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train · 797k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
Markimvsv/Teste	Markimvsv	2024-02-25T12:59:06Z	0	0	asteroid	[ "asteroid", "text-classification", "av", "dataset:teknium/OpenHermes-2.5", "license:apache-2.0", "region:us" ]	text-classification	2024-02-25T12:57:24Z	--- license: apache-2.0 datasets: - teknium/OpenHermes-2.5 language: - av metrics: - accuracy library_name: asteroid pipeline_tag: text-classification ---
farzanrahmani/vilt_finetuned_200	farzanrahmani	2024-02-25T12:58:21Z	64	0	transformers	[ "transformers", "tensorboard", "safetensors", "vilt", "visual-question-answering", "generated_from_trainer", "dataset:vqa", "base_model:dandelin/vilt-b32-mlm", "base_model:finetune:dandelin/vilt-b32-mlm", "license:apache-2.0", "endpoints_compatible", "region:us" ]	visual-question-answering	2024-02-25T12:19:05Z	--- license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer datasets: - vqa model-index: - name: vilt_finetuned_200 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. --> # vilt_finetuned_200 This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the vqa dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Priyanshuchaudhary2425/EmotiNet	Priyanshuchaudhary2425	2024-02-25T12:57:23Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-25T12:31:22Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: EmotiNet 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. --> # EmotiNet This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3224 - Accuracy: 0.9242 - Precision: 0.8830 - Recall: 0.8990 - F1: 0.8902 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.1494 \| 1.0 \| 1500 \| 0.3036 \| 0.9237 \| 0.8783 \| 0.9166 \| 0.8921 \| \| 0.1481 \| 2.0 \| 3000 \| 0.2944 \| 0.9242 \| 0.8893 \| 0.8867 \| 0.8877 \| \| 0.072 \| 3.0 \| 4500 \| 0.3224 \| 0.9242 \| 0.8830 \| 0.8990 \| 0.8902 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.2
codesagar/prompt-guard-v1	codesagar	2024-02-25T12:57:09Z	0	0	null	[ "tensorboard", "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T12:57:04Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
jennfar/Current_Model	jennfar	2024-02-25T12:54:55Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-25T12:54:55Z	--- license: creativeml-openrail-m ---
zayjean/llama-2-13b_verify-bo-lora-r8-a32-d0_3K-E20	zayjean	2024-02-25T12:54:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-25T12:54:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ThuyNT03/SOMD-train-scibert	ThuyNT03	2024-02-25T12:50:32Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:allenai/scibert_scivocab_cased", "base_model:finetune:allenai/scibert_scivocab_cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-25T08:55:33Z	--- base_model: allenai/scibert_scivocab_cased tags: - generated_from_trainer metrics: - f1 model-index: - name: SOMD-train-scibert 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. --> # SOMD-train-scibert This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - F1: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 1243 \| 0.0053 \| 0.7304 \| \| No log \| 2.0 \| 2486 \| 0.0029 \| 0.8024 \| \| No log \| 3.0 \| 3729 \| 0.0017 \| 0.8700 \| \| No log \| 4.0 \| 4972 \| 0.0011 \| 0.9302 \| \| No log \| 5.0 \| 6215 \| 0.0010 \| 0.9305 \| \| No log \| 6.0 \| 7458 \| 0.0008 \| 0.9545 \| \| No log \| 7.0 \| 8701 \| 0.0007 \| 0.9629 \| \| No log \| 8.0 \| 9944 \| 0.0004 \| 0.9698 \| \| No log \| 9.0 \| 11187 \| 0.0002 \| 0.9854 \| \| No log \| 10.0 \| 12430 \| 0.0002 \| 0.9871 \| \| No log \| 11.0 \| 13673 \| 0.0002 \| 0.9934 \| \| No log \| 12.0 \| 14916 \| 0.0001 \| 0.9898 \| \| No log \| 13.0 \| 16159 \| 0.0000 \| 0.9985 \| \| No log \| 14.0 \| 17402 \| 0.0001 \| 0.9940 \| \| No log \| 15.0 \| 18645 \| 0.0000 \| 0.9986 \| \| No log \| 16.0 \| 19888 \| 0.0000 \| 0.9966 \| \| No log \| 17.0 \| 21131 \| 0.0000 \| 0.9994 \| \| No log \| 18.0 \| 22374 \| 0.0000 \| 1.0 \| \| No log \| 19.0 \| 23617 \| 0.0000 \| 1.0 \| \| No log \| 20.0 \| 24860 \| 0.0000 \| 1.0 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
ThomasFG/101.25-33.75	ThomasFG	2024-02-25T12:39:32Z	76	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small.en", "base_model:finetune:openai/whisper-small.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-25T10:18:28Z	--- license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer metrics: - wer model-index: - name: 2024-02-25_11-18-26 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. --> # 2024-02-25_11-18-26 This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2631 - Wer: 9.9444 ## 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: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2109 \| 1.0 \| 612 \| 0.2631 \| 9.9444 \| ### Framework versions - Transformers 4.37.2 - Pytorch 1.13.1+cu116 - Datasets 2.17.0 - Tokenizers 0.15.2
MaziyarPanahi/Ultra-Smaug-125B-v0.1	MaziyarPanahi	2024-02-25T12:32:59Z	29	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:agpl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T12:51:30Z	--- license: agpl-3.0 --- pass_7 Quantized model in GGUF: https://huggingface.co/MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF
MaziyarPanahi/Ultra-Miqu-WizardLM-120B-v0.1	MaziyarPanahi	2024-02-25T12:30:03Z	36	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "wizardlm", "mique", "conversational", "license:agpl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-14T22:31:59Z	--- license: agpl-3.0 pipeline_tag: text-generation tags: - merge - wizardlm - mique --- ## Examples There are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step. ``` At the beginning, there were three killers in the room. 1. Someone entered the room, making it four people inside (including the new person). 2. The new person then killed one of the original three killers, so now there are two dead bodies in the room. 3. However, the riddle didn't say that the new person wasn't a killer themselves. In fact, they just proved to be one by killing one of the original three. 4. Therefore, after the murder, there are still three killers in the room: the new person who just committed a killing (making them a killer), and the remaining two from the original group of three. ``` Mark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step ``` 1) We know that Mark is faster than Mary, so we can say that Mark's speed > Mary's speed 2) We also know that Mary is faster than Joe, so we can say that Mary's speed > Joe's speed 3) Based on the information above, we can conclude that Mark'sss speed > Mary' speed > Joe's speed. 4) Therefore, Joe cannot be faster than Mark as his speed is less than both Mark and Mary. ``` If I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously? ``` It would still take approximately 5 hours. The number of shirts does not affect the drying time, assuming all other conditions remain constant (such as temperature, humidity, air flow, and amount of moisture). ```
codesagar/autotrain-jd6kz-4z28z	codesagar	2024-02-25T12:23:25Z	0	0	null	[ "tensorboard", "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T12:23:20Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
ActiveLearningAGI/zephyr-7b-sft-qlora	ActiveLearningAGI	2024-02-25T12:19:58Z	5	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "sft", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-22T09:26:57Z	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/ultrachat_200k base_model: mistralai/Mistral-7B-v0.1 model-index: - name: zephyr-7b-sft-qlora 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. --> # zephyr-7b-sft-qlora This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: 0.9536 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9757 \| 1.0 \| 8714 \| 0.9536 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
Litzy619/V0224P2	Litzy619	2024-02-25T12:18:32Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-02-25T08:40:59Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0224P2 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. --> # V0224P2 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7339 ## 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0572 \| 0.13 \| 10 \| 0.9574 \| \| 0.9041 \| 0.26 \| 20 \| 0.8622 \| \| 0.8373 \| 0.39 \| 30 \| 0.8201 \| \| 0.7968 \| 0.52 \| 40 \| 0.7984 \| \| 0.784 \| 0.65 \| 50 \| 0.7833 \| \| 0.7691 \| 0.78 \| 60 \| 0.7743 \| \| 0.7479 \| 0.91 \| 70 \| 0.7653 \| \| 0.735 \| 1.04 \| 80 \| 0.7590 \| \| 0.6958 \| 1.17 \| 90 \| 0.7547 \| \| 0.7106 \| 1.3 \| 100 \| 0.7509 \| \| 0.7013 \| 1.43 \| 110 \| 0.7463 \| \| 0.6914 \| 1.55 \| 120 \| 0.7422 \| \| 0.6826 \| 1.68 \| 130 \| 0.7398 \| \| 0.6926 \| 1.81 \| 140 \| 0.7380 \| \| 0.6971 \| 1.94 \| 150 \| 0.7349 \| \| 0.6674 \| 2.07 \| 160 \| 0.7370 \| \| 0.6406 \| 2.2 \| 170 \| 0.7360 \| \| 0.6539 \| 2.33 \| 180 \| 0.7351 \| \| 0.6457 \| 2.46 \| 190 \| 0.7352 \| \| 0.6487 \| 2.59 \| 200 \| 0.7343 \| \| 0.6498 \| 2.72 \| 210 \| 0.7339 \| \| 0.6483 \| 2.85 \| 220 \| 0.7340 \| \| 0.6499 \| 2.98 \| 230 \| 0.7339 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
cl-nagoya/shioriha-large-pt	cl-nagoya	2024-02-25T12:12:00Z	7	5	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-13T10:04:19Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # cl-nagoya/shioriha-large-pt 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('cl-nagoya/shioriha-large-pt') 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('cl-nagoya/shioriha-large-pt') model = AutoModel.from_pretrained('cl-nagoya/shioriha-large-pt') # 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=cl-nagoya/shioriha-large-pt) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Bul11k/finetuned-tiny-bert_second_attempt	Bul11k	2024-02-25T12:10:24Z	120	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:cointegrated/rubert-tiny", "base_model:finetune:cointegrated/rubert-tiny", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-02-25T11:49:45Z	--- license: mit base_model: cointegrated/rubert-tiny tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned-tiny-bert_second_attempt 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. --> # finetuned-tiny-bert_second_attempt This model is a fine-tuned version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2433 - Accuracy: 0.5666 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
rorito/jug19light	rorito	2024-02-25T12:09:32Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-02-25T12:09:32Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- a young woman, swimming cap, brown hair, green eyes, upper body, from above, facing the viewer, floating on the water, wet, pensive, one-piece swimming suit, realistic, skin texture, best quality parameters: negative_prompt: bad quality, worst quality, low quality, semi realistic, 2d, cartoon, output: url: images/00057-2415965909.jpeg.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null --- # jug <Gallery /> ## Download model [Download](/rorito/jug19light/tree/main) them in the Files & versions tab.
Jayantez/ppo-LunarLander-v2	Jayantez	2024-02-25T11:51:43Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-25T11:51:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.25 +/- 16.17 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
lvcalucioli/llamantino7b_2_2_syntetic-question-answering_merged	lvcalucioli	2024-02-25T11:50:09Z	76	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-25T11:42:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rorito/jugxl9	rorito	2024-02-25T11:39:49Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-02-25T11:39:49Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- a young woman, swimming cap, brown hair, green eyes, upper body, from above, facing the viewer, floating on the water, wet, pensive, one-piece swimming suit, realistic, skin texture, best quality parameters: negative_prompt: bad quality, worst quality, low quality, semi realistic, 2d, cartoon, output: url: images/00057-2415965909.jpeg.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null --- # jugger <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/rorito/jugxl9/tree/main) them in the Files & versions tab.
