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train · 879k rows

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utahnlp/ag_news_gpt2-large_seed-1	utahnlp	2024-04-04T19:54:08Z	103	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:52:50Z	--- 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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utahnlp/ag_news_gpt2-medium_seed-3	utahnlp	2024-04-04T19:52:42Z	103	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:52:05Z	--- 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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anonauthors/food101-swinT	anonauthors	2024-04-04T19:51:10Z	183	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:54:01Z	--- 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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utahnlp/ag_news_gpt2_seed-3	utahnlp	2024-04-04T19:50:23Z	104	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:50:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/ag_news_gpt2_seed-2	utahnlp	2024-04-04T19:50:05Z	103	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:49:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
anonauthors/food101-timm-vit_base_patch16_224.orig_in21k_ft_in1k	anonauthors	2024-04-04T19:49:13Z	15	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:37:07Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for food101-timm-vit_base_patch16_224.orig_in21k_ft_in1k
anonauthors/fgvc_aircraft-swinT	anonauthors	2024-04-04T19:48:25Z	186	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:53:45Z	--- 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]
anonauthors/fgvc_aircraft-ConvNeXt-base	anonauthors	2024-04-04T19:48:00Z	165	0	transformers	[ "transformers", "safetensors", "convnext", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:48:06Z	--- 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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anonauthors/fgvc_aircraft-ViT-b32	anonauthors	2024-04-04T19:47:36Z	171	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:47:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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anonauthors/fgvc_aircraft-timm-convnext_base.fb_in1k	anonauthors	2024-04-04T19:47:02Z	16	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:34:42Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for fgvc_aircraft-timm-convnext_base.fb_in1k
anonauthors/fgvc_aircraft-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k	anonauthors	2024-04-04T19:46:19Z	15	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:34:17Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for fgvc_aircraft-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k
anonauthors/fgvc_aircraft-timm-vit_base_patch16_224.orig_in21k_ft_in1k	anonauthors	2024-04-04T19:45:37Z	15	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:33:59Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for fgvc_aircraft-timm-vit_base_patch16_224.orig_in21k_ft_in1k
anonauthors/dtd-swinT	anonauthors	2024-04-04T19:45:11Z	183	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:53:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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anonauthors/dtd-ConvNeXt-base	anonauthors	2024-04-04T19:44:51Z	163	0	transformers	[ "transformers", "safetensors", "convnext", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:46:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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anonauthors/dtd-ViT-b32	anonauthors	2024-04-04T19:44:25Z	165	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:45:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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assskelad/paraphrase-multilingual-mpnet-base-v2_hh_cos_sim	assskelad	2024-04-04T19:44:00Z	9	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-04-04T19:41:54Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # assskelad/paraphrase-multilingual-mpnet-base-v2_hh_cos_sim This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('assskelad/paraphrase-multilingual-mpnet-base-v2_hh_cos_sim') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('assskelad/paraphrase-multilingual-mpnet-base-v2_hh_cos_sim') model = AutoModel.from_pretrained('assskelad/paraphrase-multilingual-mpnet-base-v2_hh_cos_sim') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=assskelad/paraphrase-multilingual-mpnet-base-v2_hh_cos_sim) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2732 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1366, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Reza8848/MUFFIN-Llama2-lora-13B	Reza8848	2024-04-04T19:41:56Z	0	1	null	[ "safetensors", "dataset:Reza8848/MUFFIN_68k", "arxiv:2312.02436", "license:mit", "region:us" ]	null	2024-02-29T21:35:14Z	--- license: mit datasets: - Reza8848/MUFFIN_68k --- <img src="https://cdn-uploads.huggingface.co/production/uploads/6434a6e8ea46c009904c617e/J_4FHXmtM6TuRnN3aL06y.png" width="38" height="38"> This is the Llama2 LoRA weight that was fine-tuned on MUFFIN (Multi-Faceted Instructions). We fine-tune the [Llama2-13B](https://huggingface.co/meta-llama/Llama-2-13b-hf) on [MUFFIN dataset](https://arxiv.org/abs/2312.02436) with LoRA (low-rank adaption). We released the LoRA weights of both Llama2 7B and 13B models: \|Model\|LoRA Target Modules\| \|-\|-\| \|[MUFFIN-Llama2-7B](https://huggingface.co/Reza8848/MUFFIN-Llama2-lora-7B)\|`Q, K, V, O`\| \|[MUFFIN-Llama2-13B](https://huggingface.co/Reza8848/MUFFIN-Llama2-lora-13B)\|`Q, K, V, O`\| You can also find the T5-based models [here](https://huggingface.co/Reza8848/MUFFIN-T5-3B). ## Model Usage ### 1. Inference code We use [Alpaca-lora](https://github.com/tloen/alpaca-lora) as our fine-tuning code. So, when adopting the released model weights for inference, it should be better to use the [generation code](https://github.com/tloen/alpaca-lora/blob/main/generate.py) of Alpaca-lora to reproduce our performance. Please follow the document of Alpaca-lora to set up the correct Python environments first. > Our released lora weights are in `.safetensors` format rather than the common `.bin` torch model files. > Wrong transformers and torch versions may result in [PEFT compatibility errors](https://github.com/huggingface/transformers/issues/27397) when using the released lora weighs. ### 2. Prompt template Please use the following prompt template (save the following dict as a JSON file under ['template' folder](https://github.com/tloen/alpaca-lora/tree/main/templates)): ```json { "description": "Template used by muffin.", "prompt_input": "### Input:\n{input}\n\n### Instruction:\n{instruction}\n\n### Response:\n", "prompt_no_input": "### Input:\nNone\n\n### Instruction:\n{instruction}\n\n### Response:\n", "response_split": "### Response:" } ``` ### 3. Generation hyper-parameters We use the default generation