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leeloolee/boosted-qwen2-7b	leeloolee	2024-07-17T14:38:43Z	5	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T06:09:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
sinequa/passage-ranker.pistachio	sinequa	2024-07-17T14:37:27Z	252	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "de", "en", "es", "fr", "it", "ja", "nl", "pt", "zh", "pl", "arxiv:1901.04085", "arxiv:1611.09268", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-11T14:02:06Z	--- language: - de - en - es - fr - it - ja - nl - pt - zh - pl --- # Model Card for `passage-ranker.pistachio` This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is used to order search results. Model name: `passage-ranker.pistachio` ## Supported Languages The model was trained and tested in the following languages: - English - French - German - Spanish - Italian - Dutch - Japanese - Portuguese - Chinese (simplified) - Polish Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during the pretraining of the base model (see [list of languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages)). ## Scores \| Metric \| Value \| \|:----------------------------\|------:\| \| English Relevance (NDCG@10) \| 0.474 \| \| Polish Relevance (NDCG@10) \| 0.380 \| Note that the relevance score is computed as an average over several retrieval datasets (see [details below](#evaluation-metrics)). ## Inference Times \| GPU \| Quantization type \| Batch size 1 \| Batch size 32 \| \|:------------------------------------------\|:------------------\|---------------:\|---------------:\| \| NVIDIA A10 \| FP16 \| 2 ms \| 28 ms \| \| NVIDIA A10 \| FP32 \| 4 ms \| 82 ms \| \| NVIDIA T4 \| FP16 \| 3 ms \| 65 ms \| \| NVIDIA T4 \| FP32 \| 14 ms \| 369 ms \| \| NVIDIA L4 \| FP16 \| 3 ms \| 38 ms \| \| NVIDIA L4 \| FP32 \| 5 ms \| 123 ms \| ## Gpu Memory usage \| Quantization type \| Memory \| \|:-------------------------------------------------\|-----------:\| \| FP16 \| 850 MiB \| \| FP32 \| 1200 MiB \| Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which can be around 0.5 to 1 GiB depending on the used GPU. ## Requirements - Minimal Sinequa version: 11.10.0 - Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0 - [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use) ## Model Details ### Overview - Number of parameters: 167 million - Base language model: [Multilingual BERT-Base](https://huggingface.co/bert-base-multilingual-uncased) - Insensitive to casing and accents - Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085) ### Training Data - MS MARCO Passage Ranking ([Paper](https://arxiv.org/abs/1611.09268), [Official Page](https://microsoft.github.io/msmarco/), [English & translated datasets on the HF dataset hub](https://huggingface.co/datasets/unicamp-dl/mmarco), [translated dataset in Polish on the HF dataset hub](https://huggingface.co/datasets/clarin-knext/msmarco-pl)) - Original English dataset - Translated datasets for the other nine supported languages ### Evaluation Metrics ##### English To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the [BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English. \| Dataset \| NDCG@10 \| \|:------------------\|--------:\| \| Average \| 0.474 \| \| \| \| \| Arguana \| 0.539 \| \| CLIMATE-FEVER \| 0.230 \| \| DBPedia Entity \| 0.369 \| \| FEVER \| 0.765 \| \| FiQA-2018 \| 0.329 \| \| HotpotQA \| 0.694 \| \| MS MARCO \| 0.413 \| \| NFCorpus \| 0.337 \| \| NQ \| 0.486 \| \| Quora \| 0.714 \| \| SCIDOCS \| 0.144 \| \| SciFact \| 0.649 \| \| TREC-COVID \| 0.651 \| \| Webis-Touche-2020 \| 0.312 \| #### Polish This model has polish capacities, that are being evaluated over a subset of the [PIRBenchmark](https://github.com/sdadas/pirb) with BM25 as the first stage retrieval. \| Dataset \| NDCG@10 \| \|:--------------\|--------:\| \| Average \| 0.380 \| \| \| \| \| arguana-pl \| 0.285 \| \| dbpedia-pl \| 0.283 \| \| fiqa-pl \| 0.223 \| \| hotpotqa-pl \| 0.603 \| \| msmarco-pl \| 0.259 \| \| nfcorpus-pl \| 0.293 \| \| nq-pl \| 0.355 \| \| quora-pl \| 0.613 \| \| scidocs-pl \| 0.128 \| \| scifact-pl \| 0.581 \| \| trec-covid-pl \| 0.560 \| #### Other languages We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) to test its multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics for the existing languages. \| Language \| NDCG@10 \| \|:----------------------\|--------:\| \| French \| 0.439 \| \| German \| 0.418 \| \| Spanish \| 0.487 \| \| Japanese \| 0.517 \| \| Chinese (simplified) \| 0.454 \|
Diluzx/gemma-2-9b-bnb-4bit	Diluzx	2024-07-17T14:37:06Z	20	0	transformers	[ "transformers", "safetensors", "gguf", "gemma2", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/gemma-2-9b-bnb-4bit", "base_model:quantized:unsloth/gemma-2-9b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-17T13:35:51Z	--- base_model: unsloth/gemma-2-9b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma2 - trl --- # Uploaded model - Developed by: Diluzx - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-9b-bnb-4bit This gemma2 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)
anasmkh/fintuned_pythia_ubuntu_commands	anasmkh	2024-07-17T14:25:06Z	167	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "en", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T14:02:52Z	--- library_name: transformers language: - en --- # 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: [Anas Mkh] - Funded by [optional]: [] - Shared by [optional]: [Anas Mkh] - Model type: [Question Answering] - Language(s) (NLP): [English] - License: [NA] - Finetuned from model [optional]: [pythia-410m] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [anasmkh/fintuned_pythia_ubuntu_commands] - Paper [optional]: [NA] - Demo [optional]: [NA] ## 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]
4Fun2Old/Kai_v1-DiscoPhoenix	4Fun2Old	2024-07-17T14:22:23Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:DRXD1000/Phoenix-7B", "base_model:merge:DRXD1000/Phoenix-7B", "base_model:Keynote-Technology/KAI-7B-v0.1", "base_model:merge:Keynote-Technology/KAI-7B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T14:11:03Z	--- base_model: - Keynote-Technology/KAI-7B-v0.1 - DRXD1000/Phoenix library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Keynote-Technology/KAI-7B-v0.1](https://huggingface.co/Keynote-Technology/KAI-7B-v0.1) * [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Keynote-Technology/KAI-7B-v0.1 layer_range: [0, 32] - model: DRXD1000/Phoenix layer_range: [0, 32] merge_method: slerp base_model: Keynote-Technology/KAI-7B-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors dtype: float16 ```
vitamin-c-sharp/multiview-diffusion-generator	vitamin-c-sharp	2024-07-17T14:17:04Z	16	0	diffusers	[ "diffusers", "safetensors", "image-to-3d", "arxiv:2312.02201", "license:openrail", "diffusers:MVDreamPipeline", "region:us" ]	image-to-3d	2024-07-17T13:54:28Z	--- license: openrail pipeline_tag: image-to-3d --- This is a copy of [ashawkey/imagedream-ipmv-diffusers](https://huggingface.co/ashawkey/imagedream-ipmv-diffusers). It is hosted here for persistence throughout the ML for 3D course. # MVDream-diffusers Model Card This is a port of https://huggingface.co/Peng-Wang/ImageDream into diffusers. For usage, please check: https://github.com/ashawkey/mvdream_diffusers ## Citation ``` @article{wang2023imagedream, title={ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation}, author={Wang, Peng and Shi, Yichun}, journal={arXiv preprint arXiv:2312.02201}, year={2023} } ``` ## Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Dichitha/bert_mrpc_trained	Dichitha	2024-07-17T14:16:44Z	108	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T13:46:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mahendra0203/whisper-small-hi-test	mahendra0203	2024-07-17T14:15:02Z	78	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-17T14:07:40Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hindi test 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. --> # Whisper Small Hindi test This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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: 16 - 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 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.4 - Pytorch 2.2.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
gokulsrinivasagan/gpt_132	gokulsrinivasagan	2024-07-17T14:15:01Z	113	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_raw_dataset", "base_model:gokulsrinivasagan/gpt_120", "base_model:finetune:gokulsrinivasagan/gpt_120", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T00:39:20Z	--- license: mit base_model: gokulsrinivasagan/gpt_120 tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_raw_dataset metrics: - accuracy model-index: - name: gpt_132 results: - task: name: Causal Language Modeling type: text-generation dataset: name: gokuls/wiki_book_corpus_complete_raw_dataset type: gokuls/wiki_book_corpus_complete_raw_dataset metrics: - name: Accuracy type: accuracy value: 0.38595119982890264 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/gokulsrinivasagan/huggingface/runs/bbd1d9wi) # gpt_132 This model is a fine-tuned version of [gokulsrinivasagan/gpt_120](https://huggingface.co/gokulsrinivasagan/gpt_120) on the gokuls/wiki_book_corpus_complete_raw_dataset dataset. It achieves the following results on the evaluation set: - Loss: 3.0759 - Accuracy: 0.3860 ## 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: 48 - eval_batch_size: 48 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
