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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-05 12:28:32	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 468 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-05 12:27:45	card stringlengths 11 1.01M
prathamjagga/output2	prathamjagga	2024-03-19T14:52:03Z	162	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T14:51:41Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: output2 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. --> # output2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2120 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6754 \| 1.0 \| 500 \| 1.2120 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Tokenizers 0.15.2
Elkelouizajo/BERT_mnli_xlarge_10K	Elkelouizajo	2024-03-19T14:49:30Z	5	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T10:45:57Z	--- license: apache-2.0 base_model: google-bert/bert-large-cased tags: - generated_from_trainer model-index: - name: results_bert_xlarge 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_bert_xlarge This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 8446 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11.0 ### Training results ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
textminr/ner-bert	textminr	2024-03-19T14:49:21Z	124	0	transformers	[ "transformers", "safetensors", "bert", "token-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" ]	token-classification	2024-03-19T13:39:56Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ner-bert 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. --> # ner-bert This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Precision: 1.0 - Recall: 0.9993 - F1: 0.9997 - Accuracy: 1.0000 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0005 \| 0.1 \| 250 \| 0.0047 \| 0.9998 \| 0.9861 \| 0.9929 \| 0.9994 \| \| 0.009 \| 0.2 \| 500 \| 0.0041 \| 0.9961 \| 0.9864 \| 0.9912 \| 0.9994 \| \| 0.0004 \| 0.3 \| 750 \| 0.0024 \| 0.9977 \| 0.9895 \| 0.9936 \| 0.9995 \| \| 0.0001 \| 0.4 \| 1000 \| 0.0010 \| 0.9984 \| 0.9975 \| 0.9980 \| 0.9999 \| \| 0.0001 \| 0.51 \| 1250 \| 0.0008 \| 1.0 \| 0.9975 \| 0.9987 \| 0.9999 \| \| 0.0001 \| 0.61 \| 1500 \| 0.0005 \| 1.0 \| 0.9975 \| 0.9987 \| 0.9999 \| \| 0.0003 \| 0.71 \| 1750 \| 0.0003 \| 1.0 \| 0.9991 \| 0.9995 \| 1.0000 \| \| 0.0001 \| 0.81 \| 2000 \| 0.0002 \| 1.0 \| 0.9993 \| 0.9997 \| 1.0000 \| \| 0.0 \| 0.91 \| 2250 \| 0.0002 \| 1.0 \| 0.9993 \| 0.9997 \| 1.0000 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
quocviethere/alberta-finetuned-squadv2	quocviethere	2024-03-19T14:45:18Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "albert", "question-answering", "generated_from_trainer", "base_model:albert/albert-base-v2", "base_model:finetune:albert/albert-base-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-03-19T13:32:51Z	--- license: apache-2.0 base_model: albert/albert-base-v2 tags: - generated_from_trainer model-index: - name: alberta-finetuned-squadv2 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. --> # alberta-finetuned-squadv2 This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.2.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
OEvortex/HelpingAI-Vision	OEvortex	2024-03-19T14:38:53Z	26	6	transformers	[ "transformers", "safetensors", "HelpingAI", "image-text-to-text", "custom_code", "en", "base_model:visheratin/MC-LLaVA-3b", "base_model:finetune:visheratin/MC-LLaVA-3b", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-01-19T11:27:11Z	--- language: - en license: other license_name: hsul license_link: https://huggingface.co/OEvortex/vortex-3b/raw/main/LICENSE.md library_name: transformers base_model: visheratin/MC-LLaVA-3b widget: - text: What animal is it? src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg - text: Where is it? src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg pipeline_tag: image-text-to-text --- # HelpingAI-Vision <a target="_blank" href="https://colab.research.google.com/drive/1t2OAMVSKsiqVgvuHq7rhyNv28b67u0D8"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> ## Model details The fundamental concept behind HelpingAI-Vision is to generate one token embedding per N parts of an image, as opposed to producing N visual token embeddings for the entire image. This approach, based on the HelpingAI-Lite and incorporating the LLaVA adapter, aims to enhance scene understanding by capturing more detailed information. For every crop of the image, an embedding is generated using the full SigLIP encoder (size [1, 1152]). Subsequently, all N embeddings undergo processing through the LLaVA adapter, resulting in a token embedding of size [N, 2560]. Currently, these tokens lack explicit information about their position in the original image, with plans to incorporate positional information in a later update. HelpingAI-Vision was fine-tuned from MC-LLaVA-3b. The model adopts the ChatML prompt format, suggesting its potential application in chat-based scenarios. If you have specific queries or would like further details, feel free ask ``` <\|im_start\|>system You are Vortex, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## How to use Install dependencies ```bash !pip install -q open_clip_torch timm einops ``` Download modeling files ```python from huggingface_hub import hf_hub_download hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="configuration_llava.py", local_dir="./", force_download=True) hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="configuration_phi.py", local_dir="./", force_download=True) hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="modeling_llava.py", local_dir="./", force_download=True) hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="modeling_phi.py", local_dir="./", force_download=True) hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="processing_llava.py", local_dir="./", force_download=True) ``` Create a model ```python from modeling_llava import LlavaForConditionalGeneration import torch model = LlavaForConditionalGeneration.from_pretrained("OEvortex/HelpingAI-Vision", torch_dtype=torch.float16) model = model.to("cuda") ``` Create processors ```python from transformers import AutoTokenizer from processing_llava import LlavaProcessor, OpenCLIPImageProcessor tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-Vision") image_processor = OpenCLIPImageProcessor(model.config.preprocess_config) processor = LlavaProcessor(image_processor, tokenizer) ``` Set image and text ```python from PIL import Image import requests image_file = "https://images.unsplash.com/photo-1439246854758-f686a415d9da" raw_image = Image.open(requests.get(image_file, stream=True).raw) prompt = """<\|im_start\|>system A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. The assistant does not hallucinate and pays very close attention to the details.<\|im_end\|> <\|im_start\|>user <image> Describe the image.<\|im_end\|> <\|im_start\|>assistant """ ``` Process inputs ```python with torch.inference_mode(): inputs = processor(prompt, raw_image, model, return_tensors='pt') inputs['input_ids'] = inputs['input_ids'].to(model.device) inputs['attention_mask'] = inputs['attention_mask'].to(model.device) from transformers import TextStreamer streamer = TextStreamer(tokenizer) ``` Generate the data ```python %%time with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=200, do_sample=True, top_p=0.9, temperature=1.2, eos_token_id=tokenizer.eos_token_id, streamer=streamer) print(tokenizer.decode(output[0]).replace(prompt, "").replace("<\|im_end\|>", "")) ```
sessex/kvn-LoRA	sessex	2024-03-19T14:38:26Z	3	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-19T14:38:23Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: in the style of kvn photography widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - sessex/kvn-LoRA <Gallery /> ## Model description These are sessex/kvn-LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use in the style of kvn photography to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](sessex/kvn-LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
EngTig/distilroberta-base-finetuned-wikitext2	EngTig	2024-03-19T14:38:05Z	61	0	transformers	[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-19T14:36:53Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_keras_callback model-index: - name: EngTig/distilroberta-base-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # EngTig/distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.7852 - Validation Loss: 1.6809 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.7852 \| 1.6809 \| 0 \| ### Framework versions - Transformers 4.38.2 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
kavya301/falcon-7b-sharded-bf16-finetuned-mental-health-conversational	kavya301	2024-03-19T14:35:51Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:adapter:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2024-03-19T13:00:09Z	--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-sharded-bf16-finetuned-mental-health-conversational results: [] library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # falcon-7b-sharded-bf16-finetuned-mental-health-conversational This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - _load_in_8bit: False - _load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 - load_in_4bit: True - load_in_8bit: False ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 320 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.5.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.13.1 - Tokenizers 0.15.2
tedad09/PolizzeDonut-ConDOC-3Epochs-60img	tedad09	2024-03-19T14:34:47Z	48	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:tedad09/PolizzeDonut-SoloPDF-3Epochs", "base_model:finetune:tedad09/PolizzeDonut-SoloPDF-3Epochs", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-03-19T14:28:59Z	--- license: mit base_model: tedad09/PolizzeDonut-SoloPDF-3Epochs tags: - generated_from_trainer datasets: - imagefolder model-index: - name: PolizzeDonut-ConDOC-3Epochs-60img 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. --> # PolizzeDonut-ConDOC-3Epochs-60img This model is a fine-tuned version of [tedad09/PolizzeDonut-SoloPDF-3Epochs](https://huggingface.co/tedad09/PolizzeDonut-SoloPDF-3Epochs) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5633 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0383 \| 1.0 \| 30 \| 0.7194 \| \| 0.7271 \| 2.0 \| 60 \| 0.5648 \| \| 0.5757 \| 3.0 \| 90 \| 0.5633 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
nwhamed/Marcoro14-7B-slerp	nwhamed	2024-03-19T14:33:22Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "AIDC-ai-business/Marcoroni-7B-v3", "EmbeddedLLM/Mistral-7B-Merge-14-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-13T12:04:15Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - AIDC-ai-business/Marcoroni-7B-v3 - EmbeddedLLM/Mistral-7B-Merge-14-v0.1 --- # Marcoro14-7B-slerp Marcoro14-7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) * [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) ## 🧩 Configuration ```yaml slices: - sources: - model: AIDC-ai-business/Marcoroni-7B-v3 layer_range: [0, 32] - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1 layer_range: [0, 32] merge_method: slerp base_model: AIDC-ai-business/Marcoroni-7B-v3 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 ```
mpasila/Capybara-Finnish-V1-8B	mpasila	2024-03-19T14:32:54Z	5	0	transformers	[ "transformers", "safetensors", "bloom", "text-generation", "fi", "dataset:Finnish-NLP/Capybara-fi-deepl-translated-sft", "dataset:mpasila/Capybara-fi-deepl-translated-sft-alpaca", "base_model:mpasila/gpt3-finnish-8B-gptq-4bit", "base_model:finetune:mpasila/gpt3-finnish-8B-gptq-4bit", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T22:04:42Z	--- language: - fi pipeline_tag: text-generation base_model: mpasila/gpt3-finnish-8B-gptq-4bit license: apache-2.0 datasets: - Finnish-NLP/Capybara-fi-deepl-translated-sft - mpasila/Capybara-fi-deepl-translated-sft-alpaca --- # Model Card for Capybara-Finnish-V1-8B This is a merge of [mpasila/Capybara-Finnish-V1-8B-LoRA](https://huggingface.co/mpasila/Capybara-Finnish-V1-8B-LoRA/). Base model used: [mpasila/gpt3-finnish-8B-gptq-4bit](https://huggingface.co/mpasila/gpt3-finnish-8B-gptq-4bit/) and the original unquantized model: [TurkuNLP/gpt3-finnish-8B](https://huggingface.co/TurkuNLP/gpt3-finnish-8B/). Dataset used with the LoRA is [Finnish-NLP/Capybara-fi-deepl-translated-sft](https://huggingface.co/datasets/Finnish-NLP/Capybara-fi-deepl-translated-sft/) with some modifications so it uses Alpaca formatting [modified dataset](https://huggingface.co/datasets/mpasila/Capybara-fi-deepl-translated-sft-alpaca/). It uses Alpaca format but with a translated instruction at the start: ``` { "instruction,output": "Alla on ohje, jossa kuvataan tehtävä. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%", "instruction,input,output": "Alla on ohje, jossa kuvataan tehtävä ja joka on yhdistetty kontekstia lisäävään syötteeseen. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%" } ``` Merged using this [Colab notebook](https://colab.research.google.com/drive/1a76Y21GfPtmVs71Uztlgk2xzPA4_vVjs?usp=sharing). It might not be the best way to merge a quantized LoRA on to a float16 model but I just wanted to quickly do something. You can try merging it better if you want. ### Framework versions - PEFT 0.8.2
