025-06-16T08:55:43Z	0	0	null	[ "gguf", "merge", "mergekit", "lazymergekit", "LatitudeGames/Harbinger-24B", "ReadyArt/Broken-Tutu-24B-Unslop-v2.0", "llama-cpp", "gguf-my-repo", "base_model:Aleteian/TerraIncognita-24B", "base_model:quantized:Aleteian/TerraIncognita-24B", "endpoints_compatible", "region:us" ]	null	2025-06-16T08:54:39Z	--- base_model: Aleteian/TerraIncognita-24B tags: - merge - mergekit - lazymergekit - LatitudeGames/Harbinger-24B - ReadyArt/Broken-Tutu-24B-Unslop-v2.0 - llama-cpp - gguf-my-repo --- # Aleteian/TerraIncognita-24B-Q4_K_M-GGUF This model was converted to GGUF format from [`Aleteian/TerraIncognita-24B`](https://huggingface.co/Aleteian/TerraIncognita-24B) 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/Aleteian/TerraIncognita-24B) 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 Aleteian/TerraIncognita-24B-Q4_K_M-GGUF --hf-file terraincognita-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Aleteian/TerraIncognita-24B-Q4_K_M-GGUF --hf-file terraincognita-24b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Aleteian/TerraIncognita-24B-Q4_K_M-GGUF --hf-file terraincognita-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Aleteian/TerraIncognita-24B-Q4_K_M-GGUF --hf-file terraincognita-24b-q4_k_m.gguf -c 2048 ```
zen17/llama-3.1-8b-finetuned	zen17	2025-06-16T08:55:07Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T07:32:27Z	--- base_model: unsloth/llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: zen17 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-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)
hirundo-io/telecom-ft-500-persons-llama-3.2-3b-id-injection-unlearned	hirundo-io	2025-06-16T08:54:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T11:11:34Z	--- 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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JunSotohigashi/effortless-serenity-531	JunSotohigashi	2025-06-16T08:53:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T08:52:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-330	veddhanth	2025-06-16T08:51:58Z	0	0	diffusers	[ "diffusers", "tensorboard", "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	2025-06-16T08:31:59Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a realistic portrait of sks face 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 - veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-330 <Gallery /> ## Model description These are veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-330 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: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a realistic portrait of sks face to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-330/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]
Marksdo/WhisperMate	Marksdo	2025-06-16T08:50:09Z	156	5	null	[ "gguf", "region:us" ]	null	2023-09-21T08:41:51Z	Macos native UI app for Whisper AI processing https://whispermate.app ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6453149849b6b9a2383cb1d9/SByUagCHS1n0VfoQ4RAqh.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6453149849b6b9a2383cb1d9/xj8XzuT14abhmVgKcHG5v.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6453149849b6b9a2383cb1d9/7mBh4rxx22pgN-KRICaQ6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6453149849b6b9a2383cb1d9/o_KJxLEczPDJD975_vq5C.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6453149849b6b9a2383cb1d9/T8oq85Wh1MOqpC2R0Pk_2.png)
