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Model Name stringlengths 5 122	URL stringlengths 28 145	Crawled Text stringlengths 1 199k ⌀	text stringlengths 180 199k
gokulraj/preon-whisper-tiny-trial-4	https://huggingface.co/gokulraj/preon-whisper-tiny-trial-4	This model is a fine-tuned version of openai/whisper-medium on the custom dataset dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : gokulraj/preon-whisper-tiny-trial-4 ### Model URL : https://huggingface.co/gokulraj/preon-whisper-tiny-trial-4 ### Model Description : This model is a fine-tuned version of openai/whisper-medium on the custom dataset dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
tom192180/distilbert-base-uncased_odm_zphr_0st10sd	https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st10sd	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tom192180/distilbert-base-uncased_odm_zphr_0st10sd ### Model URL : https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st10sd ### Model Description : No model card New: Create and edit this model card directly on the website!
ashawkey/LGM	https://huggingface.co/ashawkey/LGM	This model contains the pretrained weights for LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation. LGM can generate 3D objects from image or text within 5 seconds at high-resolution based on Gaussian Splatting. The model is trained on a ~80K subset of Objaverse. For more details, please refer to our paper. To download the model: Please refer to our repo for more details on loading and inference.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : ashawkey/LGM ### Model URL : https://huggingface.co/ashawkey/LGM ### Model Description : This model contains the pretrained weights for LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation. LGM can generate 3D objects from image or text within 5 seconds at high-resolution based on Gaussian Splatting. The model is trained on a ~80K subset of Objaverse. For more details, please refer to our paper. To download the model: Please refer to our repo for more details on loading and inference.
SolaireOfTheSun/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned	https://huggingface.co/SolaireOfTheSun/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : SolaireOfTheSun/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned ### Model URL : https://huggingface.co/SolaireOfTheSun/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned ### Model Description : No model card New: Create and edit this model card directly on the website!
varun-v-rao/t5-base-bn-adapter-1.79M-snli-model3	https://huggingface.co/varun-v-rao/t5-base-bn-adapter-1.79M-snli-model3	This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : varun-v-rao/t5-base-bn-adapter-1.79M-snli-model3 ### Model URL : https://huggingface.co/varun-v-rao/t5-base-bn-adapter-1.79M-snli-model3 ### Model Description : This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
varun-v-rao/opt-1.3b-lora-3.15M-snli-model3	https://huggingface.co/varun-v-rao/opt-1.3b-lora-3.15M-snli-model3	This model is a fine-tuned version of facebook/opt-1.3b on the None dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : varun-v-rao/opt-1.3b-lora-3.15M-snli-model3 ### Model URL : https://huggingface.co/varun-v-rao/opt-1.3b-lora-3.15M-snli-model3 ### Model Description : This model is a fine-tuned version of facebook/opt-1.3b on the None dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
gokulraj/whisper-small-trail-5-preon	https://huggingface.co/gokulraj/whisper-small-trail-5-preon	This model is a fine-tuned version of openai/whisper-small on the custom dataset dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : gokulraj/whisper-small-trail-5-preon ### Model URL : https://huggingface.co/gokulraj/whisper-small-trail-5-preon ### Model Description : This model is a fine-tuned version of openai/whisper-small on the custom dataset dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
chathuranga-jayanath/codet5-small-v21	https://huggingface.co/chathuranga-jayanath/codet5-small-v21	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : chathuranga-jayanath/codet5-small-v21 ### Model URL : https://huggingface.co/chathuranga-jayanath/codet5-small-v21 ### Model Description : No model card New: Create and edit this model card directly on the website!
GSalimp/ChatUFOPTreinado	https://huggingface.co/GSalimp/ChatUFOPTreinado	[More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Use the code below to get started with the model. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] BibTeX: [More Information Needed] APA: [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed]	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : GSalimp/ChatUFOPTreinado ### Model URL : https://huggingface.co/GSalimp/ChatUFOPTreinado ### Model Description : [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Use the code below to get started with the model. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] BibTeX: [More Information Needed] APA: [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed]
lecslab/byt5-translation-all_st_unseg-v2	https://huggingface.co/lecslab/byt5-translation-all_st_unseg-v2	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : lecslab/byt5-translation-all_st_unseg-v2 ### Model URL : https://huggingface.co/lecslab/byt5-translation-all_st_unseg-v2 ### Model Description : No model card New: Create and edit this model card directly on the website!
tom192180/distilbert-base-uncased_odm_zphr_0st11sd	https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st11sd	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tom192180/distilbert-base-uncased_odm_zphr_0st11sd ### Model URL : https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st11sd ### Model Description : No model card New: Create and edit this model card directly on the website!