beomi/Mistral-Ko-Inst-dev	beomi	2024-03-19T14:32:13Z	14	4	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "pytorch", "conversational", "ko", "en", "dataset:heegyu/kowikitext", "dataset:heegyu/kowiki-sentences", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T10:00:46Z	--- license: apache-2.0 datasets: - heegyu/kowikitext - heegyu/kowiki-sentences language: - ko - en library_name: transformers tags: - pytorch --- Experimental Repository :) Contents will updated without any notice at all. If you plan to use this repository, please use with `revision` with git hash. This experiment is aimed to: - Maintain NLU capability of Mistral-Instruct model(mistralai/Mistral-7B-Instruct-v0.1) - Adapt new Korean vocab seamlessly - Use minimal dataset (used Korean wikipedia only) - Computationally efficient method - Let model answer using English knowledge and NLU capability even the question/answer is Korean only. Here's some test: ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( 'beomi/Mistral-Ko-Inst-dev', torch_dtype='auto', device_map='auto', ) tokenizer = AutoTokenizer.from_pretrained('beomi/Mistral-Ko-Inst-dev') pipe = pipeline( 'text-generation', model=model, tokenizer=tokenizer, do_sample=True, max_new_tokens=350, return_full_text=False, no_repeat_ngram_size=6, eos_token_id=1, # not yet tuned to gen </s>, use <s> instead. ) def gen(x): chat = tokenizer.apply_chat_template([ {"role": "user", "content": x}, # {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, # {"role": "user", "content": "Do you have mayonnaise recipes? please say in Korean."} ], tokenize=False) print(pipe(chat)[0]['generated_text'].strip()) gen("스타벅스와 스타벅스 코리아의 차이는?") # (생성 예시) # 스타벅스는 전 세계적으로 운영하고 있는 커피 전문사이다. 한국에는 스타벅스 코리아라는 이름으로 운영되고 있다. # 스타벅스 코리아는 대한민국에 입점한 이후 2009년과 2010년에 두 차례의 브랜드과의 재검토 및 새로운 디자인을 통해 새로운 브랜드다. 커피 전문의 프리미엄 이미지를 유지하고 있고, 스타벅스 코리아는 한국을 대표하는 프리미엄 커피 전문 브랜드을 만들고 있다. ```
automerger/Experiment26Yamshadow-7B	automerger	2024-03-19T14:31:52Z	49	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:automerger/YamShadow-7B", "base_model:finetune:automerger/YamShadow-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-15T02:17:47Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - automerger/YamShadow-7B --- # Experiment26Yamshadow-7B Experiment26Yamshadow-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B) ## 🧩 Configuration ```yaml models: - model: rwitz/experiment26-truthy-iter-0 # No parameters necessary for base model - model: automerger/YamShadow-7B parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: rwitz/experiment26-truthy-iter-0 parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Experiment26Yamshadow-7B" 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"]) ```
mpasila/Capybara-Finnish-V1-8B-LoRA	mpasila	2024-03-19T14:31:39Z	0	0	peft	[ "peft", "safetensors", "fi", "dataset:Finnish-NLP/Capybara-fi-deepl-translated-sft", "dataset:mpasila/Capybara-fi-deepl-translated-sft-alpaca", "base_model:mpasila/gpt3-finnish-8B-gptq-4bit", "base_model:adapter:mpasila/gpt3-finnish-8B-gptq-4bit", "license:apache-2.0", "region:us" ]	null	2024-03-06T20:37:09Z	--- language: - fi library_name: peft base_model: mpasila/gpt3-finnish-8B-gptq-4bit license: apache-2.0 datasets: - Finnish-NLP/Capybara-fi-deepl-translated-sft - mpasila/Capybara-fi-deepl-translated-sft-alpaca --- # Model Card for Capybara-Finnish-V1-8B-LoRA LoRA trained using [mpasila/gpt3-finnish-8B-gptq-4bit](https://huggingface.co/mpasila/gpt3-finnish-8B-gptq-4bit/) as the base model. Also the quantized model is based on this [TurkuNLP/gpt3-finnish-8B](https://huggingface.co/TurkuNLP/gpt3-finnish-8B/). Dataset used with the LoRA is [Finnish-NLP/Capybara-fi-deepl-translated-sft](https://huggingface.co/datasets/Finnish-NLP/Capybara-fi-deepl-translated-sft/) with some modifications so it uses Alpaca formatting [modified dataset](https://huggingface.co/datasets/mpasila/Capybara-fi-deepl-translated-sft-alpaca/). It uses Alpaca format but with a translated instruction at the start: ``` { "instruction,output": "Alla on ohje, jossa kuvataan tehtävä. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%", "instruction,input,output": "Alla on ohje, jossa kuvataan tehtävä ja joka on yhdistetty kontekstia lisäävään syötteeseen. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%" } ``` Using the following settings: ```json { "lora_name": "Capybara_Finnish_V1", "always_override": false, "q_proj_en": true, "v_proj_en": true, "k_proj_en": false, "o_proj_en": false, "gate_proj_en": false, "down_proj_en": false, "up_proj_en": false, "save_steps": 250.0, "micro_batch_size": 4, "batch_size": 128, "epochs": 3.0, "learning_rate": "3e-4", "lr_scheduler_type": "linear", "lora_rank": 32, "lora_alpha": 64, "lora_dropout": 0.05, "cutoff_len": 256, "dataset": "capybara_finnish_v1.1", "eval_dataset": "None", "format": "alpaca-format-finnish", "eval_steps": 100.0, "raw_text_file": "None", "overlap_len": 128, "newline_favor_len": 128, "higher_rank_limit": false, "warmup_steps": 100.0, "optimizer": "adamw_torch", "hard_cut_string": "\\n\\n\\n", "train_only_after": "", "stop_at_loss": 0, "add_eos_token": false, "min_chars": 0.0, "report_to": "None" } ``` ### Framework versions - PEFT 0.8.2
ijwatson98/sft-bart-xsum-1903	ijwatson98	2024-03-19T14:27:17Z	174	0	transformers	[ "transformers", "safetensors", "bart", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T14:24:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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harsh798/pet	harsh798	2024-03-19T14:27:09Z	2	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-19T14:23:14Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### pet Dreambooth model trained by harsh798 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 25500121058 Sample pictures of this concept: ![0](https://huggingface.co/harsh798/pet/resolve/main/sample_images/pet_(8).jpg)
Denath-Khor/dolphin-2.2.1-mistral-7b-GGUF	Denath-Khor	2024-03-19T14:24:30Z	2	0	null	[ "gguf", "fr", "en", "dataset:cognitivecomputations/dolphin", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-19T13:11:37Z	--- datasets: - cognitivecomputations/dolphin language: - fr - en --- Fenris MOBILE GGUF - Q4_K_M - Q8_0 Dolphin-2.2.1-mistral-7b - GGUF Model creator : Cognitive Computations Original model : Dolphin-2.2.1-mistral-7b : https://huggingface.co/cognitivecomputations/dolphin-2.2.1-mistral-7b Description : This repo contains GGUF format model files for Cognitive Computations's Dolphin-2.2.1-mistral-7b. Finetuned from : mistralai/Mistral-7B-v0.1
tincans-ai/gazelle-v0.1	tincans-ai	2024-03-19T14:24:13Z	7	7	transformers	[ "transformers", "safetensors", "gazelle", "text2text-generation", "text-generation", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T20:46:14Z	--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation --- # Gazelle Gazelle v0.1 is a research preview of Tincan's joint speech-language models. The corresponding v0.1 [release notes](https://tincans.ai/slm2) have more details. We suggest using the v0.2 series: [base](https://huggingface.co/tincans-ai/gazelle-v0.2) or [dpo](https://huggingface.co/tincans-ai/gazelle-v0.2-dpo).
sarthakharne/bert-base-70-ep-pretrain-on-textbooks	sarthakharne	2024-03-19T14:23:45Z	195	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-19T14:22:09Z	--- 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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tedad09/PolizzeDonut-ConDOC-3Epochs-20img	tedad09	2024-03-19T14:22:32Z	53	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:tedad09/PolizzeDonut-SoloPDF-3Epochs", "base_model:finetune:tedad09/PolizzeDonut-SoloPDF-3Epochs", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-03-19T14:20:04Z	--- license: mit base_model: tedad09/PolizzeDonut-SoloPDF-3Epochs tags: - generated_from_trainer datasets: - imagefolder model-index: - name: PolizzeDonut-ConDOC-3Epochs-20img 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. --> # PolizzeDonut-ConDOC-3Epochs-20img This model is a fine-tuned version of [tedad09/PolizzeDonut-SoloPDF-3Epochs](https://huggingface.co/tedad09/PolizzeDonut-SoloPDF-3Epochs) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7138 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.11 \| 1.0 \| 10 \| 0.8166 \| \| 0.845 \| 2.0 \| 20 \| 0.7362 \| \| 0.8439 \| 3.0 \| 30 \| 0.7138 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
cstr/Flora_7B-laser	cstr	2024-03-19T14:20:40Z	1	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-18T13:00:39Z	--- license: cc-by-sa-4.0 --- experimental laserRMT version of ResplendentAI/Flora_7B
mvpmaster/MistralDpoPearl-7b-slerp	mvpmaster	2024-03-19T14:16:33Z	50	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "louisbrulenaudet/Pearl-7B-slerp", "NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "base_model:merge:NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "base_model:louisbrulenaudet/Pearl-7B-slerp", "base_model:merge:louisbrulenaudet/Pearl-7B-slerp", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T13:51:23Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - louisbrulenaudet/Pearl-7B-slerp - NousResearch/Nous-Hermes-2-Mistral-7B-DPO base_model: - louisbrulenaudet/Pearl-7B-slerp - NousResearch/Nous-Hermes-2-Mistral-7B-DPO --- # MistralDpoPearl-7b-slerp MistralDpoPearl-7b-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [louisbrulenaudet/Pearl-7B-slerp](https://huggingface.co/louisbrulenaudet/Pearl-7B-slerp) * [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO) ## 🧩 Configuration ```yaml slices: - sources: - model: louisbrulenaudet/Pearl-7B-slerp layer_range: [0, 32] - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO layer_range: [0, 32] merge_method: slerp base_model: louisbrulenaudet/Pearl-7B-slerp 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 = "mvpmaster/MistralDpoPearl-7b-slerp" 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"]) ```
tedad09/PolizzeDonut-ConXLS-3Epochs-40img	tedad09	2024-03-19T14:14:39Z	48	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:tedad09/PolizzeDonut-SoloPDF-3Epochs", "base_model:finetune:tedad09/PolizzeDonut-SoloPDF-3Epochs", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-03-19T14:10:25Z	--- license: mit base_model: tedad09/PolizzeDonut-SoloPDF-3Epochs tags: - generated_from_trainer datasets: - imagefolder model-index: - name: PolizzeDonut-ConXLS-3Epochs-40img 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. --> # PolizzeDonut-ConXLS-3Epochs-40img This model is a fine-tuned version of [tedad09/PolizzeDonut-SoloPDF-3Epochs](https://huggingface.co/tedad09/PolizzeDonut-SoloPDF-3Epochs) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3055 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0333 \| 1.0 \| 20 \| 0.3278 \| \| 0.8567 \| 2.0 \| 40 \| 0.2948 \| \| 0.6921 \| 3.0 \| 60 \| 0.3055 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Commencis/Commencis-LLM	Commencis	2024-03-19T14:12:59Z	2,915	13	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "tr", "en", "dataset:uonlp/CulturaX", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-15T17:03:55Z	--- license: apache-2.0 datasets: - uonlp/CulturaX language: - tr - en pipeline_tag: text-generation metrics: - accuracy - bleu base_model: mistralai/Mistral-7B-Instruct-v0.1 --- # Commencis-LLM <!-- Provide a quick summary of what the model is/does. --> Commencis LLM is a generative model based on the Mistral 7B model. The base model adapts Mistral 7B to Turkish Banking specifically by training on a diverse dataset obtained through various methods, encompassing general Turkish and banking data. ## Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [Commencis](https://www.commencis.com) - Language(s): Turkish - Finetuned from model: [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) - Blog Post: [LLM Blog](https://www.commencis.com/thoughts/commencis-introduces-its-purpose-built-turkish-fluent-llm-for-banking-and-finance-industry-a-detailed-overview/) ## Training Details Alignment phase consists of two stages: supervised fine-tuning (SFT) and Reward Modeling with Reinforcement learning from human feedback (RLHF). The SFT phase was done on the a mixture of synthetic datasets generated from comprehensive banking dictionary data, synthetic datasets generated from banking-based domain and sub-domain headings, and derived from the CulturaX Turkish dataset by filtering. It was trained with three epochs. We used a learning rate 2e-5, lora rank 64 and maximum sequence length 1024 tokens. ### Usage ### Suggested Inference Parameters - Temperature: 0.5 - Repetition penalty: 1.0 - Top-p: 0.9 ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline class TextGenerationAssistant: def __init__(self, model_id:str): self.tokenizer = AutoTokenizer.from_pretrained(model_id) self.model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto',load_in_8bit=True,load_in_4bit=False) self.pipe = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer, device_map="auto", max_new_tokens=1024, return_full_text=True, repetition_penalty=1.0 ) self.sampling_params = dict(do_sample=True, temperature=0.5, top_k=50, top_p=0.9) self.system_prompt = "Sen yardımcı bir asistansın. Sana verilen talimat ve girdilere en uygun cevapları üreteceksin. \n\n\n" def format_prompt(self, user_input): return "[INST] " + self.system_prompt + user_input + " [/INST]" def generate_response(self, user_query): prompt = self.format_prompt(user_query) outputs = self.pipe(prompt, **self.sampling_params) return outputs[0]["generated_text"].split("[/INST]")[1].strip() assistant = TextGenerationAssistant(model_id="Commencis/Commencis-LLM") # Enter your query here. user_query = "Faiz oranı yükseldiğinde kredi maliyetim nasıl etkilenir?" response = assistant.generate_response(user_query) print(response) ``` ### Chat Template ```python from transformers import AutoTokenizer import transformers import torch model = "Commencis/Commencis-LLM" messages = [{"role": "user", "content": "Faiz oranı yükseldiğinde kredi maliyetim nasıl etkilenir?"}] 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=1024, do_sample=True, temperature=0.5, top_k=50, top_p=0.9) print (outputs[0]["generated_text"].split("[/INST]")[1].strip()) ``` # Quantized Models: GGUF: https://huggingface.co/Commencis/Commencis-LLM-GGUF ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Like all LLMs, Commencis-LLM has certain limitations: - Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information. - Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output. - Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses. - Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited. - Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content.
