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MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_1-Node_26ZrHTei	MoTHer-VTHR	2024-05-28T14:43:14Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:42:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_Bhu6dQL9	MoTHer-VTHR	2024-05-28T14:42:28Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:42:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Likich/mistral-finetune-qualcoding_1000_prompt1_dot	Likich	2024-05-28T14:42:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:42:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_ZT6isy4W	MoTHer-VTHR	2024-05-28T14:42:07Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:41:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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C4Scale/deberta-v3-base_finetuned_bluegennx_run2.19_5e	C4Scale	2024-05-28T14:41:56Z	126	0	transformers	[ "transformers", "safetensors", "deberta-v2", "token-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-28T10:50:59Z	--- license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: deberta-v3-base_finetuned_bluegennx_run2.19_5e 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. --> # deberta-v3-base_finetuned_bluegennx_run2.19_5e This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0196 - Overall Precision: 0.9773 - Overall Recall: 0.9870 - Overall F1: 0.9822 - Overall Accuracy: 0.9957 - Aadhar Card F1: 0.9908 - Age F1: 0.9708 - City F1: 0.9879 - Country F1: 0.9825 - Creditcardcvv F1: 0.9915 - Creditcardnumber F1: 0.9428 - Date F1: 0.9626 - Dateofbirth F1: 0.9056 - Email F1: 0.9928 - Expirydate F1: 0.9898 - Organization F1: 0.9925 - Pan Card F1: 0.9866 - Person F1: 0.9887 - Phonenumber F1: 0.9880 - Pincode F1: 0.9897 - Secondaryaddress F1: 0.9891 - State F1: 0.9912 - Time F1: 0.9831 - Url F1: 0.9955 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| Aadhar Card F1 \| Age F1 \| City F1 \| Country F1 \| Creditcardcvv F1 \| Creditcardnumber F1 \| Date F1 \| Dateofbirth F1 \| Email F1 \| Expirydate F1 \| Organization F1 \| Pan Card F1 \| Person F1 \| Phonenumber F1 \| Pincode F1 \| Secondaryaddress F1 \| State F1 \| Time F1 \| Url F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\|:--------------:\|:------:\|:-------:\|:----------:\|:----------------:\|:-------------------:\|:-------:\|:--------------:\|:--------:\|:-------------:\|:---------------:\|:-----------:\|:---------:\|:--------------:\|:----------:\|:-------------------:\|:--------:\|:-------:\|:------:\| \| 0.0356 \| 1.0 \| 15321 \| 0.0383 \| 0.9535 \| 0.9675 \| 0.9604 \| 0.9915 \| 0.9542 \| 0.9221 \| 0.9617 \| 0.9816 \| 0.9243 \| 0.9195 \| 0.9235 \| 0.8262 \| 0.9826 \| 0.9477 \| 0.9882 \| 0.9529 \| 0.9785 \| 0.9684 \| 0.9187 \| 0.9734 \| 0.9665 \| 0.9723 \| 0.9888 \| \| 0.0231 \| 2.0 \| 30642 \| 0.0265 \| 0.9607 \| 0.9814 \| 0.9709 \| 0.9937 \| 0.9586 \| 0.9437 \| 0.9808 \| 0.9821 \| 0.9799 \| 0.9006 \| 0.9488 \| 0.8788 \| 0.9864 \| 0.9768 \| 0.9843 \| 0.9837 \| 0.9824 \| 0.9809 \| 0.9840 \| 0.9820 \| 0.9906 \| 0.9749 \| 0.9784 \| \| 0.0182 \| 3.0 \| 45963 \| 0.0219 \| 0.9726 \| 0.9854 \| 0.9789 \| 0.9951 \| 0.9842 \| 0.9631 \| 0.9856 \| 0.9843 \| 0.9854 \| 0.9424 \| 0.9553 \| 0.8962 \| 0.9890 \| 0.9878 \| 0.9921 \| 0.9869 \| 0.9859 \| 0.9815 \| 0.9867 \| 0.9884 \| 0.9917 \| 0.9767 \| 0.9962 \| \| 0.0106 \| 4.0 \| 61284 \| 0.0196 \| 0.9773 \| 0.9870 \| 0.9822 \| 0.9957 \| 0.9908 \| 0.9708 \| 0.9879 \| 0.9825 \| 0.9915 \| 0.9428 \| 0.9626 \| 0.9056 \| 0.9928 \| 0.9898 \| 0.9925 \| 0.9866 \| 0.9887 \| 0.9880 \| 0.9897 \| 0.9891 \| 0.9912 \| 0.9831 \| 0.9955 \| \| 0.0044 \| 5.0 \| 76605 \| 0.0214 \| 0.9787 \| 0.9876 \| 0.9831 \| 0.9959 \| 0.9934 \| 0.9710 \| 0.9885 \| 0.9846 \| 0.9915 \| 0.9453 \| 0.9646 \| 0.9125 \| 0.9931 \| 0.9898 \| 0.9937 \| 0.9875 \| 0.9886 \| 0.9893 \| 0.9907 \| 0.9903 \| 0.9924 \| 0.9837 \| 0.9958 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_57CsHpG7	MoTHer-VTHR	2024-05-28T14:41:46Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:41:33Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_1-Node_aBR8mQgk	MoTHer-VTHR	2024-05-28T14:41:26Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:41:12Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_wh3Gj4h7	MoTHer-VTHR	2024-05-28T14:40:22Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:40:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Dandan0K/Pilot_xls-r-1-Ref_french	Dandan0K	2024-05-28T14:39:18Z	78	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "hf-asr-leaderboard", "it", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-28T14:29:49Z	--- language: - it license: apache-2.0 tags: - automatic-speech-recognition - hf-asr-leaderboard - it - mozilla-foundation/common_voice_8_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: XLS-R Wav2Vec2 Italian by Jonatas Grosman results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: it metrics: - name: Test WER type: wer value: 9.04 - name: Test CER type: cer value: 2.2 - name: Test WER (+LM) type: wer value: 6.75 - name: Test CER (+LM) type: cer value: 1.76 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: it metrics: - name: Dev WER type: wer value: 23.38 - name: Dev CER type: cer value: 9.41 - name: Dev WER (+LM) type: wer value: 15.84 - name: Dev CER (+LM) type: cer value: 8.93 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: it metrics: - name: Test WER type: wer value: 18.34 --- # Fine-tuned XLS-R 1B model for speech recognition in Italian Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Italian using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual TEDx](http://www.openslr.org/100), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool, and thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) ## Usage Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-italian") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "it" MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-italian" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) ``` ## Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-italian --dataset mozilla-foundation/common_voice_8_0 --config it --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-italian --dataset speech-recognition-community-v2/dev_data --config it --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr-1b-italian, title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {I}talian}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-italian}}, year={2022} } ```
ferrazzipietro/Meta-Llama-3-8B_adapters_SLO_NoQuant_torch.bfloat16_16_64_0.01_4_0.0002	ferrazzipietro	2024-05-28T14:38:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:38:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_gbsFAQSL	MoTHer-VTHR	2024-05-28T14:38:20Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:38:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
DiederikMartens/eBERT_sa_cv_13_fold2	DiederikMartens	2024-05-28T14:37:28Z	108	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	2024-05-28T14:15:23Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_13_fold2 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. --> # eBERT_sa_cv_13_fold2 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4830 - F1: 0.6086 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.5455 \| 0.4669 \| \| 0.6251 \| 2.0 \| 650 \| 0.5646 \| 0.4961 \| \| 0.6251 \| 3.0 \| 975 \| 0.4830 \| 0.6086 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2	Zoyd	2024-05-28T14:35:51Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-28T14:06:45Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_1-Node_FMXngTqR	MoTHer-VTHR	2024-05-28T14:35:45Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:35:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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DiederikMartens/tsBERT_sa_cv_13_fold2	DiederikMartens	2024-05-28T14:35:34Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T14:14:03Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_13_fold2 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. --> # tsBERT_sa_cv_13_fold2 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4487 - F1: 0.6696 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4215 \| 0.6434 \| \| 0.4417 \| 2.0 \| 650 \| 0.4487 \| 0.6696 \| \| 0.4417 \| 3.0 \| 975 \| 0.5166 \| 0.6614 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_hpgQiK4Q	MoTHer-VTHR	2024-05-28T14:35:03Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:34:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2	Zoyd	2024-05-28T14:34:52Z	6	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T14:13:47Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 6.