ruige2002/finetuning-sentiment-model-500-hand-labelled-cashtag-tweets-distilbert-base-uncased	ruige2002	2024-02-25T11:26:05Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-25T11:20:48Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-500-hand-labelled-cashtag-tweets-distilbert-base-uncased 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. --> # finetuning-sentiment-model-500-hand-labelled-cashtag-tweets-distilbert-base-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1248 - Accuracy: 0.9167 - F1: 0.9556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Warkawik/code_example	Warkawik	2024-02-25T11:20:26Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:bigcode/starcoderbase-1b", "base_model:adapter:bigcode/starcoderbase-1b", "license:bigcode-openrail-m", "region:us" ]	null	2024-02-25T06:12:55Z	--- license: bigcode-openrail-m library_name: peft tags: - generated_from_trainer base_model: bigcode/starcoderbase-1b model-index: - name: code_example 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. --> # code_example This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0596 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9662 \| 0.05 \| 100 \| 0.9184 \| \| 0.9899 \| 0.1 \| 200 \| 0.9461 \| \| 0.6517 \| 0.15 \| 300 \| 0.9698 \| \| 0.8963 \| 0.2 \| 400 \| 0.9823 \| \| 0.9498 \| 0.25 \| 500 \| 0.9727 \| \| 0.5741 \| 0.3 \| 600 \| 1.0098 \| \| 0.7985 \| 0.35 \| 700 \| 1.0212 \| \| 0.8268 \| 0.4 \| 800 \| 1.0123 \| \| 0.5209 \| 0.45 \| 900 \| 1.0178 \| \| 0.7512 \| 0.5 \| 1000 \| 1.0302 \| \| 0.7718 \| 0.55 \| 1100 \| 1.0342 \| \| 0.4746 \| 0.6 \| 1200 \| 1.0492 \| \| 0.6964 \| 0.65 \| 1300 \| 1.0394 \| \| 0.6844 \| 0.7 \| 1400 \| 1.0471 \| \| 0.5396 \| 0.75 \| 1500 \| 1.0495 \| \| 0.6569 \| 0.8 \| 1600 \| 1.0553 \| \| 0.6005 \| 0.85 \| 1700 \| 1.0609 \| \| 0.6015 \| 0.9 \| 1800 \| 1.0632 \| \| 0.5552 \| 0.95 \| 1900 \| 1.0620 \| \| 0.5883 \| 1.0 \| 2000 \| 1.0596 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
fzzhang/mistral_gsm8k_tuneSfull	fzzhang	2024-02-25T11:16:47Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-25T03:34:51Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral_gsm8k_tuneSfull 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. --> # mistral_gsm8k_tuneSfull 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. ## 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: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.0
Shariar433/bert-finetuned-ner-bangla	Shariar433	2024-02-25T11:10:46Z	112	0	transformers	[ "transformers", "safetensors", "electra", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-24T18:40:11Z	--- tags: - generated_from_trainer model-index: - name: bert-finetuned-ner-bangla results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner-bangla This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1194 \| 0.84 \| 500 \| 0.1120 \| \| 0.1027 \| 1.68 \| 1000 \| 0.1048 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
OmarAlsaabi/gemma-7b-it-q4_k_m	OmarAlsaabi	2024-02-25T10:53:14Z	1	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us" ]	null	2024-02-25T10:42:07Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- # Gemma-7B-it GGUF Quantized ## Usage This model can be used with the latest version of llama.cpp and LM Studio >0.2.16.
Dagonez/DialoGPT-small-Barney-Bot	Dagonez	2024-02-25T10:47:19Z	45	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "chatbot", "conversational", "text2text-generation", "en", "base_model:microsoft/DialoGPT-small", "base_model:finetune:microsoft/DialoGPT-small", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-16T19:45:21Z	--- language: - en library_name: transformers pipeline_tag: text2text-generation tags: - chatbot - conversational inference: True base_model: "microsoft/DialoGPT-small" ---
zayjean/llama-2-13b_draft-bo-lora-r8-a32-d0_3K-E20	zayjean	2024-02-25T10:35:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-25T10:35:34Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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woody72/albert-no-variable-items-length	woody72	2024-02-25T10:34:44Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:deepseek-ai/deepseek-math-7b-base", "base_model:finetune:deepseek-ai/deepseek-math-7b-base", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T08:29:47Z	--- license: other base_model: deepseek-ai/deepseek-math-7b-base tags: - trl - sft - generated_from_trainer model-index: - name: albert-no-variable-items-length 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. --> # albert-no-variable-items-length This model is a fine-tuned version of [deepseek-ai/deepseek-math-7b-base](https://huggingface.co/deepseek-ai/deepseek-math-7b-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0201 ## 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: 3e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7887 \| 0.01 \| 1 \| 0.3267 \| \| 0.8195 \| 0.01 \| 2 \| 0.3220 \| \| 0.7704 \| 0.02 \| 3 \| 0.3179 \| \| 0.7745 \| 0.03 \| 4 \| 0.3148 \| \| 0.7651 \| 0.03 \| 5 \| 0.3115 \| \| 0.7484 \| 0.04 \| 6 \| 0.3085 \| \| 0.7621 \| 0.04 \| 7 \| 0.3065 \| \| 0.7409 \| 0.05 \| 8 \| 0.3033 \| \| 0.7138 \| 0.06 \| 9 \| 0.3003 \| \| 0.7204 \| 0.06 \| 10 \| 0.2985 \| \| 0.6912 \| 0.07 \| 11 \| 0.2952 \| \| 0.7163 \| 0.08 \| 12 \| 0.2922 \| \| 0.6911 \| 0.08 \| 13 \| 0.2901 \| \| 0.6774 \| 0.09 \| 14 \| 0.2878 \| \| 0.6692 \| 0.1 \| 15 \| 0.2851 \| \| 0.6055 \| 0.1 \| 16 \| 0.2825 \| \| 0.4833 \| 0.11 \| 17 \| 0.2801 \| \| 0.4824 \| 0.12 \| 18 \| 0.2765 \| \| 0.4753 \| 0.12 \| 19 \| 0.2752 \| \| 0.4653 \| 0.13 \| 20 \| 0.2722 \| \| 0.466 \| 0.13 \| 21 \| 0.2696 \| \| 0.4599 \| 0.14 \| 22 \| 0.2685 \| \| 0.4484 \| 0.15 \| 23 \| 0.2647 \| \| 0.4508 \| 0.15 \| 24 \| 0.2615 \| \| 0.4436 \| 0.16 \| 25 \| 0.2594 \| \| 0.4526 \| 0.17 \| 26 \| 0.2565 \| \| 0.4332 \| 0.17 \| 27 \| 0.2551 \| \| 0.4246 \| 0.18 \| 28 \| 0.2515 \| \| 0.4241 \| 0.19 \| 29 \| 0.2480 \| \| 0.4173 \| 0.19 \| 30 \| 0.2453 \| \| 0.4031 \| 0.2 \| 31 \| 0.2435 \| \| 0.4122 \| 0.2 \| 32 \| 0.2400 \| \| 0.408 \| 0.21 \| 33 \| 0.2386 \| \| 0.3971 \| 0.22 \| 34 \| 0.2361 \| \| 0.4002 \| 0.22 \| 35 \| 0.2337 \| \| 0.3881 \| 0.23 \| 36 \| 0.2310 \| \| 0.3965 \| 0.24 \| 37 \| 0.2272 \| \| 0.3731 \| 0.24 \| 38 \| 0.2245 \| \| 0.3743 \| 0.25 \| 39 \| 0.2211 \| \| 0.3625 \| 0.26 \| 40 \| 0.2191 \| \| 0.3619 \| 0.26 \| 41 \| 0.2167 \| \| 0.3557 \| 0.27 \| 42 \| 0.2149 \| \| 0.3539 \| 0.28 \| 43 \| 0.2107 \| \| 0.3367 \| 0.28 \| 44 \| 0.2089 \| \| 0.3427 \| 0.29 \| 45 \| 0.2061 \| \| 0.333 \| 0.29 \| 46 \| 0.2026 \| \| 0.308 \| 0.3 \| 47 \| 0.1996 \| \| 0.2572 \| 0.31 \| 48 \| 0.1969 \| \| 0.2568 \| 0.31 \| 49 \| 0.1934 \| \| 0.2452 \| 0.32 \| 50 \| 0.1915 \| \| 0.2406 \| 0.33 \| 51 \| 0.1874 \| \| 0.2395 \| 0.33 \| 52 \| 0.1834 \| \| 0.2337 \| 0.34 \| 53 \| 0.1805 \| \| 0.2223 \| 0.35 \| 54 \| 0.1767 \| \| 0.2295 \| 0.35 \| 55 \| 0.1732 \| \| 0.222 \| 0.36 \| 56 \| 0.1692 \| \| 0.2174 \| 0.36 \| 57 \| 0.1653 \| \| 0.2064 \| 0.37 \| 58 \| 0.1637 \| \| 0.2075 \| 0.38 \| 59 \| 0.1591 \| \| 0.2014 \| 0.38 \| 60 \| 0.1559 \| \| 0.1963 \| 0.39 \| 61 \| 0.1519 \| \| 0.1908 \| 0.4 \| 62 \| 0.1485 \| \| 0.1963 \| 0.4 \| 63 \| 0.1445 \| \| 0.1793 \| 0.41 \| 64 \| 0.1408 \| \| 0.179 \| 0.42 \| 65 \| 0.1377 \| \| 0.1645 \| 0.42 \| 66 \| 0.1334 \| \| 0.1651 \| 0.43 \| 67 \| 0.1299 \| \| 0.1632 \| 0.44 \| 68 \| 0.1264 \| \| 0.1549 \| 0.44 \| 69 \| 0.1224 \| \| 0.1479 \| 0.45 \| 70 \| 0.1186 \| \| 0.1491 \| 0.45 \| 71 \| 0.1153 \| \| 0.1397 \| 0.46 \| 72 \| 0.1109 \| \| 0.1384 \| 0.47 \| 73 \| 0.1069 \| \| 0.1308 \| 0.47 \| 74 \| 0.1025 \| \| 0.1144 \| 0.48 \| 75 \| 0.0987 \| \| 0.1191 \| 0.49 \| 76 \| 0.0946 \| \| 0.1132 \| 0.49 \| 77 \| 0.0910 \| \| 0.1057 \| 0.5 \| 78 \| 0.0867 \| \| 0.0911 \| 0.51 \| 79 \| 0.0841 \| \| 0.1005 \| 0.51 \| 80 \| 0.0812 \| \| 0.0885 \| 0.52 \| 81 \| 0.0790 \| \| 0.091 \| 0.52 \| 82 \| 0.0766 \| \| 0.0841 \| 0.53 \| 83 \| 0.0742 \| \| 0.0808 \| 0.54 \| 84 \| 0.0728 \| \| 0.0752 \| 0.54 \| 85 \| 0.0708 \| \| 0.0717 \| 0.55 \| 86 \| 0.0690 \| \| 0.0767 \| 0.56 \| 87 \| 0.0676 \| \| 0.069 \| 0.56 \| 88 \| 0.0658 \| \| 0.0721 \| 0.57 \| 89 \| 0.0643 \| \| 0.074 \| 0.58 \| 90 \| 0.0635 \| \| 0.0633 \| 0.58 \| 91 \| 0.0621 \| \| 0.0706 \| 0.59 \| 92 \| 0.0609 \| \| 0.0575 \| 0.6 \| 93 \| 0.0587 \| \| 0.0641 \| 0.6 \| 94 \| 0.0584 \| \| 0.0574 \| 0.61 \| 95 \| 0.0572 \| \| 0.0658 \| 0.61 \| 96 \| 0.0567 \| \| 0.0696 \| 0.62 \| 97 \| 0.0553 \| \| 0.0603 \| 0.63 \| 98 \| 0.0544 \| \| 0.0571 \| 0.63 \| 99 \| 0.0537 \| \| 0.057 \| 0.64 \| 100 \| 0.0527 \| \| 0.0569 \| 0.65 \| 101 \| 0.0514 \| \| 0.0521 \| 0.65 \| 102 \| 0.0511 \| \| 0.052 \| 0.66 \| 103 \| 0.0501 \| \| 0.0529 \| 0.67 \| 104 \| 0.0490 \| \| 0.0523 \| 0.67 \| 105 \| 0.0490 \| \| 0.0567 \| 0.68 \| 106 \| 0.0480 \| \| 0.0547 \| 0.68 \| 107 \| 0.0475 \| \| 0.0509 \| 0.69 \| 108 \| 0.0468 \| \| 0.0506 \| 0.7 \| 109 \| 0.0462 \| \| 0.0533 \| 0.7 \| 110 \| 0.0458 \| \| 0.0544 \| 0.71 \| 111 \| 0.0449 \| \| 0.0521 \| 0.72 \| 112 \| 0.0442 \| \| 0.0527 \| 0.72 \| 113 \| 0.0439 \| \| 0.0495 \| 0.73 \| 114 \| 0.0436 \| \| 0.0471 \| 0.74 \| 115 \| 0.0430 \| \| 0.0442 \| 0.74 \| 116 \| 0.0424 \| \| 0.0432 \| 0.75 \| 117 \| 0.0421 \| \| 0.0451 \| 0.76 \| 118 \| 0.0420 \| \| 0.0498 \| 0.76 \| 119 \| 0.0409 \| \| 0.0468 \| 0.77 \| 120 \| 0.0409 \| \| 0.0474 \| 0.77 \| 121 \| 0.0403 \| \| 0.0462 \| 0.78 \| 122 \| 0.0399 \| \| 0.037 \| 0.79 \| 123 \| 0.0397 \| \| 0.041 \| 0.79 \| 124 \| 0.0393 \| \| 0.045 \| 0.8 \| 125 \| 0.0387 \| \| 0.045 \| 0.81 \| 126 \| 0.0393 \| \| 0.0416 \| 0.81 \| 127 \| 0.0381 \| \| 0.0418 \| 0.82 \| 128 \| 0.0383 \| \| 0.0382 \| 0.83 \| 129 \| 0.0377 \| \| 0.0444 \| 0.83 \| 130 \| 0.0376 \| \| 0.0404 \| 0.84 \| 131 \| 0.0373 \| \| 0.0438 \| 0.84 \| 132 \| 0.0369 \| \| 0.0411 \| 0.85 \| 133 \| 0.0365 \| \| 0.041 \| 0.86 \| 134 \| 0.0364 \| \| 0.0425 \| 0.86 \| 135 \| 0.0362 \| \| 0.0418 \| 0.87 \| 136 \| 0.0356 \| \| 0.0407 \| 0.88 \| 137 \| 0.0358 \| \| 0.04 \| 0.88 \| 138 \| 0.0362 \| \| 0.0382 \| 0.89 \| 139 \| 0.0358 \| \| 0.0415 \| 0.9 \| 140 \| 0.0351 \| \| 0.0374 \| 0.9 \| 141 \| 0.0353 \| \| 0.0377 \| 0.91 \| 142 \| 0.0350 \| \| 0.0368 \| 0.92 \| 143 \| 0.0348 \| \| 0.0389 \| 0.92 \| 144 \| 0.0348 \| \| 0.035 \| 0.93 \| 145 \| 0.0344 \| \| 0.0377 \| 0.93 \| 146 \| 0.0346 \| \| 0.0394 \| 0.94 \| 147 \| 0.0344 \| \| 0.0349 \| 0.95 \| 148 \| 0.0344 \| \| 0.0379 \| 0.95 \| 149 \| 0.0341 \| \| 0.0336 \| 0.96 \| 150 \| 0.0340 \| \| 0.037 \| 0.97 \| 151 \| 0.0338 \| \| 0.0338 \| 0.97 \| 152 \| 0.0337 \| \| 0.0384 \| 0.98 \| 153 \| 0.0337 \| \| 0.0385 \| 0.99 \| 154 \| 0.0333 \| \| 0.0345 \| 0.99 \| 155 \| 0.0333 \| \| 0.0354 \| 1.0 \| 156 \| 0.0330 \| \| 0.0359 \| 1.0 \| 157 \| 0.0324 \| \| 0.0372 \| 1.01 \| 158 \| 0.0328 \| \| 