varun-v-rao/bert-large-cased-bn-adapter-3.17M-snli-model2	https://huggingface.co/varun-v-rao/bert-large-cased-bn-adapter-3.17M-snli-model2	This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : varun-v-rao/bert-large-cased-bn-adapter-3.17M-snli-model2 ### Model URL : https://huggingface.co/varun-v-rao/bert-large-cased-bn-adapter-3.17M-snli-model2 ### Model Description : This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
contenfire/antrea_issue_v1	https://huggingface.co/contenfire/antrea_issue_v1	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : contenfire/antrea_issue_v1 ### Model URL : https://huggingface.co/contenfire/antrea_issue_v1 ### Model Description : No model card New: Create and edit this model card directly on the website!
ankhamun/IIIIIxII0-0IIxIIIII	https://huggingface.co/ankhamun/IIIIIxII0-0IIxIIIII	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : ankhamun/IIIIIxII0-0IIxIIIII ### Model URL : https://huggingface.co/ankhamun/IIIIIxII0-0IIxIIIII ### Model Description : No model card New: Create and edit this model card directly on the website!
tyzhu/lmind_hotpot_train8000_eval7405_v1_qa_gpt2-xl	https://huggingface.co/tyzhu/lmind_hotpot_train8000_eval7405_v1_qa_gpt2-xl	This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind_hotpot_train8000_eval7405_v1_qa dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tyzhu/lmind_hotpot_train8000_eval7405_v1_qa_gpt2-xl ### Model URL : https://huggingface.co/tyzhu/lmind_hotpot_train8000_eval7405_v1_qa_gpt2-xl ### Model Description : This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind_hotpot_train8000_eval7405_v1_qa dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa_gpt2-xl	https://huggingface.co/tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa_gpt2-xl	This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa_gpt2-xl ### Model URL : https://huggingface.co/tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa_gpt2-xl ### Model Description : This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx_gpt2-xl	https://huggingface.co/tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx_gpt2-xl	This model is a fine-tuned version of gpt2-xl on an unknown dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx_gpt2-xl ### Model URL : https://huggingface.co/tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx_gpt2-xl ### Model Description : This model is a fine-tuned version of gpt2-xl on an unknown dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
mathreader/ppo-LunarLander-v2	https://huggingface.co/mathreader/ppo-LunarLander-v2	This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. TODO: Add your code	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : mathreader/ppo-LunarLander-v2 ### Model URL : https://huggingface.co/mathreader/ppo-LunarLander-v2 ### Model Description : This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. TODO: Add your code
RMarvinMT/PoliticaYeconomia	https://huggingface.co/RMarvinMT/PoliticaYeconomia	null	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : RMarvinMT/PoliticaYeconomia ### Model URL : https://huggingface.co/RMarvinMT/PoliticaYeconomia ### Model Description :
blaze999/finetuned-ner-conll	https://huggingface.co/blaze999/finetuned-ner-conll	This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : blaze999/finetuned-ner-conll ### Model URL : https://huggingface.co/blaze999/finetuned-ner-conll ### Model Description : This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
thrunlab/sparse_sparse_80_percent_pretraining_warmup	https://huggingface.co/thrunlab/sparse_sparse_80_percent_pretraining_warmup	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : thrunlab/sparse_sparse_80_percent_pretraining_warmup ### Model URL : https://huggingface.co/thrunlab/sparse_sparse_80_percent_pretraining_warmup ### Model Description : No model card New: Create and edit this model card directly on the website!
chenhugging/mistral-7b-medqa-v1	https://huggingface.co/chenhugging/mistral-7b-medqa-v1	This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the medical_meadow_medqa dataset. The following hyperparameters were used during training: hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-medqa-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : chenhugging/mistral-7b-medqa-v1 ### Model URL : https://huggingface.co/chenhugging/mistral-7b-medqa-v1 ### Model Description : This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the medical_meadow_medqa dataset. The following hyperparameters were used during training: hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-medqa-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1
tom192180/distilbert-base-uncased_odm_zphr_0st12sd	https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st12sd	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tom192180/distilbert-base-uncased_odm_zphr_0st12sd ### Model URL : https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st12sd ### Model Description : No model card New: Create and edit this model card directly on the website!