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2	Zoyd	2024-05-28T14:34:43Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-05-28T13:55:54Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 4.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2	Zoyd	2024-05-28T14:34:34Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T13:30:38Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_AqZcPQjB	MoTHer-VTHR	2024-05-28T14:34:19Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:34:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2	Zoyd	2024-05-28T14:33:40Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T13:48:20Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 3.75 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2	Zoyd	2024-05-28T14:33:31Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-28T13:37:17Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 3.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_KCmyhjVC	MoTHer-VTHR	2024-05-28T14:32:53Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:32:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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Weni/runpod_debug	Weni	2024-05-28T14:32:13Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-28T13:07:29Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: runpod_debug results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/weni-tech/WeniGPT/runs/yux9v24u) # runpod_debug This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2127 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.03 - training_steps: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5929 \| 0.1198 \| 10 \| 1.2905 \| \| 1.2188 \| 0.2395 \| 20 \| 1.2275 \| \| 1.2161 \| 0.3593 \| 30 \| 1.2127 \| ### Framework versions - PEFT 0.11.0 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_FBytVXcq	MoTHer-VTHR	2024-05-28T14:31:32Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:31:18Z	--- 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. 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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]
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_9uqVHXRb	MoTHer-VTHR	2024-05-28T14:31:11Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:30:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DiederikMartens/gBERT_sa_cv_13_fold2	DiederikMartens	2024-05-28T14:30:56Z	113	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T14:11:59Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_13_fold2 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. --> # gBERT_sa_cv_13_fold2 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5242 - F1: 0.7093 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4023 \| 0.5374 \| \| 0.4314 \| 2.0 \| 650 \| 0.4111 \| 0.6705 \| \| 0.4314 \| 3.0 \| 975 \| 0.5242 \| 0.7093 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_gpnAyv4k	MoTHer-VTHR	2024-05-28T14:30:50Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:30:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_1-Node_eCWU4rGY	MoTHer-VTHR	2024-05-28T14:30:29Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:30:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_0-Node_ULcuMZfv	MoTHer-VTHR	2024-05-28T14:30:06Z	160	0	transformers	[ "transformers", "safetensors", "vit", "image-feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-feature-extraction	2024-05-28T14:29:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Bramwel/persuasion_v0.8	Bramwel	2024-05-28T14:29:54Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-05-28T06:55:07Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_MtfXeta7	MoTHer-VTHR	2024-05-28T14:28:40Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:28:24Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_1-Node_kYHiKA98	MoTHer-VTHR	2024-05-28T14:28:17Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:28:04Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_H9XbpYWt	MoTHer-VTHR	2024-05-28T14:27:36Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:27:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_dk7z88JM	MoTHer-VTHR	2024-05-28T14:27:16Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:27:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_1-Node_BhVuRDoZ	MoTHer-VTHR	2024-05-28T14:26:35Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:26:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cyr19/gpt2-small-en-quatrain-conditioned	cyr19	2024-05-28T14:26:13Z	136	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T14:25:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_nLwZvVZq	MoTHer-VTHR	2024-05-28T14:25:54Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:25:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_UEC63FdE	MoTHer-VTHR	2024-05-28T14:25:34Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:25:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_bd6KL2rJ	MoTHer-VTHR	2024-05-28T14:25:14Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:25:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_1-Node_gW3N7Rwh	MoTHer-VTHR	2024-05-28T14:24:55Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:24:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_D7YNXK3N	MoTHer-VTHR	2024-05-28T14:24:38Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:22:12Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_LT3PNvAg	MoTHer-VTHR	2024-05-28T14:24:34Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:21:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_dMA67Lxh	MoTHer-VTHR	2024-05-28T14:24:32Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:21:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_1-Node_6hLsBteR	MoTHer-VTHR	2024-05-28T14:24:29Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:09:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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HelloOoOooo/results_3	HelloOoOooo	2024-05-28T14:24:28Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:abhi317/results_2", "base_model:finetune:abhi317/results_2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-28T14:12:16Z	--- tags: - generated_from_trainer base_model: abhi317/results_2 model-index: - name: results_3 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. --> # results_3 This model is a fine-tuned version of [abhi317/results_2](https://huggingface.co/abhi317/results_2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1557 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 2.4711 \| \| No log \| 2.0 \| 2 \| 2.3635 \| \| No log \| 3.0 \| 3 \| 2.2591 \| \| No log \| 4.0 \| 4 \| 2.1869 \| \| No log \| 5.0 \| 5 \| 2.1121 \| \| No log \| 6.0 \| 6 \| 2.0433 \| \| No log \| 7.0 \| 7 \| 1.9845 \| \| No log \| 8.0 \| 8 \| 1.9252 \| \| No log \| 9.0 \| 9 \| 1.8642 \| \| No log \| 10.0 \| 10 \| 1.8104 \| \| No log \| 11.0 \| 11 \| 1.7649 \| \| No log \| 12.0 \| 12 \| 1.7260 \| \| No log \| 13.0 \| 13 \| 1.6873 \| \| No log \| 14.0 \| 14 \| 1.6532 \| \| No log \| 15.0 \| 15 \| 1.6242 \| \| No log \| 16.0 \| 16 \| 1.6066 \| \| No log \| 17.0 \| 17 \| 1.5801 \| \| No log \| 18.0 \| 18 \| 1.5596 \| \| No log \| 19.0 \| 19 \| 1.5346 \| \| No log \| 20.0 \| 20 \| 1.5040 \| \| No log \| 21.0 \| 21 \| 1.4759 \| \| No log \| 22.0 \| 22 \| 1.4507 \| \| No log \| 23.0 \| 23 \| 1.4294 \| \| No log \| 24.0 \| 24 \| 1.4083 \| \| No log \| 25.0 \| 25 \| 1.4008 \| \| No log \| 26.0 \| 26 \| 1.3787 \| \| No log \| 27.0 \| 27 \| 1.3444 \| \| No log \| 28.0 \| 28 \| 1.3196 \| \| No log \| 29.0 \| 29 \| 1.2965 \| \| No log \| 30.0 \| 30 \| 1.2714 \| \| No log \| 31.0 \| 31 \| 1.2447 \| \| No log \| 32.0 \| 32 \| 1.2207 \| \| No log \| 33.0 \| 33 \| 1.1911 \| \| No log \| 34.0 \| 34 \| 1.1596 \| \| No log \| 35.0 \| 35 \| 1.1291 \| \| No log \| 36.0 \| 36 \| 1.1054 \| \| No log \| 37.0 \| 37 \| 1.0787 \| \| No log \| 38.0 \| 38 \| 1.0492 \| \| No log \| 39.0 \| 39 \| 1.0278 \| \| No log \| 40.0 \| 40 \| 1.0058 \| \| No log \| 41.0 \| 41 \| 0.9850 \| \| No log \| 42.0 \| 42 \| 0.9644 \| \| No log \| 43.0 \| 43 \| 0.9525 \| \| No log \| 44.0 \| 44 \| 0.9405 \| \| No log \| 45.0 \| 45 \| 0.9255 \| \| No log \| 46.0 \| 46 \| 0.9018 \| \| No log \| 47.0 \| 47 \| 0.8715 \| \| No log \| 48.0 \| 48 \| 0.8439 \| \| No log \| 49.0 \| 49 \| 0.8271 \| \| No log \| 50.0 \| 50 \| 0.8079 \| \| No log \| 51.0 \| 51 \| 0.7844 \| \| No log \| 52.0 \| 52 \| 0.7619 \| \| No log \| 53.0 \| 53 \| 0.7389 \| \| No log \| 54.0 \| 54 \| 0.7216 \| \| No log \| 55.0 \| 55 \| 0.7085 \| \| No log \| 56.0 \| 56 \| 0.6971 \| \| No log \| 57.0 \| 57 \| 0.6864 \| \| No log \| 58.0 \| 58 \| 0.6771 \| \| No log \| 59.0 \| 59 \| 0.6650 \| \| No log \| 60.0 \| 60 \| 0.6552 \| \| No log \| 61.0 \| 61 \| 0.6451 \| \| No log \| 62.0 \| 62 \| 0.6375 \| \| No log \| 63.0 \| 63 \| 0.6317 \| \| No log \| 64.0 \| 64 \| 0.6252 \| \| No log \| 65.0 \| 65 \| 0.6179 \| \| No log \| 66.0 \| 66 \| 0.6081 \| \| No log \| 67.0 \| 67 \| 0.5980 \| \| No log \| 68.0 \| 68 \| 0.5844 \| \| No log \| 69.0 \| 69 \| 0.5751 \| \| No log \| 70.0 \| 70 \| 0.5651 \| \| No log \| 71.0 \| 71 \| 0.5603 \| \| No log \| 72.0 \| 72 \| 0.5540 \| \| No log \| 73.0 \| 73 \| 0.5442 \| \| No log \| 74.0 \| 74 \| 0.5342 \| \| No log \| 75.0 \| 75 \| 0.5228 \| \| No log \| 76.0 \| 76 \| 0.5093 \| \| No log \| 77.0 \| 77 \| 0.4987 \| \| No log \| 78.0 \| 78 \| 0.4859 \| \| No log \| 79.0 \| 79 \| 0.4728 \| \| No log \| 80.0 \| 80 \| 0.4602 \| \| No log \| 81.0 \| 81 \| 0.4523 \| \| No log \| 82.0 \| 82 \| 0.4444 \| \| No log \| 83.0 \| 83 \| 0.4349 \| \| No log \| 84.0 \| 84 \| 0.4250 \| \| No log \| 85.0 \| 85 \| 0.4154 \| \| No log \| 86.0 \| 86 \| 0.4078 \| \| No log \| 87.0 \| 87 \| 0.3995 \| \| No log \| 88.0 \| 88 \| 0.3929 \| \| No log \| 89.0 \| 89 \| 0.3863 \| \| No log \| 90.0 \| 90 \| 0.3796 \| \| No log \| 91.0 \| 91 \| 0.3737 \| \| No log \| 92.0 \| 92 \| 0.3663 \| \| No log \| 93.0 \| 93 \| 0.3624 \| \| No log \| 94.0 \| 94 \| 0.3592 \| \| No log \| 95.0 \| 95 \| 0.3537 \| \| No log \| 96.0 \| 96 \| 0.3467 \| \| No log \| 97.0 \| 97 \| 0.3424 \| \| No log \| 98.0 \| 98 \| 0.3381 \| \| No log \| 99.0 \| 99 \| 0.3332 \| \| No log \| 100.0 \| 100 \| 0.3276 \| \| No log \| 101.0 \| 101 \| 0.3245 \| \| No log \| 102.0 \| 102 \| 0.3208 \| \| No log \| 103.0 \| 103 \| 0.3170 \| \| No log \| 104.0 \| 104 \| 0.3148 \| \| No log \| 105.0 \| 105 \| 0.3132 \| \| No log \| 106.0 \| 106 \| 0.3106 \| \| No log \| 107.0 \| 107 \| 0.3086 \| \| No log \| 108.0 \| 108 \| 0.3053 \| \| No log \| 109.0 \| 109 \| 0.3038 \| \| No log \| 110.0 \| 110 \| 0.3020 \| \| No log \| 111.0 \| 111 \| 0.2998 \| \| No log \| 112.0 \| 112 \| 0.2966 \| \| No log \| 113.0 \| 113 \| 0.2931 \| \| No log \| 114.0 \| 114 \| 0.2887 \| \| No log \| 115.0 \| 115 \| 0.2838 \| \| No log \| 116.0 \| 116 \| 0.2785 \| \| No log \| 117.0 \| 117 \| 0.2735 \| \| No log \| 118.0 \| 118 \| 0.2688 \| \| No log \| 119.0 \| 119 \| 0.2644 \| \| No log \| 120.0 \| 120 \| 0.2624 \| \| No log \| 121.0 \| 121 \| 0.2610 \| \| No log \| 122.0 \| 122 \| 0.2593 \| \| No log \| 123.0 \| 123 \| 0.2564 \| \| No log \| 124.0 \| 124 \| 0.2537 \| \| No log \| 125.0 \| 125 \| 0.2506 \| \| No log \| 126.0 \| 126 \| 0.2465 \| \| No log \| 127.0 \| 127 \| 0.2441 \| \| No log \| 128.0 \| 128 \| 0.2408 \| \| No log \| 129.0 \| 129 \| 0.2380 \| \| No log \| 130.0 \| 130 \| 0.2348 \| \| No log \| 131.0 \| 131 \| 0.2313 \| \| No log \| 132.0 \| 132 \| 0.2277 \| \| No log \| 133.0 \| 133 \| 0.2238 \| \| No log \| 134.0 \| 134 \| 0.2197 \| \| No log \| 135.0 \| 135 \| 0.2155 \| \| No log \| 136.0 \| 136 \| 0.2118 \| \| No log \| 137.0 \| 137 \| 0.2090 \| \| No log \| 138.0 \| 138 \| 0.2067 \| \| No log \| 139.0 \| 139 \| 0.2044 \| \| No log \| 140.0 \| 140 \| 0.2020 \| \| No log \| 141.0 \| 141 \| 0.1995 \| \| No log \| 142.0 \| 142 \| 0.1970 \| \| No log \| 143.0 \| 143 \| 0.1950 \| \| No log \| 144.0 \| 144 \| 0.1929 \| \| No log \| 145.0 \| 145 \| 0.1906 \| \| No log \| 146.0 \| 146 \| 0.1884 \| \| No log \| 147.0 \| 147 \| 0.1876 \| \| No log \| 148.0 \| 148 \| 0.1868 \| \| No log \| 149.0 \| 149 \| 0.1860 \| \| No log \| 150.0 \| 150 \| 0.1851 \| \| No log \| 151.0 \| 151 \| 0.1838 \| \| No log \| 152.0 \| 152 \| 0.1829 \| \| No log \| 153.0 \| 153 \| 0.1818 \| \| No log \| 154.0 \| 154 \| 0.1811 \| \| No log \| 155.0 \| 155 \| 0.1810 \| \| No log \| 156.0 \| 156 \| 0.1802 \| \| No log \| 157.0 \| 157 \| 0.1791 \| \| No log \| 158.0 \| 158 \| 0.1777 \| \| No log \| 159.0 \| 159 \| 0.1763 \| \| No log \| 160.0 \| 160 \| 0.1748 \| \| No log \| 161.0 \| 161 \| 0.1739 \| \| No log \| 162.0 \| 162 \| 0.1726 \| \| No log \| 163.0 \| 163 \| 0.1716 \| \| No log \| 164.0 \| 164 \| 0.1710 \| \| No log \| 165.0 \| 165 \| 0.1702 \| \| No log \| 166.0 \| 166 \| 0.1694 \| \| No log \| 167.0 \| 167 \| 0.1693 \| \| No log \| 168.0 \| 168 \| 0.1688 \| \| No log \| 169.0 \| 169 \| 0.1680 \| \| No log \| 170.0 \| 170 \| 0.1669 \| \| No log \| 171.0 \| 171 \| 0.1661 \| \| No log \| 172.0 \| 172 \| 0.1655 \| \| No log \| 173.0 \| 173 \| 0.1649 \| \| No log \| 174.0 \| 174 \| 0.1647 \| \| No log \| 175.0 \| 175 \| 0.1644 \| \| No log \| 176.0 \| 176 \| 0.1643 \| \| No log \| 177.0 \| 177 \| 0.1639 \| \| No log \| 178.0 \| 178 \| 0.1634 \| \| No log \| 179.0 \| 179 \| 0.1628 \| \| No log \| 180.0 \| 180 \| 0.1622 \| \| No log \| 181.0 \| 181 \| 0.1616 \| \| No log \| 182.0 \| 182 \| 0.1610 \| \| No log \| 183.0 \| 183 \| 0.1605 \| \| No log \| 184.0 \| 184 \| 0.1598 \| \| No log \| 185.0 \| 185 \| 0.1593 \| \| No log \| 186.0 \| 186 \| 0.1589 \| \| No log \| 187.0 \| 187 \| 0.1584 \| \| No log \| 188.0 \| 188 \| 0.1581 \| \| No log \| 189.0 \| 189 \| 0.1578 \| \| No log \| 190.0 \| 190 \| 0.1576 \| \| No log \| 191.0 \| 191 \| 0.1573 \| \| No log \| 192.0 \| 192 \| 0.1571 \| \| No log \| 193.0 \| 193 \| 0.1568 \| \| No log \| 194.0 \| 194 \| 0.1565 \| \| No log \| 195.0 \| 195 \| 0.1563 \| \| No log \| 196.0 \| 196 \| 0.1560 \| \| No log \| 197.0 \| 197 \| 0.1559 \| \| No log \| 198.0 \| 198 \| 0.1558 \| \| No log \| 199.0 \| 199 \| 0.1557 \| \| No log \| 200.0 \| 200 \| 0.1557 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_0-Node_2Fch5Myt	MoTHer-VTHR	2024-05-28T14:24:27Z	161	0	transformers	[ "transformers", "safetensors", "vit", "image-feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-feature-extraction	2024-05-28T14:20:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Likich/falcon-finetune-qualcoding_1000_prompt1_dot	Likich	2024-05-28T14:23:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:23:18Z	--- 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]
SidXXD/training_prompt_dog-image_cat	SidXXD	2024-05-28T14:22:36Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-28T14:16:28Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1> dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/training_prompt_dog-image_cat These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1> dog using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