speechbrain/hifigan-wavlm-l1-3-7-12-18-23-continuous-LibriTTS	speechbrain	2024-07-17T14:13:15Z	33	0	speechbrain	[ "speechbrain", "Vocoder", "HiFIGAN", "speech-synthesis", "en", "dataset:LibriTTS", "arxiv:2406.10735", "arxiv:2406.14294", "license:apache-2.0", "region:us" ]	null	2024-05-22T08:06:05Z	--- language: "en" inference: false tags: - Vocoder - HiFIGAN - speech-synthesis - speechbrain license: "apache-2.0" datasets: - LibriTTS --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # Vocoder with HiFIGAN Unit trained on LibriTTS This repository provides all the necessary tools for using a [scalable HiFiGAN Unit](https://arxiv.org/abs/2406.10735) vocoder trained with [LibriTTS](https://www.openslr.org/141/). The pre-trained model take as input continous self-supervised representations and produces a waveform as output. This is suitable for a wide range of generative tasks such as speech enhancement, separation, text-to-speech, voice cloning, etc. Please read [DASB - Discrete Audio and Speech Benchmark](https://arxiv.org/abs/2406.14294) for more information. To generate the continuous self-supervised representations, we use `microsoft/wavlm-large`. ## Install SpeechBrain First of all, please install tranformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Using the Vocoder ```python import torch from speechbrain.inference.vocoders import UnitHIFIGAN hifi_gan_unit = UnitHIFIGAN.from_hparams(source="speechbrain/hifigan-wavlm-l1-3-7-12-18-23-continuous-LibriTTS", savedir="pretrained_models/vocoder") codes = torch.rand(100, 1, 1024) waveform = hifi_gan_unit.decode_unit(codes) ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` #### About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain
aritrasen/bge-base-en-v1.5-ft	aritrasen	2024-07-17T14:13:02Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:21", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "base_model:finetune:BAAI/bge-base-en-v1.5", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-07-17T14:12:45Z	--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:21 - loss:MultipleNegativesRankingLoss widget: - source_sentence: '\| Config \| Model \| Epochs \| Max seq length \| Micro batch size \| Machine \| Training runtime \| Cost \| Peak memory \| Validation loss \| Validation perplexity \| Multitask score (MMLU) \| \| --------------------------------- \| ---------------------- \| ------ \| -------------- \| ---------------- \| ------- \| ---------------- \| ---- \| ----------- \| --------------- \| --------------------- \| --------------- \| \| falcon-7b/lora.yaml \| falcon-7b \| 4 \| 512 \| 1 \| 1xA10G \| 24.84 min \| $0.7 \| 16.69 GB \| 0.945 \| 2.573 \| 26.2% \| \| falcon-7b/lora.yaml \| falcon-7b \| 4 \| 512 \| 1 \| 4xA10G \| 24.94 min \| $2.0 \| 16.69 GB \| 0.945 \| 2.573 \| 26.4% \| \| falcon-7b/qlora.yaml \| falcon-7b \| 4 \| 512 \| 1 \| 1xA10G \| 50.85 min \| $1.5 \| 9.44 GB \| 0.993 \| 2.699 \| 26.3% \| \| falcon-7b/qlora.yaml \| falcon-7b \| 4 \| 512 \| 1 \| 4xA10G \| 50.88 min \| $4.1 \| 9.44 GB \| 0.993 \| 2.699 \| 26.3% \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| gemma-2b/full.yaml \| gemma-2b \| 1 \| 512 \| 1 \| 4xA10G \| 14.06 min \| $1.1 \| 17.43 GB \| 1.021 \| 2.777 \| 32.4% \| \| gemma-2b/lora.yaml \| gemma-2b \| 2 \| 512 \| 2 \| 1xA10G \| 9.41 min \| $0.3 \| 12.62 GB \| 0.981 \| 2.666 \| 34.4% \|' sentences: - 'What is the command to download the pretrained model weights for the Llama-2-7b-hf model? ' - 'What is the version of nvfuser\_cu121 used? ' - 'What is the training runtime for the gemma-2b model with the lora configuration? ' - source_sentence: "# Serve and Deploy LLMs\n\nThis document shows how you can serve\ \ a LitGPT for deployment. \n\n \n## Serve an LLM\n\nThis section illustrates\ \ how we can set up an inference server for a phi-2 LLM using `litgpt serve` that\ \ is minimal and highly scalable.\n\n\n \n## Step 1: Start the inference\ \ server\n\n\n```bash\n# 1) Download a pretrained model (alternatively, use your\ \ own finetuned model)\nlitgpt download --repo_id microsoft/phi-2\n\n# 2) Start\ \ the server\nlitgpt serve --checkpoint_dir checkpoints/microsoft/phi-2\n```\n\ \n> [!TIP]\n> Use `litgpt serve --help` to display additional options, including\ \ the port, devices, LLM temperature setting, and more.\n\n\n \n## Step 2:\ \ Query the inference server\n\nYou can now send requests to the inference server\ \ you started in step 2. For example, in a new Python session, we can send requests\ \ to the inference server as follows:\n\n\n```python\nimport requests, json\n\n\ response = requests.post(\n \"http://127.0.0.1:8000/predict\", \n json={\"\ prompt\": \"Fix typos in the following sentence: Exampel input\"}\n)\n\nprint(response.json()[\"\ output\"])\n```\n\nExecuting the code above prints the following output:\n\n```\n\ Instruct: Fix typos in the following sentence: Exampel input\nOutput: Example\ \ input.\n```" sentences: - 'What command do I use to convert the finetuned model to a HF transformer model? ' - 'How do you merge LoRA weights into the original model''s checkpoint? ' - 'How can I start an inference server for a phi-2 LLM using litgpt serve? ' --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("aritrasen/bge-base-en-v1.5-ft") # Run inference sentences = [ '# Serve and Deploy LLMs\n\nThis document shows how you can serve a LitGPT for deployment. \n\n \n## Serve an LLM\n\nThis section illustrates how we can set up an inference server for a phi-2 LLM using `litgpt serve` that is minimal and highly scalable.\n\n\n \n## Step 1: Start the inference server\n\n\n```bash\n# 1) Download a pretrained model (alternatively, use your own finetuned model)\nlitgpt download --repo_id microsoft/phi-2\n\n# 2) Start the server\nlitgpt serve --checkpoint_dir checkpoints/microsoft/phi-2\n```\n\n> [!TIP]\n> Use `litgpt serve --help` to display additional options, including the port, devices, LLM temperature setting, and more.\n\n\n \n## Step 2: Query the inference server\n\nYou can now send requests to the inference server you started in step 2. For example, in a new Python session, we can send requests to the inference server as follows:\n\n\n```python\nimport requests, json\n\nresponse = requests.post(\n "http://127.0.0.1:8000/predict", \n json={"prompt": "Fix typos in the following sentence: Exampel input"}\n)\n\nprint(response.json()["output"])\n```\n\nExecuting the code above prints the following output:\n\n```\nInstruct: Fix typos in the following sentence: Exampel input\nOutput: Example input.\n```', 'How can I start an inference server for a phi-2 LLM using litgpt serve?\n', 'What command do I use to convert the finetuned model to a HF transformer model?\n', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 21 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:-------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 51 tokens</li><li>mean: 424.62 tokens</li><li>max: 512 tokens</li></ul> \| <ul><li>min: 12 tokens</li><li>mean: 17.19 tokens</li><li>max: 26 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| <code>\| 7 B \| Llama 2 \| bnb.nf4 \| 1 \| 4,194,304 \| 14.14 GB \| 3.68 min \|<br>\| 7 B \| Llama 2 \| bnb.nf4-dq \| 1 \| 4,194,304 \| 13.84 GB \| 3.83 min \|<br>\| 7 B \| Llama 2 \| None \| 2 \| 4,194,304 \| 29.07 GB \| 2.52 min \|<br>\| 7 B \| Llama 2 \| None \| 4 \| 4,194,304 \| OOM \| - \|<br>\| \| \| \| \| \| \| \|<br>\| 13 B \| Llama 2 \| None \| 1 \| 6,553,600 \| 38.12 GB \| 3.19 min \|<br>\| 13 B \| Llama 2 \| bnb.nf4 \| 1 \| 6,553,600 \| 23.14 GB \| 6.38 min \|<br>\| 13 B \| Llama 2 \| bnb.nf4-dq \| 1 \| 6,553,600 \| 22.55 GB \| 6.55 min \|<br>\| 13 B \| Llama 2 \| None \| 2 \| 6,553,600 \| OOM \| - \|<br>\| 13 B \| Llama 2 \| None \| 4 \| 6,553,600 \| OOM \| - \|<br>\| \| \| \| \| \| \| \|<br>\| 40 B \| Falcon \| None \| 1 \| 12,042,240 \| OOM \| - \|<br>\| 40 B \| Falcon \| bnb.nf4 \| 1 \| 12,042,240 \| OOM \| - \|<br>\| 40 B \| Falcon \| bnb.nf4-dq \| 1 \| 12,042,240 \| OOM \| - \|</code> \| <code>What is the memory usage of Llama 2 with 7B when using bnb.nf4-dq?<br></code> \| \| <code>1. Follow the instructions above to load the model into a Hugging Face transformers model.<br><br>2. Create a `model.safetensor` file:<br><br>```python<br>model.save_pretrained("out/hf-tinyllama/converted/")<br>```<br><br>3. Copy the tokenizer files into the model-containing directory:<br><br>```bash<br>cp checkpoints/$repo_id/tokenizer* out/hf-tinyllama/converted<br>```<br><br>4. Run the evaluation harness, for example:<br><br>```bash<br>lm_eval --model hf \<br> --model_args pretrained=out/hf-tinyllama/converted \<br> --tasks "hellaswag,gsm8k,truthfulqa_mc2,mmlu,winogrande,arc_challenge" \<br> --device "cuda:0" \<br> --batch_size 4<br>```</code> \| <code>What is the command to run the evaluation harness?