hyper-parameters as identified in [this line](https://github.com/tloen/alpaca-lora/blob/main/generate.py#L90). Besides, be aware of the following hyper-parameters: - `max_input_len == 1024`. This is the max_input_len of training. But it's fine to use any length in the inference since our evaluation batch size is 1. - `num_beams == 1`. In our experiments, we set beam size to 1. But we recommend you try with a larger beam size to get better responses from models. - When doing batched inference, please make sure `tokenizer.padding_side = "left" `, as we left padded all the batched instances when doing tuning (though it shall not have a big impact on the inference results). ## Zero-Shot Evaluation Performances We use the [metric calculation scripts](https://github.com/yizhongw/Tk-Instruct/blob/main/src/compute_metrics.py) of [Tk-Instruct](https://github.com/yizhongw/Tk-Instruct/tree/main) (i.e., `ROUGE-L` and `Exact-Match`). <div style="text-align:center"><img src="https://cdn-uploads.huggingface.co/production/uploads/6434a6e8ea46c009904c617e/IjeMYWLMRO_qGOOiXxemP.png" alt="performances.png" width="600"/></div> ## 🥳 Citation Please kindly cite our paper if you use any resources in this repository: ```bibtex @inproceedings{Lou2023MUFFIN, title={{MUFFIN}: Curating Multi-Faceted Instructions for Improving Instruction Following}, author={Renze Lou and Kai Zhang and Jian Xie and Yuxuan Sun and Janice Ahn and Hanzi Xu and Yu su and Wenpeng Yin}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=1vrS1zwekw} } ```
anonauthors/cifar100-ViT-b32	anonauthors	2024-04-04T19:41:45Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-01T23:56:10Z	--- 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. 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anonauthors/cifar100-timm-convnext_base.fb_in1k	anonauthors	2024-04-04T19:41:23Z	14	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:32:33Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for cifar100-timm-convnext_base.fb_in1k
utahnlp/ag_news_facebook_opt-6.7b_seed-1	utahnlp	2024-04-04T19:41:20Z	3	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:37:40Z	--- 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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anonauthors/cifar100-timm-vit_base_patch16_224.orig_in21k_ft_in1k	anonauthors	2024-04-04T19:40:13Z	18	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:31:56Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for cifar100-timm-vit_base_patch16_224.orig_in21k_ft_in1k
anonauthors/cifar10-swinT	anonauthors	2024-04-04T19:39:34Z	182	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:52:16Z	--- 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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anonauthors/cifar10-ConvNeXt-base	anonauthors	2024-04-04T19:39:17Z	165	0	transformers	[ "transformers", "safetensors", "convnext", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-01T23:55:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
anonauthors/cifar10-timm-convnext_base.fb_in1k	anonauthors	2024-04-04T19:38:37Z	14	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:31:12Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for cifar10-timm-convnext_base.fb_in1k
anonauthors/cifar10-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k	anonauthors	2024-04-04T19:38:01Z	15	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:30:56Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for cifar10-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k
anonauthors/cifar10-timm-vit_base_patch16_224.orig_in21k_ft_in1k	anonauthors	2024-04-04T19:37:32Z	19	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:30:28Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for cifar10-timm-vit_base_patch16_224.orig_in21k_ft_in1k
anonauthors/caltech101-swinT	anonauthors	2024-04-04T19:37:05Z	184	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-03T23:51:54Z	--- 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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anonauthors/caltech101-ViT-b32	anonauthors	2024-04-04T19:36:30Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-01T23:49:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Mariano12/q-Taxi-v3	Mariano12	2024-04-04T19:36:05Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-04-04T19:36:01Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Mariano12/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
anonauthors/caltech101-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k	anonauthors	2024-04-04T19:35:34Z	15	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:27:30Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for caltech101-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k
utahnlp/ag_news_facebook_opt-2.7b_seed-1	utahnlp	2024-04-04T19:30:53Z	3	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:28:06Z	--- 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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anonauthors/stanford_cars-ViT-b32	anonauthors	2024-04-04T19:30:40Z	216	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-02T03:49:16Z	--- 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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anonauthors/stanford_cars-timm-vit_base_patch16_224.orig_in21k_ft_in1k	anonauthors	2024-04-04T19:28:58Z	14	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:40:07Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for stanford_cars-timm-vit_base_patch16_224.orig_in21k_ft_in1k
anonauthors/oxford_pet-swinT	anonauthors	2024-04-04T19:28:20Z	218	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-02T03:48:55Z	--- 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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anonauthors/oxford_pet-ViT-b32	anonauthors	2024-04-04T19:27:48Z	199	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-02T03:48:30Z	--- 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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anonauthors/oxford_pet-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k	anonauthors	2024-04-04T19:26:54Z	15	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-31T01:38:58Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for oxford_pet-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k
anonauthors/flowers102-swinT	anonauthors	2024-04-04T19:26:23Z	216	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-02T03:48:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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luiz-and-robert-thesis/all-mpnet-base-v2-margin-5-epoch-1	luiz-and-robert-thesis	2024-04-04T19:24:54Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-04-04T19:24:44Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # luiz-and-robert-thesis/all-mpnet-base-v2-margin-5-epoch-1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('luiz-and-robert-thesis/all-mpnet-base-v2-margin-5-epoch-1') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=luiz-and-robert-thesis/all-mpnet-base-v2-margin-5-epoch-1) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 5885 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