Sacbe/ViT_SAM_Classification	https://huggingface.co/Sacbe/ViT_SAM_Classification	El modelo fue entrenado usando el modelo base de VisionTransformer junto con el optimizador SAM de Google y la función de perdida Negative log likelihood, sobre los datos Wildfire. Los resultados muestran que el clasificador alcanzó una precisión del 97% con solo 10 épocas de entrenamiento. La teoría de se muestra a continuación. Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale. To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute. Along the way, we refine the architecture and training of ViT, reducing memory consumption and increasing accuracy of the resulting models. As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well for few-shot transfer, for example, reaching 84.86% top-1 accuracy on ImageNet with only 10 examples per class. [1] A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. arXiv, el 3 de junio de 2021. Consultado: el 12 de noviembre de 2023. [En línea]. Disponible en: http://arxiv.org/abs/2010.11929 SAM simultaneously minimizes loss value and loss sharpness. In particular, it seeks parameters that lie in neighborhoods having uniformly low loss. SAM improves model generalization and yields SoTA performance for several datasets. Additionally, it provides robustness to label noise on par with that provided by SoTA procedures that specifically target learning with noisy labels. ResNet loss landscape at the end of training with and without SAM. Sharpness-aware updates lead to a significantly wider minimum, which then leads to better generalization properties. [2] P. Foret, A. Kleiner, y H. Mobahi, “Sharpness-Aware Minimization For Efficiently Improving Generalization”, 2021. It is useful to train a classification problem with $C$ classes. If provided, the optional argument weight should be a 1D Tensor assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set. The input given through a forward call is expected to contain log-probabilities of each class. input has to be a Tensor of size either (minibatch, $C$ ) or ( minibatch, $C, d_1, d_2, \ldots, d_K$ ) with $K \geq 1$ for the $K$-dimensional case. The latter is useful for higher dimension inputs, such as computing NLL loss per-pixel for 2D images. Obtaining log-probabilities in a neural network is easily achieved by adding a LogSoftmax layer in the last layer of your network. You may use CrossEntropyLoss instead, if you prefer not to add an extra layer. The target that this loss expects should be a class index in the range $[0, C-1]$ where $C$ number of classes; if ignore_index is specified, this loss also accepts this class index (this index may not necessarily be in the class range). The unreduced (i.e. with reduction set to 'none ') loss can be described as: ℓ(x,y)=L={l1,…,lN}⊤,ln=−wynxn,yn,wc= weight [c]⋅1 \ell(x, y)=L=\left\{l_1, \ldots, l_N\right\}^{\top}, \quad l_n=-w_{y_n} x_{n, y_n}, \quad w_c=\text { weight }[c] \cdot 1 ℓ(x,y)=L={l1,…,lN}⊤,ln=−wynxn,yn,wc= weight [c]⋅1 where $x$ is the input, $y$ is the target, $w$ is the weight, and $N$ is the batch size. If reduction is not 'none' (default 'mean'), then ℓ(x,y)={∑n=1N1∑n=1Nwynln, if reduction = ’mean’ ∑n=1Nln, if reduction = ’sum’ \ell(x, y)= \begin{cases}\sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, & \text { if reduction }=\text { 'mean' } \\ \sum_{n=1}^N l_n, & \text { if reduction }=\text { 'sum' }\end{cases} ℓ(x,y)={∑n=1N∑n=1Nwyn1ln,∑n=1Nln, if reduction = ’mean’ if reduction = ’sum’	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Sacbe/ViT_SAM_Classification ### Model URL : https://huggingface.co/Sacbe/ViT_SAM_Classification ### Model Description : El modelo fue entrenado usando el modelo base de VisionTransformer junto con el optimizador SAM de Google y la función de perdida Negative log likelihood, sobre los datos Wildfire. Los resultados muestran que el clasificador alcanzó una precisión del 97% con solo 10 épocas de entrenamiento. La teoría de se muestra a continuación. Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale. To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute. Along the way, we refine the architecture and training of ViT, reducing memory consumption and increasing accuracy of the resulting models. As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well for few-shot transfer, for example, reaching 84.86% top-1 accuracy on ImageNet with only 10 examples per class. [1] A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. arXiv, el 3 de junio de 2021. Consultado: el 12 de noviembre de 2023. [En línea]. Disponible en: http://arxiv.org/abs/2010.11929 SAM simultaneously minimizes loss value and loss sharpness. In particular, it seeks parameters that lie in neighborhoods having uniformly low loss. SAM improves model generalization and yields SoTA performance for several datasets. Additionally, it provides robustness to label noise on par with that provided by SoTA procedures that specifically target learning