<br></code> \| \| <code>The LM Evaluation Harness requires a tokenizer to be present in the model checkpoint folder, which we can copy from the original download checkpoint:<br><br>```bash<br># Copy the tokenizer needed by the Eval Harness<br>cp checkpoints/microsoft/phi-2/tokenizer<br>out/converted_model<br>```<br><br>Then, we can run the Evaluation Harness as follows:<br><br>```bash<br>lm_eval --model hf \<br> --model_args pretrained="out/converted_model" \<br> --tasks "hellaswag,gsm8k,truthfulqa_mc2,mmlu,winogrande,arc_challenge" \<br> --device "cuda:0" \<br> --batch_size 4<br>```<br><br> <br><br>> [!TIP]<br>> The Evaluation Harness tasks above are those used in Open LLM Leaderboard. You can find a list all supported tasks [here](https://github.com/EleutherAI/lm-evaluation-harness/blob/master/docs/task_table.md).<br><br><br><br> <br>More information and additional resources<br><br>- [tutorials/convert_lit_models](./convert_lit_models.md): Tutorial on converting LitGPT weights<br><br><br><br> <br><br>## Get involved!<br><br>We appreciate your feedback and contributions. If you have feature requests, questions, or want to contribute code or config files, please don't hesitate to use the [GitHub Issue](https://github.com/Lightning-AI/litgpt/issues) tracker.<br><br>We welcome all individual contributors, regardless of their level of experience or hardware. Your contributions are valuable, and we are excited to see what you can accomplish in this collaborative and supportive environment.<br><br> <br><br>> [!TIP]<br>> Unsure about contributing? Check out our [How to Contribute to LitGPT](https://lightning.ai/pages/community/tutorial/how-to-contribute-to-litgpt/) guide.<br><br> <br><br>If you have general questions about building with LitGPT, please [join our Discord](https://discord.gg/VptPCZkGNa).</code> \| <code>What is the command to run the Evaluation Harness?<br></code> \| Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 10 evaluation samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:-------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 273 tokens</li><li>mean: 460.8 tokens</li><li>max: 512 tokens</li></ul> \| <ul><li>min: 10 tokens</li><li>mean: 20.1 tokens</li><li>max: 34 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------\| \| <code>(this table was sourced from the author's [README](https://github.com/jzhang38/TinyLlama/))<br><br> <br>## Download datasets<br><br>You can download the data using git lfs:<br><br>```bash<br># Make sure you have git-lfs installed (https://git-lfs.com):<br>sudo apt install git-lfs<br>```<br><br>```bash<br>git clone https://huggingface.co/datasets/cerebras/slimpajama-627b data/slimpajama-raw<br>git clone https://huggingface.co/datasets/bigcode/starcoderdata data/starcoderdata-raw<br>```<br><br>Around 1.2 TB of disk space is required to store both datasets.<br><br> <br>## Prepare the datasets for training<br><br>In order to start pretraining litgpt on it, you need to read, tokenize, and write the data in binary chunks. This will leverage the `litdata` optimization pipeline and streaming dataset.<br><br>First, install additional dependencies for preprocessing:<br><br>```bash<br>pip install '.[all]'<br>```<br><br>You will need to have the tokenizer config available:<br><br>```bash<br>litgpt download \<br> --repo_id meta-llama/Llama-2-7b-hf \<br> --access_token your_hf_token \<br> --tokenizer_only true<br>```<br><br>Then, run the preprocessing script for each dataset and split.<br>You will require 1.1 TB of disk space for Starcoder and 2.5 TB of space for the SlimPajama dataset.<br><br>Starcoder:<br><br>```bash<br>python litgpt/data/prepare_starcoder.py \<br> --input_dir data/starcoderdata-raw \<br> --output_dir data/starcoder \<br> --tokenizer_path checkpoints/meta-llama/Llama-2-7b-hf<br>```<br><br>SlimPajama:<br><br>```bash<br>python litgpt/data/prepare_slimpajama.py \<br> --input_dir data/slimpajama-raw/validation \<br> --output_dir data/slimpajama/val \<br> --tokenizer_path checkpoints/meta-llama/Llama-2-7b-hf<br><br>python litgpt/data/prepare_slimpajama.py \<br> --input_dir data/slimpajama-raw/test \<br> --output_dir data/slimpajama/test \<br> --tokenizer_path checkpoints/meta-llama/Llama-2-7b-hf<br><br>python litgpt/data/prepare_slimpajama.py \<br> --input_dir data/slimpajama-raw/train \<br> --output_dir data/slimpajama/train \<br> --tokenizer_path checkpoints/meta-llama/Llama-2-7b-hf<br>```</code> \| <code>How much disk space is required to store the SlimPajama dataset?<br></code> \| \| <code># Serve and Deploy LLMs<br><br>This document shows how you can serve a LitGPT for deployment. <br><br> <br>## Serve an LLM<br><br>This section illustrates how we can set up an inference server for a phi-2 LLM using `litgpt serve` that is minimal and highly scalable.<br><br><br> <br>## Step 1: Start the inference server<br><br><br>```bash<br># 1) Download a pretrained model (alternatively, use your own finetuned model)<br>litgpt download --repo_id microsoft/phi-2<br><br># 2) Start the server<br>litgpt serve --checkpoint_dir checkpoints/microsoft/phi-2<br>```<br><br>> [!TIP]<br>> Use `litgpt serve --help` to display additional options, including the port, devices, LLM temperature setting, and more.<br><br><br> <br>## Step 2: Query the inference server<br><br>You can now send requests to the inference server you started in step 2. For example, in a new Python session, we can send requests to the inference server as follows:<br><br><br>```python<br>import requests, json<br><br>response = requests.post(<br> "http://127.0.0.1:8000/predict", <br> json={"prompt": "Fix typos in the following sentence: Exampel input"}<br>)<br><br>print(response.json()["output"])<br>```<br><br>Executing the code above prints the following output:<br><br>```<br>Instruct: Fix typos in the following sentence: Exampel input<br>Output: Example input.<br>```</code> \| <code>How can I start an inference server for a phi-2 LLM using litgpt serve?<br></code> \| \| <code># TPU support<br><br>This project utilizes [`Fabric`](https://lightning.ai/docs/fabric/stable), which supports TPUs via [PyTorch XLA](https://github.com/pytorch/xla).<br><br>> [!NOTE]<br>> This guide assumes that you have already set-up your [Google Cloud environment](https://cloud.google.com/run/docs/setup).<br><br>To set up a Google Cloud instance with a TPU v4 VM, run the following commands:<br><br>```shell<br>gcloud compute tpus tpu-vm create litgpt --version=tpu-vm-v4-base --accelerator-type=v4-8 --zone=us-central2-b<br>gcloud compute tpus tpu-vm ssh litgpt --zone=us-central2-b<br>```<br><br>You can also choose a different TPU type. To do so, change the `version`, `accelerator-type`, and `zone` arguments. Find all regions and zones [here](https://cloud.google.com/tpu/docs/regions-zones).<br><br><details><br><summary>Multihost caveats</summary><br><br>TPU v4-8 uses a single host. SSH'ing into the machine and running commands manually will only work when using a single host (1 slice in the TPU pod).<br>In multi-host environments, such as larger TPU pod slices, it's necessary to launch all commands on all hosts simultaneously to avoid hangs.<br>For local development, it is advisable to upload a zip file containing all your current changes and execute it inside the VM from your personal computer:<br><br>```shell<br># Zip the local directory, excluding large directories from the zip. You may want to keep them.<br>zip -r local_changes.zip . -x ".git/" "checkpoints/" "data/" "out/"<br># Copy the .zip file to the TPU VM<br>gcloud compute tpus tpu-vm scp --worker=all local_changes.zip "litgpt:~"<br># Unzip on each host<br>gcloud compute tpus tpu-vm ssh litgpt --worker=all --command="cd ~; unzip -q -o local_changes.zip"<br><br># Example of a typical workflow<br>gcloud compute tpus tpu-vm ssh tmp --worker=all --command="cd ~; bash install_dependencies.sh"<br>gcloud compute tpus tpu-vm ssh tmp --worker=all --command="cd ~; bash prepare_checkpoints.sh"<br>gcloud compute tpus tpu-vm ssh tmp --worker=all --command="cd ~; bash run_desired_script.sh"</code> \| <code>How does this project support TPUs?<br></code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 5 - `per_device_eval_batch_size`: 5 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 5 - `per_device_eval_batch_size`: 5 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| loss \| \|:-----:\|:----:\|:-------------:\|:------:\| \| 0.4 \| 2 \| 0.6407 \| 0.4190 \| \| 0.8 \| 4 \| 0.7873 \| 0.2789 \| \| 1.2 \| 6 \| 0.1871 \| 0.2089 \| \| 1.6 \| 8 \| 0.2125 \| 0.1718 \| \| 2.0 \| 10 \| 0.0374 \| 0.1648 \| \| 2.4 \| 12 \| 0.1923 \| 0.1695 \| \| 2.8 \| 14 \| 0.0183 \| 0.1723 \| \| 3.2 \| 16 \| 0.1582 \| 0.1770 \| \| 3.6 \| 18 \| 0.0032 \| 0.1824 \| \| 4.0 \| 20 \| 0.0015 \| 0.1870 \| \| 4.4 \| 22 \| 0.1399 \| 0.1901 \| \| 4.8 \| 24 \| 0.002 \| 0.1914 \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.0+cu121 - Accelerate: 0.27.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
John6666/pony-photo-real-merge-x3-v1-sdxl	John6666	2024-07-17T14:09:39Z	108	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-07-17T14:02:51Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - pony --- Original model is [here](https://civitai.com/models/584093/pony-photo-real-merge-x3?modelVersionId=651674).