anonauthors/cub200-swinT	anonauthors	2024-04-04T19:24:36Z	217	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-02T03:46:31Z	--- 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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anonauthors/cub200-ViT-b32	anonauthors	2024-04-04T19:24:08Z	217	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-01T23:45:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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anonauthors/cub200-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k	anonauthors	2024-04-04T19:23:43Z	20	0	timm	[ "timm", "pytorch", "image-classification", "license:apache-2.0", "region:us" ]	image-classification	2024-03-28T19:25:25Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 --- # Model card for cub200-timm-swin_base_patch4_window7_224.ms_in22k_ft_in1k
utahnlp/ag_news_facebook_opt-350m_seed-3	utahnlp	2024-04-04T19:20:33Z	103	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:19:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Mariano12/q-FrozenLake-v1-4x4-Slippery2	Mariano12	2024-04-04T19:19:42Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-04-04T19:19:39Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.55 +/- 0.50 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Mariano12/q-FrozenLake-v1-4x4-Slippery2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
utahnlp/ag_news_facebook_opt-350m_seed-1	utahnlp	2024-04-04T19:19:03Z	103	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:18:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/ag_news_facebook_opt-125m_seed-2	utahnlp	2024-04-04T19:17:48Z	103	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:17:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sinhala-nlp/NSINA-Headlines-mt5-large	sinhala-nlp	2024-04-04T19:17:26Z	5	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "si", "dataset:sinhala-nlp/NSINA-Headlines", "dataset:sinhala-nlp/NSINA", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T19:10:05Z	--- license: cc-by-sa-4.0 datasets: - sinhala-nlp/NSINA-Headlines - sinhala-nlp/NSINA language: - si --- # Sinhala Headline Generation This is a text generation task created with the [NSINA dataset](https://github.com/Sinhala-NLP/NSINA). This dataset is also released with the same license as NSINA. The objective of the task is to generate news headlines based on the provided news content. ## Data We used the same instances from NSINA 1.0 as all the news articles had headlines. We divided this dataset into a training and test set following a 0.8 split. Data can be loaded into pandas dataframes using the following code. ```python from datasets import Dataset from datasets import load_dataset train = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Headlines', split='train')) test = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Headlines', split='test')) ``` ## Citation If you are using the dataset or the models, please cite the following paper. ~~~ @inproceedings{Nsina2024, author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu}, title={{NSINA: A News Corpus for Sinhala}}, booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, year={2024}, month={May}, } ~~~
utahnlp/ag_news_microsoft_deberta-v3-large_seed-3	utahnlp	2024-04-04T19:16:57Z	105	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:16:01Z	--- 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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utahnlp/ag_news_microsoft_deberta-v3-large_seed-2	utahnlp	2024-04-04T19:15:54Z	105	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:14:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/ag_news_microsoft_deberta-v3-large_seed-1	utahnlp	2024-04-04T19:14:53Z	105	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:13:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/ag_news_microsoft_deberta-v3-base_seed-3	utahnlp	2024-04-04T19:13:51Z	103	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:13:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/ag_news_microsoft_deberta-v3-base_seed-1	utahnlp	2024-04-04T19:12:55Z	104	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:12:34Z	--- 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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utahnlp/ag_news_roberta-large_seed-1	utahnlp	2024-04-04T19:10:53Z	106	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:10:01Z	--- 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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utahnlp/ag_news_roberta-base_seed-3	utahnlp	2024-04-04T19:09:48Z	107	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:09:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/ag_news_roberta-base_seed-1	utahnlp	2024-04-04T19:08:59Z	107	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T19:08:39Z	--- 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. 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utahnlp/mnli_t5-11b_seed-1	utahnlp	2024-04-04T19:08:35Z	3	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T19:02:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/mnli_t5-3b_seed-3	utahnlp	2024-04-04T19:02:13Z	3	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T18:59:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/mnli_t5-3b_seed-2	utahnlp	2024-04-04T18:59:14Z	4	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T18:56:50Z	--- 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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dbourget/philai-tsdae-6e-bge-ft-5e	dbourget	2024-04-04T18:56:07Z	3	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-04-04T18:54:49Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # dbourget/philai-tsdae-6e-bge-ft-5e This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('dbourget/philai-tsdae-6e-bge-ft-5e') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=dbourget/philai-tsdae-6e-bge-ft-5e) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
BhavanaMalla/Mask2former_ancient-totem-4	BhavanaMalla	2024-04-04T18:55:53Z	35	0	transformers	[ "transformers", "safetensors", "mask2former", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-04T18:55:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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BeenaSamuel/logs	BeenaSamuel	2024-04-04T18:47:36Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T14:51:39Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: logs 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. --> # logs This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3006 - eval_rouge1: 0.5924 - eval_rouge2: 0.326 - eval_rougeL: 0.5425 - eval_gen_len: 82.8793 - eval_runtime: 174.5683 - eval_samples_per_second: 6.124 - eval_steps_per_second: 0.768 - epoch: 2.79 - step: 1000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