LiteLLMs/French-Alpaca-Llama3-8B-Instruct-v1.0-GGUF	LiteLLMs	2024-05-28T14:20:58Z	306	4	transformers	[ "transformers", "gguf", "llama3", "french", "llama-3-8B", "GGUF", "fr", "en", "dataset:jpacifico/French-Alpaca-dataset-Instruct-110K", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-28T14:26:40Z	--- language: - fr - en license: apache-2.0 library_name: transformers tags: - llama3 - french - llama-3-8B - GGUF datasets: - jpacifico/French-Alpaca-dataset-Instruct-110K quantized_by: andrijdavid --- # French-Alpaca-Llama3-8B-Instruct-v1.0-GGUF - Original model: [French-Alpaca-Llama3-8B-Instruct-v1.0](https://huggingface.co/jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0) <!-- description start --> ## Description This repo contains GGUF format model files for [French-Alpaca-Llama3-8B-Instruct-v1.0](https://huggingface.co/jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/French-Alpaca-Llama3-8B-Instruct-v1.0-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/French-Alpaca-Llama3-8B-Instruct-v1.0-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/French-Alpaca-Llama3-8B-Instruct-v1.0-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/French-Alpaca-Llama3-8B-Instruct-v1.0-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: French-Alpaca-Llama3-8B-Instruct-v1.0 ## Model Card for Model ID French-Alpaca based on Llama3-8B-Instruct ![image/jpeg](https://github.com/jpacifico/French-Alpaca/blob/main/Assets/French-Alpaca_500px.png?raw=true) ### Model Description fine-tuned from the original French-Alpaca-dataset entirely generated with OpenAI GPT-3.5-turbo. French-Alpaca is a general model and can itself be finetuned to be specialized for specific use cases. The fine-tuning method is inspired from https://crfm.stanford.edu/2023/03/13/alpaca.html Quantized Q4_K_M GGUF 4bits version available : jpacifico/french-alpaca-llama3-8B-Q4-GGUF ### Usage ```python model_id = "jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0" model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0}) tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True, padding_side='left') streamer = TextStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) def stream_frenchalpaca(user_prompt): runtimeFlag = "cuda:0" system_prompt = 'Tu trouveras ci-dessous une instruction qui décrit une tâche. Rédige une réponse qui complète de manière appropriée la demande.\n\n' B_INST, E_INST = "### Instruction:\n", "### Response:\n" prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n\n{E_INST}" inputs = tokenizer([prompt], return_tensors="pt").to(runtimeFlag) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) _ = model.generate(inputs, streamer=streamer, max_new_tokens=500) stream_frenchalpaca("your prompt here") ``` Colab notebook available on my Github : https://github.com/jpacifico/French-Alpaca/blob/main/French_Alpaca_Llama3_inference_test_colab.ipynb ### Limitations The French-Alpaca model is a quick demonstration that a base 8B model can be easily fine-tuned to specialize in a particular language. It does not have any moderation mechanisms. - Developed by: Jonathan Pacifico, 2024 - Model type: LLM - Language(s) (NLP): French - License:** MIT <!-- original-model-card end -->
beansandbytes/Llama3-German-8B-Q4_K_M-GGUF	beansandbytes	2024-05-28T14:20:25Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "de", "license:llama3", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:20:09Z	--- language: - de license: llama3 library_name: transformers tags: - llama-cpp - gguf-my-repo --- # ma1lmana/Llama3-German-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`DiscoResearch/Llama3-German-8B`](https://huggingface.co/DiscoResearch/Llama3-German-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/DiscoResearch/Llama3-German-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo ma1lmana/Llama3-German-8B-Q4_K_M-GGUF --model llama3-german-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo ma1lmana/Llama3-German-8B-Q4_K_M-GGUF --model llama3-german-8b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m llama3-german-8b-q4_k_m.gguf -n 128 ```
CMU-AIR2/math-llama_3_instruct-model-arith-6k	CMU-AIR2	2024-05-28T14:15:37Z	1	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-27T22:05:14Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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] ### Framework versions - PEFT 0.8.2
CMU-AIR2/math-llama_3_instruct-model-arith-10k	CMU-AIR2	2024-05-28T14:15:14Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-27T22:07:12Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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] ### Framework versions - PEFT 0.8.2
DiederikMartens/eBERT_sa_cv_13_fold1	DiederikMartens	2024-05-28T14:15:14Z	108	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	2024-05-28T13:53:01Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_13_fold1 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. --> # eBERT_sa_cv_13_fold1 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5872 - F1: 0.5515 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.5790 \| 0.4718 \| \| 0.591 \| 2.0 \| 650 \| 0.5019 \| 0.5017 \| \| 0.591 \| 3.0 \| 975 \| 0.5872 \| 0.5515 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
OwOpeepeepoopoo/AndDesertYou	OwOpeepeepoopoo	2024-05-28T14:14:53Z	90	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "mergekit", "merge", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T09:03:00Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # output_fastn_on This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * /notebooks/dippy-bittensor-subnet/clone_hgnoi_wOxkiBc6i1gdS0su * /notebooks/dippy-bittensor-subnet/clone_BagleMeetCoffee_s11fc-10 ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: /notebooks/dippy-bittensor-subnet/clone_hgnoi_wOxkiBc6i1gdS0su layer_range: [0, 24] - model: /notebooks/dippy-bittensor-subnet/clone_BagleMeetCoffee_s11fc-10 layer_range: [0, 24] merge_method: slerp base_model: /notebooks/dippy-bittensor-subnet/clone_hgnoi_wOxkiBc6i1gdS0su parameters: t: - filter: self_attn value: [0, 0.7, 0.5, 0.3, 1] - filter: mlp value: [1, 0.3, 0.5, 0.7, 0] - value: 0.5 dtype: bfloat16 ```
DiederikMartens/tsBERT_sa_cv_13_fold1	DiederikMartens	2024-05-28T14:13:54Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T13:52:25Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_13_fold1 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. --> # tsBERT_sa_cv_13_fold1 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5209 - F1: 0.6815 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4228 \| 0.6745 \| \| 0.4392 \| 2.0 \| 650 \| 0.4182 \| 0.6386 \| \| 0.4392 \| 3.0 \| 975 \| 0.5209 \| 0.6815 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
lilferrit/ft-wmt14	lilferrit	2024-05-28T14:12:55Z	13	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-05T10:55:50Z	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: ft-wmt14 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. --> # ft-wmt14 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7607 - Bleu: 23.421 - Gen Len: 27.6243 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adafactor - lr_scheduler_type: linear - training_steps: 100000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:------:\|:------:\|:---------------:\|:-------:\|:-------:\| \| 1.7882 \| 0.2778 \| 10000 \| 1.9278 \| 19.7853 \| 28.4147 \| \| 1.6619 \| 0.5556 \| 20000 \| 1.8710 \| 21.3803 \| 27.667 \| \| 1.6007 \| 0.8333 \| 30000 \| 1.8397 \| 22.2715 \| 27.317 \| \| 1.5269 \| 1.1111 \| 40000 \| 1.8205 \| 21.9329 \| 27.704 \| \| 1.498 \| 1.3889 \| 50000 \| 1.8134 \| 22.4836 \| 27.63 \| \| 1.4801 \| 1.6667 \| 60000 \| 1.7941 \| 22.727 \| 27.582 \| \| 1.462 \| 1.9444 \| 70000 \| 1.7766 \| 23.0372 \| 27.5903 \| \| 1.4182 \| 2.2222 \| 80000 \| 1.7724 \| 23.6231 \| 27.4233 \| \| 1.4079 \| 2.5 \| 90000 \| 1.7663 \| 23.2604 \| 27.7623 \| \| 1.4037 \| 2.7778 \| 100000 \| 1.7607 \| 23.421 \| 27.6243 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
LauraAlexandra/my_awesome_opus_books_model	LauraAlexandra	2024-05-28T14:12:13Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-28T11:54:29Z	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: my_awesome_opus_books_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_opus_books_model This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6087 - Bleu: 5.5958 - Gen Len: 17.6132 ## 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: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 1.8644 \| 1.0 \| 6355 \| 1.6334 \| 5.403 \| 17.6172 \| \| 1.8252 \| 2.0 \| 12710 \| 1.6087 \| 5.5958 \| 17.6132 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ferrazzipietro/Meta-Llama-3-8B_adapters_SLO_NoQuant_torch.bfloat16_16_32_0.01_4_0.0002	ferrazzipietro	2024-05-28T14:11:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:11:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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John6666/real-pony-real-anime-v4-sdxl	John6666	2024-05-28T14:09:19Z	36	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-28T14:03:24Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime --- Original model is [here](https://civitai.com/models/365041/real-pony?modelVersionId=515456).