shreyasfadnavis/proc_bert_merge_finmed	shreyasfadnavis	2024-07-17T14:04:53Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "arxiv:2203.05482", "base_model:PAIXAI/Astrid-7B-LLama-Med", "base_model:merge:PAIXAI/Astrid-7B-LLama-Med", "base_model:cxllin/Llama2-7b-Finance", "base_model:merge:cxllin/Llama2-7b-Finance", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T13:30:28Z	--- base_model: - cxllin/Llama2-7b-Finance - PAIXAI/Astrid-7B-LLama-Med library_name: transformers tags: - mergekit - merge --- # proc_merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [cxllin/Llama2-7b-Finance](https://huggingface.co/cxllin/Llama2-7b-Finance) * [PAIXAI/Astrid-7B-LLama-Med](https://huggingface.co/PAIXAI/Astrid-7B-LLama-Med) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: PAIXAI/Astrid-7B-LLama-Med parameters: weight: 0.5 - model: cxllin/Llama2-7b-Finance parameters: weight: 0.5 merge_method: linear dtype: float16 ```
speechbrain/hifigan-wav2vec-l1-3-7-12-18-23-k1000-LibriTTS	speechbrain	2024-07-17T14:04:40Z	11	0	speechbrain	[ "speechbrain", "Vocoder", "HiFIGAN", "speech-synthesis", "en", "dataset:LibriTTS", "arxiv:2406.10735", "arxiv:2406.14294", "license:apache-2.0", "region:us" ]	null	2024-05-22T08:04:16Z	--- language: "en" inference: false tags: - Vocoder - HiFIGAN - speech-synthesis - speechbrain license: "apache-2.0" datasets: - LibriTTS --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # Vocoder with HiFIGAN Unit trained on LibriTTS This repository provides all the necessary tools for using a [scalable HiFiGAN Unit](https://arxiv.org/abs/2406.10735) vocoder trained with [LibriTTS](https://www.openslr.org/141/). The pre-trained model take as input discrete self-supervised representations and produces a waveform as output. This is suitable for a wide range of generative tasks such as speech enhancement, separation, text-to-speech, voice cloning, etc. Please read [DASB - Discrete Audio and Speech Benchmark](https://arxiv.org/abs/2406.14294) for more information. To generate the discrete self-supervised representations, we employ a K-means clustering model trained using `facebook/wav2vec2-large-960h-lv60-self` hidden layers, with k=1000. ## Install SpeechBrain First of all, please install tranformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Using the Vocoder ```python import torch from speechbrain.inference.vocoders import UnitHIFIGAN hifi_gan_unit = UnitHIFIGAN.from_hparams(source="speechbrain/hifigan-wav2vec-l1-3-7-12-18-23-k1000-LibriTTS", savedir="pretrained_models/vocoder") codes = torch.randint(0, 99, (100, 1)) waveform = hifi_gan_unit.decode_unit(codes) ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` #### About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain
ArrayDice/car_orientation_classification	ArrayDice	2024-07-17T14:01:16Z	26	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-07-17T09:21:35Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: car_orientation_classification2 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. --> # car_orientation_classification2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6800 - Accuracy: 0.6926 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9933 \| 1.0 \| 68 \| 1.9084 \| 0.4099 \| \| 1.4721 \| 2.0 \| 136 \| 1.2870 \| 0.5124 \| \| 1.1677 \| 3.0 \| 204 \| 1.0780 \| 0.5265 \| \| 0.9919 \| 4.0 \| 272 \| 0.9454 \| 0.5760 \| \| 0.8392 \| 5.0 \| 340 \| 0.8184 \| 0.6926 \| \| 0.7778 \| 6.0 \| 408 \| 0.8311 \| 0.6431 \| \| 0.7341 \| 7.0 \| 476 \| 0.7425 \| 0.6572 \| \| 0.6695 \| 8.0 \| 544 \| 0.6800 \| 0.6926 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
Deeokay/Phi-3-medium-4k-MYP-GGUF	Deeokay	2024-07-17T13:54:08Z	34	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-07-17T13:12:43Z	--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - Developed by: Deeokay - License: apache-2.0 - Finetuned from model : unsloth/phi-3-medium-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) # README This is a test model on a the following - a private dataset focused for Students in MYP (IB) Program for my niece - Works with Ollama create with just "FROM path/to/model" as Modelfile (standard template works no issues) # HOW TO USE The whole point of conversion for me was I wanted to be able to to use it through Ollama or (other local options) For Ollama, it required to be a GGUF file. Once you have this it is pretty straight forward (if it is in llama3 which this model is) Quick Start: - You must already have Ollama running in your setting - Download the unsloth.Q4_K_M.gguf model from Files - In the same directory create a file call "Modelfile" - Inside the "Modelfile" type ```python FROM ./unsloth.Q4_K_M.gguf # or which ever GGUF file ``` - Save a go back to the folder (folder where model + Modelfile exisit) - Now in terminal make sure you are in the same location of the folder and type in the following command ```python ollama create mycustomai # "mycustomai" <- you can name it anything u want ``` This GGUF is based on unsloth/Phi-3-medium-4k-instruct thus ollama doesn't need anything else to auto configure this model After than you should be able to use this model to chat! # NOTE: DISCLAIMER Please note this is not for the purpose of production, but results of self tought Fine Tuning The Special Tokens where kept the same and the training data has the following Template: ``` <s><\|user\|>{question}<\|end\|> <\|assistant\|>{answer}<\|end\|></s> ```
kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF	kingabzpro	2024-07-17T13:52:29Z	7	0	transformers	[ "transformers", "gguf", "medical", "llama-cpp", "gguf-my-repo", "question-answering", "en", "dataset:ruslanmv/ai-medical-chatbot", "base_model:kingabzpro/llama-3-8b-chat-doctor", "base_model:quantized:kingabzpro/llama-3-8b-chat-doctor", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	question-answering	2024-07-17T13:52:07Z	--- base_model: kingabzpro/llama-3-8b-chat-doctor datasets: - ruslanmv/ai-medical-chatbot language: - en library_name: transformers license: apache-2.0 pipeline_tag: question-answering tags: - medical - llama-cpp - gguf-my-repo --- # kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF This model was converted to GGUF format from [`kingabzpro/llama-3-8b-chat-doctor`](https://huggingface.co/kingabzpro/llama-3-8b-chat-doctor) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/kingabzpro/llama-3-8b-chat-doctor) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF --hf-file llama-3-8b-chat-doctor-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF --hf-file llama-3-8b-chat-doctor-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF --hf-file llama-3-8b-chat-doctor-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF --hf-file llama-3-8b-chat-doctor-q4_k_m.gguf -c 2048 ```
Deeokay/Phi-3-medium-4k-MYP-4bit	Deeokay	2024-07-17T13:51:53Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T13:41:44Z	--- base_model: unsloth/phi-3-medium-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- # Uploaded model - Developed by: Deeokay - License: apache-2.0 - Finetuned from model : unsloth/phi-3-medium-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Sajjad-underline/ppo-Huggy	Sajjad-underline	2024-07-17T13:38:54Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-07-17T13:38:41Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Sajjad-underline/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Tinsae/Florence-Fish-8	Tinsae	2024-07-17T13:35:42Z	108	0	transformers	[ "transformers", "safetensors", "florence2", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2024-07-16T21:38:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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udomsak/layoutlmv3-document-classification	udomsak	2024-07-17T13:34:06Z	134	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T13:33:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Felladrin/gguf-Qwen1.5-0.5B-Chat	Felladrin	2024-07-17T13:30:46Z	155	2	null	[ "gguf", "base_model:Qwen/Qwen1.5-0.5B-Chat", "base_model:quantized:Qwen/Qwen1.5-0.5B-Chat", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-29T11:27:19Z	--- base_model: Qwen/Qwen1.5-0.5B-Chat --- GGUF version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat).