nwztest/outputs_cs_only	nwztest	2024-03-19T14:11:23Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b-it", "base_model:adapter:google/gemma-2b-it", "license:other", "region:us" ]	null	2024-03-19T14:02:36Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b-it model-index: - name: outputs_cs_only 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. --> # outputs_cs_only This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 11 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.15.2
hellie/conversation-summary	hellie	2024-03-19T14:10:46Z	177	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-19T14:10:02Z	--- 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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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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namratanwani/gemma-finetuned-stack-exchange	namratanwani	2024-03-19T14:10:40Z	3	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "region:us" ]	null	2024-03-19T03:28:45Z	--- library_name: peft base_model: google/gemma-2b --- # 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 0.8.2
tedad09/PolizzeDonut-ConXLS-3Epochs-30img	tedad09	2024-03-19T14:09:26Z	49	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:tedad09/PolizzeDonut-SoloPDF-3Epochs", "base_model:finetune:tedad09/PolizzeDonut-SoloPDF-3Epochs", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-03-19T14:06:15Z	--- license: mit base_model: tedad09/PolizzeDonut-SoloPDF-3Epochs tags: - generated_from_trainer datasets: - imagefolder model-index: - name: PolizzeDonut-ConXLS-3Epochs-30img 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. --> # PolizzeDonut-ConXLS-3Epochs-30img This model is a fine-tuned version of [tedad09/PolizzeDonut-SoloPDF-3Epochs](https://huggingface.co/tedad09/PolizzeDonut-SoloPDF-3Epochs) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3119 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1939 \| 1.0 \| 15 \| 0.2879 \| \| 0.9005 \| 2.0 \| 30 \| 0.3223 \| \| 0.7769 \| 3.0 \| 45 \| 0.3119 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ijwatson98/sft-bart-xsum-1803	ijwatson98	2024-03-19T14:08:55Z	177	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-18T15:41:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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gosshh/finetuned_ConvNext	gosshh	2024-03-19T14:07:51Z	189	0	transformers	[ "transformers", "safetensors", "convnext", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-19T13:19:37Z	--- 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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cenfis/XGLM_TR_FineTune_alpha-original	cenfis	2024-03-19T14:04:03Z	0	2	peft	[ "peft", "safetensors", "xglm", "text-generation", "tr", "dataset:TFLai/Turkish-Alpaca", "license:apache-2.0", "region:us" ]	text-generation	2023-10-06T05:45:55Z	--- datasets: - TFLai/Turkish-Alpaca language: - tr library_name: peft pipeline_tag: text-generation license: apache-2.0 --- # (ALPHA) Turkish Instruct LLM Based On Facebook-XGLM This is one of our early experimental models based on Facebook's XGLM, fine-tuned using PEFT loRA on a Turkish Instruction dataset. Our goal is to share a Turkish LLM model in the future. The model provided originates from a checkpoint at the 5700th step. ## Inference Please note, utilizing this model necessitates the download of substantial data files, necessitating a minimum of 24GB VRAM. Given its experimental nature, it is very likely that this model can produce garbage (biased & wrong) output from time to time, so use it with caution. In order to install PEFT modules, please visit: * https://github.com/huggingface/peft ### Load the Model ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) model = AutoModelForCausalLM.from_pretrained("facebook/xglm-7.5B", quantization_config=bnb_config, device_map="auto") model = PeftModel.from_pretrained(model, "myzens/XGLM_TR_FineTune_alpha") tokenizer = AutoTokenizer.from_pretrained("facebook/xglm-7.5B") ``` ### Text Generation Examples Here's a quick and dirty implementation of a post-processing function in case you want to get rid of any garbage letters etc (if any exists): ```python import re def post_process(text, how_many_sentences=5): start_index = text.find("### Cevap: ") + len("### Cevap: ") end_index = text.find("️", start_index) extracted_text = text[start_index:end_index].strip() sentences = extracted_text.split('.') sentences = '.'.join(sentences[:how_many_sentences]) cleaned_text = ''.join(ch for ch in sentences if ch.isalnum() or ch.isspace() or ch == ".") cleaned_text = cleaned_text.replace(" ", " ") cleaned_text = re.sub('\.\.+', '.', cleaned_text) cleaned_text = cleaned_text.strip() return cleaned_text ``` #### Basic Sentiment Analysis ```python PROMPT = """Aşağıda, daha fazla bağlam sağlayan bir girdi ile eşleştirilmiş bir görevi açıklayan bir talimat bulunmaktadır. İsteği uygun şekilde tamamlayan bir yanıt yazın. ### Talimat: Sana verilen cümleyi olumlu, olumsuz veya tarafsız olarak sınıflandır ### Giriş: Tamam iyi güzel kargo zamanında geliyor. HB ile ilgili bi sıkıntı yok. Ama bu ürün çok kullanışsız. ### Cevap:""" inputs = tokenizer(PROMPT, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, temperature=0.1, top_p=0.2, repetition_penalty=1.2, max_length=128, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id ) for generated_text in generation_output: story = tokenizer.decode(generated_text, skip_special_tokens=True) print(post_process(story, 1)) # Olumsuz ``` #### General Use `how_many_sentences = -1`. ```python PROMPT = """Aşağıda, daha fazla bağlam sağlayan bir girdi ile eşleştirilmiş bir görevi açıklayan bir talimat bulunmaktadır. İsteği uygun şekilde tamamlayan bir yanıt yazın. ### Talimat: ### Giriş: Sanatın toplum için önemini açıkla. ### Cevap:""" inputs = tokenizer(PROMPT, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, temperature=0.8, top_p=0.9, repetition_penalty=1.2, max_length=512, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id ) """ Sanat insanlığın gelişimine katkıda bulunan önemli bir araçtır ve birçok farklı alanda kullanılabilir. Örneğin sanat eserleri görsel olarak bilgiyi aktarmak veya duyguları ifade etmek gibi çeşitli amaçlar sağlayabilirler. Ayrıca sanatçılar yaratıcılıklarını kullanarak yeni fikirler üretebilmelerine olanak tanıyarak toplumun ilerlemesine yardımcı olabilirler. Son olarak sanatsal ürünler insanlar arasında iletişim kurmaya da yardımcı olurken aynı zamanda kültürel etkileşimde de büyük rol oynarlar. Bu nedenle sanatın toplumsal faydası yadsınamazdır """ ``` #### Suggestions ```python PROMPT = """Aşağıda, daha fazla bağlam sağlayan bir girdi ile eşleştirilmiş bir görevi açıklayan bir talimat bulunmaktadır. İsteği uygun şekilde tamamlayan bir yanıt yazın. ### Talimat: ### Giriş: Ders çalışmak için önerilerde bulun. ### Cevap:""" generation_output = model.generate( input_ids=input_ids, temperature=0.7, top_p=0.9, repetition_penalty=1.2, max_length=512, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id ) """ 1. Çalışmak istediğiniz konuyu belirleyin ve ders çalışmaya başlamadan önce o konudaki kaynakları araştırın. 2. Dersi takip etmek için düzenli olarak zaman ayırmaya çalışın. 3. Birden çok görev yapmayı deneyin - bu sayede her gün birkaç saatinizi verimli kullanabilirsiniz. 4. Herhangi bir soru veya sorunla karşılaştığınız anda öğretmeninize danışın. 5. Kendinizi motive etmenin yollarına odaklanın - böylece kendinize güvenebilirsiniz ve motivasyonunuzu sürdürmek kolaylaşır. 6. Öğrenme sürecinde hata yapmaktan korkmayın! Hatalar öğrenmenin doğal bir parçasıdır; ancak yanlış yaptığınızda düzeltmeniz gereken şeylerin farkına varmanız önemlidir. 7. Başarısızlık duygusunu kabul edin ve başarının yolunu bulmanıza yardımcı olacak yeni yollar arayın. 8. Başkalarına yardım etmeye çalışarak kendi gelişiminize katkı sağlayın. 9. Sınavlara hazırlık yaparken dikkat dağıtıcı aktivitelerden kaçının. 10. Sonunda başarılı olduğunuzun garantisini vermeyin. Bu sadece sizi stresli hale getirebilir. 11. Deneme sınavlarını tekrarlayın ve sonuçları değerlendirerek ilerlemeyi izleyin. 12. Daha iyi sonuçlar elde etme yolunda adım atmak için sabırlı olun. 13. Yeni bilgiler öğrendiğinizde bunları paylaşmayı unutmayın. 14. Motivasyonunu kaybetmekten korkan öğrencilerle iletişim kurmaktan çekinmeyin. 15. Soru sorduğunuzda olumlu geri bildirim almak önemli olduğundan emin olun. 16. Arkadaşlarınızla birlikte çalışırken destekleyici olmakta fayda vardır. 17. Öğretmeniniz tarafından verilen ödevleri eksiksiz yapın. 18. Dikkat dağıtan diğer etkinliklerden uzak durun. 19. Zaman yönetimini öğrenin ve planlı hareket ederek zamanı etkili kullanın. 20. Uyku düzenini koruyun ve sağlıklı beslenmeye özen gösterin. 21. Stresle başa çıkma becerilerinizi geliştirin. 22. Hayatınızda başkalarının da sizin gibi zorluklar yaşadığını hatırlayın. 23. Farkındalık yaratmak için farklı yöntemler deneyerek zihinsel sağlığınızı korumaya çalışın. 24. Eğer yeterince konsantre olamıyorsanız dinlenmeye izin verin. """ ``` ### Future Steps 1) We are aiming to decrease the inference cost while maintaining the quality in the outputs. 2) We'll continue to improve our dataset. ### Contact We would like to thank [Ünver Çiftçi](https://www.linkedin.com/in/unverciftci/) who has connected us in the first place. \| \| Task \| LinkedIn \| GitHub \| \|:---:\|:---:\|:---:\|:---:\| \| Kaan Bıçakcı \| Training + Data Preprocessing \| [Contact](https://www.linkedin.com/in/kaanbicakci/) \| https://github.com/Frightera \| \| Talha Rüzgar Akkuş \| Data Collection + General Idea \| [Contact](https://www.linkedin.com/in/talha-r%C3%BCzgar-akku%C5%9F-1b5457264/) \| https://github.com/LegallyCoder \| \| Ethem Yağız Çalık \| Data Collection + General Idea \| [Contact](https://www.linkedin.com/in/ethem-ya%C4%9F%C4%B1z-%C3%A7al%C4%B1k-799a73275/) \| https://github.com/Weyaxi \|
desarrolloasesoreslocales/bert-corpus-ft	desarrolloasesoreslocales	2024-03-19T14:03:20Z	106	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-19T14:02:17Z	--- 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]
Commencis/Commencis-LLM-GGUF	Commencis	2024-03-19T13:59:28Z	40	7	transformers	[ "transformers", "gguf", "commencis", "mistral", "turkish", "text-generation", "tr", "en", "base_model:Commencis/Commencis-LLM", "base_model:quantized:Commencis/Commencis-LLM", "license:apache-2.0", "region:us", "conversational" ]	text-generation	2024-03-18T06:12:48Z	--- model_name: Commencis-LLM model_creator: Commencis base_model: Commencis/Commencis-LLM language: - tr - en pipeline_tag: text-generation license: apache-2.0 library_name: transformers inference: false tags: - commencis - mistral - turkish --- ## Commencis LLM Quantized models and methods \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Commencis-LLM.Q2_K.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q2_K.gguf) \| Q2_K \| 2 \| 2.72 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [Commencis-LLM.Q3_K_S.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB \| very small, high quality loss \| \| [Commencis-LLM.Q3_K_M.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB \| very small, high quality loss \| \| [Commencis-LLM.Q3_K_L.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB \| small, substantial quality loss \| \| [Commencis-LLM.Q4_0.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Commencis-LLM.Q4_K_S.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB \| small, greater quality loss \| \| [Commencis-LLM.Q4_K_M.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB \| medium, balanced quality - recommended \| \| [Commencis-LLM.Q5_0.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q5_0.gguf) \| Q5_0 \| 5 \| 5 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Commencis-LLM.Q5_K_S.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5 GB \| large, low quality loss - recommended \| \| [Commencis-LLM.Q5_K_M.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| large, very low quality loss - recommended \| \| [Commencis-LLM.Q6_K.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB \| very large, extremely low quality loss \| \| [Commencis-LLM.Q8_0.gguf](https://huggingface.co/Commencis/Commencis-LLM-GGUF/blob/main/commencis-llm-Q8_0.gguf) \| Q8_0 \| 8 \| 7.7 GB \| very large, extremely low quality loss - not recommended \|
arpanl/finetuned_model	arpanl	2024-03-19T13:56:12Z	192	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-19T13:36:46Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: finetuned_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. --> # finetuned_model This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
SeeonQwQ/blip2_frame_v3.3	SeeonQwQ	2024-03-19T13:55:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-19T13:55:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
arvnoodle/hcl-gemma-2b-javascript-lotuscript	arvnoodle	2024-03-19T13:50:29Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-03-19T13:50:24Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-2b-bnb-4bit --- # Uploaded model - Developed by: arvnoodle - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-bnb-4bit This gemma 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)
Samuael/amhat5-base-finetuned	Samuael	2024-03-19T13:45:12Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-09T22:40:10Z	--- tags: - generated_from_trainer model-index: - name: amhat5-base-finetuned 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. --> # amhat5-base-finetuned This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0005 - 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: 50 ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ZJZAC/dog6_nop	ZJZAC	2024-03-19T13:41:19Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-19T13:11:39Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - ZJZAC/dog6_nop This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
sarthakharne/bert-base-65-ep-pretrain-on-textbooks	sarthakharne	2024-03-19T13:40:08Z	195	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-19T13:37:47Z	--- 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. 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]