psyche/llama3-8b-instruct-ko	psyche	2024-05-28T14:08:57Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T14:05:16Z	--- 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. 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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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DiederikMartens/tsBERT_sa_cv_13_fold0	DiederikMartens	2024-05-28T13:52:16Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T13:30:34Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_13_fold0 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. --> # tsBERT_sa_cv_13_fold0 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4995 - F1: 0.6768 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4188 \| 0.6443 \| \| 0.4413 \| 2.0 \| 650 \| 0.3954 \| 0.6675 \| \| 0.4413 \| 3.0 \| 975 \| 0.4995 \| 0.6768 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
haturusinghe/LLAMA3-Finetune-v1-1.46_loss-May-28-2024	haturusinghe	2024-05-28T13:52:16Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T13:50:32Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: haturusinghe - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
myrkur/shotor	myrkur	2024-05-28T13:52:00Z	13	4	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "fa", "en", "dataset:myrkur/persian-alpaca-deep-clean", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T07:04:32Z	--- license: apache-2.0 language: - fa - en library_name: transformers pipeline_tag: text-generation datasets: - myrkur/persian-alpaca-deep-clean --- # Shotor (Llama 3 8B Instruction Tuned on Farsi) <a href="https://ibb.co/PwCN3VF"><img src="https://i.ibb.co/0hJc8zm/shotor.png" alt="shotor" border="0"></a> Shotor is a Persian language model built upon the llama 3 8B architecture, a multilingual Large Language Model (LLM). It has been fine-tuned using supervised learning techniques and the Dora method for efficient fine-tuning. The model has been specifically tailored and trained on Persian datasets, particularly leveraging the dataset provided by [persian-alpaca-deep-clean](https://huggingface.co/datasets/myrkur/persian-alpaca-deep-clean). ## Usage Here's a sample Python code snippet demonstrating how to use Shotor for text generation: ```python import transformers import torch # Load the Shotor model model_id = "myrkur/shotor" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) # Define user messages messages = [ {"role": "user", "content": "علم بهتر است یا ثروت؟"}, ] # Apply chat template and generate text prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=512, eos_token_id=terminators, do_sample=True, temperature=0.5, top_p=0.9, repetition_penalty=1.1 ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Contributions Contributions to Shotor are welcome! Whether it's enhancing the model's capabilities, improving its performance on specific tasks, or evaluating its performance, your contributions can help advance Persian natural language processing. ## Contact For questions or further information, please contact: - Amir Masoud Ahmadi: [[email protected]](mailto:[email protected]) - Sahar Mirzapour: [[email protected]](mailto:[email protected])
LiteLLMs/aya-23-8B-GGUF	LiteLLMs	2024-05-28T13:51:46Z	62	2	transformers	[ "transformers", "gguf", "GGUF", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "arxiv:2405.15032", "license:cc-by-nc-4.0", "region:us", "conversational" ]	null	2024-05-24T14:26:08Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 library_name: transformers tags: - GGUF inference: false quantized_by: andrijdavid --- # aya-23-8B-GGUF - Original model: [aya-23-8B](https://huggingface.co/CohereForAI/aya-23-8B) <!-- description start --> ## Description This repo contains GGUF format model files for [aya-23-8B](https://huggingface.co/CohereForAI/aya-23-8B). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/aya-23-8B-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/aya-23-8B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/aya-23-8B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/aya-23-8B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: aya-23-8B # Model Card for Aya-23-8B Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 8-billion version of the Aya 23 model. We also released a 35-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-35B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 8 billion parameters ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install transformers==4.41.1 from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-8B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-8B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aryabumi2024aya, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi and John Dang and Dwarak Talupuru and Saurabh Dash and David Cairuz and Hangyu Lin and Bharat Venkitesh and Madeline Smith and Kelly Marchisio and Sebastian Ruder and Acyr Locatelli and Julia Kreutzer and Nick Frosst and Phil Blunsom and Marzieh Fadaee and Ahmet Üstün and Sara Hooker}, year={2024}, eprint={2405.15032}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- original-model-card end -->
DiederikMartens/gBERT_sa_cv_13_fold0	DiederikMartens	2024-05-28T13:51:24Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T13:30:38Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_13_fold0 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. --> # gBERT_sa_cv_13_fold0 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3616 - F1: 0.6869 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4943 \| 0.5589 \| \| 0.4328 \| 2.0 \| 650 \| 0.3616 \| 0.6869 \| \| 0.4328 \| 3.0 \| 975 \| 0.5263 \| 0.6846 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
delphi-suite/stories-llama2-50k	delphi-suite	2024-05-28T13:51:10Z	25	1	delphi	[ "delphi", "safetensors", "llama", "en", "dataset:delphi-suite/stories", "license:apache-2.0", "region:us" ]	null	2024-05-27T08:25:43Z	--- language: - en license: apache-2.0 datasets: - delphi-suite/stories library_name: delphi --- This is a part of `stories-llama2-*` model family: name \| params \| layers \| hidden_size \| query heads \| key & value heads -\|-\|-\|-\|-\|- stories-llama2-50k \| 49,554 \| 1 \| 6 \| 3 \| 1 stories-llama2-100k \| 99,924 \| 1 \| 12 \| 2 \| 1 stories-llama2-250k \| 246,820 \| 2 \| 28 \| 2 \| 1 stories-llama2-500k \| 527,912 \| 2 \| 56 \| 4 \| 2 stories-llama2-1m \| 1,019,508 \| 4 \| 84 \| 6 \| 3 stories-llama2-2.5m \| 2,437,280 \| 4 \| 160 \| 8 \| 4 stories-llama2-5m \| 5,136,720 \| 5 \| 240 \| 10 \| 5 stories-llama2-10m \| 10,421,340 \| 6 \| 340 \| 10 \| 5 stories-llama2-25m \| 24,215,520 \| 8 \| 480 \| 16 \| 8 stories-llama2-50m \| 49,387,712 \| 8 \| 704 \| 16 \| 8 You can access W&B logs [here](https://wandb.ai/delphi-suite/delphi). This model was trained using [delphi](https://github.com/delphi-suite/delphi). See `training_config.json` and `run_context.json` for details.
wop/kosmox-tiny-gguf	wop	2024-05-28T13:45:55Z	6	1	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:quantized:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-28T13:43:54Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: wop - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
John6666/osorubeshi-xl-nsfw-hyper-v1-sdxl	John6666	2024-05-28T13:44:41Z	48	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-28T13:37:54Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime --- Original model is [here](https://civitai.com/models/120090?modelVersionId=283551).
Fischerboot/InternLM2-ToxicRP-QLORA-4Bit	Fischerboot	2024-05-28T13:42:37Z	7	0	peft	[ "peft", "llama", "generated_from_trainer", "base_model:intervitens/internlm2-limarp-chat-20b", "base_model:adapter:intervitens/internlm2-limarp-chat-20b", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-28T11:35:04Z	--- license: other library_name: peft tags: - generated_from_trainer base_model: intervitens/internlm2-limarp-chat-20b model-index: - name: outputs/qlora-out 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. --> Compute power from g4rg. Big Thanks. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml mlflow_tracking_uri: http://127.0.0.1:2340 mlflow_experiment_name: Default base_model: intervitens/internlm2-limarp-chat-20b model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ResplendentAI/Alpaca_NSFW_Shuffled type: alpaca - path: diffnamehard/toxic-dpo-v0.1-NoWarning-alpaca type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.1 output_dir: ./outputs/qlora-out adapter: qlora lora_model_dir: sequence_len: 8192 sample_packing: false pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # outputs/qlora-out This model is a fine-tuned version of [intervitens/internlm2-limarp-chat-20b](https://huggingface.co/intervitens/internlm2-limarp-chat-20b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9896 ## 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: 7 - gradient_accumulation_steps: 4 - total_train_batch_size: 56 - total_eval_batch_size: 14 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4668 \| 0.0476 \| 1 \| 1.4615 \| \| 1.3541 \| 0.2857 \| 6 \| 1.4253 \| \| 1.2057 \| 0.5714 \| 12 \| 1.2120 \| \| 1.0818 \| 0.8571 \| 18 \| 1.1259 \| \| 1.0835 \| 1.1429 \| 24 \| 1.0750 \| \| 1.0503 \| 1.4286 \| 30 \| 1.0451 \| \| 1.0031 \| 1.7143 \| 36 \| 1.0288 \| \| 0.9728 \| 2.0 \| 42 \| 1.0137 \| \| 0.8879 \| 2.2857 \| 48 \| 1.0082 \| \| 0.8981 \| 2.5714 \| 54 \| 0.9956 \| \| 0.8613 \| 2.8571 \| 60 \| 0.9926 \| \| 0.8608 \| 3.1429 \| 66 \| 0.9903 \| \| 0.7841 \| 3.4286 \| 72 \| 0.9903 \| \| 0.9237 \| 3.7143 \| 78 \| 0.9899 \| \| 0.868 \| 4.0 \| 84 \| 0.9896 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
xk-huang/quartet_meshes	xk-huang	2024-05-28T13:39:29Z	0	0	null	[ "region:us" ]	null	2024-05-27T09:19:32Z	For tet template of "EMA: Efficient Meshy Neural Fields for Animatable Human Avatars" (https://github.com/xk-huang/ema).