botbot-ai/CabraLlama3-70b	botbot-ai	2024-07-17T13:23:43Z	274	6	transformers	[ "transformers", "safetensors", "llama", "text-generation", "portuguese", "cabra", "llama-3", "conversational", "pt", "dataset:botbot-ai/Cabra3k", "license:llama3", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-19T16:03:03Z	--- language: - pt license: llama3 library_name: transformers tags: - portuguese - llama - cabra - llama-3 datasets: - botbot-ai/Cabra3k model-index: - name: CabraLlama3-70b results: - task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics: - type: acc value: 82.02 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics: - type: acc value: 70.1 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics: - type: acc value: 68.52 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 93.21 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics: - type: pearson value: 83.32 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics: - type: f1_macro value: 80.6 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 81.62 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 72.72 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 73.85 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b name: Open Portuguese LLM Leaderboard --- # Cabra Llama-3 70B O Cabra Llama-3 70B é uma versão aprimorada do Meta Llama 3 70B Instruct, refinado com o uso do dataset Cabra 30k. Este modelo foi especialmente otimizado para compreender e responder em português (pt-br). Conheça os nossos outros [modelos e datasets](https://huggingface.co/collections/botbot-ai/models-6604c2069ceef04f834ba99b), e o [Cabra Llama 3 8b](https://huggingface.co/botbot-ai/CabraLlama3-8b). ## Detalhes do modelo base ### Modelo: Meta-Llama-3-70B-Instruct A Meta desenvolveu e lançou a família de modelos Llama 3, uma coleção de modelos de texto generativos pré-treinados e ajustados por instruções nos tamanhos de 8B e 70B. Os modelos Llama 3 ajustados por instruções são otimizados para casos de uso em diálogos e superam muitos dos modelos de chat de código aberto disponíveis em benchmarks comuns da indústria. Além disso, ao desenvolver esses modelos, tomamos grande cuidado para otimizar a utilidade e a segurança. Arquitetura do Modelo: Llama 3 é um modelo de linguagem auto-regressivo que usa uma arquitetura de transformador otimizada. As versões ajustadas utilizam o aprimoramento supervisionado (SFT) e aprendizado por reforço com feedback humano (RLHF) para se alinhar às preferências humanas quanto à utilidade e segurança. ### Dataset: Cabra 30k Dataset interno para fine-tuning. Vamos lançar em breve. ### Quantização / GGUF Colocamos diversas versões (GGUF) quantanizadas no branch "quantanization". # Open Portuguese LLM Leaderboard Evaluation Results Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/botbot-ai/CabraLlama3-70b) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) \| Metric \| Value \| \|--------------------------\|---------\| \|Average \|78.44\| \|ENEM Challenge (No Images)\| 82.02\| \|BLUEX (No Images) \| 70.10\| \|OAB Exams \| 68.52\| \|Assin2 RTE \| 93.21\| \|Assin2 STS \| 83.32\| \|FaQuAD NLI \| 80.60\| \|HateBR Binary \| 81.62\| \|PT Hate Speech Binary \| 72.72\| \|tweetSentBR \| 73.85\|
CAUKiel/JavaBERT	CAUKiel	2024-07-17T13:21:37Z	212	12	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "code", "arxiv:2110.10404", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: - code license: apache-2.0 widget: - text: public [MASK] isOdd(Integer num) {if (num % 2 == 0) {return "even";} else {return "odd";}} --- # Model Card for JavaBERT A BERT-like model pretrained on Java software code. # Model Details ## Model Description A BERT-like model pretrained on Java software code. - Developed by: Christian-Albrechts-University of Kiel (CAUKiel) - Shared by [Optional]: Hugging Face - Model type: Fill-Mask - Language(s) (NLP): en - License: Apache-2.0 - Related Models: A version of this model using an uncased tokenizer is available at [CAUKiel/JavaBERT-uncased](https://huggingface.co/CAUKiel/JavaBERT-uncased). - Parent Model: BERT - Resources for more information: - [Associated Paper](https://arxiv.org/pdf/2110.10404.pdf) # Uses ## Direct Use Fill-Mask ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. { see paper= word something) # Training Details ## Training Data The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A ```bert-base-cased``` tokenizer is used by this model. ## Training Procedure ### Training Objective A MLM (Masked Language Model) objective was used to train this model. ### Preprocessing More information needed. ### Speeds, Sizes, Times More information needed. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed. ### Factors ### Metrics More information needed. ## Results More information needed. # Model Examination More information needed. # Environmental Impact 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 BibTeX: ``` @inproceedings{De_Sousa_Hasselbring_2021, address={Melbourne, Australia}, title={JavaBERT: Training a Transformer-Based Model for the Java Programming Language}, rights={https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html}, ISBN={9781665435833}, url={https://ieeexplore.ieee.org/document/9680322/}, DOI={10.1109/ASEW52652.2021.00028}, booktitle={2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)}, publisher={IEEE}, author={Tavares de Sousa, Nelson and Hasselbring, Wilhelm}, year={2021}, month=nov, pages={90–95} } ``` APA: More information needed. # Glossary [optional] More information needed. # More Information [optional] More information needed. # Model Card Authors [optional] Christian-Albrechts-University of Kiel (CAUKiel) in collaboration with Ezi Ozoani and the team at Hugging Face # Model Card Contact More information needed. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import pipeline pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT') output = pipe(CODE) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code. ``` </details>
PrunaAI/RLHFlow-pair-preference-model-LLaMA3-8B-AWQ-4bit-smashed	PrunaAI	2024-07-17T13:12:29Z	80	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:RLHFlow/pair-preference-model-LLaMA3-8B", "base_model:quantized:RLHFlow/pair-preference-model-LLaMA3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-07-17T13:09:47Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: RLHFlow/pair-preference-model-LLaMA3-8B metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with awq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo RLHFlow/pair-preference-model-LLaMA3-8B installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install autoawq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from awq import AutoAWQForCausalLM model = AutoAWQForCausalLM.from_quantized("PrunaAI/RLHFlow-pair-preference-model-LLaMA3-8B-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("RLHFlow/pair-preference-model-LLaMA3-8B") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model RLHFlow/pair-preference-model-LLaMA3-8B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
quim-motger/reviewRoBERTa-large	quim-motger	2024-07-17T13:10:05Z	164	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-07-17T12:55:25Z	--- license: gpl-3.0 --- # reviewRoBERTa-large This model is a fine-tuned version of [`roberta-large`](https://huggingface.co/FacebookAI/roberta-large) on a large dataset of mobile app reviews. The model is designed to understand and process text from mobile app reviews, providing enhanced performance for tasks such as feature extraction, sentiment analysis and review summarization from app reviews. ## Model Details - Model Architecture: RoBERTa (Robustly Optimized BERT Approach) - Base Model: `roberta-large` - Pre-training Extension: Mobile app reviews dataset - Language: English ## Dataset The extended pre-training was performed using a diverse dataset of mobile app reviews collected from various app stores. The dataset includes reviews of different lengths, sentiments, and topics, providing a robust foundation for understanding the nuances of mobile app user feedback. ## Training Procedure The model was fine-tuned using the following parameters: - Batch Size: 8 - Learning Rate: 2e-5 - Epochs: 2 ## Usage ### Load the model ```python from transformers import RobertaTokenizer, RobertaForSequenceClassification tokenizer = RobertaTokenizer.from_pretrained('quim-motger/reviewRoBERTa-large') model = RobertaForSequenceClassification.from_pretrained('quim-motger/reviewRoBERTa-large') ``` ### Example: Sentiment Analysis ```python from transformers import pipeline nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) review = "This app is fantastic! I love the user-friendly interface and features." result = nlp(review) print(result) # Output: [{'label': 'POSITIVE', 'score': 0.98}] ``` ### Example: Review Summarization ```python from transformers import pipeline summarizer = pipeline('summarization', model=model, tokenizer=tokenizer) long_review = "I have been using this app for a while and it has significantly improved my productivity. The range of features is excellent, and the user interface is intuitive. However, there are occasional bugs that need fixing." summary = summarizer(long_review, max_length=50, min_length=25, do_sample=False) print(summary) # Output: [{'summary_text': 'The app has significantly improved my productivity with its excellent features and intuitive user interface. However, occasional bugs need fixing.'}] ```
PrunaAI/RLHFlow-pair-preference-model-LLaMA3-8B-HQQ-1bit-smashed	PrunaAI	2024-07-17T13:07:37Z	4	0	transformers	[ "transformers", "llama", "text-generation", "pruna-ai", "conversational", "base_model:RLHFlow/pair-preference-model-LLaMA3-8B", "base_model:finetune:RLHFlow/pair-preference-model-LLaMA3-8B", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T13:06:04Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: RLHFlow/pair-preference-model-LLaMA3-8B metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo RLHFlow/pair-preference-model-LLaMA3-8B installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/RLHFlow-pair-preference-model-LLaMA3-8B-HQQ-1bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/RLHFlow-pair-preference-model-LLaMA3-8B-HQQ-1bit-smashed") tokenizer = AutoTokenizer.from_pretrained("RLHFlow/pair-preference-model-LLaMA3-8B") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model RLHFlow/pair-preference-model-LLaMA3-8B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