Kaushik1705/detr-resnet-50_finetuned_cppe5	Kaushik1705	2024-03-19T13:34:02Z	188	0	transformers	[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2024-03-19T11:39:01Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer model-index: - name: detr-resnet-50_finetuned_cppe5 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. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
zxe123456/corgy_dog_LoRA	zxe123456	2024-03-19T13:26:08Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-19T10:16:05Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of couplepeople widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - zxe123456/corgy_dog_LoRA <Gallery /> ## Model description These are zxe123456/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of couplepeople to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](zxe123456/corgy_dog_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
winglian/mistral-orpo	winglian	2024-03-19T13:25:52Z	4	3	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T13:23:49Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: 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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: false strict: false rl: orpo chat_template: inst datasets: - path: argilla/ultrafeedback-binarized-preferences-cleaned type: orpo.chat_template chat_template: inst dataset_prepared_path: val_set_size: 0.0 output_dir: ./out remove_unused_columns: false sequence_len: 1024 sample_packing: false pad_to_sequence_len: false eval_sample_packing: false wandb_project: orpo wandb_entity: oaaic wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 3e-6 orpo_alpha: 0.1 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 100 evals_per_epoch: eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 4 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # out This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - 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: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.0
adedamola26/bart-finetuned-samsum	adedamola26	2024-03-19T13:25:44Z	122	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "summarization", "en", "dataset:samsum", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2024-03-11T01:03:01Z	--- pipeline_tag: summarization datasets: - samsum language: - en metrics: - rouge library_name: transformers widget: - text: \| Rita: I'm so bloody tired. Falling asleep at work. :-( Tina: I know what you mean. Tina: I keep on nodding off at my keyboard hoping that the boss doesn't notice.. Rita: The time just keeps on dragging on and on and on.... Rita: I keep on looking at the clock and there's still 4 hours of this drudgery to go. Tina: Times like these I really hate my work. Rita: I'm really not cut out for this level of boredom. Tina: Neither am I. - text: \| Beatrice: I am in town, shopping. They have nice scarfs in the shop next to the church. Do you want one? Leo: No, thanks Beatrice: But you don't have a scarf. Leo: Because I don't need it. Beatrice: Last winter you had a cold all the time. A scarf could help. Leo: I don't like them. Beatrice: Actually, I don't care. You will get a scarf. Leo: How understanding of you! Beatrice: You were complaining the whole winter that you're going to die. I've had enough. Leo: Eh. - text: \| Jack: Cocktails later? May: YES!!! May: You read my mind... Jack: Possibly a little tightly strung today? May: Sigh... without question. Jack: Thought so. May: A little drink will help! Jack: Maybe two! model-index: - name: bart-finetuned-samsum results: - task: name: Text Summarization type: summarization dataset: name: SamSum type: samsum metrics: - name: Validation ROUGE-1 type: rouge-1 value: 53.6163 - name: Validation ROUGE-2 type: rouge-2 value: 28.914 - name: Validation ROUGE-L type: rougeL value: 44.1443 - name: Validation ROUGE-L Sum type: rougeLsum value: 49.2995 --- # Description This model was trained by fine-tuning the [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) model using [these parameters](#training-parameters) and the [samsum dataset](https://huggingface.co/datasets/samsum). ## Development - Jupyter Notebook: [Text Summarization With BART](https://github.com/adedamola26/text-summarization/blob/main/Text_Summarization_with_BART.ipynb) ## Usage ```python from transformers import pipeline model = pipeline("summarization", model="adedamolade26/bart-finetuned-samsum") conversation = '''Jack: Cocktails later? May: YES!!! May: You read my mind... Jack: Possibly a little tightly strung today? May: Sigh... without question. Jack: Thought so. May: A little drink will help! Jack: Maybe two! ''' model(conversation) ``` ## Training Parameters ```python evaluation_strategy = "epoch", save_strategy = 'epoch', load_best_model_at_end = True, metric_for_best_model = 'eval_loss', seed = 42, learning_rate=2e-5, per_device_train_batch_size=4, per_device_eval_batch_size=4, gradient_accumulation_steps=2, weight_decay=0.01, save_total_limit=2, num_train_epochs=4, predict_with_generate=True, fp16=True, report_to="none" ``` ## References Model Training process was adapted from Luis Fernando Torres's [Kaggle Notebook](https://www.kaggle.com/code/lusfernandotorres/text-summarization-with-large-language-models): 📝 Text Summarization with Large Language Models
sinhala-nlp/NSINA-Category-sinbert-small	sinhala-nlp	2024-03-19T13:25:32Z	108	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "si", "dataset:sinhala-nlp/NSINA", "dataset:sinhala-nlp/NSINA-Categories", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T13:24:21Z	--- license: cc-by-sa-4.0 datasets: - sinhala-nlp/NSINA - sinhala-nlp/NSINA-Categories language: - si --- # Sinhala News Category Prediction This is a text classification task created with the [NSINA dataset](https://github.com/Sinhala-NLP/NSINA). This dataset is also released with the same license as NSINA. Given the news content, the ML models should predict a pre-defined category for the news. ## Data First, for this task, we dropped all the news articles in NSINA 1.0 without a category as some news sources prefer not to categorise them. Next, we identified the top 100 news categories from the available news instances. We grouped possible categories into four main categories: local news, international news, sports news, and business news. To avoid bias, we undersampled the dataset. We only used 10,000 instances from each category, and for the ``Business" category, we used the original number of instances which was 8777 articles. We divided this dataset into a training and test set following a 0.8 split Data can be loaded into pandas dataframes using the following code. ```python from datasets import Dataset from datasets import load_dataset train = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Categories', split='train')) test = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Categories', split='test')) ``` ## Citation If you are using the dataset or the models, please cite the following paper. ~~~ @inproceedings{Nsina2024, author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu}, title={{NSINA: A News Corpus for Sinhala}}, booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, year={2024}, month={May}, } ~~~
IntelLabs/eftnas-s2-bert-medium	IntelLabs	2024-03-19T13:24:24Z	0	1	null	[ "en", "dataset:nyu-mll/glue", "license:apache-2.0", "region:us" ]	null	2024-03-15T12:13:15Z	--- language: en license: apache-2.0 datasets: - nyu-mll/glue --- # EFTNAS Model Card: eftnas-s2-bert-medium The super-networks fine-tuned on BERT-medium with [GLUE benchmark](https://gluebenchmark.com/) using EFTNAS. ## Model Details ### Information - Model name: eftnas-s2-bert-medium-[TASK] - Base model: [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) - Subnetwork version: Super-network - NNCF Configurations: [eftnas_configs](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/EFTNAS/eftnas_configs) ### Training and Evaluation [GLUE benchmark](https://gluebenchmark.com/) ## Results Results of the optimal sub-network discoverd from the super-network: \| Model \| GFLOPs \| GLUE Avg. \| MNLI-m \| QNLI \| QQP \| SST-2 \| CoLA \| MRPC \| RTE \| \|-------------------------------\|-----------\|---------------\|----------\|------\|----------\|----------\|----------\|----------\|------\| \| Test Set: \| \| [EFTNAS-S1]() \| 5.7 \| 77.7 \| 83.7 \| 89.9 \| 71.8 \| 93.4 \| 52.6 \| 87.6 \| 65.0 \| \| [EFTNAS-S2]() \| 2.2 \| 75.2 \| 82.0 \| 87.8 \| 70.6 \| 91.4 \| 44.5 \| 86.1 \| 64.0 \| ## Model Sources - Repository: [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/EFTNAS](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/EFTNAS) - Paper: [Searching for Efficient Language Models in First-Order Weight-Reordered Super-Networks]() ## Citation ```bibtex @inproceedings{ eftnas2024, title={Searching for Efficient Language Models in First-Order Weight-Reordered Super-Networks}, author={J. Pablo Munoz and Yi Zheng and Nilesh Jain}, booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation}, year={2024}, url={} } ``` ## License Apache-2.0
sinhala-nlp/NSINA-Category-sinbert-large	sinhala-nlp	2024-03-19T13:23:54Z	105	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "si", "dataset:sinhala-nlp/NSINA", "dataset:sinhala-nlp/NSINA-Categories", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T13:19:58Z	--- license: cc-by-sa-4.0 datasets: - sinhala-nlp/NSINA - sinhala-nlp/NSINA-Categories language: - si --- # Sinhala News Category Prediction This is a text classification task created with the [NSINA dataset](https://github.com/Sinhala-NLP/NSINA). This dataset is also released with the same license as NSINA. Given the news content, the ML models should predict a pre-defined category for the news. ## Data First, for this task, we dropped all the news articles in NSINA 1.0 without a category as some news sources prefer not to categorise them. Next, we identified the top 100 news categories from the available news instances. We grouped possible categories into four main categories: local news, international news, sports news, and business news. To avoid bias, we undersampled the dataset. We only used 10,000 instances from each category, and for the ``Business" category, we used the original number of instances which was 8777 articles. We divided this dataset into a training and test set following a 0.8 split Data can be loaded into pandas dataframes using the following code. ```python from datasets import Dataset from datasets import load_dataset train = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Categories', split='train')) test = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Categories', split='test')) ``` ## Citation If you are using the dataset or the models, please cite the following paper. ~~~ @inproceedings{Nsina2024, author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu}, title={{NSINA: A News Corpus for Sinhala}}, booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, year={2024}, month={May}, } ~~~
Aryanne/CalderaAI_Hexoteric-7B-F16	Aryanne	2024-03-19T13:17:43Z	20	1	transformers	[ "transformers", "safetensors", "gguf", "mistral", "text-generation", "mergekit", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-18T18:40:24Z	--- library_name: transformers tags: - mergekit license: apache-2.0 --- ### This is not a Merge I just used mergekit to convert to f16 this model [CalderaAI/Hexoteric-7B](https://huggingface.co/CalderaAI/Hexoteric-7B). ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: model: path: CalderaAI/Hexoteric-7B dtype: float16 merge_method: task_arithmetic slices: - sources: - layer_range: [0, 32] model: model: path: CalderaAI/Hexoteric-7B parameters: weight: 1.0 ```
sinhala-nlp/NSINA-Category-xlmr-large	sinhala-nlp	2024-03-19T13:11:33Z	8	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "si", "dataset:sinhala-nlp/NSINA", "dataset:sinhala-nlp/NSINA-Categories", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-18T20:59:43Z	--- license: cc-by-sa-4.0 datasets: - sinhala-nlp/NSINA - sinhala-nlp/NSINA-Categories language: - si --- # Sinhala News Category Prediction This is a text classification task created with the [NSINA dataset](https://github.com/Sinhala-NLP/NSINA). This dataset is also released with the same license as NSINA. Given the news content, the ML models should predict a pre-defined category for the news. ## Data First, for this task, we dropped all the news articles in NSINA 1.0 without a category as some news sources prefer not to categorise them. Next, we identified the top 100 news categories from the available news instances. We grouped possible categories into four main categories: local news, international news, sports news, and business news. To avoid bias, we undersampled the dataset. We only used 10,000 instances from each category, and for the ``Business" category, we used the original number of instances which was 8777 articles. We divided this dataset into a training and test set following a 0.8 split Data can be loaded into pandas dataframes using the following code. ```python from datasets import Dataset from datasets import load_dataset train = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Categories', split='train')) test = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Categories', split='test')) ``` ## Citation If you are using the dataset or the models, please cite the following paper. ~~~ @inproceedings{Nsina2024, author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu}, title={{NSINA: A News Corpus for Sinhala}}, booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, year={2024}, month={May}, } ~~~
OEvortex/PixelGen	OEvortex	2024-03-19T13:01:39Z	54	11	diffusers	[ "diffusers", "pytorch", "safetensors", "PixelGen", "HelpingAI", "text-to-image", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-03-18T15:10:33Z	--- pipeline_tag: text-to-image license_name: hsul license_link: https://huggingface.co/OEvortex/vortex-3b/raw/main/LICENSE.md language: - en tags: - PixelGen - HelpingAI license: other --- ## PixelGen: Text-to-Image Generation Model ### Overview PixelGen, developed by HelpingAI, is a Text-to-Image generation model. It enables the generation of high-quality images from textual descriptions, offering a versatile tool for various creative and practical applications. ### Features - Text-to-Image Generation: PixelGen translates textual descriptions into visually appealing images. - Large Model Size: With 3.47 billion parameters, PixelGen can capture intricate details in generated images.