omarimc/musicgen-small	omarimc	2024-04-04T18:47:20Z	4	0	transformers	[ "transformers", "pytorch", "safetensors", "musicgen", "text-to-audio", "arxiv:2306.05284", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-to-audio	2024-04-04T16:33:55Z	--- inference: true tags: - musicgen license: cc-by-nc-4.0 pipeline_tag: text-to-audio widget: - text: "a funky house with 80s hip hop vibes" example_title: "Prompt 1" - text: "a chill song with influences from lofi, chillstep and downtempo" example_title: "Prompt 2" - text: "a catchy beat for a podcast intro" example_title: "Prompt 3" --- # MusicGen - Small - 300M MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. Four checkpoints are released: - [small (this checkpoint)](https://huggingface.co/facebook/musicgen-small) - [medium](https://huggingface.co/facebook/musicgen-medium) - [large](https://huggingface.co/facebook/musicgen-large) - [melody](https://huggingface.co/facebook/musicgen-melody) ## Example Try out MusicGen yourself! * Audiocraft Colab: <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Colab: <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Demo: <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> </a> ## 🤗 Transformers Usage You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) and scipy: ``` pip install --upgrade pip pip install --upgrade transformers scipy ``` 2. Run inference via the `Text-to-Audio` (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code! ```python from transformers import pipeline import scipy synthesiser = pipeline("text-to-audio", "facebook/musicgen-small") music = synthesiser("lo-fi music with a soothing melody", forward_params={"do_sample": True}) scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], data=music["audio"]) ``` 3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control. ```python from transformers import AutoProcessor, MusicgenForConditionalGeneration processor = AutoProcessor.from_pretrained("facebook/musicgen-small") model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") inputs = processor( text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], padding=True, return_tensors="pt", ) audio_values = model.generate(inputs, max_new_tokens=256) ``` 3. Listen to the audio samples either in an ipynb notebook: ```python from IPython.display import Audio sampling_rate = model.config.audio_encoder.sampling_rate Audio(audio_values[0].numpy(), rate=sampling_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```python import scipy sampling_rate = model.config.audio_encoder.sampling_rate scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy()) ``` For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen). ## Audiocraft Usage You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft): 1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft) ``` pip install git+https://github.com/facebookresearch/audiocraft.git ``` 2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed: ``` apt-get install ffmpeg ``` 3. Run the following Python code: ```py from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write model = MusicGen.get_pretrained("small") model.set_generation_params(duration=8) # generate 8 seconds. descriptions = ["happy rock", "energetic EDM"] wav = model.generate(descriptions) # generates 2 samples. for idx, one_wav in enumerate(wav): # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness") ``` ## Model details Organization developing the model: The FAIR team of Meta AI. Model date: MusicGen was trained between April 2023 and May 2023. Model version: This is the version 1 of the model. Model type: MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. Paper or resources for more information: More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284). Citation details: ``` @misc{copet2023simple, title={Simple and Controllable Music Generation}, author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, year={2023}, eprint={2306.05284}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` License: Code is released under MIT, model weights are released under CC-BY-NC 4.0. Where to send questions or comments about the model: Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. ## Intended use Primary intended use: The primary use of MusicGen is research on AI-based music generation, including: - Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science - Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs Primary intended users: The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. Out-of-scope use cases: The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. ## Metrics Models performance measures: We used the following objective measure to evaluate the model on a standard music benchmark: - Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) - Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) - CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: - Overall quality of the music samples; - Text relevance to the provided text input; - Adherence to the melody for melody-guided music generation. More details on performance measures and human studies can be found in the paper. Decision thresholds: Not applicable. ## Evaluation datasets The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set. ## Training datasets The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing. ## Evaluation results Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper. \| Model \| Frechet Audio Distance \| KLD \| Text Consistency \| Chroma Cosine Similarity \| \|---\|---\|---\|---\|---\| \| facebook/musicgen-small \| 4.88 \| 1.42 \| 0.27 \| - \| \| facebook/musicgen-medium \| 5.14 \| 1.38 \| 0.28 \| - \| \| facebook/musicgen-large \| 5.48 \| 1.37 \| 0.28 \| - \| \| facebook/musicgen-melody \| 4.93 \| 1.41 \| 0.27 \| 0.44 \| More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284), in the Results section. ## Limitations and biases Data: The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. Mitigations: Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). Limitations: - The model is not able to generate realistic vocals. - The model has been trained with English descriptions and will not perform as well in other languages. - The model does not perform equally well for all music styles and cultures. - The model sometimes generates end of songs, collapsing to silence. - It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. Biases: The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. Risks and harms: Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