tsavage68/Summary4500_L3_1000steps_1e7rate_03beta_CSFTDPO	tsavage68	2024-07-17T13:06:08Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/Summary4500_L3_100steps_1e6rate_SFT", "base_model:finetune:tsavage68/Summary4500_L3_100steps_1e6rate_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T12:59:57Z	--- license: llama3 base_model: tsavage68/Summary4500_L3_100steps_1e6rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: Hyponatremia_L3_1000steps_1e7rate_03beta_CSFTDPO 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. --> # Hyponatremia_L3_1000steps_1e7rate_03beta_CSFTDPO This model is a fine-tuned version of [tsavage68/Summary4500_L3_100steps_1e6rate_SFT](https://huggingface.co/tsavage68/Summary4500_L3_100steps_1e6rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0025 - Rewards/chosen: 0.4191 - Rewards/rejected: -7.9725 - Rewards/accuracies: 0.9980 - Rewards/margins: 8.3916 - Logps/rejected: -159.7724 - Logps/chosen: -82.7927 - Logits/rejected: -1.1012 - Logits/chosen: -1.0642 ## 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-07 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6455 \| 0.0112 \| 50 \| 0.6189 \| 0.0168 \| -0.1483 \| 0.7940 \| 0.1651 \| -133.6916 \| -84.1338 \| -1.0991 \| -1.0690 \| \| 0.1915 \| 0.0224 \| 100 \| 0.2381 \| 0.0998 \| -1.2728 \| 0.9980 \| 1.3727 \| -137.4402 \| -83.8570 \| -1.1007 \| -1.0693 \| \| 0.0069 \| 0.0336 \| 150 \| 0.0340 \| 0.2244 \| -3.5445 \| 0.9980 \| 3.7690 \| -145.0125 \| -83.4417 \| -1.1014 \| -1.0678 \| \| 0.0017 \| 0.0448 \| 200 \| 0.0098 \| 0.2714 \| -5.2540 \| 0.9980 \| 5.5254 \| -150.7106 \| -83.2852 \| -1.1013 \| -1.0664 \| \| 0.0014 \| 0.0559 \| 250 \| 0.0058 \| 0.3321 \| -6.1233 \| 0.9980 \| 6.4554 \| -153.6084 \| -83.0827 \| -1.1013 \| -1.0655 \| \| 0.0001 \| 0.0671 \| 300 \| 0.0044 \| 0.3409 \| -6.6530 \| 0.9980 \| 6.9939 \| -155.3742 \| -83.0536 \| -1.1000 \| -1.0641 \| \| 0.0005 \| 0.0783 \| 350 \| 0.0037 \| 0.3524 \| -7.0398 \| 0.9980 \| 7.3922 \| -156.6634 \| -83.0152 \| -1.1004 \| -1.0643 \| \| 0.0001 \| 0.0895 \| 400 \| 0.0031 \| 0.3703 \| -7.3960 \| 0.9980 \| 7.7663 \| -157.8508 \| -82.9556 \| -1.1006 \| -1.0643 \| \| 0.0 \| 0.1007 \| 450 \| 0.0029 \| 0.4041 \| -7.5392 \| 0.9980 \| 7.9433 \| -158.3280 \| -82.8429 \| -1.1006 \| -1.0640 \| \| 0.0 \| 0.1119 \| 500 \| 0.0028 \| 0.3938 \| -7.6566 \| 0.9980 \| 8.0503 \| -158.7193 \| -82.8773 \| -1.1011 \| -1.0644 \| \| 0.0 \| 0.1231 \| 550 \| 0.0027 \| 0.3960 \| -7.7988 \| 0.9980 \| 8.1949 \| -159.1935 \| -82.8697 \| -1.1004 \| -1.0635 \| \| 0.0001 \| 0.1343 \| 600 \| 0.0026 \| 0.4050 \| -7.8907 \| 0.9980 \| 8.2958 \| -159.4998 \| -82.8397 \| -1.1008 \| -1.0638 \| \| 0.0 \| 0.1454 \| 650 \| 0.0025 \| 0.4102 \| -7.9529 \| 0.9980 \| 8.3630 \| -159.7068 \| -82.8226 \| -1.1006 \| -1.0637 \| \| 0.0 \| 0.1566 \| 700 \| 0.0025 \| 0.4105 \| -7.9650 \| 0.9980 \| 8.3755 \| -159.7473 \| -82.8215 \| -1.1011 \| -1.0642 \| \| 0.0037 \| 0.1678 \| 750 \| 0.0025 \| 0.4133 \| -7.9730 \| 0.9980 \| 8.3863 \| -159.7740 \| -82.8120 \| -1.1009 \| -1.0641 \| \| 0.0 \| 0.1790 \| 800 \| 0.0025 \| 0.4059 \| -7.9812 \| 0.9980 \| 8.3871 \| -159.8014 \| -82.8367 \| -1.1012 \| -1.0644 \| \| 0.0004 \| 0.1902 \| 850 \| 0.0025 \| 0.4003 \| -7.9906 \| 0.9980 \| 8.3909 \| -159.8326 \| -82.8553 \| -1.1015 \| -1.0645 \| \| 0.0 \| 0.2014 \| 900 \| 0.0025 \| 0.4050 \| -7.9764 \| 0.9980 \| 8.3814 \| -159.7853 \| -82.8397 \| -1.1014 \| -1.0645 \| \| 0.0 \| 0.2126 \| 950 \| 0.0025 \| 0.4187 \| -7.9726 \| 0.9980 \| 8.3913 \| -159.7726 \| -82.7940 \| -1.1012 \| -1.0642 \| \| 0.0 \| 0.2238 \| 1000 \| 0.0025 \| 0.4191 \| -7.9725 \| 0.9980 \| 8.3916 \| -159.7724 \| -82.7927 \| -1.1012 \| -1.0642 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1
PrunaAI/RLHFlow-pair-preference-model-LLaMA3-8B-bnb-4bit-smashed	PrunaAI	2024-07-17T13:00:27Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:RLHFlow/pair-preference-model-LLaMA3-8B", "base_model:quantized:RLHFlow/pair-preference-model-LLaMA3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T12:57:46Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: RLHFlow/pair-preference-model-LLaMA3-8B metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo RLHFlow/pair-preference-model-LLaMA3-8B installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/RLHFlow-pair-preference-model-LLaMA3-8B-bnb-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("RLHFlow/pair-preference-model-LLaMA3-8B") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model RLHFlow/pair-preference-model-LLaMA3-8B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
crapthings/beans	crapthings	2024-07-17T12:58:02Z	195	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "vision", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-07-17T12:50:20Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - vision - generated_from_trainer metrics: - accuracy model-index: - name: beans 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. --> # beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0677 - Accuracy: 0.9925 ## 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: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2825 \| 1.0 \| 130 \| 0.2142 \| 0.9624 \| \| 0.1331 \| 2.0 \| 260 \| 0.1347 \| 0.9699 \| \| 0.1427 \| 3.0 \| 390 \| 0.0999 \| 0.9774 \| \| 0.0808 \| 4.0 \| 520 \| 0.0677 \| 0.9925 \| \| 0.1156 \| 5.0 \| 650 \| 0.0839 \| 0.9774 \| ### Framework versions - Transformers 4.43.0.dev0 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
nvhf/coedit-xl-composite-Q6_K-GGUF	nvhf	2024-07-17T12:52:47Z	5	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "dataset:asset", "dataset:wi_locness", "dataset:GEM/wiki_auto_asset_turk", "dataset:discofuse", "dataset:zaemyung/IteraTeR_plus", "dataset:jfleg", "dataset:grammarly/coedit", "base_model:grammarly/coedit-xl-composite", "base_model:quantized:grammarly/coedit-xl-composite", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-07-17T12:52:37Z	--- base_model: grammarly/coedit-xl-composite datasets: - asset - wi_locness - GEM/wiki_auto_asset_turk - discofuse - zaemyung/IteraTeR_plus - jfleg - grammarly/coedit language: - en license: cc-by-nc-4.0 metrics: - sari - bleu - accuracy tags: - llama-cpp - gguf-my-repo --- # nvhf/coedit-xl-composite-Q6_K-GGUF This model was converted to GGUF format from [`grammarly/coedit-xl-composite`](https://huggingface.co/grammarly/coedit-xl-composite) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/grammarly/coedit-xl-composite) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo nvhf/coedit-xl-composite-Q6_K-GGUF --hf-file coedit-xl-composite-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo nvhf/coedit-xl-composite-Q6_K-GGUF --hf-file coedit-xl-composite-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo nvhf/coedit-xl-composite-Q6_K-GGUF --hf-file coedit-xl-composite-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo nvhf/coedit-xl-composite-Q6_K-GGUF --hf-file coedit-xl-composite-q6_k.gguf -c 2048 ```
emplitude/rubywork	emplitude	2024-07-17T12:51:46Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:2309.00071", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T12:45:16Z	--- license: apache-2.0 --- Model Name: Qwen2 orca_mini_v7_7b # Qwen2 orca_mini_v7_7b is trained with various SFT Datasets <img src="https://huggingface.co/pankajmathur/orca_mini_v5_8b/resolve/main/orca_minis_small.jpeg" width="auto" /> <strong> Passionate about Generative AI? I help companies to privately train and deploy custom LLM/MLLM affordably. For startups, I can even assist with securing GPU grants to get you started. Let's chat! <a href="https://www.linkedin.com/in/pankajam" target="_blank">https://www.linkedin.com/in/pankajam</a> Looking forward to connecting! </strong> <br> ### NOTICE By providing proper credit and attribution, you are granted permission to use this model as a foundational base for further Full fine tuning, DPO, PPO or ORPO tuning and any kind of Merges. I actively encourage users to customize and enhance the model according to their specific needs, as this version is designed to be a comprehensive general model. Dive in and innovate! ### Evaluation Coming Soon.. ### Example Usage Here is the ChatML prompt format ``` <\|im_start\|>system You are Orca Mini, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user Hello Orca Mini, what can you do for me?<\|im_end\|> <\|im_start\|>assistant ``` Below shows a code example on how to use this model ```python from transformers import AutoModel, AutoTokenizer model_slug = "pankajmathur/orca_mini_v7_7b" model = AutoModel.from_pretrained(model_slug) tokenizer = AutoTokenizer.from_pretrained(model_slug) messages = [ {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."}, {"role": "user", "content": "Hello Orca Mini, what can you do for me?"} ] gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt") model.generate(gen_input) ``` Quants GGUF : Coming Soon AWQ: Coming Soon ### Processing Long Texts (Based upon Qwen2-7B-Instruct suggestions at https://huggingface.co/Qwen/Qwen2-7B-Instruct) To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps: 1. Install vLLM: You can install vLLM by running the following command. ```bash pip install "vllm>=0.4.3" ``` Or you can install vLLM from [source](https://github.com/vllm-project/vllm/). 2. Configure Model Settings: After downloading the model weights, modify the `config.json` file by including the below snippet: ```json { "architectures": [ "Qwen2ForCausalLM" ], // ... "vocab_size": 152064, // adding the following snippets "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` This snippet enable YARN to support longer contexts. 3. Model Deployment: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command: ```bash python -u -m vllm.entrypoints.openai.api_server --model pankajmathur/orca_mini_v7_7b ``` Then you can access the Chat API by: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "pankajmathur/orca_mini_v7_7b", "messages": [ {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."}, {"role": "user", "content": "Hello Orca Mini, what can you do for me?"} ] }' ``` Note: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required.
mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF	mradermacher	2024-07-17T12:48:50Z	27	0	transformers	[ "transformers", "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "groq", "tool-use", "function-calling", "en", "base_model:Groq/Llama-3-Groq-8B-Tool-Use", "base_model:quantized:Groq/Llama-3-Groq-8B-Tool-Use", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-07-17T09:02:31Z	--- base_model: Groq/Llama-3-Groq-8B-Tool-Use language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - facebook - meta - pytorch - llama - llama-3 - groq - tool-use - function-calling --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Groq/Llama-3-Groq-8B-Tool-Use <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Groq-8B-Tool-Use-GGUF/resolve/main/Llama-3-Groq-8B-Tool-Use.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
limaatulya/my_awesome_billsum_model_2	limaatulya	2024-07-17T12:41:34Z	125	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-17T10:38:47Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer model-index: - name: my_awesome_billsum_model_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. --> # my_awesome_billsum_model_2 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
choiruzzia/best_berita_bert_model_fold_5	choiruzzia	2024-07-17T12:38:21Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa", "base_model:finetune:ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T12:37:54Z	--- license: mit base_model: ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: best_berita_bert_model_fold_5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>]() # best_berita_bert_model_fold_5 This model is a fine-tuned version of [ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0859 - Accuracy: 0.9833 - Precision: 0.9834 - Recall: 0.9830 - F1: 0.9832 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5542 \| 1.0 \| 601 \| 0.3531 \| 0.9142 \| 0.9204 \| 0.9129 \| 0.9108 \| \| 0.266 \| 2.0 \| 1202 \| 0.1554 \| 0.9625 \| 0.9634 \| 0.9620 \| 0.9618 \| \| 0.1215 \| 3.0 \| 1803 \| 0.0859 \| 0.9833 \| 0.9834 \| 0.9830 \| 0.9832 \| \| 0.0721 \| 4.0 \| 2404 \| 0.1634 \| 0.9725 \| 0.9736 \| 0.9721 \| 0.9720 \| \| 0.0227 \| 5.0 \| 3005 \| 0.4132 \| 0.9484 \| 0.9527 \| 0.9475 \| 0.9470 \| \| 0.0242 \| 6.0 \| 3606 \| 0.2816 \| 0.9609 \| 0.9632 \| 0.9602 \| 0.9599 \| \| 0.0083 \| 7.0 \| 4207 \| 0.2295 \| 0.9717 \| 0.9731 \| 0.9712 \| 0.9712 \| \| 0.0 \| 8.0 \| 4808 \| 0.1644 \| 0.9792 \| 0.9800 \| 0.9788 \| 0.9789 \| \| 0.0002 \| 9.0 \| 5409 \| 0.1868 \| 0.9784 \| 0.9792 \| 0.9780 \| 0.9781 \| \| 0.0 \| 10.0 \| 6010 \| 0.1901 \| 0.9784 \| 0.9792 \| 0.9780 \| 0.9781 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
m1b/act_koch_pick_red_lego_distributed	m1b	2024-07-17T12:34:48Z	51	0	transformers	[ "transformers", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-07-17T12:34:47Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
PrunaAI/Replete-AI-Llama-3-11.5B-Instruct-V2-HQQ-2bit-smashed	PrunaAI	2024-07-17T12:31:49Z	4	0	transformers	[ "transformers", "llama", "text-generation", "pruna-ai", "conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T12:29:30Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: Replete-AI/Llama-3-11.5B-Instruct-V2 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo Replete-AI/Llama-3-11.5B-Instruct-V2 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/Replete-AI-Llama-3-11.5B-Instruct-V2-HQQ-2bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/Replete-AI-Llama-3-11.5B-Instruct-V2-HQQ-2bit-smashed") tokenizer = AutoTokenizer.from_pretrained("Replete-AI/Llama-3-11.5B-Instruct-V2") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model Replete-AI/Llama-3-11.5B-Instruct-V2 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
nvhf/coedit-large-Q6_K-GGUF	nvhf	2024-07-17T12:26:49Z	28	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "dataset:facebook/asset", "dataset:wi_locness", "dataset:GEM/wiki_auto_asset_turk", "dataset:discofuse", "dataset:zaemyung/IteraTeR_plus", "dataset:jfleg", "dataset:grammarly/coedit", "base_model:grammarly/coedit-large", "base_model:quantized:grammarly/coedit-large", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-07-17T12:26:45Z	--- base_model: grammarly/coedit-large datasets: - facebook/asset - wi_locness - GEM/wiki_auto_asset_turk - discofuse - zaemyung/IteraTeR_plus - jfleg - grammarly/coedit language: - en license: cc-by-nc-4.0 metrics: - sari - bleu - accuracy tags: - llama-cpp - gguf-my-repo widget: - text: 'Fix the grammar: When I grow up, I start to understand what he said is quite right.' example_title: Fluency - text: 'Make this text coherent: Their flight is weak. They run quickly through the tree canopy.' example_title: Coherence - text: 'Rewrite to make this easier to understand: A storm surge is what forecasters consider a hurricane''s most treacherous aspect.' example_title: Simplification - text: 'Paraphrase this: Do you know where I was born?' example_title: Paraphrase - text: 'Write this more formally: omg i love that song im listening to it right now' example_title: Formalize - text: 'Write in a more neutral way: The authors'' exposé on nutrition studies.' example_title: Neutralize --- # nvhf/coedit-large-Q6_K-GGUF This model was converted to GGUF format from [`grammarly/coedit-large`](https://huggingface.co/grammarly/coedit-large) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/grammarly/coedit-large) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo nvhf/coedit-large-Q6_K-GGUF --hf-file coedit-large-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo nvhf/coedit-large-Q6_K-GGUF --hf-file coedit-large-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo nvhf/coedit-large-Q6_K-GGUF --hf-file coedit-large-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo nvhf/coedit-large-Q6_K-GGUF --hf-file coedit-large-q6_k.gguf -c 2048 ```
m3face/FaceControlNet	m3face	2024-07-17T12:26:20Z	5	0	null	[ "region:us" ]	null	2024-07-17T09:44:52Z	--- {} --- # Model Card for Face ControlNet Models You can find the multilingual and English controlnet model checkpoints in the corresponding revisions: - Multilingual: [`segmentation-mlin`](https://huggingface.co/m3face/ControlnetModels/tree/segmentation-mlin) and [`landmark-mlin`](https://huggingface.co/m3face/ControlnetModels/tree/landmark-mlin). - English: [`segmentation-english`](https://huggingface.co/m3face/ControlnetModels/tree/segmentation-english) and [`landmark-english`](https://huggingface.co/m3face/ControlnetModels/tree/landmark-english).