0.0337 \| 1.02 \| 159 \| 0.0321 \| \| 0.0344 \| 1.02 \| 160 \| 0.0322 \| \| 0.0351 \| 1.03 \| 161 \| 0.0319 \| \| 0.0324 \| 1.04 \| 162 \| 0.0324 \| \| 0.034 \| 1.04 \| 163 \| 0.0320 \| \| 0.0287 \| 1.05 \| 164 \| 0.0321 \| \| 0.03 \| 1.06 \| 165 \| 0.0320 \| \| 0.0314 \| 1.06 \| 166 \| 0.0319 \| \| 0.0275 \| 1.07 \| 167 \| 0.0315 \| \| 0.0213 \| 1.08 \| 168 \| 0.0321 \| \| 0.0277 \| 1.08 \| 169 \| 0.0321 \| \| 0.0275 \| 1.09 \| 170 \| 0.0319 \| \| 0.0182 \| 1.09 \| 171 \| 0.0320 \| \| 0.0236 \| 1.1 \| 172 \| 0.0319 \| \| 0.0319 \| 1.11 \| 173 \| 0.0312 \| \| 0.0358 \| 1.11 \| 174 \| 0.0319 \| \| 0.0328 \| 1.12 \| 175 \| 0.0319 \| \| 0.0337 \| 1.13 \| 176 \| 0.0318 \| \| 0.0382 \| 1.13 \| 177 \| 0.0319 \| \| 0.0315 \| 1.14 \| 178 \| 0.0312 \| \| 0.0308 \| 1.15 \| 179 \| 0.0320 \| \| 0.0314 \| 1.15 \| 180 \| 0.0325 \| \| 0.0312 \| 1.16 \| 181 \| 0.0324 \| \| 0.0326 \| 1.16 \| 182 \| 0.0327 \| \| 0.0278 \| 1.17 \| 183 \| 0.0332 \| \| 0.0264 \| 1.18 \| 184 \| 0.0329 \| \| 0.0307 \| 1.18 \| 185 \| 0.0334 \| \| 0.0255 \| 1.19 \| 186 \| 0.0336 \| \| 0.0284 \| 1.2 \| 187 \| 0.0338 \| \| 0.029 \| 1.2 \| 188 \| 0.0342 \| \| 0.0299 \| 1.21 \| 189 \| 0.0343 \| \| 0.0317 \| 1.22 \| 190 \| 0.0342 \| \| 0.0319 \| 1.22 \| 191 \| 0.0345 \| \| 0.0281 \| 1.23 \| 192 \| 0.0344 \| \| 0.0293 \| 1.24 \| 193 \| 0.0341 \| \| 0.0256 \| 1.24 \| 194 \| 0.0344 \| \| 0.0253 \| 1.25 \| 195 \| 0.0345 \| \| 0.0255 \| 1.25 \| 196 \| 0.0344 \| \| 0.0238 \| 1.26 \| 197 \| 0.0343 \| \| 0.0247 \| 1.27 \| 198 \| 0.0338 \| \| 0.0239 \| 1.27 \| 199 \| 0.0344 \| \| 0.0248 \| 1.28 \| 200 \| 0.0345 \| \| 0.0253 \| 1.29 \| 201 \| 0.0343 \| \| 0.0252 \| 1.29 \| 202 \| 0.0340 \| \| 0.0275 \| 1.3 \| 203 \| 0.0340 \| \| 0.0376 \| 1.31 \| 204 \| 0.0336 \| \| 0.0374 \| 1.31 \| 205 \| 0.0333 \| \| 0.0397 \| 1.32 \| 206 \| 0.0331 \| \| 0.0361 \| 1.32 \| 207 \| 0.0322 \| \| 0.0402 \| 1.33 \| 208 \| 0.0319 \| \| 0.0307 \| 1.34 \| 209 \| 0.0317 \| \| 0.0305 \| 1.34 \| 210 \| 0.0309 \| \| 0.0285 \| 1.35 \| 211 \| 0.0307 \| \| 0.0301 \| 1.36 \| 212 \| 0.0307 \| \| 0.0298 \| 1.36 \| 213 \| 0.0306 \| \| 0.0278 \| 1.37 \| 214 \| 0.0305 \| \| 0.0283 \| 1.38 \| 215 \| 0.0303 \| \| 0.0311 \| 1.38 \| 216 \| 0.0304 \| \| 0.0314 \| 1.39 \| 217 \| 0.0306 \| \| 0.0301 \| 1.4 \| 218 \| 0.0304 \| \| 0.0301 \| 1.4 \| 219 \| 0.0303 \| \| 0.0297 \| 1.41 \| 220 \| 0.0299 \| \| 0.0298 \| 1.41 \| 221 \| 0.0300 \| \| 0.0316 \| 1.42 \| 222 \| 0.0299 \| \| 0.0258 \| 1.43 \| 223 \| 0.0296 \| \| 0.0297 \| 1.43 \| 224 \| 0.0297 \| \| 0.0307 \| 1.44 \| 225 \| 0.0289 \| \| 0.0256 \| 1.45 \| 226 \| 0.0285 \| \| 0.0291 \| 1.45 \| 227 \| 0.0285 \| \| 0.0295 \| 1.46 \| 228 \| 0.0286 \| \| 0.0263 \| 1.47 \| 229 \| 0.0283 \| \| 0.0301 \| 1.47 \| 230 \| 0.0284 \| \| 0.0289 \| 1.48 \| 231 \| 0.0285 \| \| 0.0272 \| 1.48 \| 232 \| 0.0286 \| \| 0.0297 \| 1.49 \| 233 \| 0.0286 \| \| 0.0261 \| 1.5 \| 234 \| 0.0286 \| \| 0.0254 \| 1.5 \| 235 \| 0.0286 \| \| 0.0298 \| 1.51 \| 236 \| 0.0284 \| \| 0.0329 \| 1.52 \| 237 \| 0.0278 \| \| 0.0325 \| 1.52 \| 238 \| 0.0281 \| \| 0.0297 \| 1.53 \| 239 \| 0.0280 \| \| 0.0274 \| 1.54 \| 240 \| 0.0281 \| \| 0.0291 \| 1.54 \| 241 \| 0.0277 \| \| 0.0271 \| 1.55 \| 242 \| 0.0279 \| \| 0.0283 \| 1.56 \| 243 \| 0.0278 \| \| 0.0258 \| 1.56 \| 244 \| 0.0277 \| \| 0.0271 \| 1.57 \| 245 \| 0.0276 \| \| 0.0279 \| 1.57 \| 246 \| 0.0273 \| \| 0.0282 \| 1.58 \| 247 \| 0.0274 \| \| 0.0286 \| 1.59 \| 248 \| 0.0272 \| \| 0.0248 \| 1.59 \| 249 \| 0.0268 \| \| 0.0268 \| 1.6 \| 250 \| 0.0272 \| \| 0.0239 \| 1.61 \| 251 \| 0.0271 \| \| 0.0321 \| 1.61 \| 252 \| 0.0268 \| \| 0.0305 \| 1.62 \| 253 \| 0.0266 \| \| 0.0307 \| 1.63 \| 254 \| 0.0263 \| \| 0.0245 \| 1.63 \| 255 \| 0.0266 \| \| 0.0261 \| 1.64 \| 256 \| 0.0268 \| \| 0.0264 \| 1.64 \| 257 \| 0.0262 \| \| 0.0268 \| 1.65 \| 258 \| 0.0264 \| \| 0.0253 \| 1.66 \| 259 \| 0.0261 \| \| 0.0267 \| 1.66 \| 260 \| 0.0261 \| \| 0.0276 \| 1.67 \| 261 \| 0.0262 \| \| 0.0269 \| 1.68 \| 262 \| 0.0260 \| \| 0.0265 \| 1.68 \| 263 \| 0.0262 \| \| 0.0267 \| 1.69 \| 264 \| 0.0262 \| \| 0.0256 \| 1.7 \| 265 \| 0.0260 \| \| 0.0285 \| 1.7 \| 266 \| 0.0257 \| \| 0.0305 \| 1.71 \| 267 \| 0.0259 \| \| 0.0302 \| 1.72 \| 268 \| 0.0262 \| \| 0.0294 \| 1.72 \| 269 \| 0.0258 \| \| 0.0295 \| 1.73 \| 270 \| 0.0255 \| \| 0.027 \| 1.73 \| 271 \| 0.0255 \| \| 0.0276 \| 1.74 \| 272 \| 0.0256 \| \| 0.0256 \| 1.75 \| 273 \| 0.0257 \| \| 0.0248 \| 1.75 \| 274 \| 0.0257 \| \| 0.0305 \| 1.76 \| 275 \| 0.0253 \| \| 0.029 \| 1.77 \| 276 \| 0.0253 \| \| 0.0296 \| 1.77 \| 277 \| 0.0251 \| \| 0.0307 \| 1.78 \| 278 \| 0.0250 \| \| 0.0247 \| 1.79 \| 279 \| 0.0250 \| \| 0.0235 \| 1.79 \| 280 \| 0.0250 \| \| 0.0267 \| 1.8 \| 281 \| 0.0250 \| \| 0.0278 \| 1.8 \| 282 \| 0.0246 \| \| 0.0277 \| 1.81 \| 283 \| 0.0248 \| \| 0.0266 \| 1.82 \| 284 \| 0.0242 \| \| 0.0232 \| 1.82 \| 285 \| 0.0244 \| \| 0.0288 \| 1.83 \| 286 \| 0.0243 \| \| 0.0276 \| 1.84 \| 287 \| 0.0243 \| \| 0.0295 \| 1.84 \| 288 \| 0.0241 \| \| 0.0253 \| 1.85 \| 289 \| 0.0240 \| \| 0.0267 \| 1.86 \| 290 \| 0.0244 \| \| 0.0286 \| 1.86 \| 291 \| 0.0242 \| \| 0.0272 \| 1.87 \| 292 \| 0.0237 \| \| 0.0267 \| 1.88 \| 293 \| 0.0240 \| \| 0.0251 \| 1.88 \| 294 \| 0.0243 \| \| 0.0257 \| 1.89 \| 295 \| 0.0239 \| \| 0.0283 \| 1.89 \| 296 \| 0.0237 \| \| 0.0246 \| 1.9 \| 297 \| 0.0238 \| \| 0.0241 \| 1.91 \| 298 \| 0.0238 \| \| 0.0259 \| 1.91 \| 299 \| 0.0241 \| \| 0.0246 \| 1.92 \| 300 \| 0.0237 \| \| 0.0253 \| 1.93 \| 301 \| 0.0239 \| \| 0.0257 \| 1.93 \| 302 \| 0.0237 \| \| 0.0232 \| 1.94 \| 303 \| 0.0238 \| \| 0.0241 \| 1.95 \| 304 \| 0.0237 \| \| 0.0254 \| 1.95 \| 305 \| 0.0237 \| \| 0.0227 \| 1.96 \| 306 \| 0.0238 \| \| 0.023 \| 1.96 \| 307 \| 0.0238 \| \| 0.0232 \| 1.97 \| 308 \| 0.0238 \| \| 0.0268 \| 1.98 \| 309 \| 0.0234 \| \| 0.027 \| 1.98 \| 310 \| 0.0237 \| \| 0.0243 \| 1.99 \| 311 \| 0.0235 \| \| 0.025 \| 2.0 \| 312 \| 0.0235 \| \| 0.0185 \| 2.0 \| 313 \| 0.0235 \| \| 0.0153 \| 2.01 \| 314 \| 0.0234 \| \| 0.0119 \| 2.02 \| 315 \| 0.0236 \| \| 0.0094 \| 2.02 \| 316 \| 0.0235 \| \| 0.0171 \| 2.03 \| 317 \| 0.0237 \| \| 0.0121 \| 2.04 \| 318 \| 0.0235 \| \| 0.0135 \| 2.04 \| 319 \| 0.0231 \| \| 0.0182 \| 2.05 \| 320 \| 0.0235 \| \| 0.0128 \| 2.05 \| 321 \| 0.0232 \| \| 0.014 \| 2.06 \| 322 \| 0.0235 \| \| 0.0142 \| 2.07 \| 323 \| 0.0237 \| \| 0.0084 \| 2.07 \| 324 \| 0.0236 \| \| 0.0137 \| 2.08 \| 325 \| 0.0235 \| \| 0.0144 \| 2.09 \| 326 \| 0.0238 \| \| 0.0114 \| 2.09 \| 327 \| 0.0237 \| \| 0.0104 \| 2.1 \| 328 \| 0.0239 \| \| 0.0205 \| 2.11 \| 329 \| 0.0234 \| \| 0.0234 \| 2.11 \| 330 \| 0.0233 \| \| 0.0227 \| 2.12 \| 331 \| 0.0237 \| \| 0.0217 \| 2.12 \| 332 \| 0.0235 \| \| 0.025 \| 2.13 \| 333 \| 0.0237 \| \| 0.0208 \| 2.14 \| 334 \| 0.0245 \| \| 0.0192 \| 2.14 \| 335 \| 0.0245 \| \| 0.0195 \| 2.15 \| 336 \| 0.0249 \| \| 0.0203 \| 2.16 \| 337 \| 0.0253 \| \| 0.0234 \| 2.16 \| 338 \| 0.0252 \| \| 0.0176 \| 2.17 \| 339 \| 0.0259 \| \| 0.018 \| 2.18 \| 340 \| 0.0260 \| \| 0.0188 \| 2.18 \| 341 \| 0.0265 \| \| 0.0198 \| 2.19 \| 342 \| 0.0262 \| \| 0.0172 \| 2.2 \| 343 \| 0.0268 \| \| 0.0184 \| 2.2 \| 344 \| 0.0271 \| \| 0.0192 \| 2.21 \| 345 \| 0.0273 \| \| 0.0203 \| 2.21 \| 346 \| 0.0277 \| \| 0.0221 \| 2.22 \| 347 \| 0.0283 \| \| 0.0189 \| 2.23 \| 348 \| 0.0282 \| \| 0.0196 \| 2.23 \| 349 \| 0.0289 \| \| 0.0205 \| 2.24 \| 350 \| 0.0288 \| \| 0.0168 \| 2.25 \| 351 \| 0.0291 \| \| 0.0176 \| 2.25 \| 352 \| 0.0294 \| \| 0.0165 \| 2.26 \| 353 \| 0.0295 \| \| 0.0148 \| 2.27 \| 354 \| 0.0301 \| \| 0.0178 \| 2.27 \| 355 \| 0.0296 \| \| 0.0163 \| 2.28 \| 356 \| 0.0301 \| \| 0.0203 \| 2.28 \| 357 \| 0.0303 \| \| 0.0163 \| 2.29 \| 358 \| 0.0301 \| \| 0.0183 \| 2.3 \| 359 \| 0.0301 \| \| 0.0266 \| 2.3 \| 360 \| 0.0299 \| \| 0.0295 \| 2.31 \| 361 \| 0.0295 \| \| 0.0293 \| 2.32 \| 362 \| 0.0291 \| \| 0.0248 \| 2.32 \| 363 \| 0.0281 \| \| 0.0285 \| 2.33 \| 364 \| 0.0273 \| \| 0.0252 \| 2.34 \| 365 \| 0.0262 \| \| 0.0222 \| 2.34 \| 366 \| 0.0257 \| \| 0.0208 \| 2.35 \| 367 \| 0.0245 \| \| 0.0194 \| 2.36 \| 368 \| 0.0239 \| \| 0.0217 \| 2.36 \| 369 \| 0.0238 \| \| 0.0183 \| 2.37 \| 370 \| 0.0237 \| \| 0.0218 \| 2.37 \| 371 \| 0.0238 \| \| 0.0224 \| 2.38 \| 372 \| 0.0233 \| \| 0.023 \| 2.39 \| 373 \| 0.0235 \| \| 0.0205 \| 2.39 \| 374 \| 0.0235 \| \| 0.0215 \| 2.4 \| 375 \| 0.0237 \| \| 0.0189 \| 2.41 \| 376 \| 0.0238 \| \| 0.0233 \| 2.41 \| 377 \| 0.0236 \| \| 0.0225 \| 2.42 \| 378 \| 0.0238 \| \| 0.0196 \| 2.43 \| 379 \| 0.0233 \| \| 0.0224 \| 2.43 \| 380 \| 0.0232 \| \| 0.0214 \| 2.44 \| 381 \| 0.0234 \| \| 0.0187 \| 2.44 \| 382 \| 0.0233 \| \| 0.0199 \| 2.45 \| 383 \| 0.0231 \| \| 0.0227 \| 2.46 \| 384 \| 0.0231 \| \| 0.0199 \| 2.46 \| 385 \| 0.0231 \| \| 0.0248 \| 2.47 \| 386 \| 0.0231 \| \| 0.0196 \| 2.48 \| 387 \| 0.0231 \| \| 0.0214 \| 2.48 \| 388 \| 0.0231 \| \| 0.022 \| 2.49 \| 389 \| 0.0230 \| \| 0.0201 \| 2.5 \| 390 \| 0.0232 \| \| 0.0205 \| 2.5 \| 391 \| 0.0233 \| \| 0.0221 \| 2.51 \| 392 \| 0.0231 \| \| 0.0255 \| 2.52 \| 393 \| 0.0233 \| \| 0.0235 \| 2.52 \| 394 \| 0.0232 \| \| 0.0235 \| 2.53 \| 395 \| 0.0231 \| \| 0.0237 \| 2.53 \| 396 \| 0.0232 \| \| 0.0216 \| 2.54 \| 397 \| 0.0236 \| \| 0.0229 \| 2.55 \| 398 \| 0.0232 \| \| 0.0191 \| 2.55 \| 399 \| 0.0231 \| \| 0.0211 \| 2.56 \| 400 \| 0.0231 \| \| 0.02 \| 2.57 \| 401 \| 0.0232 \| \| 0.0217 \| 2.57 \| 402 \| 0.0228 \| \| 0.0228 \| 2.58 \| 403 \| 0.0228 \| \| 0.0222 \| 2.59 \| 404 \| 0.0230 \| \| 0.0197 \| 2.59 \| 405 \| 0.0226 \| \| 0.0195 \| 2.6 \| 406 \| 0.0225 \| \| 0.0208 \| 2.6 \| 407 \| 0.0227 \| \| 0.0238 \| 2.61 \| 408 \| 0.0226 \| \| 0.0247 \| 2.62 \| 409 \| 0.0222 \| \| 0.0229 \| 2.62 \| 410 \| 0.0223 \| \| 0.0208 \| 2.63 \| 411 \| 0.0222 \| \| 0.0197 \| 2.64 \| 412 \| 0.0220 \| \| 0.0215 \| 2.64 \| 413 \| 0.0222 \| \| 0.0216 \| 2.65 \| 414 \| 0.0221 \| \| 0.0194 \| 2.66 \| 415 \| 0.0221 \| \| 0.0223 \| 2.66 \| 416 \| 0.0220 \| \| 0.022 \| 2.67 \| 417 \| 0.0220 \| \| 0.0204 \| 2.68 \| 418 \| 0.0218 \| \| 0.0211 \| 2.68 \| 419 \| 0.0219 \| \| 0.0205 \| 2.69 \| 420 \| 0.0218 \| \| 0.021 \| 2.69 \| 421 \| 0.0213 \| \| 0.0206 \| 2.7 \| 422 \| 0.0216 \| \| 0.0261 \| 2.71 \| 423 \| 0.0215 \| \| 0.0234 \| 2.71 \| 424 \| 0.0216 \| \| 0.0246 \| 2.72 \| 425 \| 0.0215 \| \| 0.0242 \| 2.73 \| 426 \| 0.0217 \| \| 0.0204 \| 2.73 \| 427 \| 0.0217 \| \| 0.0214 \| 2.74 \| 428 \| 0.0215 \| \| 0.0235 \| 2.75 \| 429 \| 0.0216 \| \| 0.0196 \| 2.75 \| 430 \| 0.0213 \| \| 0.0223 \| 2.76 \| 431 \| 0.0213 \| \| 0.0239 \| 2.76 \| 432 \| 0.0211 \| \| 0.0224 \| 2.77 \| 433 \| 0.0210 \| \| 0.025 \| 2.78 \| 434 \| 0.0210 \| \| 0.0228 \| 2.78 \| 435 \| 0.0210 \| \| 0.0182 \| 2.79 \| 436 \| 0.0207 \| \| 0.0214 \| 2.8 \| 437 \| 0.0208 \| \| 0.022 \| 2.8 \| 438 \| 0.0212 \| \| 0.0233 \| 2.81 \| 439 \| 0.0205 \| \| 0.0209 \| 2.82 \| 440 \| 0.0208 \| \| 0.0194 \| 2.82 \| 441 \| 0.0206 \| \| 0.0215 \| 2.83 \| 442 \| 0.0205 \| \| 0.0242 \| 2.84 \| 443 \| 0.0204 \| \| 0.0219 \| 2.84 \| 444 \| 0.0205 \| \| 0.0217 \| 2.85 \| 445 \| 0.0205 \| \| 0.0237 \| 2.85 \| 446 \| 0.0205 \| \| 0.0214 \| 2.86 \| 447 \| 0.0205 \| \| 0.0224 \| 2.87 \| 448 \| 0.0201 \| \| 0.0199 \| 2.87 \| 449 \| 0.0206 \| \| 0.0219 \| 2.88 \| 450 \| 0.0202 \| \| 0.022 \| 2.89 \| 451 \| 0.0202 \| \| 0.0224 \| 2.89 \| 452 \| 0.0204 \| \| 0.0214 \| 2.9 \| 453 \| 0.0200 \| \| 0.0185 \| 2.91 \| 454 \| 0.0201 \| \| 0.0219 \| 2.91 \| 455 \| 0.0202 \| \| 0.0204 \| 2.92 \| 456 \| 0.0203 \| \| 0.0198 \| 2.92 \| 457 \| 0.0201 \| \| 0.0214 \| 2.93 \| 458 \| 0.0202 \| \| 0.0184 \| 2.94 \| 459 \| 0.0203 \| \| 0.0191 \| 2.94 \| 460 \| 0.0200 \| \| 0.0221 \| 2.95 \| 461 \| 0.0205 \| \| 0.0192 \| 2.96 \| 462 \| 0.0204 \| \| 0.0186 \| 2.96 \| 463 \| 0.0204 \| \| 0.0176 \| 2.97 \| 464 \| 0.0201 \| \| 0.0209 \| 2.98 \| 465 \| 0.0203 \| \| 0.0233 \| 2.98 \| 466 \| 0.0200 \| \| 0.0189 \| 2.99 \| 467 \| 0.0204 \| \| 0.0214 \| 3.0 \| 468 \| 0.0201 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0a0+32f93b1 - Datasets 2.17.1 - Tokenizers 0.15.2