AlignmentResearch/robust_llm_pythia-tt-410m-mz-advt-v0-ts-20000-s-1	AlignmentResearch	2024-04-04T18:45:35Z	103	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-410m", "base_model:finetune:EleutherAI/pythia-410m", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T18:44:25Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-410m model-index: - name: robust_llm_pythia-tt-410m-mz-advt-v0-ts-20000-s-1 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. --> # robust_llm_pythia-tt-410m-mz-advt-v0-ts-20000-s-1 This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
utahnlp/mnli_t5-large_seed-2	utahnlp	2024-04-04T18:42:33Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T18:41:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BhavanaMalla/Mask2former_sweet-cosmos-3	BhavanaMalla	2024-04-04T18:42:03Z	34	0	transformers	[ "transformers", "safetensors", "mask2former", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-04T18:41:47Z	--- 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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utahnlp/mnli_t5-large_seed-1	utahnlp	2024-04-04T18:40:52Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T18:39:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hlitio/Mistral-planner_v3	hlitio	2024-04-04T18:39:38Z	8	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-04T18:36:46Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: hlitio - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
utahnlp/mnli_t5-base_seed-3	utahnlp	2024-04-04T18:39:11Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T18:38:41Z	--- 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. 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utahnlp/mnli_t5-base_seed-2	utahnlp	2024-04-04T18:38:36Z	108	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T18:38:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/mnli_t5-small_seed-3	utahnlp	2024-04-04T18:37:34Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T18:37:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/mnli_gpt2-xl_seed-3	utahnlp	2024-04-04T18:36:52Z	97	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T18:34:31Z	--- 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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omarimc/musicgen-medium	omarimc	2024-04-04T18:35:34Z	1	0	transformers	[ "transformers", "pytorch", "musicgen", "text-to-audio", "arxiv:2306.05284", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-to-audio	2024-04-04T16:33:13Z	--- inference: true tags: - musicgen license: cc-by-nc-4.0 pipeline_tag: text-to-audio widget: - text: a funky house with 80s hip hop vibes example_title: Prompt 1 - text: a chill song with influences from lofi, chillstep and downtempo example_title: Prompt 2 - text: a catchy beat for a podcast intro example_title: Prompt 3 --- # MusicGen - Medium - 1.5B MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. Four checkpoints are released: - [small](https://huggingface.co/facebook/musicgen-small) - [medium (this checkpoint)](https://huggingface.co/facebook/musicgen-medium) - [large](https://huggingface.co/facebook/musicgen-large) - [melody](https://huggingface.co/facebook/musicgen-melody) ## Example Try out MusicGen yourself! * Audiocraft Colab: <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Colab: <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Demo: <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> </a> ## 🤗 Transformers Usage You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) and scipy: ``` pip install --upgrade pip pip install --upgrade transformers scipy ``` 2. Run inference via the `Text-to-Audio` (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code! ```python from transformers import pipeline import scipy synthesiser = pipeline("text-to-audio", "facebook/musicgen-medium") music = synthesiser("lo-fi music with a soothing melody", forward_params={"do_sample": True}) scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], data=music["audio"]) ``` 3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control. ```python from transformers import AutoProcessor, MusicgenForConditionalGeneration processor = AutoProcessor.from_pretrained("facebook/musicgen-medium") model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-medium") inputs = processor( text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], padding=True, return_tensors="pt", ) audio_values = model.generate(inputs, max_new_tokens=256) ``` 3. Listen to the audio samples either in an ipynb notebook: ```python from IPython.display import Audio sampling_rate = model.config.audio_encoder.sampling_rate Audio(audio_values[0].numpy(), rate=sampling_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```python import scipy sampling_rate = model.config.audio_encoder.sampling_rate scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy()) ``` For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen). ## Audiocraft Usage You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft): 1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft) ``` pip install git+https://github.com/facebookresearch/audiocraft.git ``` 2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed: ``` apt-get install ffmpeg ``` 3. Run the following Python code: ```py from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write model = MusicGen.get_pretrained("medium") model.set_generation_params(duration=8) # generate 8 seconds. descriptions = ["happy rock", "energetic EDM"] wav = model.generate(descriptions) # generates 2 samples. for idx, one_wav in enumerate(wav): # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness") ``` ## Model details Organization developing the model: The FAIR team of Meta AI. Model date: MusicGen was trained between April 2023 and May 2023. Model version: This is the version 1 of the model. Model type: MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. Paper or resources for more information: More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284). Citation details: ``` @misc{copet2023simple, title={Simple and Controllable Music Generation}, author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, year={2023}, eprint={2306.05284}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` License: Code is released under MIT, model weights are released under CC-BY-NC 4.0. Where to send questions or comments about the model: Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. ## Intended use Primary intended use: The primary use of MusicGen is research on AI-based music generation, including: - Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science - Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs Primary intended users: The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. Out-of-scope use cases: The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. ## Metrics Models performance measures: We used the following objective measure to evaluate the model on a standard music benchmark: - Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) - Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) - CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: - Overall quality of the music samples; - Text relevance to the provided text input; - Adherence to the melody for melody-guided music generation. More details on performance measures and human studies can be found in the paper. Decision thresholds: Not applicable. ## Evaluation datasets The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set. ## Training datasets The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing. ## Evaluation results Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper. \| Model \| Frechet Audio Distance \| KLD \| Text Consistency \| Chroma Cosine Similarity \| \|---\|---\|---\|---\|---\| \| facebook/musicgen-small \| 4.88 \| 1.42 \| 0.27 \| - \| \| facebook/musicgen-medium \| 5.14 \| 1.38 \| 0.28 \| - \| \| facebook/musicgen-large \| 5.48 \| 1.37 \| 0.28 \| - \| \| facebook/musicgen-melody \| 4.93 \| 1.41 \| 0.27 \| 0.44 \| More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284), in the Results section. ## Limitations and biases Data: The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. Mitigations: Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). Limitations: - The model is not able to generate realistic vocals. - The model has been trained with English descriptions and will not perform as well in other languages. - The model does not perform equally well for all music styles and cultures. - The model sometimes generates end of songs, collapsing to silence. - It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. Biases: The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. Risks and harms: Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
utahnlp/mnli_gpt2-xl_seed-2	utahnlp	2024-04-04T18:34:09Z	98	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T18:31:45Z	--- 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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OwOOwO/here_we_go_again5	OwOOwO	2024-04-04T18:27:57Z	90	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-04T18:25:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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flexnotop/imdb-v1	flexnotop	2024-04-04T18:24:52Z	105	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T13:50:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/mnli_gpt2-medium_seed-1	utahnlp	2024-04-04T18:19:21Z	104	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T18:17:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahessamb/sbert_allmini_morl	ahessamb	2024-04-04T18:19:20Z	8	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-04-04T16:57:10Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # ahessamb/sbert_allmini_morl This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ahessamb/sbert_allmini_morl') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ahessamb/sbert_allmini_morl) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 1844 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters: ``` {'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 2, 'size_average': True} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 0, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Rahulrayudu/Flan-T5-AgriQA	Rahulrayudu	2024-04-04T18:18:31Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-04T18:17:45Z	--- 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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utahnlp/mnli_gpt2_seed-2	utahnlp	2024-04-04T18:16:17Z	104	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T18:15:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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alielfilali01/jais-13b-chat-4bits	alielfilali01	2024-04-04T18:11:02Z	48	0	transformers	[ "transformers", "safetensors", "jais", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-04-04T18:05:53Z	--- 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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utahnlp/mnli_facebook_opt-6.7b_seed-1	utahnlp	2024-04-04T18:06:58Z	4	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T18:02:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Josephgflowers/Tinyllama-1.5B-Cinder-Test-1	Josephgflowers	2024-04-04T18:03:33Z	170	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-04T17:54:09Z	--- license: mit --- This is a depth up scalled model of the 616M cinder model and Cinder v2. This model still needs further training. Putting it up for testing. More information coming. Maybe. Lol. Here is a brief desc of the project: Im mixing a lot of techniques I guess that I found interesting and have been testing, HF Cosmo is not great but decent and was fully trained in 4 days using a mix of more fine tuned directed datasets and some synthetic textbook style datasets. So I used pruning and a similar mix as Cosmo on tinyllama (trained on a ton of data for an extended time for its size) to keep the tinyllama model coherent during pruning. Now I am trying to depth up scale it using my pruned model and an original, Then taking a majority of each and combining them to create a larger model. Then it needs more training, then fine tuning. Then theoretically it will be a well performing 1.5B model (that didn't need full scale training).
utahnlp/mnli_facebook_opt-2.7b_seed-3	utahnlp	2024-04-04T18:02:36Z	5	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T18:00:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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speakleash/Bielik-7B-Instruct-v0.1-3bit-HQQ	speakleash	2024-04-04T18:01:30Z	5	1	transformers	[ "transformers", "mistral", "text-generation", "finetuned", "hqq", "conversational", "pl", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]	text-generation	2024-04-04T15:29:34Z	--- language: - pl license: cc-by-nc-4.0 library_name: transformers tags: - finetuned - hqq inference: false --- <p align="center"> <img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png"> </p> # Bielik-7B-Instruct-v0.1-3bit-HQQ This repo contains HQQ (3-bit) format model files for [SpeakLeash](https://speakleash.org/)'s [Bielik-7B-Instruct-v0.1](https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1). <b><u>DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!</u></b><br> Simple Colab notebook for testing: https://colab.research.google.com/drive/1Al9glPVCuOXbtDsks8cMcuzkuu8YDzpg?usp=sharing ### Model description: * Developed by: [SpeakLeash](https://speakleash.org/) * Language: Polish * Model type: causal decoder-only * Quant from: [Bielik-7B-Instruct-v0.1](https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1) * Finetuned from: [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1) * License: CC BY NC 4.0 (non-commercial use) * Model ref: speakleash:e38140bea0d48f1218540800bbc67e89 ## Contact Us If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/3G9DVM39).