thisisakz/WOOGPT	thisisakz	2025-06-16T08:49:38Z	0	1	null	[ "wizardofoz", "gpt", "woogpt", "en", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "region:us" ]	null	2025-06-16T08:32:30Z	--- license: mit language: - en base_model: - openai-community/gpt2 tags: - wizardofoz - gpt - woogpt --- # 🧙 WOOGPT WOOGPT is a custom language model fine-tuned on Wizard of Oz books and movie scripts. It aims to generate whimsical, story-rich, and character-driven text in the tone of L. Frank Baum’s magical universe. --- ## 🧠 Model Details - Base model: GPT-2 architecture - Architecture: - Embedding dimension: 384 - Attention heads: 8 - Transformer layers: 8 - Dropout: 0.2 - Sequence length (block size): 128 tokens - Language: English - License: MIT - Trained by: Aksshat Govind --- ## 🏋️ Training Configuration \| Hyperparameter \| Value \| \|--------------------\|--------------\| \| Batch size \| 64 \| \| Context length \| 128 tokens \| \| Max iterations \| 200 \| \| Evaluation interval\| 100 iters \| \| Learning rate \| 3e-4 \| \| Embedding dim \| 384 \| \| # of heads \| 8 \| \| # of layers \| 8 \| \| Dropout \| 0.2 \| \| Optimizer \| AdamW \| \| Scheduler \| Cosine \| > Note: Training was performed on Wizard of Oz text data including books and screenplay dialogue. Training used a causal language modeling objective with teacher forcing. --- ## 📚 Training Dataset WOOGPT was trained on: - Public domain books by L. Frank Baum - Wizard of Oz film dialogue transcripts - Stage play and derivative text adaptations Training epochs: 3000 Tokenizer: GPT-2 tokenizer (byte-level BPE) Device: MPS and CUDA --- ## 🚀 Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("thisisakz/WOOGPT") tokenizer = AutoTokenizer.from_pretrained("thisisakz/WOOGPT") prompt = "Dorothy looked at the yellow brick road and said," inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0]))
Nourix44/TrimiqWeightloss5	Nourix44	2025-06-16T08:49:18Z	0	0	null	[ "region:us" ]	null	2025-06-16T08:47:05Z	Trimiq Weight Loss is a premium, powder-based dietary supplement designed to support sustainable weight management for UK residents. Crafted with a blend of natural, science-backed ingredients like Green Tea Extract, Konjac Root, and a targeted probiotic blend, Trimiq promotes fat burning, curbs cravings, and sustains energy levels without harsh stimulants. Its refreshing citrus flavor makes daily use enjoyable, as the powder dissolves effortlessly in 200-250ml of cold water, ensuring rapid absorption. Ideal for busy lifestyles, Trimiq integrates seamlessly into routines, supporting thermogenesis, appetite control, and gut health. 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While Trimiq is designed to support weight loss, it’s not a magic bullet—sustainable results come from combining the supplement with healthy habits. Avoid taking Trimiq too close to bedtime, as its energy-boosting ingredients, like Green Tea Extract, may interfere with sleep. For UK residents, Trimiq is available exclusively through its official website to ensure authenticity and avoid counterfeit products. Prices typically range from £30-£50 for a single pouch, with discounts available for bundle packs or subscriptions. Always check the official site for the latest offers and follow the manufacturer’s guidelines to avoid overconsumption, which could lead to mild side effects like jitters or digestive discomfort. ### Why Trimiq Stands Out in the UK Market The UK weight loss market is saturated with products promising quick fixes, but Trimiq distinguishes itself through its focus on natural ingredients, transparency, and practicality. Unlike many supplements that rely on proprietary blends or undisclosed formulas, Trimiq clearly lists its ingredients and their dosages, building trust with consumers. Its powder form is also a unique selling point, offering faster absorption and greater convenience compared to pills. Moreover, Trimiq’s 100% money-back guarantee reflects the brand’s confidence in its product. For