WoopsFactory/pretrained	WoopsFactory	2024-02-25T10:21:34Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T22:05:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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RishavB777/Gemma-2B-finetuned-4bit_q	RishavB777	2024-02-25T10:07:01Z	77	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "dataset:Abirate/english_quotes", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-25T09:15:34Z	--- license: mit datasets: - Abirate/english_quotes ---
mlwong/NPC-Bert	mlwong	2024-02-25T10:04:53Z	115	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "Nasopharyngeal carcinoma", "Cancer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-09-10T07:53:46Z	--- thumbnail: "url to a thumbnail used in social sharing" tags: - Nasopharyngeal carcinoma - Cancer license: "mit" widget: - text: "Nasopharyngeal carcinoma confined in the [MASK]." example_title: "Example 1" - text: "Nodal metastases in the left side of the [MASK]." example_title: "Example 2" - text: "Small bilateral cervical [MASK] with unusual distribution." example_title: "Example 3" --- # IMPORTANT >>> This is an outdated model, please see my [space](https://huggingface.co/spaces/mlwong/npc-bert-demo) for a more updated version. <<< --- # Background-- This model was built on Microsoft's BERT trained on PubMed uncased database (`microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext`). A number of (~500) radiology reports for staging nasopharyngeal carcinoma (NPC) written in our center by board-certified radiologist were retrospectively retrieved with ethics approval . To focus on NPC, incidental findings and unrelated observations are removed prior to training. In addition, the abbreviations for structures were replaced by the original words to facilitate the model of learning suffixes and prefixes that might indicate geographical locations (e.g. L neck -> left neck, IJC -> internal jugular chain). A tokenizer was trained based on the original PubMed version, and the radiology reports were used to fine tune the PubMedBert. This fine tuned model has the weakness of unable to identify phrase or multi-word nouns, e.g. "nodal metastatases" is considered two separate words such that the BERT module tends to fill "nodes" when these two words are masked. This model serve as a pilot analysis of whether it is possible to adopt a transformer based deep learning for radiology report corpus of NPC. # Affiliations Imaging and Interventional Radiology, Chinese University of Hong Kong
ryusangwon/3230_Llama-2-7b-hf	ryusangwon	2024-02-25T09:45:08Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-25T09:45:02Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - samsum model-index: - name: 3230_Llama-2-7b-hf results: [] library_name: peft --- <!-- 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. --> # 3230_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
mwalol/loutish-dalmatian	mwalol	2024-02-25T09:11:33Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "conversational", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-02-25T09:06:32Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.36.1 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCESS_TOKEN>) ``` - Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="mwalol/loutish-dalmatian", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=0, max_new_tokens=1, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?</s><\|answer\|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "mwalol/loutish-dalmatian", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "mwalol/loutish-dalmatian", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=0, max_new_tokens=1, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "mwalol/loutish-dalmatian" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?</s><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=0, max_new_tokens=1, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` MistralForCausalLM( (model): MistralModel( (embed_tokens): Embedding(32000, 4096, padding_idx=2) (layers): ModuleList( (0-31): 32 x MistralDecoderLayer( (self_attn): MistralAttention( (q_proj): Linear(in_features=4096, out_features=4096, bias=False) (k_proj): Linear(in_features=4096, out_features=1024, bias=False) (v_proj): Linear(in_features=4096, out_features=1024, bias=False) (o_proj): Linear(in_features=4096, out_features=4096, bias=False) (rotary_emb): MistralRotaryEmbedding() ) (mlp): MistralMLP( (gate_proj): Linear(in_features=4096, out_features=14336, bias=False) (up_proj): Linear(in_features=4096, out_features=14336, bias=False) (down_proj): Linear(in_features=14336, out_features=4096, bias=False) (act_fn): SiLU() ) (input_layernorm): MistralRMSNorm() (post_attention_layernorm): MistralRMSNorm() ) ) (norm): MistralRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
kittysec/kittykatgamer_RVC_V2	kittysec	2024-02-25T08:51:08Z	0	0	null	[ "region:us" ]	null	2024-02-25T08:47:26Z	A RVC_V2 model from KittyKatGamer, trained for 100 epoch
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_nllf_v0_signal_it_129	furrutiav	2024-02-25T08:48:21Z	104	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-25T08:45:28Z	--- 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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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]
AbetoRivas/Cantautor32	AbetoRivas	2024-02-25T08:39:25Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-02-25T08:39:21Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/IMG-20231225-WA0018.jpg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null --- # Alberto <Gallery /> ## Model description Cantando este Chico ![received_1056250945482813~2.jpeg](https://cdn-uploads.huggingface.co/production/uploads/65d0c75375318341f2e5e56b/sGLe2f4H8C1IBmnPctPuZ.jpeg) ## Download model [Download](/AbetoRivas/Cantautor32/tree/main) them in the Files & versions tab.
Litzy619/V0224P1	Litzy619	2024-02-25T08:38:06Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-02-25T05:00:18Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0224P1 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. --> # V0224P1 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7339 ## 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0572 \| 0.13 \| 10 \| 0.9574 \| \| 0.9041 \| 0.26 \| 20 \| 0.8622 \| \| 0.8373 \| 0.39 \| 30 \| 0.8201 \| \| 0.7968 \| 0.52 \| 40 \| 0.7984 \| \| 0.784 \| 0.65 \| 50 \| 0.7833 \| \| 0.7691 \| 0.78 \| 60 \| 0.7743 \| \| 0.7479 \| 0.91 \| 70 \| 0.7653 \| \| 0.735 \| 1.04 \| 80 \| 0.7590 \| \| 0.6958 \| 1.17 \| 90 \| 0.7547 \| \| 0.7106 \| 1.3 \| 100 \| 0.7509 \| \| 0.7013 \| 1.43 \| 110 \| 0.7463 \| \| 0.6914 \| 1.55 \| 120 \| 0.7422 \| \| 0.6826 \| 1.68 \| 130 \| 0.7398 \| \| 0.6926 \| 1.81 \| 140 \| 0.7380 \| \| 0.6971 \| 1.94 \| 150 \| 0.7349 \| \| 0.6674 \| 2.07 \| 160 \| 0.7370 \| \| 0.6406 \| 2.2 \| 170 \| 0.7360 \| \| 0.6539 \| 2.33 \| 180 \| 0.7351 \| \| 0.6457 \| 2.46 \| 190 \| 0.7352 \| \| 0.6487 \| 2.59 \| 200 \| 0.7343 \| \| 0.6498 \| 2.72 \| 210 \| 0.7339 \| \| 0.6483 \| 2.85 \| 220 \| 0.7340 \| \| 0.6499 \| 2.98 \| 230 \| 0.7339 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
haripritam/gemma2b-it-openhermes20k	haripritam	2024-02-25T08:36:19Z	108	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T08:31:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel_Roberta-MISTRAL_FULL-notrescaled_70000	DragosGorduza	2024-02-25T08:25:59Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-25T08:25:21Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') 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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 48151 with parameters: ``` {'batch_size': 48, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` 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": 90000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel_Roberta-MISTRAL_FULL-notrescaled_10000	DragosGorduza	2024-02-25T08:25:20Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-20T15:31:26Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') 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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 48151 with parameters: ``` {'batch_size': 48, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` 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": 90000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel_Roberta-MISTRAL_FULL-notrescaled_80000	DragosGorduza	2024-02-25T08:24:45Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-25T08:24:03Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') 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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 48151 with parameters: ``` {'batch_size': 48, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` 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": 90000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel_Roberta-MISTRAL_FULL-notrescaled_60000	DragosGorduza	2024-02-25T08:24:01Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-20T15:30:53Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') 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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 48151 with parameters: ``` {'batch_size': 48, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` 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": 90000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel_Roberta-MISTRAL_FULL-notrescaled_40000	DragosGorduza	2024-02-25T08:22:56Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-20T15:32:42Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') 