with noisy labels. ResNet loss landscape at the end of training with and without SAM. Sharpness-aware updates lead to a significantly wider minimum, which then leads to better generalization properties. [2] P. Foret, A. Kleiner, y H. Mobahi, “Sharpness-Aware Minimization For Efficiently Improving Generalization”, 2021. It is useful to train a classification problem with $C$ classes. If provided, the optional argument weight should be a 1D Tensor assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set. The input given through a forward call is expected to contain log-probabilities of each class. input has to be a Tensor of size either (minibatch, $C$ ) or ( minibatch, $C, d_1, d_2, \ldots, d_K$ ) with $K \geq 1$ for the $K$-dimensional case. The latter is useful for higher dimension inputs, such as computing NLL loss per-pixel for 2D images. Obtaining log-probabilities in a neural network is easily achieved by adding a LogSoftmax layer in the last layer of your network. You may use CrossEntropyLoss instead, if you prefer not to add an extra layer. The target that this loss expects should be a class index in the range $[0, C-1]$ where $C$ number of classes; if ignore_index is specified, this loss also accepts this class index (this index may not necessarily be in the class range). The unreduced (i.e. with reduction set to 'none ') loss can be described as: ℓ(x,y)=L={l1,…,lN}⊤,ln=−wynxn,yn,wc= weight [c]⋅1 \ell(x, y)=L=\left\{l_1, \ldots, l_N\right\}^{\top}, \quad l_n=-w_{y_n} x_{n, y_n}, \quad w_c=\text { weight }[c] \cdot 1 ℓ(x,y)=L={l1,…,lN}⊤,ln=−wynxn,yn,wc= weight [c]⋅1 where $x$ is the input, $y$ is the target, $w$ is the weight, and $N$ is the batch size. If reduction is not 'none' (default 'mean'), then ℓ(x,y)={∑n=1N1∑n=1Nwynln, if reduction = ’mean’ ∑n=1Nln, if reduction = ’sum’ \ell(x, y)= \begin{cases}\sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, & \text { if reduction }=\text { 'mean' } \\ \sum_{n=1}^N l_n, & \text { if reduction }=\text { 'sum' }\end{cases} ℓ(x,y)={∑n=1N∑n=1Nwyn1ln,∑n=1Nln, if reduction = ’mean’ if reduction = ’sum’
tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa_gpt2-xl	https://huggingface.co/tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa_gpt2-xl	This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa_gpt2-xl ### Model URL : https://huggingface.co/tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa_gpt2-xl ### Model Description : This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa dataset. It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training:
Krisbiantoro/mixtral-id-chatml-700	https://huggingface.co/Krisbiantoro/mixtral-id-chatml-700	[More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Use the code below to get started with the model. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] BibTeX: [More Information Needed] APA: [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed]	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Krisbiantoro/mixtral-id-chatml-700 ### Model URL : https://huggingface.co/Krisbiantoro/mixtral-id-chatml-700 ### Model Description : [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Use the code below to get started with the model. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] BibTeX: [More Information Needed] APA: [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed]
deepnetguy/e97c9ebe-zeta	https://huggingface.co/deepnetguy/e97c9ebe-zeta	Failed to access https://huggingface.co/deepnetguy/e97c9ebe-zeta - HTTP Status Code: 404	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : deepnetguy/e97c9ebe-zeta ### Model URL : https://huggingface.co/deepnetguy/e97c9ebe-zeta ### Model Description : Failed to access https://huggingface.co/deepnetguy/e97c9ebe-zeta - HTTP Status Code: 404
mertbozkir/mistral-gsm8k-finetune	https://huggingface.co/mertbozkir/mistral-gsm8k-finetune	This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Use the code below to get started with the model. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] BibTeX: [More Information Needed] APA: [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed]	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : mertbozkir/mistral-gsm8k-finetune ### Model URL : https://huggingface.co/mertbozkir/mistral-gsm8k-finetune ### Model Description : This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Use the code below to get started with the model. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] BibTeX: [More Information Needed] APA: [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed]
tom192180/distilbert-base-uncased_odm_zphr_0st13sd	https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st13sd	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tom192180/distilbert-base-uncased_odm_zphr_0st13sd ### Model URL : https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st13sd ### Model Description : No model card New: Create and edit this model card directly on the website!