vibber/decim8	vibber	2024-03-19T13:00:32Z	3	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-19T13:00:25Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: in the style of <s0><s1> output: url: image-0.png - text: in the style of <s0><s1> output: url: image-1.png - text: in the style of <s0><s1> output: url: image-2.png - text: in the style of <s0><s1> output: url: image-3.png - text: in the style of <s0><s1> output: url: image-4.png - text: in the style of <s0><s1> output: url: image-5.png - text: in the style of <s0><s1> output: url: image-6.png - text: in the style of <s0><s1> output: url: image-7.png - text: in the style of <s0><s1> output: url: image-8.png - text: in the style of <s0><s1> output: url: image-9.png - text: in the style of <s0><s1> output: url: image-10.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: in the style of <s0><s1> license: openrail++ --- # SDXL LoRA DreamBooth - vibber/decim8 <Gallery /> ## Model description ### These are vibber/decim8 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`decim8.safetensors` here 💾](/vibber/decim8/blob/main/decim8.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:decim8:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - Embeddings: download [`decim8_emb.safetensors` here 💾](/vibber/decim8/blob/main/decim8_emb.safetensors). - Place it on it on your `embeddings` folder - Use it by adding `decim8_emb` to your prompt. For example, `in the style of decim8_emb` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('vibber/decim8', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='vibber/decim8', filename='decim8_emb.safetensors' repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('in the style of <s0><s1>').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/vibber/decim8/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
zxe123456/corgy_couplepeople_LoRA	zxe123456	2024-03-19T12:59:42Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-19T12:56:50Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK dog widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - zxe123456/corgy_couplepeople_LoRA <Gallery /> ## Model description These are zxe123456/corgy_couplepeople_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](zxe123456/corgy_couplepeople_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
tomaszki/mistral-14-copy	tomaszki	2024-03-19T12:58:24Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T12:55:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sarthakharne/bert-base-60-ep-pretrain-on-textbooks	sarthakharne	2024-03-19T12:55:51Z	196	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-19T12:54:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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deepnet/SN6-70M8	deepnet	2024-03-19T12:52:07Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T11:21:09Z	--- 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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kalese/opus-mt-en-bkm-10e64	kalese	2024-03-19T12:45:37Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "dataset:arrow", "base_model:Helsinki-NLP/opus-mt-en-ro", "base_model:finetune:Helsinki-NLP/opus-mt-en-ro", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-19T11:27:17Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-ro tags: - generated_from_trainer datasets: - arrow metrics: - bleu model-index: - name: opus-mt-en-bkm-10e64 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: arrow type: arrow config: default split: train args: default metrics: - name: Bleu type: bleu value: 7.795 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-bkm-10e64 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the arrow dataset. It achieves the following results on the evaluation set: - Loss: 1.4935 - Bleu: 7.795 - Gen Len: 60.8227 ## 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: 64 - eval_batch_size: 64 - 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 \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 279 \| 2.2834 \| 1.3309 \| 68.9546 \| \| 3.1609 \| 2.0 \| 558 \| 1.8907 \| 4.0661 \| 60.289 \| \| 3.1609 \| 3.0 \| 837 \| 1.7445 \| 5.2814 \| 59.5739 \| \| 1.9255 \| 4.0 \| 1116 \| 1.6564 \| 6.0017 \| 61.6317 \| \| 1.9255 \| 5.0 \| 1395 \| 1.5978 \| 6.5742 \| 60.6533 \| \| 1.7347 \| 6.0 \| 1674 \| 1.5572 \| 7.0291 \| 61.3661 \| \| 1.7347 \| 7.0 \| 1953 \| 1.5287 \| 7.4536 \| 60.8991 \| \| 1.642 \| 8.0 \| 2232 \| 1.5092 \| 7.6945 \| 60.6611 \| \| 1.5888 \| 9.0 \| 2511 \| 1.4971 \| 7.7436 \| 60.8317 \| \| 1.5888 \| 10.0 \| 2790 \| 1.4935 \| 7.795 \| 60.8227 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
lokaspire/simplrops_rule_based	lokaspire	2024-03-19T12:39:19Z	4	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-03-19T12:12:48Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: simplrops_rule_based 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. --> # simplrops_rule_based This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
kallam/Mixtral-8x7B-It-v0.1	kallam	2024-03-19T12:37:42Z	4	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "fr", "it", "de", "es", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T12:37:41Z	--- license: apache-2.0 language: - fr - it - de - es - en inference: parameters: temperature: 0.5 widget: - messages: - role: user content: What is your favorite condiment? --- # Model Card for Mixtral-8x7B The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested. For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/). ## Warning This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF. ## Instruction format This format must be strictly respected, otherwise the model will generate sub-optimal outputs. The template used to build a prompt for the Instruct model is defined as follows: ``` <s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST] ``` Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings. As reference, here is the pseudo-code used to tokenize instructions during fine-tuning: ```python def tokenize(text): return tok.encode(text, add_special_tokens=False) [BOS_ID] + tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_1) + [EOS_ID] + … tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_N) + [EOS_ID] ``` In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space. In the Transformers library, one can use [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating) which make sure the right format is applied. ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem: ### In half-precision Note `float16` precision only works on GPU devices <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Lower precision using (8-bit & 4-bit) using `bitsandbytes` <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto") text = "Hello my name is" messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Load the model with Flash Attention 2 <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True, device_map="auto") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ## Limitations The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. # The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
moficodes/gemma-7b-sql-context	moficodes	2024-03-19T12:37:18Z	37	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-14T10:17:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AlignmentResearch/robust_llm_pythia-tt-1b-mz-ada-v3-s-3	AlignmentResearch	2024-03-19T12:36:28Z	90	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b-deduped", "base_model:finetune:EleutherAI/pythia-1b-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:34:29Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-1b-deduped model-index: - name: robust_llm_pythia-tt-1b-mz-ada-v3-s-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. --> # robust_llm_pythia-tt-1b-mz-ada-v3-s-3 This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
freewheelin/free-solar-dpo-v0.2	freewheelin	2024-03-19T12:33:42Z	60	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "en", "arxiv:2312.15166", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T08:20:13Z	--- language: - ko - en license: mit --- # Model Card for free-solar-dpo-v0.2 ## Developed by : [Freewheelin](https://freewheelin-recruit.oopy.io/) AI Technical Team ## Hardware and Software * Training Factors: We fine-tuned this model using the [HuggingFace TRL Trainer](https://huggingface.co/docs/trl/trainer) ## Method - This model was trained using the learning method introduced in the [SOLAR paper](https://arxiv.org/pdf/2312.15166.pdf). ## Base Model - [davidkim205/nox-solar-10.7b-v4](https://huggingface.co/davidkim205/nox-solar-10.7b-v4)
ZJZAC/dog6	ZJZAC	2024-03-19T12:28:09Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-19T11:48:05Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - ZJZAC/dog6 This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
abhishek/autotrain-8kfjk-b3gva	abhishek	2024-03-19T12:26:49Z	232	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T11:14:40Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
AlignmentResearch/robust_llm_pythia-tt-410m-mz-ada-v3-s-1	AlignmentResearch	2024-03-19T12:26:07Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-deduped", "base_model:finetune:EleutherAI/pythia-410m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:25:12Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-410m-deduped model-index: - name: robust_llm_pythia-tt-410m-mz-ada-v3-s-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-tt-410m-mz-ada-v3-s-1 This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
Donnyed/PhysicsMistralAlpaca	Donnyed	2024-03-19T12:22:59Z	16	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-18T21:29:56Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - Developed by: Donnyed - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-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)
AlignmentResearch/robust_llm_pythia-tt-160m-mz-ada-v3-s-3	AlignmentResearch	2024-03-19T12:22:12Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m-deduped", "base_model:finetune:EleutherAI/pythia-160m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:21:49Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m-deduped model-index: - name: robust_llm_pythia-tt-160m-mz-ada-v3-s-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. --> # robust_llm_pythia-tt-160m-mz-ada-v3-s-3 This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-160m-mz-ada-v3-s-2	AlignmentResearch	2024-03-19T12:19:44Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m-deduped", "base_model:finetune:EleutherAI/pythia-160m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:19:18Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m-deduped model-index: - name: robust_llm_pythia-tt-160m-mz-ada-v3-s-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-tt-160m-mz-ada-v3-s-2 This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-70m-mz-ada-v3-s-3	AlignmentResearch	2024-03-19T12:17:49Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:17:34Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-70m-deduped model-index: - name: robust_llm_pythia-tt-70m-mz-ada-v3-s-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. --> # robust_llm_pythia-tt-70m-mz-ada-v3-s-3 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-31m-mz-ada-v3-s-1	AlignmentResearch	2024-03-19T12:17:17Z	107	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "base_model:finetune:EleutherAI/pythia-31m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:17:09Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-31m model-index: - name: robust_llm_pythia-tt-31m-mz-ada-v3-s-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-tt-31m-mz-ada-v3-s-1 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-70m-mz-ada-v3-s-2	AlignmentResearch	2024-03-19T12:16:33Z	106	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:16:20Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-70m-deduped model-index: - name: robust_llm_pythia-tt-70m-mz-ada-v3-s-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-tt-70m-mz-ada-v3-s-2 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-31m-mz-ada-v3-s-3	AlignmentResearch	2024-03-19T12:15:46Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "base_model:finetune:EleutherAI/pythia-31m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:15:38Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-31m model-index: - name: robust_llm_pythia-tt-31m-mz-ada-v3-s-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. --> # robust_llm_pythia-tt-31m-mz-ada-v3-s-3 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-14m-mz-ada-v3-s-2	AlignmentResearch	2024-03-19T12:15:44Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:15:38Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-14m model-index: - name: robust_llm_pythia-tt-14m-mz-ada-v3-s-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-tt-14m-mz-ada-v3-s-2 This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-14m-mz-ada-v3-s-3	AlignmentResearch	2024-03-19T12:15:30Z	104	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:15:24Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-14m model-index: - name: robust_llm_pythia-tt-14m-mz-ada-v3-s-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. --> # robust_llm_pythia-tt-14m-mz-ada-v3-s-3 This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