gillesdewaha/dpo_reference_model	gillesdewaha	2024-05-28T13:38:10Z	109	0	transformers	[ "transformers", "safetensors", "openelm", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2024-05-28T13:21:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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johnsutor/mixture-of-gemmas-slerp	johnsutor	2024-05-28T13:36:55Z	8	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "base_model:google/codegemma-7b", "base_model:merge:google/codegemma-7b", "base_model:google/gemma-7b", "base_model:merge:google/gemma-7b", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:27:39Z	--- base_model: - google/gemma-7b - google/codegemma-7b library_name: transformers tags: - mergekit - merge license: mit --- # slerp This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [google/gemma-7b](https://huggingface.co/google/gemma-7b) * [google/codegemma-7b](https://huggingface.co/google/codegemma-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: google/gemma-7b - model: google/codegemma-7b merge_method: slerp base_model: google/gemma-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 ```
sroecker/granite-3b-code-instruct-llamafile	sroecker	2024-05-28T13:35:35Z	26	0	null	[ "llamafile", "license:apache-2.0", "region:us" ]	null	2024-05-28T12:52:03Z	--- license: apache-2.0 ---
0xfaskety/Qwen-Qwen1.5-7B-1716902615	0xfaskety	2024-05-28T13:30:13Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:23:38Z	--- 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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mrsarthakgupta/onnxtry2	mrsarthakgupta	2024-05-28T13:26:43Z	4	0	transformers	[ "transformers", "onnx", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T13:17:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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ryan0712/llama-3-8b-slow-DUS-max-layer-method2	ryan0712	2024-05-28T13:26:36Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "ryan0712/llama-3-8b-slow-DUS-max-layer1-method2", "ryan0712/llama-3-8b-slow-DUS-max-layer2-method2", "base_model:ryan0712/llama-3-8b-slow-DUS-max-layer1-method2", "base_model:merge:ryan0712/llama-3-8b-slow-DUS-max-layer1-method2", "base_model:ryan0712/llama-3-8b-slow-DUS-max-layer2-method2", "base_model:merge:ryan0712/llama-3-8b-slow-DUS-max-layer2-method2", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:21:42Z	--- tags: - merge - mergekit - lazymergekit - ryan0712/llama-3-8b-slow-DUS-max-layer1-method2 - ryan0712/llama-3-8b-slow-DUS-max-layer2-method2 base_model: - ryan0712/llama-3-8b-slow-DUS-max-layer1-method2 - ryan0712/llama-3-8b-slow-DUS-max-layer2-method2 license: llama3 --- # llama-3-8b-slow-DUS-max-layer-method2 llama-3-8b-slow-DUS-max-layer-method2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [ryan0712/llama-3-8b-slow-DUS-max-layer1-method2](https://huggingface.co/ryan0712/llama-3-8b-slow-DUS-max-layer1-method2) * [ryan0712/llama-3-8b-slow-DUS-max-layer2-method2](https://huggingface.co/ryan0712/llama-3-8b-slow-DUS-max-layer2-method2) ## 🧩 Configuration ```yaml slices: - sources: - model: ryan0712/llama-3-8b-slow-DUS-max-layer1-method2 layer_range: [0, 16] - model: ryan0712/llama-3-8b-slow-DUS-max-layer2-method2 layer_range: [0, 16] merge_method: slerp base_model: ryan0712/llama-3-8b-slow-DUS-max-layer1-method2 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "ryan0712/llama-3-8b-slow-DUS-max-layer-method2" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
stepanom/XLMRoberta-base-amazon-massive-NER	stepanom	2024-05-28T13:26:21Z	141	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "en", "ru", "dataset:AmazonScience/massive", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-24T10:30:24Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: XLMRoberta-base-amazon-massive-NER results: [] widget: - text: Maria has an exam at five am this week datasets: - AmazonScience/massive language: - en - ru --- <!-- 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. --> # XLMRoberta-base-amazon-massive-NER This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the MASSIVE dataset. It achieves the following results on the evaluation set: - Loss: 0.2907 - Precision: 0.6189 - Recall: 0.6243 - F1: 0.6123 - Accuracy: 0.9200 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.9645 \| 1.0 \| 720 \| 0.4148 \| 0.4631 \| 0.4177 \| 0.4154 \| 0.8950 \| \| 0.4421 \| 2.0 \| 1440 \| 0.3181 \| 0.5808 \| 0.6001 \| 0.5780 \| 0.9154 \| \| 0.2514 \| 3.0 \| 2160 \| 0.2907 \| 0.6189 \| 0.6243 \| 0.6123 \| 0.9200 \| \| 0.2117 \| 4.0 \| 2880 \| 0.2967 \| 0.6522 \| 0.6351 \| 0.6352 \| 0.9252 \| \| 0.1592 \| 5.0 \| 3600 \| 0.3090 \| 0.6288 \| 0.6923 \| 0.6520 \| 0.9233 \| \| 0.131 \| 6.0 \| 4320 \| 0.2961 \| 0.6619 \| 0.6693 \| 0.6546 \| 0.9282 \| \| 0.1054 \| 7.0 \| 5040 \| 0.3147 \| 0.6424 \| 0.6762 \| 0.6498 \| 0.9260 \| \| 0.0923 \| 8.0 \| 5760 \| 0.3171 \| 0.6447 \| 0.6945 \| 0.6614 \| 0.9257 \| \| 0.0845 \| 9.0 \| 6480 \| 0.3328 \| 0.6434 \| 0.6791 \| 0.6539 \| 0.9256 \| \| 0.0691 \| 10.0 \| 7200 \| 0.3314 \| 0.6628 \| 0.6834 \| 0.6635 \| 0.9264 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
selmisskilig/EGTLM-Qwen1.5-1.8B-instruct	selmisskilig	2024-05-28T13:25:05Z	131	1	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T10:43:48Z	--- license: apache-2.0 --- ## EGTLM-Qwen1.5-1.8B-instruct EGTLM-Qwen1.5-1.8B-instruct EGTLM is our hybrid Embedding and text generation task model trained on the Qwen model. It has a score of 61.2 in the MTEB Chinese review list and also has a good text generation capability in the Chinese language set. - Instruction shunting and training using hybrid loss to make the model hybrid task capable - Bidirectional attention mechanism to enhance the contextual understanding of the model - Uses carefully generated and filtered Embedding data, as well as a large amount of open-source dialogue data ## Model Information - Model Size: 1.8B - Embedding Dimension: 4096 - Max Input Tokens: 32k ## Requirements ``` accelerate>=0.26.1 transformers>=4.37.2 datasets>=2.16.1 wandb mteb[beir] ``` ## Model Highlights To train the model, a mixed-task approach is used. The loss functions involved are as follows: The generative loss function, $\mathcal{L}_{Gen}\$, is defined as: $$ \mathcal{L}_{Gen} = -\frac{1}{T} \sum_{t=1}^{T} \left( s_{y_t} - \log \sum_{y' \in \mathcal{V}} e^{s_{y'}} \right) $$ This loss measures the quality of text generation by averaging the scores over the sequence length $T$. The embedding loss function, $\mathcal{L}_{Emb}\$, is given by: $$ \mathcal{L}_{Emb}(x, y, y') = (1 - l) \cdot D(f(x), f(y))^2 + l \cdot \max\left(0, \alpha - D(f(x), f(y'))\right)^2 $$ This loss ensures that the embeddings are learned effectively by balancing the distance between the correct pairs $(x, y)\$ and the incorrect pairs $(x, y')\$. The combined loss function, $\mathcal{L}_{Mix}\$, used for training the model is: $$ \mathcal{L}_{Mix}=\lambda_{Emb}\mathcal{L}_{Emb}+\lambda_{Gen}\mathcal{L}_{Gen} $$ This mixed loss function integrates both the embedding and generative tasks, where $\lambda_{Emb}\$ and $\lambda_{Gen}\$ are the respective weights for each loss component. By using this mixed-task training approach, the model is capable of both text generation and embedding tasks effectively. ## Usage ```python from egtlm import EgtLM from tqdm import tqdm from scipy.spatial.distance import cosine model = EgtLM( "selmisskilig/EGTLM-Qwen1.5-1.8B-instruct", mode="unified", torch_dtype="auto", attn_implementation="eager" ) messages_list = [ [{"role": "user", "content": "请帮我写一首李白的诗"}], [{"role": "user", "content": "多少岁才能够算成年？"}], [{"role": "user", "content": "请帮我写一个睡前小故事，来安慰我的宝宝睡觉。"}], [{"role": "user", "content": "请问中国有多少个朝代？"}], ] def egtlm_instruction(instruction): return ( "<\|user\|>\n" + instruction + "\n<\|embed\|>\n" if instruction else "<\|embed\|>\n" ) for messages in tqdm(messages_list): print("Query:\n", messages[0]["content"]) encoded = model.tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", ) encoded = encoded.to(model.device) gen = model.model.generate(encoded, max_new_tokens=256, do_sample=False) decoded = model.tokenizer.batch_decode(gen) print("Answer:\n") print(decoded[0], "\n====================\n") queries = ["请告诉我比特币是怎样运作的？", "请问美国有多少年的发展历史？"] documents = [ "纯粹的点对点电子现金可以让在线支付直接从一方发送到另一方，而无需通过金融机构。数字签名提供了部分解决方案，但如果仍然需要一个可信的第三方来防止双重消费，则会失去主要的好处。我们提出了一种利用点对点网络解决双重消费问题的方案。网络通过将交易散列到一个持续的基于散列的工作证明链中来为交易打上时间戳，这样就形成了一个记录，如果不重做工作证明，就无法更改该记录。最长的链不仅可以证明所见证的事件顺序，还可以证明它来自最大的 CPU 能力池。只要大部分 CPU 能力由不合作攻击网络的节点控制，它们就能生成最长的链，并超越攻击者。网络本身的结构要求极低。信息在尽最大努力的基础上进行广播，节点可以随意离开和重新加入网络，并接受最长的工作证明链作为它们离开时发生的事情的证明。", """美国作为一个独立国家的历史可以追溯到1776年7月4日,当时美国十三个殖民地通过《独立宣言》正式脱离英国统治,宣布独立。因此,从1776年独立宣言签署算起,到2023年,美利坚合众国已经有247年的历史。不过,如果从欧洲人最早在北美洲定居开始算起,美国的历史可以追溯到1607年,当时英国人在今日维尔jinnia州的詹姆斯敦建立了第一个永久性英国殖民地。从1607年算起,到2023年,美国的历史已经超过415年了。当然,在欧洲人到来之前,北美洲大陆上已经有众多印第安人部落生活了数千年。所以从更广阔的视角来看,美国这片土地上的人类历史可以追溯到更加悠久的时期。总的来说,作为一个国家,美国有247年的独立历史;作为一片土地上的人类文明,美国的历史可以追溯到早于欧洲人到来的印第安人时期,时间跨度超过數千年。""", ] d_rep, d_cache = model.encode( documents, instruction=egtlm_instruction(""), get_cache=True ) q_rep = model.encode(queries, instruction=egtlm_instruction("")) sims = { q: [1 - cosine(q_rep[i], d_rep[j]) for j in range(len(d_rep))] for i, q in enumerate(queries) } print(sims) ``` ## Evaluation ### C-MTEB You can use the [scripts/eval_mteb.py](https://huggingface.co/selmisskilig/EGTLM-Qwen1.5-1.8B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the evaluation results on C-MTEB(Chinese): \| Model Name \| C-MTEB(35) \| \|:----:\|:---:\| \| [EGTLM-Qwen1.5-1.8B-instruct](https://huggingface.co/selmisskilig/EGTLM-Qwen1.5-1.8B-instruct) \| 61.20 \|