Chillarmo/whisper-small-hy-AM	https://huggingface.co/Chillarmo/whisper-small-hy-AM	Chillarmo/whisper-small-hy-AM is an AI model designed for speech-to-text conversion specifically tailored for the Armenian language. Leveraging the power of fine-tuning, this model, named whisper-small-hy-AM, is based on openai/whisper-small and trained on the common_voice_16_1 dataset. It achieves the following results on the evaluation set: The training data consists of Mozilla Common Voice version 16.1. Plans for future improvements include continuing the training process and integrating an additional 10 hours of data from datasets such as google/fleurs and possibly google/xtreme_s. Despite its current performance, efforts are underway to further reduce the WER. The following hyperparameters were used during training:	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Chillarmo/whisper-small-hy-AM ### Model URL : https://huggingface.co/Chillarmo/whisper-small-hy-AM ### Model Description : Chillarmo/whisper-small-hy-AM is an AI model designed for speech-to-text conversion specifically tailored for the Armenian language. Leveraging the power of fine-tuning, this model, named whisper-small-hy-AM, is based on openai/whisper-small and trained on the common_voice_16_1 dataset. It achieves the following results on the evaluation set: The training data consists of Mozilla Common Voice version 16.1. Plans for future improvements include continuing the training process and integrating an additional 10 hours of data from datasets such as google/fleurs and possibly google/xtreme_s. Despite its current performance, efforts are underway to further reduce the WER. The following hyperparameters were used during training:
jyoung105/albedobase-xl-v21	https://huggingface.co/jyoung105/albedobase-xl-v21	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : jyoung105/albedobase-xl-v21 ### Model URL : https://huggingface.co/jyoung105/albedobase-xl-v21 ### Model Description : No model card New: Create and edit this model card directly on the website!
tyson0420/stack_llama-clang	https://huggingface.co/tyson0420/stack_llama-clang	This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Use the code below to get started with the model. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] BibTeX: [More Information Needed] APA: [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed]	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tyson0420/stack_llama-clang ### Model URL : https://huggingface.co/tyson0420/stack_llama-clang ### Model Description : This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Use the code below to get started with the model. [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] BibTeX: [More Information Needed] APA: [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed] [More Information Needed]
badmonk/aimiyoshikawa	https://huggingface.co/badmonk/aimiyoshikawa	Use the code below to get started with the model.	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : badmonk/aimiyoshikawa ### Model URL : https://huggingface.co/badmonk/aimiyoshikawa ### Model Description : Use the code below to get started with the model.
tom192180/distilbert-base-uncased_odm_zphr_0st14sd	https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st14sd	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : tom192180/distilbert-base-uncased_odm_zphr_0st14sd ### Model URL : https://huggingface.co/tom192180/distilbert-base-uncased_odm_zphr_0st14sd ### Model Description : No model card New: Create and edit this model card directly on the website!
janhq/stealth-finance-v1-GGUF	https://huggingface.co/janhq/stealth-finance-v1-GGUF	Jan - Discord This is a GGUF version of jan-hq/stealth-finance-v1 Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. This is a repository for the [open-source converter](https://github.com/janhq/model-converter. We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : janhq/stealth-finance-v1-GGUF ### Model URL : https://huggingface.co/janhq/stealth-finance-v1-GGUF ### Model Description : Jan - Discord This is a GGUF version of jan-hq/stealth-finance-v1 Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. This is a repository for the [open-source converter](https://github.com/janhq/model-converter. We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format
Vanns/Vannz	https://huggingface.co/Vanns/Vannz	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : Vanns/Vannz ### Model URL : https://huggingface.co/Vanns/Vannz ### Model Description : No model card New: Create and edit this model card directly on the website!
samiabat/my-lora-model-10	https://huggingface.co/samiabat/my-lora-model-10	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : samiabat/my-lora-model-10 ### Model URL : https://huggingface.co/samiabat/my-lora-model-10 ### Model Description : No model card New: Create and edit this model card directly on the website!
mauricett/lichess_sf	https://huggingface.co/mauricett/lichess_sf	Failed to access https://huggingface.co/mauricett/lichess_sf - HTTP Status Code: 404	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : mauricett/lichess_sf ### Model URL : https://huggingface.co/mauricett/lichess_sf ### Model Description : Failed to access https://huggingface.co/mauricett/lichess_sf - HTTP Status Code: 404
ctsy/drone-codes-model	https://huggingface.co/ctsy/drone-codes-model	Failed to access https://huggingface.co/ctsy/drone-codes-model - HTTP Status Code: 404	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : ctsy/drone-codes-model ### Model URL : https://huggingface.co/ctsy/drone-codes-model ### Model Description : Failed to access https://huggingface.co/ctsy/drone-codes-model - HTTP Status Code: 404
dzagardo/gcp_test_v2	https://huggingface.co/dzagardo/gcp_test_v2	No model card New: Create and edit this model card directly on the website!	Indicators looking for configurations to recommend AI models for configuring AI agents ### Model Name : dzagardo/gcp_test_v2 ### Model URL : https://huggingface.co/dzagardo/gcp_test_v2 ### Model Description : No model card New: Create and edit this model card directly on the website!