AlignmentResearch/robust_llm_pythia-tt-31m-mz-ada-v3-s-2	AlignmentResearch	2024-03-19T12:15:17Z	105	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "base_model:finetune:EleutherAI/pythia-31m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:15:10Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-31m model-index: - name: robust_llm_pythia-tt-31m-mz-ada-v3-s-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-tt-31m-mz-ada-v3-s-2 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
calewan/dqn-SpaceInvadersNoFrameskip-v4	calewan	2024-03-19T12:14:29Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-14T06:47:54Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 594.00 +/- 95.15 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga calewan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga calewan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga calewan ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
jitendra89/dataicd	jitendra89	2024-03-19T12:13:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-19T12:13:46Z	--- 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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Rajeevsh01/my-pet-dog	Rajeevsh01	2024-03-19T12:13:02Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-19T12:08:48Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by Rajeevsh01 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/Rajeevsh01/my-pet-dog/resolve/main/sample_images/xzg2.png)
roselyu/FinSent-XLMR-FinNews	roselyu	2024-03-19T12:10:31Z	115	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:09:32Z	--- 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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linuxhunter/q-FrozenLake-v1-4x4-noSlippery	linuxhunter	2024-03-19T12:08:34Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-19T12:08:31Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage model = load_from_hub(repo_id="linuxhunter/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])
Dilipan/detr-finetuned-invoice	Dilipan	2024-03-19T12:08:00Z	226	1	transformers	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	object-detection	2024-03-19T11:11:43Z	--- 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. 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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(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]
tiagomosantos/w2v-bert-2.0-pt_pt_v2	tiagomosantos	2024-03-19T12:07:10Z	8	1	transformers	[ "transformers", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "asr", "w2v-bert-2.0", "pt", "dataset:common_voice_16_1", "base_model:facebook/w2v-bert-2.0", "base_model:finetune:facebook/w2v-bert-2.0", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-17T23:42:48Z	--- license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer - asr - w2v-bert-2.0 datasets: - common_voice_16_1 metrics: - wer - cer - bertscore model-index: - name: w2v-bert-2.0-pt_pt_v2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_16_1 type: common_voice_16_1 config: pt split: validation args: pt metrics: - name: Wer type: wer value: 0.08315087821729188 language: - pt --- <!-- 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. --> # w2v-bert-2.0-pt_pt_v2 This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_1 Portuguese subset using 1XRTX 3090. It achieves the following results on the test set: - Wer: 0.10491320595991134 - Cer: 0.032070871626631914 - Bert Score: 0.9619712047981167 - Sentence Similarity: 0.93867844 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| Bert Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:----------:\| \| 1.2735 \| 1.0 \| 678 \| 0.2292 \| 0.1589 \| 0.0415 \| 0.9498 \| \| 0.1715 \| 2.0 \| 1356 \| 0.1762 \| 0.1283 \| 0.0344 \| 0.9599 \| \| 0.1158 \| 3.0 \| 2034 \| 0.1539 \| 0.1100 \| 0.0298 \| 0.9646 \| \| 0.0821 \| 4.0 \| 2712 \| 0.1362 \| 0.0949 \| 0.0258 \| 0.9703 \| \| 0.0605 \| 5.0 \| 3390 \| 0.1349 \| 0.0860 \| 0.0236 \| 0.9728 \| \| 0.0475 \| 6.0 \| 4068 \| 0.1395 \| 0.0871 \| 0.0239 \| 0.9728 \| \| 0.0355 \| 7.0 \| 4746 \| 0.1487 \| 0.0837 \| 0.0230 \| 0.9739 \| \| 0.0309 \| 8.0 \| 5424 \| 0.1452 \| 0.0873 \| 0.0240 \| 0.9728 \| \| 0.0308 \| 9.0 \| 6102 \| 0.1390 \| 0.0843 \| 0.0228 \| 0.9735 \| \| 0.0239 \| 10.0 \| 6780 \| 0.1282 \| 0.0832 \| 0.0224 \| 0.9739 \| ### Evaluation results \| Test Wer \| Test Cer \| Test Bert Score \| Runtime \| Samples per second \| \|:------------------:\|:-------------------:\|:-----------------:\|:-------:\|:---------------------:\| \| 0.09146400542583083\| 0.02643665913309742 \| 0.9702128323433327\| 266.8185\| 35.282 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.0 - Datasets 2.18.0 - Tokenizers 0.15.2
yemen2016/MeMo_BERT-WSD-DanskBERT	yemen2016	2024-03-19T12:06:11Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:vesteinn/DanskBERT", "base_model:finetune:vesteinn/DanskBERT", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T12:00:39Z	--- license: cc-by-4.0 base_model: vesteinn/DanskBERT tags: - generated_from_trainer model-index: - name: MeMo_BERT-WSD-DanskBERT 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. --> # MeMo_BERT-WSD-DanskBERT This model is a fine-tuned version of [vesteinn/DanskBERT](https://huggingface.co/vesteinn/DanskBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7755 - F1-score: 0.5209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1-score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 61 \| 1.4766 \| 0.1229 \| \| No log \| 2.0 \| 122 \| 1.4366 \| 0.1229 \| \| No log \| 3.0 \| 183 \| 1.3636 \| 0.2462 \| \| No log \| 4.0 \| 244 \| 1.2889 \| 0.3692 \| \| No log \| 5.0 \| 305 \| 1.4150 \| 0.3786 \| \| No log \| 6.0 \| 366 \| 1.5581 \| 0.3409 \| \| No log \| 7.0 \| 427 \| 1.6512 \| 0.4664 \| \| No log \| 8.0 \| 488 \| 1.7405 \| 0.4661 \| \| 0.9424 \| 9.0 \| 549 \| 1.7755 \| 0.5209 \| \| 0.9424 \| 10.0 \| 610 \| 2.4738 \| 0.4351 \| \| 0.9424 \| 11.0 \| 671 \| 2.4721 \| 0.4858 \| \| 0.9424 \| 12.0 \| 732 \| 2.9449 \| 0.4491 \| \| 0.9424 \| 13.0 \| 793 \| 2.8346 \| 0.4528 \| \| 0.9424 \| 14.0 \| 854 \| 3.0715 \| 0.4845 \| \| 0.9424 \| 15.0 \| 915 \| 3.1416 \| 0.4520 \| \| 0.9424 \| 16.0 \| 976 \| 3.0893 \| 0.5197 \| \| 0.1197 \| 17.0 \| 1037 \| 3.1668 \| 0.4764 \| \| 0.1197 \| 18.0 \| 1098 \| 3.2142 \| 0.4656 \| \| 0.1197 \| 19.0 \| 1159 \| 3.2174 \| 0.5087 \| \| 0.1197 \| 20.0 \| 1220 \| 3.2239 \| 0.5087 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Esmail-AGumaan/Transformer-model	Esmail-AGumaan	2024-03-19T12:01:43Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-03-16T09:54:52Z	--- license: mit --- # Transformer model from scratch. ![image](https://github.com/Esmail-ibraheem/transformer-architecture-library/assets/113830751/8552eab6-0b7b-496b-848e-12b0476a7820)
ritutweets46/layoutlm-funsd	ritutweets46	2024-03-19T12:00:21Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "layoutlm", "token-classification", "generated_from_trainer", "dataset:funsd", "base_model:microsoft/layoutlm-base-uncased", "base_model:finetune:microsoft/layoutlm-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-05T14:12:55Z	--- license: mit base_model: microsoft/layoutlm-base-uncased tags: - generated_from_trainer datasets: - funsd model-index: - name: layoutlm-funsd 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. --> # layoutlm-funsd This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. It achieves the following results on the evaluation set: - Loss: 1.6784 - Answer: {'precision': 0.4630541871921182, 'recall': 0.580964153275649, 'f1': 0.5153508771929824, 'number': 809} - Header: {'precision': 0.4470588235294118, 'recall': 0.31932773109243695, 'f1': 0.37254901960784315, 'number': 119} - Question: {'precision': 0.5822259136212624, 'recall': 0.6582159624413145, 'f1': 0.617893345085941, 'number': 1065} - Overall Precision: 0.5247 - Overall Recall: 0.6066 - Overall F1: 0.5627 - Overall Accuracy: 0.6411 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Answer \| Header \| Question \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------------------------------------------------------------------------------------------------:\|:------------------------------------------------------------------------------------------------------------:\|:-----------------------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 1.7288 \| 1.0 \| 10 \| 1.5435 \| {'precision': 0.022058823529411766, 'recall': 0.014833127317676144, 'f1': 0.017738359201773836, 'number': 809} \| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} \| {'precision': 0.19180633147113593, 'recall': 0.09671361502347418, 'f1': 0.1285892634207241, 'number': 1065} \| 0.1064 \| 0.0577 \| 0.0748 \| 0.3266 \| \| 1.4353 \| 2.0 \| 20 \| 1.3528 \| {'precision': 0.20369301788805538, 'recall': 0.4363411619283066, 'f1': 0.27773406766325726, 'number': 809} \| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} \| {'precision': 0.2926481084939329, 'recall': 0.38497652582159625, 'f1': 0.33252230332522303, 'number': 1065} \| 0.2427 \| 0.3828 \| 0.2971 \| 0.4262 \| \| 1.2751 \| 3.0 \| 30 \| 1.2397 \| {'precision': 0.24175084175084174, 'recall': 0.4437577255871446, 'f1': 0.3129904097646033, 'number': 809} \| {'precision': 0.03636363636363636, 'recall': 0.01680672268907563, 'f1': 0.022988505747126436, 'number': 119} \| {'precision': 0.34423076923076923, 'recall': 0.504225352112676, 'f1': 0.40914285714285714, 'number': 1065} \| 0.2897 \| 0.4506 \| 0.3526 \| 0.4736 \| \| 1.1382 \| 4.0 \| 40 \| 1.1096 \| {'precision': 0.2822349570200573, 'recall': 0.48702101359703337, 'f1': 0.3573696145124716, 'number': 809} \| {'precision': 0.2111111111111111, 'recall': 0.15966386554621848, 'f1': 0.1818181818181818, 'number': 119} \| {'precision': 0.4142857142857143, 'recall': 0.5446009389671361, 'f1': 0.4705882352941177, 'number': 1065} \| 0.3441 \| 0.4982 \| 0.4071 \| 0.5598 \| \| 1.0207 \| 5.0 \| 50 \| 1.1986 \| {'precision': 0.3122302158273381, 'recall': 0.5364647713226205, 'f1': 0.39472487494315595, 'number': 809} \| {'precision': 0.2696629213483146, 'recall': 0.20168067226890757, 'f1': 0.23076923076923078, 'number': 119} \| {'precision': 0.43670411985018726, 'recall': 0.5474178403755868, 'f1': 0.48583333333333334, 'number': 1065} \| 0.3699 \| 0.5223 \| 0.4331 \| 0.5405 \| \| 0.9534 \| 6.0 \| 60 \| 1.1446 \| {'precision': 0.32223796033994334, 'recall': 0.5624227441285538, 'f1': 0.4097253489419181, 'number': 809} \| {'precision': 0.3, 'recall': 0.17647058823529413, 'f1': 0.22222222222222224, 'number': 119} \| {'precision': 0.48997995991983967, 'recall': 0.4591549295774648, 'f1': 0.47406689287445475, 'number': 1065} \| 0.3891 \| 0.4842 \| 0.4315 \| 0.5634 \| \| 0.854 \| 7.0 \| 70 \| 1.0533 \| {'precision': 0.38318777292576417, 'recall': 0.4338689740420272, 'f1': 0.4069565217391304, 'number': 