selmisskilig/EGTLM-Mistral7b-instruct	selmisskilig	2024-05-28T13:24:45Z	5	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "conversational", "custom_code", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T09:14:57Z	--- license: apache-2.0 --- ## EGTLM-Mistral7b-instruct EGTLM-Mistral7b-instruct EGTLM is our hybrid Embedding and text generation task model trained on the Mistral model. It has a score of 64.47 in the MTEB Chinese review list and also has a good text generation capability in the Chinese language set. - Instruction shunting and training using hybrid loss to make the model hybrid task capable - Bidirectional attention mechanism to enhance the contextual understanding of the model - Uses carefully generated and filtered Embedding data, as well as a large amount of open-source dialogue data ## Model Information - Model Size: 7B - Embedding Dimension: 4096 - Max Input Tokens: 32k ## Requirements ``` accelerate>=0.26.1 transformers>=4.37.2 datasets>=2.16.1 wandb mteb[beir] ``` ## Model Highlights To train the model, a mixed-task approach is used. The loss functions involved are as follows: The generative loss function, $\mathcal{L}_{Gen}$, is defined as: $$ \mathcal{L}_{Gen} = -\frac{1}{T} \sum_{t=1}^{T} \tilde{s}_{y_t} $$ This loss measures the quality of text generation by averaging the scores over the sequence length $T$. The embedding loss function, $\mathcal{L}_{Emb}$, is given by: $$ \mathcal{L}_{Emb}(x, y, y') = (1 - l) \cdot D(f(x), f(y))^2 + l \cdot \max\left(0, \alpha - D(f(x), f(y'))\right)^2 $$ This loss ensures that the embeddings are learned effectively by balancing the distance between the correct pairs $(x, y)$ and the incorrect pairs $(x, y')$. The combined loss function, $\mathcal{L}_{Mix}$, used for training the model is: $$ \mathcal{L}_{Mix}=\lambda_{Emb}\mathcal{L}_{Emb}+\lambda_{Gen}\mathcal{L}_{Gen} $$ This mixed loss function integrates both the embedding and generative tasks, where $\lambda_{Emb}$ and $\lambda_{Gen}$ are the respective weights for each loss component. By using this mixed-task training approach, the model is capable of both text generation and embedding tasks effectively. ## Usage ```python from egtlm import EgtLM from tqdm import tqdm from scipy.spatial.distance import cosine model = EgtLM( "selmisskilig/EGTLM-Mistral7b-instruct", mode="unified", torch_dtype="auto", attn_implementation="eager" ) messages_list = [ [{"role": "user", "content": "请帮我写一首李白的诗"}], [{"role": "user", "content": "多少岁才能够算成年？"}], [{"role": "user", "content": "请帮我写一个睡前小故事，来安慰我的宝宝睡觉。"}], [{"role": "user", "content": "请问中国有多少个朝代？"}], ] def egtlm_instruction(instruction): return ( "<\|user\|>\n" + instruction + "\n<\|embed\|>\n" if instruction else "<\|embed\|>\n" ) for messages in tqdm(messages_list): print("Query:\n", messages[0]["content"]) encoded = model.tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", ) encoded = encoded.to(model.device) gen = model.model.generate(encoded, max_new_tokens=256, do_sample=False) decoded = model.tokenizer.batch_decode(gen) print("Answer:\n") print(decoded[0], "\n====================\n") queries = ["请告诉我比特币是怎样运作的？", "请问美国有多少年的发展历史？"] documents = [ "纯粹的点对点电子现金可以让在线支付直接从一方发送到另一方，而无需通过金融机构。数字签名提供了部分解决方案，但如果仍然需要一个可信的第三方来防止双重消费，则会失去主要的好处。我们提出了一种利用点对点网络解决双重消费问题的方案。网络通过将交易散列到一个持续的基于散列的工作证明链中来为交易打上时间戳，这样就形成了一个记录，如果不重做工作证明，就无法更改该记录。最长的链不仅可以证明所见证的事件顺序，还可以证明它来自最大的 CPU 能力池。只要大部分 CPU 能力由不合作攻击网络的节点控制，它们就能生成最长的链，并超越攻击者。网络本身的结构要求极低。信息在尽最大努力的基础上进行广播，节点可以随意离开和重新加入网络，并接受最长的工作证明链作为它们离开时发生的事情的证明。", """美国作为一个独立国家的历史可以追溯到1776年7月4日,当时美国十三个殖民地通过《独立宣言》正式脱离英国统治,宣布独立。因此,从1776年独立宣言签署算起,到2023年,美利坚合众国已经有247年的历史。不过,如果从欧洲人最早在北美洲定居开始算起,美国的历史可以追溯到1607年,当时英国人在今日维尔jinnia州的詹姆斯敦建立了第一个永久性英国殖民地。从1607年算起,到2023年,美国的历史已经超过415年了。当然,在欧洲人到来之前,北美洲大陆上已经有众多印第安人部落生活了数千年。所以从更广阔的视角来看,美国这片土地上的人类历史可以追溯到更加悠久的时期。总的来说,作为一个国家,美国有247年的独立历史;作为一片土地上的人类文明,美国的历史可以追溯到早于欧洲人到来的印第安人时期,时间跨度超过數千年。""", ] d_rep, d_cache = model.encode( documents, instruction=egtlm_instruction(""), get_cache=True ) q_rep = model.encode(queries, instruction=egtlm_instruction("")) sims = { q: [1 - cosine(q_rep[i], d_rep[j]) for j in range(len(d_rep))] for i, q in enumerate(queries) } print(sims) ``` ## Evaluation ### C-MTEB You can use the [scripts/eval_mteb.py](https://huggingface.co/selmisskilig/EGTLM-Mistral7b-instruct/blob/main/scripts/eval_mteb.py) to reproduce the evaluation results on C-MTEB(Chinese): \| Model Name \| C-MTEB(35) \| \|:----:\|:---:\| \| [EGTLM-Mistral7b-instruct](https://huggingface.co/selmisskilig/EGTLM-Mistral7b-instruct) \| 64.47 \|
DiederikMartens/tsBERT_sa_cv_9_fold9	DiederikMartens	2024-05-28T13:23:56Z	87	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T10:59:20Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_9_fold9 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. --> # tsBERT_sa_cv_9_fold9 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4554 - F1: 0.6488 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4371 \| 0.5831 \| \| 0.4399 \| 2.0 \| 650 \| 0.4554 \| 0.6488 \| \| 0.4399 \| 3.0 \| 975 \| 0.6180 \| 0.6395 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/mBERT_sa_cv_9_fold9	DiederikMartens	2024-05-28T13:23:46Z	88	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T10:59:28Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_9_fold9 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. --> # mBERT_sa_cv_9_fold9 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5213 - F1: 0.5334 ## 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: 4.47e-05 - train_batch_size: 16 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.5880 \| 0.2854 \| \| 0.6424 \| 2.0 \| 650 \| 0.5330 \| 0.4944 \| \| 0.6424 \| 3.0 \| 975 \| 0.5213 \| 0.5334 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
brendanduke/Llama-2-7B-f32.gguf	brendanduke	2024-05-28T13:23:19Z	4	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T13:12:37Z	--- license: apache-2.0 ---
sgarrett/Succ_31_Final	sgarrett	2024-05-28T13:18:36Z	157	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:nferruz/ProtGPT2", "base_model:finetune:nferruz/ProtGPT2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:10:45Z	--- license: apache-2.0 base_model: nferruz/ProtGPT2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: model_output_31_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_output_31_2 This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.4472 - Accuracy: 0.6444 ## 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: 0.001 - 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: 200.0 ### Training results ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.0.1 - Datasets 2.19.1 - Tokenizers 0.19.1
wop/kosmox-small	wop	2024-05-28T13:16:55Z	62	1	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T09:02:56Z	--- 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. 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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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calewan/a2c-PandaReachDense-v3	calewan	2024-05-28T13:15:24Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-28T13:11:05Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.18 +/- 0.07 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