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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 48151 with parameters: ``` {'batch_size': 48, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` 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": 90000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel_Roberta-MISTRAL_FULL-notrescaled_50000	DragosGorduza	2024-02-25T08:22:18Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-20T15:32:02Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') 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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 48151 with parameters: ``` {'batch_size': 48, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` 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": 90000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel_Roberta-MISTRAL_FULL-notrescaled_30000	DragosGorduza	2024-02-25T08:21:43Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-20T15:33:20Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') 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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 48151 with parameters: ``` {'batch_size': 48, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` 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": 90000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel_Roberta-MISTRAL_FULL-notrescaled_20000	DragosGorduza	2024-02-25T08:21:07Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-20T15:30:09Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') 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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 48151 with parameters: ``` {'batch_size': 48, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` 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": 90000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: RobertaModel (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 -->
heldJan/llama-2-7b-froozen_CLIP_test_Train_only_projection	heldJan	2024-02-25T08:19:17Z	5	0	transformers	[ "transformers", "safetensors", "VideoChatGPT", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T21:14:57Z	--- 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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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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arbitropy/BConvQA	arbitropy	2024-02-25T08:12:59Z	104	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:csebuetnlp/banglat5", "base_model:finetune:csebuetnlp/banglat5", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-25T08:10:44Z	--- base_model: csebuetnlp/banglat5 tags: - generated_from_trainer model-index: - name: BConvQA 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. --> # BConvQA This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1670 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 1.9883 \| 0.15 \| 10000 \| 1.7429 \| \| 1.6691 \| 0.3 \| 20000 \| 1.4494 \| \| 1.5742 \| 0.45 \| 30000 \| 1.3819 \| \| 1.46 \| 0.59 \| 40000 \| 1.3138 \| \| 1.4805 \| 0.74 \| 50000 \| 1.2829 \| \| 1.4381 \| 0.89 \| 60000 \| 1.2696 \| \| 1.3504 \| 1.04 \| 70000 \| 1.2356 \| \| 1.3064 \| 1.19 \| 80000 \| 1.2144 \| \| 1.2817 \| 1.34 \| 90000 \| 1.2073 \| \| 1.2709 \| 1.48 \| 100000 \| 1.2058 \| \| 1.2348 \| 1.63 \| 110000 \| 1.1814 \| \| 1.1828 \| 1.78 \| 120000 \| 1.1848 \| \| 1.324 \| 1.93 \| 130000 \| 1.1670 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
oliMLeng/distilbert-base-uncased-finetuned-emotion	oliMLeng	2024-02-25T08:12:49Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-16T20:26:07Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: split metrics: - name: Accuracy type: accuracy value: 0.931 - name: F1 type: f1 value: 0.931331557456777 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1754 - Accuracy: 0.931 - F1: 0.9313 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.645 \| 1.0 \| 500 \| 0.2154 \| 0.927 \| 0.9263 \| \| 0.1846 \| 2.0 \| 1000 \| 0.1754 \| 0.931 \| 0.9313 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.15.2
Ai-Marshal/Mixtral-8x7B-Instruct-v0.1-Finetuned	Ai-Marshal	2024-02-25T07:59:38Z	6	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-20T20:20:01Z	--- library_name: transformers tags: - trl - sft --- # 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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nguyenthanhdo/unsloth-sft-test	nguyenthanhdo	2024-02-25T07:57:54Z	1	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T07:51:40Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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yinani24/my_distilbert_ft_model_2	yinani24	2024-02-25T07:54:40Z	5	0	transformers	[ "transformers", "tf", "distilbert", "multiple-choice", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2023-11-12T04:54:23Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: yinani24/my_distilbert_ft_model_2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # yinani24/my_distilbert_ft_model_2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7662 - Validation Loss: 0.7765 - Train Accuracy: 0.6875 - Epoch: 3 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 25, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.0922 \| 1.0961 \| 0.5 \| 0 \| \| 1.0921 \| 1.0474 \| 0.6875 \| 1 \| \| 0.9923 \| 0.8858 \| 0.75 \| 2 \| \| 0.7662 \| 0.7765 \| 0.6875 \| 3 \| ### Framework versions - Transformers 4.37.2 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2
roleplay4fun/unsloth-sft-test	roleplay4fun	2024-02-25T07:51:39Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:NeverSleep/Noromaid-7b-v0.2", "base_model:finetune:NeverSleep/Noromaid-7b-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-02-25T07:51:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: NeverSleep/Noromaid-7b-v0.2 --- # Uploaded model - Developed by: roleplay4fun - License: apache-2.0 - Finetuned from model : NeverSleep/Noromaid-7b-v0.2 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)
Kar1suMAX/bert-base-japanese-v3-jsts	Kar1suMAX	2024-02-25T07:43:52Z	106	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-25T07:43:30Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
bartowski/Einstein-v4-7B-exl2	bartowski	2024-02-25T07:34:14Z	5	3	null	[ "axolotl", "generated_from_trainer", "text-generation", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	text-generation	2024-02-25T07:20:59Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: Einstein-v4-7B results: [] quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Einstein-v4-7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.14">turboderp's ExLlamaV2 v0.0.14</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/Weyaxi/Einstein-v4-7B \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Einstein-v4-7B-exl2 Einstein-v4-7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Einstein-v4-7B-exl2`: ```shell mkdir Einstein-v4-7B-exl2 huggingface-cli download bartowski/Einstein-v4-7B-exl2 --local-dir Einstein-v4-7B-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir Einstein-v4-7B-exl2-6_5 huggingface-cli download bartowski/Einstein-v4-7B-exl2 --revision 6_5 --local-dir Einstein-v4-7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir Einstein-v4-7B-exl2-6.5 huggingface-cli download bartowski/Einstein-v4-7B-exl2 --revision 6_5 --local-dir Einstein-v4-7B-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Heng666/taiwan-kapok-300m-base	Heng666	2024-02-25T07:28:17Z	106	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "zh", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T07:20:39Z	--- tags: - generated_from_trainer - mistral model-index: - name: taiwan-kapok-300M results: [] license: apache-2.0 language: - zh --- <!-- 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. --> # taiwan-kapok-300M - Loss: 2.7894 ## 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: 0.0006 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 64 - total_train_batch_size: 512 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.95) and epsilon=0.0001 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6962 \| 3.35 \| 5000 \| 2.7894 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
nickmiller795/rl_course_vizdoom_health_gathering_supreme	nickmiller795	2024-02-25T07:26:19Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-25T07:26:13Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.44 +/- 5.69 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r nickmiller795/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
chetana123/my-pet-dog	chetana123	2024-02-25T07:24:12Z	7	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-25T07:20:32Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by chetana123 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 21711A0537 Sample pictures of this concept: ![0](https://huggingface.co/chetana123/my-pet-dog/resolve/main/sample_images/catty.jpg)
LarryAIDraw/yamada_anna_lora_ver2	LarryAIDraw	2024-02-25T07:22:28Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-25T07:20:35Z	--- license: creativeml-openrail-m --- https://civitai.com/models/250431/yamada-anna-the-dangers-in-my-heart
LarryAIDraw/artkoikoi_Alisa_Southerncross_v3	LarryAIDraw	2024-02-25T07:15:10Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-25T06:51:44Z	--- license: creativeml-openrail-m --- https://civitai.com/models/49815/alisa-southerncross-keroro-gunsou-artkoikoi
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_type_nllf_v0_signal_it_129	furrutiav	2024-02-25T07:10:19Z	104	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-25T07:06:16Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ryusangwon/253_Llama-2-7b-hf	ryusangwon	2024-02-25T06:41:49Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-25T06:41:44Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - samsum model-index: - name: 253_Llama-2-7b-hf results: [] library_name: peft --- <!-- 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. --> # 253_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