UK consumers wary of scams or ineffective supplements, this guarantee provides peace of mind. The supplement’s alignment with GMP-certified manufacturing standards further ensures quality and safety, making it a reliable choice for health-conscious individuals. ## Tips for Maximizing Trimiq Weight Loss Results To get the most out of Trimiq Weight Loss, consider these practical tips tailored for UK residents: Pair with a Balanced Diet: Focus on whole foods like fruits, vegetables, lean proteins, and whole grains. Avoid processed foods and sugary drinks, which can counteract Trimiq’s effects. Stay Active: Incorporate 15-20 minutes of daily exercise, such as walking, cycling, or strength training, to boost calorie burn and enhance Trimiq’s fat-burning benefits. Hydrate: Drink at least eight glasses of water daily to support digestion and optimize Trimiq’s effects. Adding lemon or cucumber can make hydration more enjoyable. Track Progress: Keep a journal to monitor your weight, measurements, and energy levels. This helps you stay motivated and identify areas for adjustment. Consult a Professional: If you have underlying health conditions or experience side effects, consult a healthcare provider to ensure Trimiq is suitable for you. By integrating these habits, UK users can amplify Trimiq’s effectiveness and achieve sustainable weight loss. ## Potential Considerations and Precautions While Trimiq is formulated with natural ingredients, it’s not without precautions. Individuals sensitive to caffeine (from Green Tea Extract) should avoid taking it late in the day to prevent sleep disturbances. Pregnant or breastfeeding women, as well as those with medical conditions, should consult a doctor before use. Overdosing is discouraged, as it may lead to mild side effects like headaches or digestive upset. To ensure authenticity, always purchase Trimiq from the official website, as counterfeit products are a risk on third-party platforms like Amazon or eBay. The brand’s money-back guarantee provides a safety net for those unsure about committing to the supplement. ## Conclusion: Is Trimiq Weight Loss Right for You? For UK residents seeking a natural, effective, and convenient weight loss solution, Trimiq Weight Loss offers a compelling option. Its blend of science-backed ingredients, focus on holistic health, and ease of use make it a standout in a crowded market. While it’s not a miracle cure, Trimiq’s ability to support fat burning, reduce cravings, and boost energy makes it a valuable ally for those committed to a healthier lifestyle. Whether you’re a busy professional in London, a fitness enthusiast in Manchester, or someone looking to break through a weight loss plateau in Bristol, Trimiq can fit seamlessly into your routine. By combining the supplement with a balanced diet, regular exercise, and consistent habits, you can unlock its full potential and take meaningful steps toward your health goals. Visit the official Trimiq website to explore pricing, bundles, and start your journey to a healthier, more confident you. ## [Click here to order from officile website of Trimiq Weight loss](https://adtocart.xyz/trimiq-uk)
tresixy39/sdxl_collection	tresixy39	2025-06-16T08:48:18Z	5	0	null	[ "stable-diffusion-xl", "license:apache-2.0", "region:us" ]	null	2025-05-12T04:37:23Z	--- license: apache-2.0 --- # 📦 SDXL Collection > A curated suite of popular Stable Diffusion XL (.safetensors) checkpoints 本仓库收集了多款热门 SDXL 变体模型，以 `.safetensors` 格式存储，方便一键下载与加载。所有文件均通过 Git LFS 管理，支持大文件断点续传。 --- ## 📑 文件列表 \| 文件名 \| 大小 \| 说明 \| \| ----------------------------------------- \| ------- \| ------------------------- \| \| `3dCartoon.safetensors` \| 6.94 GB \| 针对 3D 卡通风格微调的 SDXL 检出点 \| \| `AnythingXL_xl.safetensors` \| 6.94 GB \| 高通用性版，适合多种风格 \| \| `Helloworld.safetensors` \| 6.94 GB \| 入门版示例权重，适合快速测试 \| \| `animagine-xl-3.1.safetensors` \| 6.94 GB \| Animagine XL v3.1 动画/插画风格 \| \| `autismmixSDXL_autismmixPony.safetensors` \| 6.94 GB \| Pony 风格混合模型 \| \| `epicrealismXL.safetensors` \| 6.94 GB \| 史诗写实风格优化版 \| \| `gamelcon.safetensors` \| 6.94 GB \| 游戏概念艺术专用微调 \| \| `juggernautXL_ragnarok.safetensors` \| 7.11 GB \| Ragnarok 主题大规模场景微调 \| \| `sdxlNiji.safetensors` \| 7.14 GB \| Niji（动漫）风格专用 \| --- ## 🚀 快速开始 1. 克隆仓库（需安装 Git LFS）： ```bash git lfs install git clone https://huggingface.co/yase88/sdxl_collection cd sdxl_collection ``` 2. Python + Diffusers 加载示例： ```python from diffusers import StableDiffusionXLPipeline import torch model_path = "yase88/sdxl_collection/epicrealismXL.safetensors" pipe = StableDiffusionXLPipeline.from_pretrained( model_path, torch_dtype=torch.float16, safety_checker=None, ).to("cuda") prompt = "A hyper-realistic fantasy landscape at sunrise" image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0] image.save("output.png") ``` 3. CLI 下载单个文件： ```bash huggingface-cli repo download yase88/sdxl_collection --pattern "sdxlNiji.safetensors" ``` --- ## 📝 许可证本仓库及其中索引文件（如 `README.md`、`config.json`）采用 Apache-2.0 开源许可。各 `.safetensors` 子模型请参照其原始发布页面的授权说明。 --- 欢迎 ⭐️ & PR，一起丰富 SDXL 模型生态！
MCINext/Hakim-unsup	MCINext	2025-06-16T08:46:31Z	0	0	null	[ "arxiv:2505.08435", "region:us" ]	null	2025-06-02T08:01:29Z	# 🧠 Hakim-unsup [![arXiv](https://img.shields.io/badge/arXiv-2505.08435-b31b1b.svg)](https://arxiv.org/abs/2505.08435) Hakim-unsup represents an intermediate stage of the state-of-the-art Hakim text embedding project for the Persian language. This model is the result of pretraining on large Persian corpora followed by an extensive unsupervised contrastive learning phase on millions of text pairs. While the fully supervised Hakim model achieves top performance on the FaMTEB benchmark, Hakim-unsup provides strong general-purpose semantic representations. It serves as a powerful foundation for further fine-tuning and is particularly useful for tasks where large labeled datasets are unavailable but understanding semantic similarity from unlabeled pairs is crucial. --- ## 📌 Model Highlights - 🧱 Strong Foundational Embeddings: Provides robust general-purpose Persian text embeddings learned from large-scale unsupervised data. - 🔄 Trained on Diverse Unlabeled Pairs: Benefits from the `Pairsia-unsup` dataset, capturing a wide array of semantic relationships. - ⚙️ Standard Size: ~124M parameters, same as the base Hakim model. - 🌱 Basis for Supervised Models: This is the model checkpoint before the supervised instruction-tuning phase that creates the final Hakim and Hakim-small models. --- ## 🏗️ Training Datasets Hakim-unsup is trained in two main phases: ### 📚 Pretraining - Corpesia: 11B tokens from 46 Persian websites across 21 domains (e.g., news, health, religion, tech). - hmBlogs: 6.8B tokens from ~20M Persian blog posts. - Queries: 8.5M anonymized search queries. ### 🔄 Unsupervised Stage (Pairsia-unsup) - Pairsia-unsup: 5M high-quality Persian text pairs from diverse sources including: - Document–title, FAQ, QA, and paper title–abstract pairs. - Machine-translated datasets (MS MARCO, SAMSum, AdversarialQA, etc.). - The model is trained using a contrastive learning objective on these pairs to learn general semantic representations. Hakim-unsup does not undergo the subsequent supervised fine-tuning stage with the `Pairsia-sup` dataset or instruction tuning. For more detailed information on the dataset creation and curation process, please refer to the [Hakim paper](https://arxiv.org/abs/2505.08435). --- ## 🧪 Benchmark Results (FaMTEB) \| Model \| Avg. Score \| Classification \| Clustering \| PairClass. \| Reranking \| Retrieval \| STS \| Summarization \| \|------------------------\|------------\|----------------\|------------\|------------\|-----------\|-----------\|-------\|----------------\| \| Hakim \| 73.81 \| 84.56 \| 70.46 \| 89.75 \| 69.46 \| 40.43 \| 76.62 \| 85.41 \| \| Hakim-small \| 70.45 \| 80.19 \| 66.31 \| 87.41 \| 67.30 \| 38.05 \| 75.53 \| 78.40 \| \| Hakim-unsup \| 64.56 \| 60.65 \| 58.89 \| 86.41 \| 67.56 \| 37.71 \| 79.36 \| 61.34 \| \| BGE-m3 \| 65.29 \| 58.75 \| 57.73 \| 85.21 \| 74.56 \| 43.38 \| 76.35 \| 61.07 \| \| Jina-embeddings-v3 \| 64.53 \| 59.93 \| 59.15 \| 83.71 \| 61.26 \| 43.51 \| 78.65 \| 65.50 \| \| multilingual-e5-large \| 64.40 \| 59.86 \| 57.19 \| 84.42 \| 74.34 \| 42.98 \| 75.38 \| 56.61 \| \| GTE-multilingual-base \| 63.64 \| 56.07 \| 57.28 \| 84.58 \| 69.72 \| 41.22 \| 75.75 \| 60.88 \| \| multilingual-e5-base \| 62.93 \| 57.62 \| 56.52 \| 84.04 \| 72.07 \| 41.20 \| 74.45 \| 54.58 \| \| Tooka-SBERT \| 60.65 \| 59.40 \| 56.45 \| 87.04 \| 58.29 \| 27.86 \| 76.42 \| 59.06 \| --- ## Model Usage You can interact with the `Hakim_unsup` model through our API. Below are examples using `curl` and Python. ### Inference with `curl` Here's how to send a request to the model using a `curl` command in your terminal. Important: Replace `your_api_key` with your actual API key. > Note: For quick testing, you can use the value `mcinext` as your API key. This will allow you to use the API with some limitations. ```bash curl -X POST 'https://mcinext.ai/api/hakim-unsup' \ -H "Content-Type: application/json" \ -H "Accept: application/json" \ -H "Authorization": "Bearer your_api_key" \ -d '{ "model": "Hakim_unsuper", "input": [ "The text of the first document.", "The text of the second document.", "And so on..." ], "encoding_format": "float", "add_special_tokens": true }' ``` ### Inference with `python` ```python import requests import json # --- Configuration --- API_KEY = "your_api_key" # Replace with your key or "mcinext" for testing API_URL = "https://mcinext.ai/api/hakim-unsup" # --- Request Details --- headers = { "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {API_KEY}" } data = { "model": "Hakim_unsuper", "input": [ "The text of the first document.", "The text of the second document.", "And so on..." ], "encoding_format": "float", "add_special_tokens": True } # --- Send Request --- try: response = requests.post(API_URL, headers=headers, data=json.dumps(data)) response.raise_for_status() print("Request successful!") print("Response JSON:") print(response.json()) except requests.exceptions.HTTPError as http_err: print(f"HTTP error occurred: {http_err}") print(f"Response content: {response.text}") except Exception as err: print(f"An other error occurred: {err}") ``` ## Citation ```bibtext @article{sarmadi2025hakim, title={Hakim: Farsi Text Embedding Model}, author={Sarmadi, Mehran and Alikhani, Morteza and Zinvandi, Erfan and Pourbahman, Zahra}, journal={arXiv preprint arXiv:2505.08435}, year={2025} } ```
Aleteian/TerraIncognita-24B	Aleteian	2025-06-16T08:43:14Z	0	2	null	[ "safetensors", "mistral", "merge", "mergekit", "lazymergekit", "LatitudeGames/Harbinger-24B", "ReadyArt/Broken-Tutu-24B-Unslop-v2.0", "base_model:LatitudeGames/Harbinger-24B", "base_model:merge:LatitudeGames/Harbinger-24B", "base_model:ReadyArt/Broken-Tutu-24B-Unslop-v2.0", "base_model:merge:ReadyArt/Broken-Tutu-24B-Unslop-v2.0", "region:us" ]	null	2025-06-16T08:34:25Z	--- base_model: - LatitudeGames/Harbinger-24B - ReadyArt/Broken-Tutu-24B-Unslop-v2.0 tags: - merge - mergekit - lazymergekit - LatitudeGames/Harbinger-24B - ReadyArt/Broken-Tutu-24B-Unslop-v2.0 --- # TerraIncognita-24B