vivekdabhi80/prompt_env	vivekdabhi80	2024-05-28T13:14:59Z	0	0	null	[ "arxiv:1910.09700", "license:mit", "region:us" ]	null	2024-05-28T13:10:43Z	--- license: mit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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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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Momorami/fnet-base-finetuned-cola	Momorami	2024-05-28T13:11:26Z	74	0	transformers	[ "transformers", "safetensors", "fnet", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:google/fnet-base", "base_model:finetune:google/fnet-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T08:24:59Z	--- language: - en license: apache-2.0 base_model: google/fnet-base tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-base-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.3934207280665654 --- <!-- 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. --> # fnet-base-finetuned-cola This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6476 - Matthews Correlation: 0.3934 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.61 \| 1.0 \| 268 \| 0.5818 \| 0.1606 \| \| 0.5265 \| 2.0 \| 536 \| 0.5489 \| 0.3415 \| \| 0.4161 \| 3.0 \| 804 \| 0.5454 \| 0.3451 \| \| 0.3324 \| 4.0 \| 1072 \| 0.5746 \| 0.3869 \| \| 0.2657 \| 5.0 \| 1340 \| 0.6476 \| 0.3934 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 1.13.1 - Datasets 2.19.1 - Tokenizers 0.19.1
cs552-mlp/phi3-dpo-h5	cs552-mlp	2024-05-28T13:11:26Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "region:us" ]	null	2024-05-28T13:11:07Z	--- library_name: peft base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
cs552-mlp/phi3-dpo-h1	cs552-mlp	2024-05-28T13:10:11Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "region:us" ]	null	2024-05-28T13:09:59Z	--- library_name: peft base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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] ### Framework versions - PEFT 0.11.1
cs552-mlp/phi3-dpo-h4	cs552-mlp	2024-05-28T13:09:25Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "region:us" ]	null	2024-05-28T13:09:06Z	--- library_name: peft base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Thodns/openai-whisper-medium-BS-1e-05	Thodns	2024-05-28T13:07:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T09:41:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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haturusinghe/LLAMA3-Finetune-v1-0.62_loss-May-28-2024	haturusinghe	2024-05-28T13:05:59Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T13:04:18Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: haturusinghe - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
IneG/RoBERTa_pretrained_litcov10K_manipulated	IneG	2024-05-28T13:04:36Z	95	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-28T13:03:46Z	--- tags: - generated_from_trainer model-index: - name: RoBERTa_pretrained_litcov10K_manipulated 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. --> # RoBERTa_pretrained_litcov10K_manipulated This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 3.0 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
SidXXD/a_v_photo_of_cat_token_ini_ktn	SidXXD	2024-05-28T13:03:53Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-28T12:58:17Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: a <v1> photo of cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/a_v_photo_of_cat_token_ini_ktn These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on a <v1> photo of cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
Likich/gemma-finetune-qualcoding_1000_prompt1_dot	Likich	2024-05-28T13:01:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T13:01:04Z	--- 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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Essacheez/grmma-7b-it-1.1-finetune-classification-10k-gemma-prompt-style	Essacheez	2024-05-28T12:57:19Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T12:36:38Z	--- 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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Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-5_0bpw_exl2	Zoyd	2024-05-28T12:56:20Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "abliterated", "conversational", "dataset:mlabonne/orpo-dpo-mix-40k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-28T12:28:23Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - abliterated --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-4_25bpw_exl2)</center> \| <center>4933 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-5_0bpw_exl2)</center> \| <center>5558 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-6_0bpw_exl2)</center> \| <center>6490 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-6_5bpw_exl2)</center> \| <center>6881 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-8_0bpw_exl2)</center> \| <center>8073 MB</center> \| <center>8</center> \| # Llama-3-8B-Instruct-abliterated-dpomix This model is an experimental DPO fine-tune of an abliterated Llama 3 8B Instruct model on the full [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) dataset. It improves Llama 3 8B Instruct's performance while being uncensored. ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B) [📄](https://gist.github.com/mlabonne/91369d9c372f80b6a42a978b454d3b5e) \| 49.65 \| 37.15 \| 69.12 \| 51.66 \| 40.67 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \| ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Llama-3-8B-Instruct-abliterated-dpomix" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-2_5bpw_exl2	Zoyd	2024-05-28T12:55:02Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "abliterated", "conversational", "dataset:mlabonne/orpo-dpo-mix-40k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T11:53:09Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - abliterated --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-4_25bpw_exl2)</center> \| <center>4933 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-5_0bpw_exl2)</center> \| <center>5558 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-6_0bpw_exl2)</center> \| <center>6490 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-6_5bpw_exl2)</center> \| <center>6881 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-8_0bpw_exl2)</center> \| <center>8073 MB</center> \| <center>8</center> \| # Llama-3-8B-Instruct-abliterated-dpomix This model is an experimental DPO fine-tune of an abliterated Llama 3 8B Instruct model on the full [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) dataset. It improves Llama 3 8B Instruct's performance while being uncensored. ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B) [📄](https://gist.github.com/mlabonne/91369d9c372f80b6a42a978b454d3b5e) \| 49.65 \| 37.15 \| 69.12 \| 51.66 \| 40.67 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \| ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Llama-3-8B-Instruct-abliterated-dpomix" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-8_0bpw_exl2	Zoyd	2024-05-28T12:54:34Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "abliterated", "conversational", "dataset:mlabonne/orpo-dpo-mix-40k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-05-28T12:41:18Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - abliterated --- Exllamav2 quant (exl2 / 8.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-4_25bpw_exl2)</center> \| <center>4933 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-5_0bpw_exl2)</center> \| <center>5558 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-6_0bpw_exl2)</center> \| <center>6490 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-6_5bpw_exl2)</center> \| <center>6881 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-8_0bpw_exl2)</center> \| <center>8073 MB</center> \| <center>8</center> \| # Llama-3-8B-Instruct-abliterated-dpomix This model is an experimental DPO fine-tune of an abliterated Llama 3 8B Instruct model on the full [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) dataset. It improves Llama 3 8B Instruct's performance while being uncensored. ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B) [📄](https://gist.github.com/mlabonne/91369d9c372f80b6a42a978b454d3b5e) \| 49.65 \| 37.15 \| 69.12 \| 51.66 \| 40.67 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \| ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Llama-3-8B-Instruct-abliterated-dpomix" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

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