809} \| {'precision': 0.264, 'recall': 0.2773109243697479, 'f1': 0.27049180327868855, 'number': 119} \| {'precision': 0.4503968253968254, 'recall': 0.6394366197183099, 'f1': 0.5285215366705472, 'number': 1065} \| 0.4172 \| 0.5344 \| 0.4685 \| 0.5950 \| \| 0.7821 \| 8.0 \| 80 \| 1.0447 \| {'precision': 0.3707482993197279, 'recall': 0.5389369592088998, 'f1': 0.43929471032745593, 'number': 809} \| {'precision': 0.3373493975903614, 'recall': 0.23529411764705882, 'f1': 0.2772277227722772, 'number': 119} \| {'precision': 0.4789594491201224, 'recall': 0.5877934272300469, 'f1': 0.5278246205733558, 'number': 1065} \| 0.4248 \| 0.5469 \| 0.4782 \| 0.6234 \| \| 0.7291 \| 9.0 \| 90 \| 1.1487 \| {'precision': 0.39887005649717516, 'recall': 0.4363411619283066, 'f1': 0.41676505312868956, 'number': 809} \| {'precision': 0.3106060606060606, 'recall': 0.3445378151260504, 'f1': 0.32669322709163345, 'number': 119} \| {'precision': 0.4737201365187713, 'recall': 0.6516431924882629, 'f1': 0.5486166007905138, 'number': 1065} \| 0.4384 \| 0.5459 \| 0.4863 \| 0.5976 \| \| 0.7222 \| 10.0 \| 100 \| 1.0862 \| {'precision': 0.36459246275197194, 'recall': 0.5142150803461063, 'f1': 0.42666666666666664, 'number': 809} \| {'precision': 0.33, 'recall': 0.2773109243697479, 'f1': 0.30136986301369867, 'number': 119} \| {'precision': 0.48, 'recall': 0.6422535211267606, 'f1': 0.5493975903614458, 'number': 1065} \| 0.4250 \| 0.5685 \| 0.4864 \| 0.6184 \| \| 0.6242 \| 11.0 \| 110 \| 1.2087 \| {'precision': 0.3969465648854962, 'recall': 0.5142150803461063, 'f1': 0.44803446418955306, 'number': 809} \| {'precision': 0.2536231884057971, 'recall': 0.29411764705882354, 'f1': 0.2723735408560311, 'number': 119} \| {'precision': 0.5061124694376528, 'recall': 0.5830985915492958, 'f1': 0.5418848167539267, 'number': 1065} \| 0.4443 \| 0.5379 \| 0.4866 \| 0.6010 \| \| 0.5972 \| 12.0 \| 120 \| 1.1726 \| {'precision': 0.43176972281449894, 'recall': 0.5006180469715699, 'f1': 0.4636519748139668, 'number': 809} \| {'precision': 0.32989690721649484, 'recall': 0.2689075630252101, 'f1': 0.2962962962962963, 'number': 119} \| {'precision': 0.5732430143945809, 'recall': 0.6356807511737089, 'f1': 0.6028495102404274, 'number': 1065} \| 0.5027 \| 0.5590 \| 0.5293 \| 0.6228 \| \| 0.5502 \| 13.0 \| 130 \| 1.2468 \| {'precision': 0.4368048533872599, 'recall': 0.5339925834363412, 'f1': 0.4805339265850946, 'number': 809} \| {'precision': 0.3181818181818182, 'recall': 0.29411764705882354, 'f1': 0.3056768558951965, 'number': 119} \| {'precision': 0.5737855178735105, 'recall': 0.5877934272300469, 'f1': 0.5807050092764378, 'number': 1065} \| 0.4991 \| 0.5484 \| 0.5226 \| 0.6096 \| \| 0.507 \| 14.0 \| 140 \| 1.2631 \| {'precision': 0.44842105263157894, 'recall': 0.5265760197775031, 'f1': 0.4843661171119955, 'number': 809} \| {'precision': 0.3017241379310345, 'recall': 0.29411764705882354, 'f1': 0.29787234042553185, 'number': 119} \| {'precision': 0.5765529308836396, 'recall': 0.6187793427230047, 'f1': 0.5969202898550725, 'number': 1065} \| 0.5070 \| 0.5620 \| 0.5331 \| 0.6096 \| \| 0.4834 \| 15.0 \| 150 \| 1.2604 \| {'precision': 0.41264559068219636, 'recall': 0.6131025957972805, 'f1': 0.4932869219293884, 'number': 809} \| {'precision': 0.3793103448275862, 'recall': 0.2773109243697479, 'f1': 0.32038834951456313, 'number': 119} \| {'precision': 0.6035015447991761, 'recall': 0.5502347417840375, 'f1': 0.5756385068762279, 'number': 1065} \| 0.4934 \| 0.5595 \| 0.5243 \| 0.6207 \| \| 0.4621 \| 16.0 \| 160 \| 1.2164 \| {'precision': 0.4373695198329854, 'recall': 0.5179233621755254, 'f1': 0.47425014148273914, 'number': 809} \| {'precision': 0.3644859813084112, 'recall': 0.3277310924369748, 'f1': 0.3451327433628319, 'number': 119} \| {'precision': 0.5385232744783307, 'recall': 0.6300469483568075, 'f1': 0.5807009952401558, 'number': 1065} \| 0.4885 \| 0.5665 \| 0.5246 \| 0.6197 \| \| 0.4211 \| 17.0 \| 170 \| 1.2596 \| {'precision': 0.4471299093655589, 'recall': 0.5488257107540173, 'f1': 0.4927857935627081, 'number': 809} \| {'precision': 0.3229166666666667, 'recall': 0.2605042016806723, 'f1': 0.28837209302325584, 'number': 119} \| {'precision': 0.5671641791044776, 'recall': 0.6422535211267606, 'f1': 0.6023778071334215, 'number': 1065} \| 0.5050 \| 0.5815 \| 0.5406 \| 0.6253 \| \| 0.3774 \| 18.0 \| 180 \| 1.2991 \| {'precision': 0.47, 'recall': 0.522867737948084, 'f1': 0.4950263311878291, 'number': 809} \| {'precision': 0.32407407407407407, 'recall': 0.29411764705882354, 'f1': 0.30837004405286345, 'number': 119} \| {'precision': 0.5509404388714734, 'recall': 0.6600938967136151, 'f1': 0.600598035027766, 'number': 1065} \| 0.5083 \| 0.5825 \| 0.5429 \| 0.6279 \| \| 0.359 \| 19.0 \| 190 \| 1.3849 \| {'precision': 0.45615763546798027, 'recall': 0.5723114956736712, 'f1': 0.5076754385964912, 'number': 809} \| {'precision': 0.36633663366336633, 'recall': 0.31092436974789917, 'f1': 0.33636363636363636, 'number': 119} \| {'precision': 0.5760576057605761, 'recall': 0.6009389671361502, 'f1': 0.5882352941176471, 'number': 1065} \| 0.5119 \| 0.5720 \| 0.5403 \| 0.6079 \| \| 0.3301 \| 20.0 \| 200 \| 1.3047 \| {'precision': 0.4407239819004525, 'recall': 0.6019777503090235, 'f1': 0.5088819226750262, 'number': 809} \| {'precision': 0.3695652173913043, 'recall': 0.2857142857142857, 'f1': 0.3222748815165877, 'number': 119} \| {'precision': 0.595724907063197, 'recall': 0.6018779342723005, 'f1': 0.5987856141989725, 'number': 1065} \| 0.5112 \| 0.5830 \| 0.5448 \| 0.6240 \| \| 0.3018 \| 21.0 \| 210 \| 1.4471 \| {'precision': 0.46602972399150744, 'recall': 0.5426452410383189, 'f1': 0.5014277555682467, 'number': 809} \| {'precision': 0.32989690721649484, 'recall': 0.2689075630252101, 'f1': 0.2962962962962963, 'number': 119} \| {'precision': 0.5692695214105793, 'recall': 0.6366197183098592, 'f1': 0.601063829787234, 'number': 1065} \| 0.5152 \| 0.5765 \| 0.5442 \| 0.6149 \| \| 0.2953 \| 22.0 \| 220 \| 1.3775 \| {'precision': 0.4605263157894737, 'recall': 0.5624227441285538, 'f1': 0.5063995548135781, 'number': 809} \| {'precision': 0.3333333333333333, 'recall': 0.3025210084033613, 'f1': 0.3171806167400881, 'number': 119} \| {'precision': 0.5631067961165048, 'recall': 0.6535211267605634, 'f1': 0.6049543676662321, 'number': 1065} \| 0.5090 \| 0.5956 \| 0.5489 \| 0.6281 \| \| 0.2615 \| 23.0 \| 230 \| 1.4087 \| {'precision': 0.45692883895131087, 'recall': 0.6032138442521632, 'f1': 0.5199786893979754, 'number': 809} \| {'precision': 0.3333333333333333, 'recall': 0.2773109243697479, 'f1': 0.30275229357798167, 'number': 119} \| {'precision': 0.5908683974932856, 'recall': 0.6197183098591549, 'f1': 0.6049495875343722, 'number': 1065} \| 0.5171 \| 0.5926 \| 0.5523 \| 0.6290 \| \| 0.254 \| 24.0 \| 240 \| 1.3707 \| {'precision': 0.4535353535353535, 'recall': 0.5550061804697157, 'f1': 0.4991662034463591, 'number': 809} \| {'precision': 0.32432432432432434, 'recall': 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0.5178173719376392, 'number': 809} \| {'precision': 0.43010752688172044, 'recall': 0.33613445378151263, 'f1': 0.37735849056603776, 'number': 119} \| {'precision': 0.5836038961038961, 'recall': 0.6751173708920187, 'f1': 0.6260339573356551, 'number': 1065} \| 0.5294 \| 0.6141 \| 0.5686 \| 0.6430 \| \| 0.099 \| 45.0 \| 450 \| 1.6565 \| {'precision': 0.4645222327341533, 'recall': 0.6069221260815822, 'f1': 0.5262593783494106, 'number': 809} \| {'precision': 0.43956043956043955, 'recall': 0.33613445378151263, 'f1': 0.380952380952381, 'number': 119} \| {'precision': 0.5934819897084048, 'recall': 0.6497652582159624, 'f1': 0.6203496190049306, 'number': 1065} \| 0.5285 \| 0.6136 \| 0.5679 \| 0.6429 \| \| 0.0993 \| 46.0 \| 460 \| 1.6493 \| {'precision': 0.46771037181996084, 'recall': 0.5908529048207664, 'f1': 0.5221190606226106, 'number': 809} \| {'precision': 0.43023255813953487, 'recall': 0.31092436974789917, 'f1': 0.36097560975609755, 'number': 119} \| {'precision': 0.5841004184100418, 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'recall': 0.5747836835599506, 'f1': 0.5132450331125828, 'number': 809} \| {'precision': 0.4418604651162791, 'recall': 0.31932773109243695, 'f1': 0.3707317073170731, 'number': 119} \| {'precision': 0.5841666666666666, 'recall': 0.6582159624413145, 'f1': 0.6189845474613687, 'number': 1065} \| 0.5260 \| 0.6041 \| 0.5624 \| 0.6392 \| \| 0.0896 \| 50.0 \| 500 \| 1.6784 \| {'precision': 0.4630541871921182, 'recall': 0.580964153275649, 'f1': 0.5153508771929824, 'number': 809} \| {'precision': 0.4470588235294118, 'recall': 0.31932773109243695, 'f1': 0.37254901960784315, 'number': 119} \| {'precision': 0.5822259136212624, 'recall': 0.6582159624413145, 'f1': 0.617893345085941, 'number': 1065} \| 0.5247 \| 0.6066 \| 0.5627 \| 0.6411 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
kaitchup/Mayonnaise-4in1-02-awq-4bit	kaitchup	2024-03-19T11:59:58Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "awq", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2024-03-19T08:37:16Z	--- language: - en license: apache-2.0 library_name: transformers tags: - merge - awq --- ## Model Details kaitchup/Mayonnaise-4in1-02 quantized to 4-bit with AutoAWQ. The original model was created using a recipe detailed in this article: [The Mayonnaise: Rank First on the Open LLM Leaderboard with TIES-Merging ](https://kaitchup.substack.com/p/the-mayonnaise-rank-first-on-the) ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [The Kaitchup](https://kaitchup.substack.com/) - Model type: Causal - Language(s) (NLP): English - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
ingeol/facets_128b	ingeol	2024-03-19T11:56:10Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-19T11:55:46Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/facets_128b This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/facets_128b') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/facets_128b') model = AutoModel.from_pretrained('ingeol/facets_128b') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/facets_128b) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3899 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
July24/deberta-v3-base-prompt-injection	July24	2024-03-19T11:55:27Z	106	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-21T11:04:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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codebyershivam/my-pet-cow-xzg	codebyershivam	2024-03-19T11:48:14Z	1	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-19T11:44:05Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cow-xzg Dreambooth model trained by codebyershivam following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/codebyershivam/my-pet-cow-xzg/resolve/main/sample_images/xzg05.jpeg)
AndromedaPL/openchat-prometheus-v0.1-GGUF	AndromedaPL	2024-03-19T11:45:41Z	2	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:openchat/openchat-3.5-0106", "base_model:quantized:openchat/openchat-3.5-0106", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-18T21:34:14Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: openchat/openchat-3.5-0106 --- # Uploaded model - Developed by: AndromedaPL - License: apache-2.0 - Finetuned from model : openchat/openchat-3.5-0106 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)
InferenceIllusionist/M7-Evil-7b-GGUF	InferenceIllusionist	2024-03-19T11:43:01Z	411	5	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-03-16T10:46:42Z	<img src="https://files.catbox.moe/40bgsm.png" width="500"/> PiVoT-0.1-Evil-a SLERPed with M7-7b through sheer chaos. <b>Use in conjunction with [this mmproj file](https://huggingface.co/koboldcpp/mmproj/resolve/main/mistral-7b-mmproj-v1.5-Q4_1.gguf) in Kobold</b> <i>Warning: Extreme experimental merge for use with mmproj files only but might be good for other cases too idk.</i> <i>Warning #2: Can insult/berrate user without provocation or warrant at times during image identification so beware.</i> <h1>Update</h1> Probably pointless to upload this since this merge is just for fun. But if anyone is interested: This merge's unique characteristics are amplified by an equally chaotic set of samplers. Latest SillyTavern staging and Kobold releases recommended. Works very well with Smoothing Curve. After testing ST is the definitve front end for this. Just make sure you configure Image Captioning in ST. <img src="https://imgur.com/wvVe6n8.jpg" width="500"/> <img src="https://imgur.com/jmzkPl9.jpg" width="500"/> <img src="https://imgur.com/IPW8meJ.jpg" width="500"/>