jakellm/output_dir	jakellm	2024-04-04T17:59:59Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-04-04T17:59:45Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: output_dir 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. --> # output_dir This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.0.1+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-160m-mz-advt-v0-ts-2000-s-2	AlignmentResearch	2024-04-04T17:57:10Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m", "base_model:finetune:EleutherAI/pythia-160m", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:56:50Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m model-index: - name: robust_llm_pythia-tt-160m-mz-advt-v0-ts-2000-s-2 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. --> # robust_llm_pythia-tt-160m-mz-advt-v0-ts-2000-s-2 This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
utahnlp/mnli_facebook_opt-1.3b_seed-2	utahnlp	2024-04-04T17:51:23Z	104	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:49:05Z	--- 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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utahnlp/mnli_facebook_opt-1.3b_seed-1	utahnlp	2024-04-04T17:48:54Z	104	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:46:20Z	--- 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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utahnlp/mnli_facebook_opt-350m_seed-3	utahnlp	2024-04-04T17:46:10Z	104	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:45:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/mnli_facebook_opt-125m_seed-3	utahnlp	2024-04-04T17:42:54Z	104	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:42:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dgver507/kyrat507	dgver507	2024-04-04T17:42:33Z	0	0	null	[ "license:other", "region:us" ]	null	2024-04-02T22:56:03Z	--- license: other license_name: kyrat507 license_link: LICENSE ---
utahnlp/mnli_facebook_opt-125m_seed-2	utahnlp	2024-04-04T17:42:10Z	105	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:41:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/mnli_facebook_opt-125m_seed-1	utahnlp	2024-04-04T17:41:25Z	104	0	transformers	[ "transformers", "safetensors", "opt", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:41:06Z	--- 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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utahnlp/mnli_microsoft_deberta-v3-large_seed-3	utahnlp	2024-04-04T17:40:52Z	109	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:39:34Z	--- 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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HPLT/sft-fpft-de-bloom-3b	HPLT	2024-04-04T17:39:31Z	2	0	transformers	[ "transformers", "pytorch", "bloom", "text-generation", "generation", "question answering", "instruction tuning", "de", "arxiv:2309.08958", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-04T17:33:14Z	--- language: - de tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with full-parameter fine-tuning and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [bloom-3b](https://huggingface.co/bloom-3b) * Instruction tuning language: German * Training method: full-parameter fine-tuning. * Best checkpoint: best cross-entropy on a validation set, trained for 3 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded using `transformers` library. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/fpft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
utahnlp/mnli_microsoft_deberta-v3-large_seed-2	utahnlp	2024-04-04T17:39:28Z	106	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:38:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mlabonne/Jambatypus-v0.1	mlabonne	2024-04-04T17:39:00Z	9	37	transformers	[ "transformers", "safetensors", "jamba", "text-generation", "axolotl", "conversational", "custom_code", "en", "base_model:ai21labs/Jamba-v0.1", "base_model:quantized:ai21labs/Jamba-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-29T16:54:50Z	--- license: apache-2.0 language: - en dataset: - chargoddard/Open-Platypus-Chat tags: - axolotl base_model: ai21labs/Jamba-v0.1 --- ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/efmF8RtLLeKgQ9OwPqfD8.webp) # Jambatypus-v0.1 This model is a QLoRA fine-tuned version of [ai21labs/Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1) on the [chargoddard/Open-Platypus-Chat](https://huggingface.co/datasets/chargoddard/Open-Platypus-Chat) dataset. It has been trained on 2xA100 80 GB using my [LazyAxolotl - Jamba](https://colab.research.google.com/drive/1alsgwZFvLPPAwIgkAxeMKHQSJYfW7DeZ?usp=sharing) notebook. This repo contains both the adapter and the merged model in FP16 precision. I recommend using the ChatML template to use this model. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: ai21labs/Jamba-v0.1 trust_remote_code: true load_in_8bit: false load_in_4bit: true strict: false datasets: - path: chargoddard/Open-Platypus-Chat type: sharegpt chat_template: chatml dataset_prepared_path: val_set_size: 0.01 output_dir: ./out sequence_len: 4096 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false use_wandb: true wandb_project: axolotl wandb_entity: wandb_watch: wandb_name: Jambatypus-v0.1 wandb_log_model: adapter: qlora lora_r: 16 lora_alpha: 32 lora_dropout: 0.05 lora_target_linear: true low_cpu_mem_usage: true gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 1 optimizer: adamw_bnb_8bit adam_beta2: 0.95 adam_epsilon: 0.00001 max_grad_norm: 1.0 lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 saves_per_epoch: 4 save_total_limit: 2 debug: deepspeed: weight_decay: 0.0 special_tokens: ``` </details><br> ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6274 \| 0.01 \| 1 \| 1.0298 \| \| 0.44 \| 0.25 \| 42 \| 0.9770 \| \| 0.4406 \| 0.5 \| 84 \| 0.9653 \| \| 0.4445 \| 0.75 \| 126 \| 0.9645 \| \| 0.4609 \| 1.0 \| 168 \| 0.9641 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.0 ## 💻 Usage The following code creates a Gradio chat interface with Jambatypus. ```python !pip install -qqq -U git+https://github.com/huggingface/transformers !pip install -qqq mamba-ssm causal-conv1d>=1.2.0 !pip install -qqq accelerate bitsandbytes torch datasets peft gradio !pip install -qqq flash-attn --no-build-isolation import torch import gradio as gr from threading import Thread from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer STOP_TOKEN = "<\|im_end\|>" def predict(message, history, system_prompt, temperature, max_new_tokens, top_k, repetition_penalty, top_p): # Format history with a given chat template stop_token = "<\|im_end\|>" instruction = '<\|im_start\|>system\n' + system_prompt + '\n<\|im_end\|>\n' for human, assistant in history: instruction += '<\|im_start\|>user\n' + human + '\n<\|im_end\|>\n<\|im_start\|>assistant\n' + assistant instruction += '\n<\|im_start\|>user\n' + message + '\n<\|im_end\|>\n<\|im_start\|>assistant\n' streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) enc = tokenizer([instruction], return_tensors="pt", padding=True, truncation=True) input_ids, attention_mask = enc.input_ids, enc.attention_mask generate_kwargs = dict( {"input_ids": input_ids.to(device), "attention_mask": attention_mask.to(device)}, streamer=streamer, do_sample=True, temperature=temperature, max_new_tokens=max_new_tokens, top_k=top_k, repetition_penalty=repetition_penalty, top_p=top_p ) t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] for new_token in streamer: if STOP_TOKEN in new_token: outputs.append(new_token[:-len(stop_token)-1]) yield "".join(outputs) break outputs.append(new_token) yield "".join(outputs) # Load model device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1") # 4-bit precision quant config quantization_config = BitsAndBytesConfig( load_in_8bit=True, llm_int8_skip_modules=["mamba"] ) # Load model and tokenizer with ChatML format model = AutoModelForCausalLM.from_pretrained( "ai21labs/Jamba-v0.1", trust_remote_code=True, torch_dtype=torch.cuda.is_bf16_supported() and torch.bfloat16 or torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, quantization_config=quantization_config ) config = PeftConfig.from_pretrained("mlabonne/Jambatypus-v0.1") model = PeftModel.from_pretrained(model, "mlabonne/Jambatypus-v0.1") # Create Gradio interface gr.ChatInterface( predict, title="Jambatypus", description="Chat with Jambatypus!", examples=[ ["Can you solve the equation 2x + 3 = 11 for x?"], ["Write an epic poem about Ancient Rome."], ["Who was the first person to walk on the Moon?"], ["Use a list comprehension to create a list of squares for numbers from 1 to 10."], ["Recommend some popular science fiction books."], ["Can you write a short story about a time-traveling detective?"] ], additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False), additional_inputs=[ gr.Textbox("Perform the task to the best of your ability.", label="System prompt"), gr.Slider(0, 1, 0.8, label="Temperature"), gr.Slider(128, 4096, 1024, label="Max new tokens"), gr.Slider(1, 80, 40, label="Top K sampling"), gr.Slider(0, 2, 1.1, label="Repetition penalty"), gr.Slider(0, 1, 0.95, label="Top P sampling"), ], theme=gr.themes.Soft(primary_hue="green"), ).queue().launch(share=True) ```
utahnlp/mnli_microsoft_deberta-v3-large_seed-1	utahnlp	2024-04-04T17:38:27Z	106	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:37:39Z	--- 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. 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utahnlp/mnli_microsoft_deberta-v3-base_seed-3	utahnlp	2024-04-04T17:37:33Z	104	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:37:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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utahnlp/mnli_roberta-large_seed-2	utahnlp	2024-04-04T17:35:02Z	127	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-04T17:34:14Z	--- 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. 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Weyaxi/Einstein-v2-7B	Weyaxi	2024-04-04T17:35:00Z	75	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "axolotl", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-02T22:17:21Z	--- license: apache-2.0 tags: - axolotl - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: einstein-v2-test-model results: [] --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/uvfa4GVWrnd8SS6yBxRJZ.jpeg) # Version 2 of [Weyaxi/Einstein-7B](https://hf.co/Weyaxi/Einstein-7B) <!-- 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: merged_all.json ds_type: json type: alpaca conversation: chatml dataset_prepared_path: last_run_prepared val_set_size: 0.005 output_dir: ./einstein-v2-test-model sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false wandb_project: huggingface wandb_entity: wandb_watch: wandb_name: wandb_log_model: hub_model_id: Weyaxi/einstein-v2-test-model save_safetensors: true gradient_accumulation_steps: 4 micro_batch_size: 1 num_epochs: 1 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 2 debug: deepspeed: zero3_bf16.json weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "<\|im_end\|>" unk_token: "<unk>" tokens: - "<\|im_start\|>" ``` </details><br> # einstein-v2-test-model This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3838 ## 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-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0376 \| 0.0 \| 1 \| 1.9459 \| \| 0.5117 \| 0.25 \| 59 \| 1.4740 \| \| 0.5293 \| 0.5 \| 118 \| 1.4116 \| \| 0.5243 \| 0.76 \| 177 \| 1.3838 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v2-7B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|63.48\| \|AI2 Reasoning Challenge (25-Shot)\|62.37\| \|HellaSwag (10-Shot) \|83.46\| \|MMLU (5-Shot) \|62.08\| \|TruthfulQA (0-shot) \|50.52\| \|Winogrande (5-shot) \|79.32\| \|GSM8k (5-shot) \|43.14\|

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