hotchpotch/Swallow-13b-stf-qa-context-jaqket-jsquad-gptq	hotchpotch	2024-02-25T06:37:12Z	4	0	transformers	[ "transformers", "llama", "text-generation", "ja", "dataset:hotchpotch/jaqket_v1_qa_wikija_context", "dataset:shunk031/JGLUE", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-02-25T06:03:08Z	--- license: apache-2.0 datasets: - hotchpotch/jaqket_v1_qa_wikija_context - shunk031/JGLUE language: - ja --- ## 🤔 これは何？ - 🧠 学習元LLMモデル - 🔗 https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-hf - 📚 利用データセット - 📖 JAQKET (dev / unuse) + JSQuAD(train・サンプリングで一部を利用) - 🛠️ 学習手法 - 📈 SFT(Supervised fine-tuning) - 🧮 量子化手法 - 🤖 AutoGPTQ - 💡 このモデルのできること - 💬 コンテキスト付きのQ&Aに対して、回答を生成 ## 🚀 実行例 ``` import torch from transformers import AutoTokenizer from auto_gptq import AutoGPTQForCausalLM # type: ignore MODEL_NAME = "hotchpotch/Swallow-13b-stf-qa-context-jaqket-jsquad-gptq" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) tokenizer.pad_token = tokenizer.eos_token device = "cuda:0" if torch.cuda.is_available() else "cpu" model = AutoGPTQForCausalLM.from_quantized( MODEL_NAME, device=device, use_safetensors=True, inject_fused_mlp=True, inject_fused_attention=True, use_flash_attention_2=True, ) def generate(prompt: str) -> str: token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") with torch.no_grad(): output_ids = model.generate( input_ids=token_ids.to(model.device), # type: ignore max_new_tokens=12, do_sample=True, top_p=0.95, temperature=0.25, repetition_penalty=1.3, pad_token_id=tokenizer.pad_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, ) output = tokenizer.decode(output_ids[0]) output = output.replace(prompt, "") output = output.split(tokenizer.eos_token)[0] return output.strip() prompt_template = """ 以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。リクエストを適切に完了するための回答を記述してください。 ### 指示: {question} ### 入力: {context} ### 応答: """.strip() question = "楽曲『約束はいらない』でデビューした、声優は誰?" context = """ Suara 大阪府立豊中高等学校を経て大阪外国語大学でインドネシア語を専攻中にバンド・ユニットを組んで音楽活動を始めた。普段はお笑い番組が大好きなこともあってよく喋るほうだが、東京の仕事で標準語の喋りをする時は、早見沙織声優デビュー時より、数多くの主題歌やキャラクターソングを担当し、バラードからポップス、ヒットソングのカバー曲や英語の楽曲など、様々な曲を歌いこなす。2009年には吉田仁美とのユニット「blue dro 約束はいらない「約束はいらない」(やくそくはいらない)は、坂本真綾のデビューシングル。約束はいらない坂本真綾の歌手デビュー作品。当時坂本はまだ無名の声優であったが、同曲がテーマソングとなったアニメ『天空のエスカフローネ』とともに知名度を上げることとなる。後に「指輪」が同アニメの劇場版映画の主題歌とな坂本真綾本格的な歌手活動は、1996年にテレビアニメ『天空のエスカフローネ』へ出演したことがきっかけで始めており、同作のオープニングテーマソングである「約束はいらない」(岩里祐穂作詞、菅野よう子作曲)をシング """.strip() prompt = prompt_template.format(question=question, context=context) print(generate(prompt)) #=> 坂本真綾（さかもとまあや / MAAY ```
cquentin48/open_domain_vector_dim_qa	cquentin48	2024-02-25T06:29:11Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-25T06:28:44Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # cquentin48/open_domain_vector_dim_qa 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('cquentin48/open_domain_vector_dim_qa') 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=cquentin48/open_domain_vector_dim_qa) ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 3646 with parameters: ``` {'batch_size': 24} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "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": 72, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): SentenceTransformer( (0): Transformer({'max_seq_length': 512, '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}) (2): Normalize() ) (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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
verach3n/translation-en-to-tr-1	verach3n	2024-02-25T06:22:41Z	42	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-tatoeba-en-tr", "base_model:finetune:Helsinki-NLP/opus-tatoeba-en-tr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-22T11:39:11Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-tatoeba-en-tr tags: - generated_from_trainer model-index: - name: opus-tatoeba-en-tr-finetuned-en-to-tr 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. --> # opus-tatoeba-en-tr-finetuned-en-to-tr This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-tr](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-tr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0240 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.4348 \| 1.0 \| 12860 \| 3.0240 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Litzy619/V0224O2	Litzy619	2024-02-25T06:22:28Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-02-25T00:06:50Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0224O2 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. --> # V0224O2 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7463 ## 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.92 \| 0.13 \| 10 \| 2.0370 \| \| 1.2885 \| 0.26 \| 20 \| 1.0103 \| \| 0.9359 \| 0.39 \| 30 \| 0.8890 \| \| 0.8522 \| 0.52 \| 40 \| 0.8425 \| \| 0.8213 \| 0.65 \| 50 \| 0.8151 \| \| 0.798 \| 0.78 \| 60 \| 0.8006 \| \| 0.7714 \| 0.91 \| 70 \| 0.7896 \| \| 0.7598 \| 1.04 \| 80 \| 0.7814 \| \| 0.7269 \| 1.17 \| 90 \| 0.7729 \| \| 0.7398 \| 1.3 \| 100 \| 0.7685 \| \| 0.7291 \| 1.43 \| 110 \| 0.7637 \| \| 0.7193 \| 1.55 \| 120 \| 0.7597 \| \| 0.7097 \| 1.68 \| 130 \| 0.7571 \| \| 0.7198 \| 1.81 \| 140 \| 0.7536 \| \| 0.7229 \| 1.94 \| 150 \| 0.7491 \| \| 0.6964 \| 2.07 \| 160 \| 0.7503 \| \| 0.6717 \| 2.2 \| 170 \| 0.7484 \| \| 0.6848 \| 2.33 \| 180 \| 0.7472 \| \| 0.6767 \| 2.46 \| 190 \| 0.7471 \| \| 0.6788 \| 2.59 \| 200 \| 0.7468 \| \| 0.6801 \| 2.72 \| 210 \| 0.7463 \| \| 0.6778 \| 2.85 \| 220 \| 0.7463 \| \| 0.6803 \| 2.98 \| 230 \| 0.7463 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
Siddartha10/gemma-2b-it_sarvam_ai_dataset	Siddartha10	2024-02-25T06:19:05Z	77	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mlx", "conversational", "dataset:sarvamai/samvaad-hi-v1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T04:15:38Z	--- license: other library_name: transformers tags: - mlx widget: - text: \| <start_of_turn>user How does the brain work?<end_of_turn> <start_of_turn>model inference: parameters: max_new_tokens: 200 extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms datasets: - sarvamai/samvaad-hi-v1 --- # Siddartha10/gemma-2b-it_sarvam_ai_dataset This model was converted to MLX format from [`google/gemma-2b-it`](). Refer to the [original model card](https://huggingface.co/google/gemma-2b-it) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Siddartha10/gemma-2b-it_sarvam_ai_dataset") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
netcat420/MHENN7	netcat420	2024-02-25T06:08:49Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T06:03:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ShaikArshiya/my-pet-dog	ShaikArshiya	2024-02-25T06:01:47Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-25T05:57:47Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by ShaikArshiya following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/ShaikArshiya/my-pet-dog/resolve/main/sample_images/annas-hummingbird-6146187_1280.jpg)
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_s_nllf_signal_it_137	furrutiav	2024-02-25T06:01:21Z	105	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-25T06:00:53Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ThuyNT03/CS505_COQE_viT5_Prompting10_ASPOL_vcheck2	ThuyNT03	2024-02-25T05:53:31Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-25T04:46:38Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting10_ASPOL_vcheck2 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. --> # CS505_COQE_viT5_Prompting10_ASPOL_vcheck2 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
Pubudu/prefix_tuning_12_par_bn_rf_4_dinamina_zwj_fix	Pubudu	2024-02-25T05:53:12Z	0	0	adapter-transformers	[ "adapter-transformers", "mbart", "adapterhub:summarization/dinamina_5100", "dataset:dinamina_5100", "region:us" ]	null	2024-02-25T05:52:34Z	--- tags: - adapter-transformers - mbart - adapterhub:summarization/dinamina_5100 datasets: - dinamina_5100 --- # Adapter `Pubudu/prefix_tuning_12_par_bn_rf_4_dinamina_zwj_fix` for facebook/mbart-large-50 An [adapter](https://adapterhub.ml) for the `facebook/mbart-large-50` model that was trained on the [summarization/dinamina_5100](https://adapterhub.ml/explore/summarization/dinamina_5100/) dataset. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("facebook/mbart-large-50") adapter_name = model.load_adapter("Pubudu/prefix_tuning_12_par_bn_rf_4_dinamina_zwj_fix", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
renubalyan/ppo-LunarLander-v2	renubalyan	2024-02-25T05:53:06Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-25T05:52:50Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 273.65 +/- 16.63 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
weifar/codellama-13b-SCdetecting-153	weifar	2024-02-25T05:44:46Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T05:36:54Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ThuyNT03/CS505_COQE_viT5_Prompting16_ASPOL	ThuyNT03	2024-02-25T05:44:02Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-25T04:54:54Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting16_ASPOL 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. --> # CS505_COQE_viT5_Prompting16_ASPOL This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
ambrosfitz/gemma-history-chat_v0.1	ambrosfitz	2024-02-25T05:39:46Z	107	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T05:18:58Z	--- library_name: transformers tags: [] ---
mi-rei/CT_clinical-longformer_I-II-III_efficient_10e	mi-rei	2024-02-25T05:34:08Z	118	0	transformers	[ "transformers", "safetensors", "longformer", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-25T05:33:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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shashankss09/mistralai-probusiness-Finetune-1	shashankss09	2024-02-25T05:32:26Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T05:27:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ThuyNT03/CS505_COQE_viT5_Prompting0_ASPOL_vcheck2	ThuyNT03	2024-02-25T05:24:12Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-25T04:29:16Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting0_ASPOL_vcheck2 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. --> # CS505_COQE_viT5_Prompting0_ASPOL_vcheck2 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
Kar1suMAX/bert-base-japanese-v3-jnli	Kar1suMAX	2024-02-25T05:23:37Z	106	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-25T05:23:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
wildzzz/36frames	wildzzz	2024-02-25T05:20:24Z	1	0	diffusers	[ "diffusers", "art", "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-25T04:59:24Z	--- license: creativeml-openrail-m pipeline_tag: text-to-image tags: - art - stable-diffusion library_name: diffusers --- Recommended settings for generation: Fine art photography, (amateur photo), (analog photo:1.2,film grain),({prompt}),vivid colours,realistic,photographic, (natural colors, correct white balance, color correction, dehaze,clarity), background rich in details, conceptual image,masterpiece, award winning photography Steps: 28, Sampler: DPM++ 2M SDE Karras, CFG scale: 6, ADetailer, Hires prompt: Hires upscale: 1.35, Hires steps: 14, Hires upscaler: 4x_NMKD-Siax_200k https://civitai.com/models/319576/36-frames ![00083-[number]-414915688.png](https://cdn-uploads.huggingface.co/production/uploads/638a6249fc10b2be22250918/sfbCsBmcEvZe5s8tcD5B5.png) ![00163-[number]-3904528518.png](https://cdn-uploads.huggingface.co/production/uploads/638a6249fc10b2be22250918/HWqZvanYOWkN1f23O6fFt.png) ![00070-[number]-414915675.png](https://cdn-uploads.huggingface.co/production/uploads/638a6249fc10b2be22250918/6yk_yWw6pLtGIAW9IWia7.png) ![00176-[number]-3904528531.png](https://cdn-uploads.huggingface.co/production/uploads/638a6249fc10b2be22250918/BYDHmjzVCuOiPuAi6bwho.png) ![00020-[number]-3173852746.png](https://cdn-uploads.huggingface.co/production/uploads/638a6249fc10b2be22250918/WjpxRhw22C1ActfaTBVgX.png) ![00527-[number]-2225445592.png](https://cdn-uploads.huggingface.co/production/uploads/638a6249fc10b2be22250918/SBV3nRXupGt_C4EVXV9F6.png) ![00549-[number]-298092460.png](https://cdn-uploads.huggingface.co/production/uploads/638a6249fc10b2be22250918/4cL18IYYhUVOw4ORcBCkA.png)