cijo-uc/lilt-demo-service-list-8	cijo-uc	2024-03-19T11:29:11Z	159	0	transformers	[ "transformers", "safetensors", "lilt", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-19T11:29:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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sinhala-nlp/NSINA-Media-sinbert-small	sinhala-nlp	2024-03-19T11:27:17Z	106	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "si", "dataset:sinhala-nlp/NSINA", "dataset:sinhala-nlp/NSINA-Media", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T11:25:04Z	--- license: cc-by-sa-4.0 datasets: - sinhala-nlp/NSINA - sinhala-nlp/NSINA-Media language: - si --- # Sinhala News Media Identification This is a text classification task created with the [NSINA dataset](https://github.com/Sinhala-NLP/NSINA). This dataset is also released with the same license as NSINA. The task is identifying news media given the news content. ## Data We only used 10,000 instances in NSINA 1.0 from each news source. For the two sources that had less than 10,000 instances ("Dinamina" and "Siyatha") we used the original number of instances they contained. We divided this dataset into a training and test set following a 0.8 split. Data can be loaded into pandas dataframes using the following code. ```python from datasets import Dataset from datasets import load_dataset train = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Media', split='train')) test = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Media', split='test')) ``` ## Citation If you are using the dataset or the models, please cite the following paper. ~~~ @inproceedings{Nsina2024, author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu}, title={{NSINA: A News Corpus for Sinhala}}, booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, year={2024}, month={May}, } ~~~
sinhala-nlp/NSINA-Media-xlmr-large	sinhala-nlp	2024-03-19T11:23:39Z	7	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "si", "dataset:sinhala-nlp/NSINA", "dataset:sinhala-nlp/NSINA-Media", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-18T20:17:08Z	--- license: cc-by-sa-4.0 datasets: - sinhala-nlp/NSINA - sinhala-nlp/NSINA-Media language: - si --- # Sinhala News Media Identification This is a text classification task created with the [NSINA dataset](https://github.com/Sinhala-NLP/NSINA). This dataset is also released with the same license as NSINA. The task is identifying news media given the news content. ## Data We only used 10,000 instances in NSINA 1.0 from each news source. For the two sources that had less than 10,000 instances ("Dinamina" and "Siyatha") we used the original number of instances they contained. We divided this dataset into a training and test set following a 0.8 split. Data can be loaded into pandas dataframes using the following code. ```python from datasets import Dataset from datasets import load_dataset train = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Media', split='train')) test = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Media', split='test')) ``` ## Citation If you are using the dataset or the models, please cite the following paper. ~~~ @inproceedings{Nsina2024, author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu}, title={{NSINA: A News Corpus for Sinhala}}, booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, year={2024}, month={May}, } ~~~
kalese/opus-mt-en-bkm-10e	kalese	2024-03-19T11:23:20Z	118	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "dataset:arrow", "base_model:Helsinki-NLP/opus-mt-en-ro", "base_model:finetune:Helsinki-NLP/opus-mt-en-ro", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-19T09:22:48Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-ro tags: - generated_from_trainer datasets: - arrow metrics: - bleu model-index: - name: opus-mt-en-bkm-10e results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: arrow type: arrow config: default split: train args: default metrics: - name: Bleu type: bleu value: 7.1794 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-bkm-10e This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the arrow dataset. It achieves the following results on the evaluation set: - Loss: 1.5644 - Bleu: 7.1794 - Gen Len: 60.0222 ## 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: 96 - eval_batch_size: 96 - 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 \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 186 \| 2.6868 \| 0.299 \| 76.178 \| \| No log \| 2.0 \| 372 \| 2.0190 \| 2.2444 \| 67.167 \| \| 3.115 \| 3.0 \| 558 \| 1.8364 \| 4.5959 \| 59.7357 \| \| 3.115 \| 4.0 \| 744 \| 1.7372 \| 5.1827 \| 61.7218 \| \| 3.115 \| 5.0 \| 930 \| 1.6732 \| 5.8295 \| 59.7346 \| \| 1.8706 \| 6.0 \| 1116 \| 1.6301 \| 6.4389 \| 60.9085 \| \| 1.8706 \| 7.0 \| 1302 \| 1.6002 \| 6.6498 \| 60.4191 \| \| 1.8706 \| 8.0 \| 1488 \| 1.5792 \| 6.8315 \| 60.2721 \| \| 1.7133 \| 9.0 \| 1674 \| 1.5680 \| 7.1239 \| 60.5609 \| \| 1.7133 \| 10.0 \| 1860 \| 1.5644 \| 7.1794 \| 60.0222 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Wilfrenm/ainew1	Wilfrenm	2024-03-19T11:23:05Z	23	0	diffusers	[ "diffusers", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-19T11:19:21Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### AINew1 Dreambooth model trained by Wilfrenm following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 232035 Sample pictures of this concept: ![0](https://huggingface.co/Wilfrenm/ainew1/resolve/main/sample_images/xzg2.jpg)
sinhala-nlp/NSINA-Media-xlmr-base	sinhala-nlp	2024-03-19T11:22:00Z	104	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "si", "dataset:sinhala-nlp/NSINA", "dataset:sinhala-nlp/NSINA-Media", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-18T20:17:43Z	--- license: cc-by-sa-4.0 datasets: - sinhala-nlp/NSINA - sinhala-nlp/NSINA-Media language: - si --- # Sinhala News Media Identification This is a text classification task created with the [NSINA dataset](https://github.com/Sinhala-NLP/NSINA). This dataset is also released with the same license as NSINA. The task is identifying news media given the news content. ## Data We only used 10,000 instances in NSINA 1.0 from each news source. For the two sources that had less than 10,000 instances ("Dinamina" and "Siyatha") we used the original number of instances they contained. We divided this dataset into a training and test set following a 0.8 split. Data can be loaded into pandas dataframes using the following code. ```python from datasets import Dataset from datasets import load_dataset train = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Media', split='train')) test = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA-Media', split='test')) ``` ## Citation If you are using the dataset or the models, please cite the following paper. ~~~ @inproceedings{Nsina2024, author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu}, title={{NSINA: A News Corpus for Sinhala}}, booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, year={2024}, month={May}, } ~~~
santhoshkammari/PEFTFinetune-Bloom7B	santhoshkammari	2024-03-19T11:17:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-19T11:05:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ldowey/llama-2-7b-sentinomics	ldowey	2024-03-19T11:13:27Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-18T17:30:22Z	--- 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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yemen2016/MeMo_BERT-WSD-01	yemen2016	2024-03-19T11:12:41Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:MiMe-MeMo/MeMo-BERT-01", "base_model:finetune:MiMe-MeMo/MeMo-BERT-01", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-19T10:47:26Z	--- base_model: MiMe-MeMo/MeMo_BERT-01 tags: - generated_from_trainer model-index: - name: MeMo_BERT-01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MeMo_BERT-01 This model is a fine-tuned version of [MiMe-MeMo/MeMo_BERT-01](https://huggingface.co/MiMe-MeMo/MeMo_BERT-01) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8264 - F1-score: 0.4148 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1-score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 61 \| 1.5574 \| 0.1229 \| \| No log \| 2.0 \| 122 \| 1.4491 \| 0.1477 \| \| No log \| 3.0 \| 183 \| 1.3661 \| 0.2112 \| \| No log \| 4.0 \| 244 \| 1.7131 \| 0.3356 \| \| No log \| 5.0 \| 305 \| 2.0026 \| 0.3592 \| \| No log \| 6.0 \| 366 \| 2.8807 \| 0.2263 \| \| No log \| 7.0 \| 427 \| 2.8357 \| 0.3625 \| \| No log \| 8.0 \| 488 \| 3.5272 \| 0.3150 \| \| 0.7496 \| 9.0 \| 549 \| 3.5185 \| 0.3402 \| \| 0.7496 \| 10.0 \| 610 \| 3.8264 \| 0.4148 \| \| 0.7496 \| 11.0 \| 671 \| 4.5875 \| 0.2999 \| \| 0.7496 \| 12.0 \| 732 \| 4.3188 \| 0.3392 \| \| 0.7496 \| 13.0 \| 793 \| 4.7118 \| 0.3098 \| \| 0.7496 \| 14.0 \| 854 \| 4.5895 \| 0.3319 \| \| 0.7496 \| 15.0 \| 915 \| 4.6764 \| 0.3033 \| \| 0.7496 \| 16.0 \| 976 \| 4.6250 \| 0.3319 \| \| 0.01 \| 17.0 \| 1037 \| 4.7393 \| 0.3176 \| \| 0.01 \| 18.0 \| 1098 \| 4.7690 \| 0.3176 \| \| 0.01 \| 19.0 \| 1159 \| 4.7655 \| 0.3176 \| \| 0.01 \| 20.0 \| 1220 \| 4.7695 \| 0.3176 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
automerger/Inex12Neural-7B	automerger	2024-03-19T11:12:39Z	3	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:Kukedlc/Neural-Krishna-Multiverse-7b-v3", "base_model:merge:Kukedlc/Neural-Krishna-Multiverse-7b-v3", "base_model:MSL7/INEX12-7b", "base_model:merge:MSL7/INEX12-7b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-19T11:11:48Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - MSL7/INEX12-7b - Kukedlc/Neural-Krishna-Multiverse-7b-v3 --- # Inex12Neural-7B Inex12Neural-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [MSL7/INEX12-7b](https://huggingface.co/MSL7/INEX12-7b) * [Kukedlc/Neural-Krishna-Multiverse-7b-v3](https://huggingface.co/Kukedlc/Neural-Krishna-Multiverse-7b-v3) ## 🧩 Configuration ```yaml slices: - sources: - model: MSL7/INEX12-7b layer_range: [0, 32] - model: Kukedlc/Neural-Krishna-Multiverse-7b-v3 layer_range: [0, 32] merge_method: slerp base_model: MSL7/INEX12-7b 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 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Inex12Neural-7B" 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"]) ```
jcjo/pjy-cat-sd-lora	jcjo	2024-03-19T11:11:00Z	1	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-19T07:04:14Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: '<pjy0><pjy1> realistic, 4k, high resolution cat, ' base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: <pjy0><pjy1> realistic, 4k, high resolution cat, license: openrail++ --- # SDXL LoRA DreamBooth - jcjo/pjy-cat-sd-lora <Gallery /> ## Model description ### These are jcjo/pjy-cat-sd-lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`/content/drive/MyDrive/Colab_Notebooks/DA Matching Algorithm/AI/pet_babyAI//ckpt/cat3.safetensors` here 💾](/jcjo/pjy-cat-sd-lora/blob/main//content/drive/MyDrive/Colab_Notebooks/DA Matching Algorithm/AI/pet_babyAI//ckpt/cat3.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:/content/drive/MyDrive/Colab_Notebooks/DA Matching Algorithm/AI/pet_babyAI//ckpt/cat3:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jcjo/pjy-cat-sd-lora', weight_name='pytorch_lora_weights.safetensors') image = pipeline('True').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words You should use <pjy0><pjy1> realistic, 4k, high resolution cat, to trigger the image generation. ## Details All [Files & versions](/jcjo/pjy-cat-sd-lora/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. True. Pivotal tuning was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

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