TerraIncognita-24B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [LatitudeGames/Harbinger-24B](https://huggingface.co/LatitudeGames/Harbinger-24B) * [ReadyArt/Broken-Tutu-24B-Unslop-v2.0](https://huggingface.co/ReadyArt/Broken-Tutu-24B-Unslop-v2.0) ## 🧩 Configuration ```yaml models: - model: LatitudeGames/Harbinger-24B parameters: weight: 1.0 - model: ReadyArt/Broken-Tutu-24B-Unslop-v2.0 parameters: weight: 1.0 merge_method: della_linear base_model: LatitudeGames/Harbinger-24B parameters: normalize: true int8_mask: true dtype: float16 tokenizer: source: union chat_template: "chatml" ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Aleteian/TerraIncognita-24B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
JunSotohigashi/peach-eon-530	JunSotohigashi	2025-06-16T08:41:30Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:llm-jp/llm-jp-3-440m", "base_model:finetune:llm-jp/llm-jp-3-440m", "endpoints_compatible", "region:us" ]	null	2025-06-16T08:41:26Z	--- base_model: llm-jp/llm-jp-3-440m library_name: transformers model_name: peach-eon-530 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for peach-eon-530 This model is a fine-tuned version of [llm-jp/llm-jp-3-440m](https://huggingface.co/llm-jp/llm-jp-3-440m). 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="JunSotohigashi/peach-eon-530", 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/jun-sotohigashi-toyota-technological-institute/misusing-corpus-jp/runs/j1qh38ci) This model was trained with SFT. ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.05_0.5_epoch1	MinaMila	2025-06-16T08:36:11Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:34:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
phospho-app/yeva11-gr00t-lerobot_kirby_01-uytxd	phospho-app	2025-06-16T08:36:05Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-06-16T08:20:37Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [yeva11/lerobot_kirby_01](https://huggingface.co/datasets/yeva11/lerobot_kirby_01) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.15_0.75_0.25_epoch2	MinaMila	2025-06-16T08:36:01Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T08:34:10Z	--- 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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FeetlessDragon/Pixtral-12b-korean-preview-Q4_K_M-GGUF	FeetlessDragon	2025-06-16T08:32:05Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "image-text-to-text", "en", "ko", "base_model:spow12/Pixtral-12b-korean-preview", "base_model:quantized:spow12/Pixtral-12b-korean-preview", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-06-16T08:31:36Z	--- language: - en - ko license: cc-by-nc-4.0 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: spow12/Pixtral-12b-korean-preview pipeline_tag: image-text-to-text --- # FeetlessDragon/Pixtral-12b-korean-preview-Q4_K_M-GGUF This model was converted to GGUF format from [`spow12/Pixtral-12b-korean-preview`](https://huggingface.co/spow12/Pixtral-12b-korean-preview) 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/spow12/Pixtral-12b-korean-preview) 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 FeetlessDragon/Pixtral-12b-korean-preview-Q4_K_M-GGUF --hf-file pixtral-12b-korean-preview-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo FeetlessDragon/Pixtral-12b-korean-preview-Q4_K_M-GGUF --hf-file pixtral-12b-korean-preview-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo FeetlessDragon/Pixtral-12b-korean-preview-Q4_K_M-GGUF --hf-file pixtral-12b-korean-preview-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo FeetlessDragon/Pixtral-12b-korean-preview-Q4_K_M-GGUF --hf-file pixtral-12b-korean-preview-q4_k_m.gguf -c 2048 ```

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