ThuyNT03/CS505_COQE_viT5_Prompting0_ASPOL_vcheck1	ThuyNT03	2024-02-25T05:18:54Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-25T04:28:52Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting0_ASPOL_vcheck1 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. --> # CS505_COQE_viT5_Prompting0_ASPOL_vcheck1 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.15.0
liminerity/phigment5-slerp	liminerity	2024-02-25T05:11:12Z	48	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "merge", "mergekit", "lazymergekit", "vince62s/phi-2-psy", "liminerity/4", "custom_code", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T05:09:28Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - vince62s/phi-2-psy - liminerity/4 --- # phigment5-slerp phigment5-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [vince62s/phi-2-psy](https://huggingface.co/vince62s/phi-2-psy) * [liminerity/4](https://huggingface.co/liminerity/4) ## 🧩 Configuration ```yaml slices: - sources: - model: vince62s/phi-2-psy layer_range: [0, 32] - model: liminerity/4 layer_range: [0, 32] merge_method: slerp base_model: vince62s/phi-2-psy parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ```
vincegmz/diffPrompt-stable-diffusion-v1-5	vincegmz	2024-02-25T05:06:26Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-25T04:51:28Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - vincegmz/diffPrompt-stable-diffusion-v1-5 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
liminerity/phigment4-slerp	liminerity	2024-02-25T05:04:14Z	106	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "merge", "mergekit", "lazymergekit", "mobiuslabsgmbh/aanaphi2-v0.1", "liminerity/3", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T05:02:07Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - mobiuslabsgmbh/aanaphi2-v0.1 - liminerity/3 --- # phigment4-slerp phigment4-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [mobiuslabsgmbh/aanaphi2-v0.1](https://huggingface.co/mobiuslabsgmbh/aanaphi2-v0.1) * [liminerity/3](https://huggingface.co/liminerity/3) ## 🧩 Configuration ```yaml slices: - sources: - model: mobiuslabsgmbh/aanaphi2-v0.1 layer_range: [0, 32] - model: liminerity/3 layer_range: [0, 32] merge_method: slerp base_model: mobiuslabsgmbh/aanaphi2-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ```
sugatoray/mlx-neuralhermes-2.5-mistral-7b-q4bits	sugatoray	2024-02-25T04:58:42Z	10	0	mlx	[ "mlx", "safetensors", "mistral", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "distillation", "dpo", "rlhf", "en", "dataset:mlabonne/chatml_dpo_pairs", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:finetune:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2024-02-25T04:40:14Z	--- language: - en license: apache-2.0 tags: - mistral - instruct - finetune - chatml - gpt4 - synthetic data - distillation - dpo - rlhf - mlx datasets: - mlabonne/chatml_dpo_pairs base_model: teknium/OpenHermes-2.5-Mistral-7B --- # sugatoray/mlx-neuralhermes-2.5-mistral-7b-q4bits This model was converted to MLX format from [`mlabonne/NeuralHermes-2.5-Mistral-7B`](). Refer to the [original model card](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("sugatoray/mlx-neuralhermes-2.5-mistral-7b-q4bits") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
brycegoh/sdxl-cn-example	brycegoh	2024-02-25T04:58:36Z	2	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "controlnet", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-25T03:26:12Z	--- license: openrail++ library_name: diffusers tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet inference: true base_model: stabilityai/stable-diffusion-xl-base-1.0 --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-brycegoh/sdxl-cn-example These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below. prompt: red circle with blue background ![images_0)](./images_0.png) prompt: cyan circle with brown floral background ![images_1)](./images_1.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
liminerity/phigment3-slerp	liminerity	2024-02-25T04:55:47Z	78	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/merge", "vince62s/phi-2-psy", "custom_code", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T04:54:08Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - liminerity/merge - vince62s/phi-2-psy --- # phigment3-slerp phigment3-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [liminerity/merge](https://huggingface.co/liminerity/merge) * [vince62s/phi-2-psy](https://huggingface.co/vince62s/phi-2-psy) ## 🧩 Configuration ```yaml slices: - sources: - model: liminerity/merge layer_range: [0, 32] - model: vince62s/phi-2-psy layer_range: [0, 32] merge_method: slerp base_model: liminerity/merge parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ```
yinani24/my_distilbert_ft_model_3	yinani24	2024-02-25T04:48:54Z	60	0	transformers	[ "transformers", "tf", "distilbert", "multiple-choice", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2024-01-31T21:00:45Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: yinani24/my_distilbert_ft_model_3 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # yinani24/my_distilbert_ft_model_3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9304 - Validation Loss: 0.9185 - Train Accuracy: 0.625 - Epoch: 3 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 25, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.1026 \| 1.0961 \| 0.625 \| 0 \| \| 1.0855 \| 1.0854 \| 0.625 \| 1 \| \| 1.0607 \| 1.0109 \| 0.625 \| 2 \| \| 0.9304 \| 0.9185 \| 0.625 \| 3 \| ### Framework versions - Transformers 4.37.2 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2
Lekhasri/my-pet-dog	Lekhasri	2024-02-25T04:45:38Z	2	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-25T04:41:28Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by Lekhasri following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/Lekhasri/my-pet-dog/resolve/main/sample_images/mario-1557240_1280.jpg)
pszemraj/beit-large-patch16-512-film-shot-classifier	pszemraj	2024-02-25T04:43:52Z	8	1	transformers	[ "transformers", "onnx", "safetensors", "beit", "image-classification", "vision", "cinematography", "film", "dataset:szymonrucinski/types-of-film-shots", "base_model:microsoft/beit-large-patch16-512", "base_model:quantized:microsoft/beit-large-patch16-512", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-22T22:59:05Z	--- license: apache-2.0 tags: - image-classification - vision - cinematography - film datasets: - szymonrucinski/types-of-film-shots metrics: - accuracy base_model: microsoft/beit-large-patch16-512 --- <!-- 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. --> # beit-large-patch16-512: types of film shots ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/9YqYvv188ZccCMSzuv0KW.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/N255KgVTEorFT59oMzqVL.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/uiricD6EMnyrkyh_7yHdv.png) ## Model description This model is a fine-tuned version of [microsoft/beit-large-patch16-512](https://huggingface.co/microsoft/beit-large-patch16-512) on the szymonrucinski/types-of-film-shots dataset. It achieves the following results on the evaluation set: - Loss: 1.2335 - Accuracy: 0.6763 ## usage ```py from transformers import pipeline from PIL import Image import requests pipe = pipeline( "image-classification", model="pszemraj/beit-large-patch16-512-film-shot-classifier", ) url = "https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/9YqYvv188ZccCMSzuv0KW.png" image = Image.open(requests.get(url, stream=True).raw) result = pipe(image)[0] print(result) ``` try some of these: ### class labels The dataset contains the following labels: ```json "id2label": { "0": "ambiguous", "1": "closeUp", "2": "detail", "3": "extremeLongShot", "4": "fullShot", "5": "longShot", "6": "mediumCloseUp", "7": "mediumShot" }, ``` as plaintext: ```txt ambiguous, close up, detail, extreme long shot, full shot, long shot, medium close up, medium shot ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 4 - seed: 24414 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 6.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0435 \| 1.0 \| 393 \| 1.4799 \| 0.4892 \| \| 1.1554 \| 2.0 \| 786 \| 1.4938 \| 0.4892 \| \| 1.5041 \| 3.0 \| 1179 \| 2.1702 \| 0.3597 \| \| 1.0457 \| 4.0 \| 1572 \| 1.5413 \| 0.5683 \| \| 0.3315 \| 5.0 \| 1965 \| 1.0769 \| 0.6978 \| \| 0.2178 \| 6.0 \| 2358 \| 1.2335 \| 0.6763 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
M9DX/rgh	M9DX	2024-02-25T04:43:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-25T04:43:19Z	--- 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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benchang1110/temp-tokenizer-a8k-d40d11	benchang1110	2024-02-25T04:35:55Z	0	0	null	[ "zh", "en", "region:us" ]	null	2024-02-24T12:12:19Z	--- language: - zh - en --- # temp-tokenizer-a8k-d40d11 This tokenizer is a part of the `temp` project. * Base tokenizer: `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T` * Built with: `word_frequency_list` * Vocab size: `40001` * Tokens added (planned/actual): `8000` / `8001` * Full config: ```json {"build_with": "word_frequency_list", "tokens_to_add": 8000, "word_frequency_list_settings": {"word_frequency_list_name": "zetavg/tw-sinica-corpus-word-frequency", "include_words": ["。", "，", "、", "？", "！", "；", "：", "……", "～", "「", "」", "『", "』", "【", "】", "〖", "〗", "（", "）", "〔", "〕", "［", "］", "｛", "｝", "《", "》", "〈", "〉", "——", "──", "－", "−", "＿", "・", "．", "·", "／", "＼", "｜", "＜", "＞"], "replace_rules": [{"match": {"regex": "�"}, "replace": null}, {"match": {"pos": ["Nb", "FW", null]}, "replace": null, "except": ["奧運", "中共", "國民黨", "民進黨", "新黨", "共產黨", "媽祖", "耶穌"]}, {"match": {"regex": ["^[Ａ-Ｚａ-ｚ０-９﹒• ]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]{2,}$", "^（[零一二兩三四五六七八九十廿卅百千萬億兆壹貳參肆伍陸柒捌玖拾佰仟０-９﹒•]+）$", "^[第數][零一二兩三四五六七八九十百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+分之[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+$", "^[零一二兩三四五六七八九十廿卅百千萬億兆０-９﹒•]+[多餘來幾成次年月日天時分點世代歲起段樓％]$", "^[零一二三四五六七八九十廿卅０-９]+(月份\|年代?\|世紀\|學?年度\|年級)$", "^(星期\|週\|周)[一二三四五六日]$"]}, "replace": null, "except": ["十分", "一起", "一點", "一時", "千萬", "兩三", "百分之百"]}, {"match": {"pos": "VHC", "regex": "^(.{2,})化$"}, "sub": "\\1"}, {"match": {"regex": "^(.+球)場$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})園區$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[鄉鎮縣市區]$"}, "sub": "\\1"}, {"match": {"pos": "Nc", "regex": "^(.{2,})[界院部會署局館系所]$"}, "sub": "\\1", "except": ["委員會", "研究所", "中研院", "國科會", "資策會", "經建會", "工研院", "電信總局", "鎮公所", "事務所", "交易所", "農委會", "鄉公所", "地檢署", "警分局", "派出所", "托兒所", "消基會", "文建會", "兩廳院", "陸委會", "市議會"]}, {"match": {"pos": "Na", "regex": "^(.{2,})人$"}, "sub": "\\1", "except": ["年輕人", "負責人", "投資人", "候選人", "一家人", "當地人", "製作人"]}, {"match": {"pos": "Na", "regex": "^(.{2,3})學?家$"}, "sub": "\\1", "except": ["女人家", "婦人家", "新儒家", "窮人家", "縱橫家", "老人家", "老東家", "闊人家", "大戶人家", "婦道人家", "小戶人家", "水上人家", "諸子百家"]}, {"match": {"pos": "Na", "regex": "^副?總?([^副總]{2,})師$"}, "sub": "\\1", "except": ["中醫師", "囝仔師", "正機師", "準教師", "獸醫師", "班導師", "練馬師", "總舖師", "老像師", "新三十師", "至聖先師", "音樂大師"]}, {"match": {"pos": "Na", "regex": "^[原前]?(?:代\|代理)?副?總?([^前代副總議警里首院部署局廳司處科組課股]{2,})[院部署局廳司處科組課股]?次?長$"}, "sub": "\\1", "except": ["董事長", "理事長", "秘書長", "執行長", "分局長", "縣市長", "一技之長", "省市長", "負成長", "高成長", "大家長", "小組長", "區組長", "低成長", "偵一組長", "停管隊長", "考選部長", "年增長", "正成長", "支店長", "公賣局長", "中宣部長", "小市長"]}, {"match": {"pos": "Na", "regex": "^副?總?正?([^副總正議委人隊]{2,})[委人隊]?員$"}, "sub": "\\1", "except": ["主跑員", "乘務員", "佐理員", "共黨員", "外務員", "從業員", "特派員", "義服員", "銜道員", "啦啦隊員", "指服團員"]}, {"match": {"pos": "Na", "regex": "^副(.{2,})$"}, "sub": "\\1", "except": ["副作用"]}, {"match": "大規模", "replace": "規模"}, {"match": "廢棄物", "replace": "廢棄"}, {"match": "共產主義", "replace": "共產"}, {"match": "資本主義", "replace": "資本"}, {"match": "進一步", "replace": ["一步", "進一步"]}, {"match": "差不多", "replace": ["不多", "差不多"]}, {"match": "忍不住", "replace": ["不住", "忍不住"]}, {"match": "不見得", "replace": ["見得", "不見得"]}, {"match": "有助於", "replace": ["助於", "有助於"]}, {"match": "舊金山", "replace": ["金山", "舊金山"]}, {"match": "大躍進", "replace": ["躍進", "大躍進"]}, {"match": "半導體", "replace": ["導體", "半導體"]}, {"match": "總幹事", "replace": ["幹事", "總幹事"]}, {"match": "兩廳院", "replace": ["廳院", "兩廳院"]}]}} ```
Litzy619/V0224O1	Litzy619	2024-02-25T04:35:55Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-02-24T22:21:32Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0224O1 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. --> # V0224O1 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7376 ## 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.5107 \| 0.13 \| 10 \| 1.3353 \| \| 1.0704 \| 0.26 \| 20 \| 0.9178 \| \| 0.8741 \| 0.39 \| 30 \| 0.8580 \| \| 0.8274 \| 0.52 \| 40 \| 0.8184 \| \| 0.8016 \| 0.65 \| 50 \| 0.7969 \| \| 0.7805 \| 0.78 \| 60 \| 0.7831 \| \| 0.7534 \| 0.91 \| 70 \| 0.7732 \| \| 0.7351 \| 1.04 \| 80 \| 0.7642 \| \| 0.6839 \| 1.17 \| 90 \| 0.7570 \| \| 0.697 \| 1.3 \| 100 \| 0.7544 \| \| 0.687 \| 1.43 \| 110 \| 0.7497 \| \| 0.6776 \| 1.55 \| 120 \| 0.7447 \| \| 0.6708 \| 1.68 \| 130 \| 0.7426 \| \| 0.6797 \| 1.81 \| 140 \| 0.7383 \| \| 0.6821 \| 1.94 \| 150 \| 0.7340 \| \| 0.6439 \| 2.07 \| 160 \| 0.7410 \| \| 0.6084 \| 2.2 \| 170 \| 0.7397 \| \| 0.6215 \| 2.33 \| 180 \| 0.7378 \| \| 0.6138 \| 2.46 \| 190 \| 0.7389 \| \| 0.6166 \| 2.59 \| 200 \| 0.7380 \| \| 0.6166 \| 2.72 \| 210 \| 0.7376 \| \| 0.615 \| 2.85 \| 220 \| 0.7377 \| \| 0.6165 \| 2.98 \| 230 \| 0.7376 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
moficodes/gemma-2b-chat-finetune	moficodes	2024-02-25T04:34:07Z	110	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-25T04:31:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tSum10101/dqn-LunarLander-v2-default	tSum10101	2024-02-25T04:30:43Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-25T04:30:07Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -45.72 +/- 19.48 name: mean_reward verified: false --- # DQN Agent playing LunarLander-v2 This is a trained model of a DQN agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Edwardwangbo/film	Edwardwangbo	2024-02-25T04:21:57Z	0	0	flair	[ "flair", "art", "text-to-video", "license:other", "region:us" ]	text-to-video	2024-02-25T04:07:35Z	--- license: other license_name: wangsfilm license_link: LICENSE library_name: flair pipeline_tag: text-to-video tags: - art ---
kuotient/Seagull-13b-translation	kuotient	2024-02-25T04:15:30Z	18	6	transformers	[ "transformers", "pytorch", "llama", "text-generation", "translate", "translation", "ko", "dataset:squarelike/sharegpt_deepl_ko_translation", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2024-02-24T06:48:43Z	--- license: cc-by-nc-sa-4.0 datasets: - squarelike/sharegpt_deepl_ko_translation language: - ko pipeline_tag: translation tags: - translate --- ## Seagull-13b-translation 📇 ![Seagull-typewriter](./Seagull-typewriter.png) Seagull-13b-translation is yet another translator model, but carefully considered the following issues from existing translation models. - Exact match of `newline` or `space` - Not using dataset with first letter removed - Code - Markdown format - LaTeX format - etc 이런 이슈들을 충분히 체크하고 학습을 진행하였지만, 모델을 사용할 때는 이런 부분에 대한 결과를 면밀하게 살펴보는 것을 추천합니다(코드가 포함된 텍스트 등). > If you're interested in building large-scale language models to solve a wide variety of problems in a wide variety of domains, you should consider joining [Allganize](https://allganize.career.greetinghr.com/o/65146). For a coffee chat or if you have any questions, please do not hesitate to contact me as well! - [email protected] This model was created as a personal experiment, unrelated to the organization I work for. ## License ## From original model author: - Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License, under LLAMA 2 COMMUNITY LICENSE AGREEMENT - Full License available at: https://huggingface.co/beomi/llama-2-koen-13b/blob/main/LICENSE # Model Details #### Developed by Jisoo Kim(kuotient) #### Base Model [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b) #### Datasets - [sharegpt_deepl_ko_translation](https://huggingface.co/datasets/squarelike/sharegpt_deepl_ko_translation) - [KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3) - AIHUB - 기술과학 분야 한-영 번역 병렬 말뭉치 데이터 - 일상생활 및 구어체 한-영 번역 병렬 말뭉치 데이터 ## Usage #### Format It follows only ChatML format. ```python <\|im_start\|>system 주어진 문장을 한국어로 번역하세요.<\|im_end\|> <\|im_start\|>user {instruction}<\|im_end\|> <\|im_start\|>assistant # Don't miss newline here ``` ```python <\|im_start\|>system 주어진 문장을 영어로 번역하세요.<\|im_end\|> <\|im_start\|>user {instruction}<\|im_end\|> <\|im_start\|>assistant # Don't miss newline here ``` #### Output example 원문: > A particle's wave function, $\psi(x)$, is given by $$\psi(x)=\begin{cases} 3x & \text{if } -1 \leq x \leq 0 \\ 3(1-x) & \text{if } 0 < x \leq 1 \\ 0 & \text{otherwise} \end{cases}$$ Compute the Fourier transform, $\tilde{\psi}(k)$, of the wave function $\psi(x)$ and show that it satisfies the Fourier inversion theorem, i.e., $\psi(x) = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{\infty} \tilde{\psi}(k) e^{ikx} \mathrm{d}k$. Seagull-13b-translation: > 입자의 파동 함수 $\psi(x)$는 다음과 같이 주어집니다. $$\psi(x)=\begin{cases} 3x & \text{if } -1 \leq x \leq 0 \\ 3(1-x) & \text{if } 0 < x \leq 1 \\ 0 & \text{otherwise} \end{cases}$$ 파동 함수 $\psi(x)$의 푸리에 변환 $\tilde{\psi}(k)$를 계산하고 푸리에 반전 정리, 즉 $\psi(x) = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{\infty} \tilde{\psi}(k) e^{ikx} \mathrm{d}k$를 만족합니다. DeepL: > 입자의 파동 함수 $\psi(x)$는 $$\psi(x)=\begin{cases}로 주어집니다. 3x & \text{if } -1 \leq x \leq 0 \\ 3(1-x) & \text{if } 0 < x \leq 1 \\ 0 & \text{기타} \end{cases}$$ 파동 함수 $\psi(x)$의 푸리에 변환인 $\tilde{\psi}(k)$를 계산하고 푸리에 반전 정리, 즉 $\psi(x) = \frac{1}{\sqrt{2\pi}}를 만족함을 증명합니다. \int_{-\infty}^{\infty} \물결표{\psi}(k) e^{ikx} \mathrm{d}k$. ...and much more awesome cases with SQL query, code and markdown! #### How to I highly recommend to inference model with vllm. I will write a guide for quick and easy inference if requested. Since, chat_template already contains insturction format above. You can use the code below. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("kuotient/Seagull-13B-translation") tokenizer = AutoTokenizer.from_pretrained("kuotient/Seagull-13B-translation") messages = [ {"role": "system", "content", "주어진 문장을 한국어로 번역하세요."} {"role": "user", "content": "Here are five examples of nutritious foods to serve your kids."}, ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ```
Lewdiculous/Prima-LelantaclesV4-7b-16k-GGUF	Lewdiculous	2024-02-25T04:15:13Z	27	6	transformers	[ "transformers", "gguf", "mistral", "quantized", "text-generation-inference", "merge", "mergekit", "text-generation", "region:us" ]	text-generation	2024-02-20T16:15:49Z	--- base_model: - Test157t/Yarncules-7b-128k - Test157t/Prima-LelantaclesV3-7b library_name: transformers tags: - mistral - quantized - text-generation-inference - merge - mergekit pipeline_tag: text-generation inference: false --- GGUF quantizations for [Test157t/Prima-LelantaclesV4-7b-16k](https://huggingface.co/Test157t/Prima-LelantaclesV4-7b-16k). If you want any specific quantization to be added, feel free to ask. All credits belong to the [creator](https://huggingface.co/Test157t/). `Base⇢ GGUF(F16)⇢ GGUF(Quants)` Using [llama.cpp](https://github.com/ggerganov/llama.cpp/)-[b2217](https://github.com/ggerganov/llama.cpp/releases/tag/b2217). # Original model card: ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/SGL2JoMhUeNG2IhVEA7KT.jpeg) The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Test157t/Yarncules-7b-128k layer_range: [0, 32] - model: Test157t/Prima-LelantaclesV3-7b layer_range: [0, 32] merge_method: slerp base_model: Test157t/Prima-LelantaclesV3-7b parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
gsstein/model-75-percent-human-opt-og	gsstein	2024-02-25T04:12:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-25T04:12:24Z	--- 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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apriandito/sentiment-cnbc	apriandito	2024-02-25T04:04:15Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain", "dataset:apriandito/autotrain-data-sentiment-cnbc", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-25T04:04:04Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - apriandito/autotrain-data-sentiment-cnbc --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.2183430939912796 f1: 0.9439252336448598 precision: 0.9351851851851852 recall: 0.9528301886792453 auc: 0.9810430758276967 accuracy: 0.9433962264150944

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