choiruzzia/best_berita_bert_model_fold_3	choiruzzia	2024-07-17T12:15:07Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa", "base_model:finetune:ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T12:14:45Z	--- license: mit base_model: ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: best_berita_bert_model_fold_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. --> # best_berita_bert_model_fold_3 This model is a fine-tuned version of [ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1732 - Accuracy: 0.9808 - Precision: 0.9809 - Recall: 0.9811 - F1: 0.9809 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5469 \| 1.0 \| 601 \| 0.2814 \| 0.9409 \| 0.9416 \| 0.9414 \| 0.9410 \| \| 0.21 \| 2.0 \| 1202 \| 0.1697 \| 0.9600 \| 0.9602 \| 0.9605 \| 0.9600 \| \| 0.1002 \| 3.0 \| 1803 \| 0.2227 \| 0.9667 \| 0.9674 \| 0.9673 \| 0.9666 \| \| 0.0847 \| 4.0 \| 2404 \| 0.2771 \| 0.9584 \| 0.9599 \| 0.9592 \| 0.9581 \| \| 0.029 \| 5.0 \| 3005 \| 0.1732 \| 0.9808 \| 0.9809 \| 0.9811 \| 0.9809 \| \| 0.0095 \| 6.0 \| 3606 \| 0.2415 \| 0.9734 \| 0.9737 \| 0.9738 \| 0.9733 \| \| 0.0134 \| 7.0 \| 4207 \| 0.2048 \| 0.9767 \| 0.9769 \| 0.9771 \| 0.9766 \| \| 0.0001 \| 8.0 \| 4808 \| 0.2916 \| 0.9692 \| 0.9697 \| 0.9698 \| 0.9691 \| \| 0.0039 \| 9.0 \| 5409 \| 0.2201 \| 0.9784 \| 0.9786 \| 0.9787 \| 0.9784 \| \| 0.0 \| 10.0 \| 6010 \| 0.2293 \| 0.9742 \| 0.9745 \| 0.9746 \| 0.9742 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
hsan512/vistral_lora_merged_ver2	hsan512	2024-07-17T12:13:02Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T11:57:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
Cnam-LMSSC/vibravox_phonemizers	Cnam-LMSSC	2024-07-17T12:11:50Z	0	4	null	[ "fr", "dataset:Cnam-LMSSC/vibravox", "arxiv:2407.11828", "license:mit", "region:us" ]	null	2024-05-30T22:17:41Z	--- license: mit datasets: - Cnam-LMSSC/vibravox language: - fr --- # Master Model Card: Vibravox Speech-to-Phonemes Models <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65302a613ecbe51d6a6ddcec/zhB1fh-c0pjlj-Tr4Vpmr.png" style="object-fit:contain; width:280px; height:280px;" > </p> ## Overview This master model card serves as an entry point for exploring [multiple speech-to-phoneme models](https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers#available-models) trained on different sensor data from the [Vibravox dataset](https://huggingface.co/datasets/Cnam-LMSSC/vibravox). These models are designed to convert French speech into sequences of International Phonetic Alphabet (IPA) encoded words, and are fine-tuned on specific sensors to address various audio capture scenarios using body conducted sound and vibration sensors. ## Disclaimer Each of these models has been trained for specific non-conventional speech sensors and is intended to be used with in-domain data. The only exception is the headset microphone phonemizer, which can certainly be used for many applications using audio data captured by airborne microphones. Please be advised that using these models outside their intended sensor data may result in suboptimal performance. ## Task Description The primary task for these models is an ASR task in the speech-to-phoneme context. Each model takes audio input and outputs a sequence of phonemes encoded in the IPA, facilitating precise phonetic transcription of French speech. Users unfamiliar with the phonetic alphabet can use tools like the [IPA reader](http://ipa-reader.xyz) to convert the transcript back to synthetic speech and evaluate the transcription quality. ## Usage All models are finetuned versions of [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2) and adapted to different sensor inputs. They are intended to be used at a sample rate of 16kHz. ## Training Procedure The models were each finetuned for 10 epochs with a constant learning rate of 1e-5. Detailed instructions for reproducing the experiments are available on the [jhauret/vibravox](https://github.com/jhauret/vibravox) Github repository and in the [VibraVox paper on arXiV](https://arxiv.org/abs/2407.11828). ## Available Models The following models are available, each trained on a different sensor on the `speech_clean` subset of (https://huggingface.co/datasets/Cnam-LMSSC/vibravox): \| Transducer \| Huggingface model link \| \|:---------------------------\|:---------------------\| \| Reference headset microphone \| [phonemizer_headset_microphone](https://huggingface.co/Cnam-LMSSC/phonemizer_headset_microphone) \| \| In-ear comply foam-embedded microphone \|[phonemizer_soft_in_ear_microphone](https://huggingface.co/Cnam-LMSSC/phonemizer_soft_in_ear_microphone) \| \| In-ear rigid earpiece-embedded microphone \| [phonemizer_rigid_in_ear_microphone](https://huggingface.co/Cnam-LMSSC/phonemizer_rigid_in_ear_microphone) \| \| Forehead miniature vibration sensor \| [phonemizer_forehead_accelerometer](https://huggingface.co/Cnam-LMSSC/phonemizer_forehead_accelerometer) \| \| Temple vibration pickup \| [phonemizer_temple_vibration_pickup](https://huggingface.co/Cnam-LMSSC/phonemizer_temple_vibration_pickup) \| \| Laryngophone \| [phonemizer_throat_microphone](https://huggingface.co/Cnam-LMSSC/phonemizer_throat_microphone) \|
Cnam-LMSSC/phonemizer_headset_microphone	Cnam-LMSSC	2024-07-17T12:10:49Z	332	4	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "phonemize", "phoneme", "fr", "dataset:Cnam-LMSSC/vibravox", "arxiv:2407.11828", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-17T09:01:16Z	--- library_name: transformers license: mit language: fr datasets: - Cnam-LMSSC/vibravox metrics: - per tags: - audio - automatic-speech-recognition - speech - phonemize - phoneme model-index: - name: Wav2Vec2-base French finetuned for Speech-to-Phoneme by LMSSC results: - task: name: Speech-to-Phoneme type: automatic-speech-recognition dataset: name: Vibravox["headset_microphone"] type: Cnam-LMSSC/vibravox args: fr metrics: - name: Test PER, in-domain training \| type: per value: 2.8 --- <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65302a613ecbe51d6a6ddcec/zhB1fh-c0pjlj-Tr4Vpmr.png" style="object-fit:contain; width:280px; height:280px;" > </p> # Model Card - Developed by: [Cnam-LMSSC](https://huggingface.co/Cnam-LMSSC) - Model type: [Wav2Vec2ForCTC](https://huggingface.co/transformers/v4.9.2/model_doc/wav2vec2.html#transformers.Wav2Vec2ForCTC) - Language: French - License: MIT - Finetuned from model: [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2) - Finetuned dataset: `headset_microphone` audio of the `speech_clean` subset of [Cnam-LMSSC/vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) (see [VibraVox paper on arXiV](https://arxiv.org/abs/2407.11828)) - Samplerate for usage: 16kHz ## Output As this model is specifically trained for a speech-to-phoneme task, the output is sequence of [IPA-encoded](https://en.wikipedia.org/wiki/International_Phonetic_Alphabet) words, without punctuation. If you don't read the phonetic alphabet fluently, you can use this excellent [IPA reader website](http://ipa-reader.xyz) to convert the transcript back to audio synthetic speech in order to check the quality of the phonetic transcription. ## Link to phonemizer models trained on other body conducted sensors : An entry point to all phonemizers models trained on different sensor data from the [Vibravox dataset](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) is available at [https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers](https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers). ### Disclaimer Each of these models has been trained for a specific non-conventional speech sensor and is intended to be used with in-domain data. The only exception is the headset microphone phonemizer, which can certainly be used for many applications using audio data captured by airborne microphones. Please be advised that using these models outside their intended sensor data may result in suboptimal performance. ## Training procedure The model has been finetuned for 10 epochs with a constant learning rate of 1e-5. To reproduce experiment please visit [jhauret/vibravox](https://github.com/jhauret/vibravox). ## Inference script : ```python import torch, torchaudio from transformers import AutoProcessor, AutoModelForCTC from datasets import load_dataset processor = AutoProcessor.from_pretrained("Cnam-LMSSC/phonemizer_headset_microphone") model = AutoModelForCTC.from_pretrained("Cnam-LMSSC/phonemizer_headset_microphone") test_dataset = load_dataset("Cnam-LMSSC/vibravox", "speech_clean", split="test", streaming=True) audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.headset_microphone"]["array"]) audio_16kHz = torchaudio.functional.resample(audio_48kHz, orig_freq=48_000, new_freq=16_000) inputs = processor(audio_16kHz, sampling_rate=16_000, return_tensors="pt") logits = model(inputs.input_values).logits predicted_ids = torch.argmax(logits,dim = -1) transcription = processor.batch_decode(predicted_ids) print("Phonetic transcription : ", transcription) ```
Cnam-LMSSC/phonemizer_temple_vibration_pickup	Cnam-LMSSC	2024-07-17T12:08:22Z	123	2	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "phonemize", "phoneme", "fr", "dataset:Cnam-LMSSC/vibravox", "arxiv:2407.11828", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-17T09:04:00Z	--- library_name: transformers license: mit language: fr datasets: - Cnam-LMSSC/vibravox metrics: - per tags: - audio - automatic-speech-recognition - speech - phonemize - phoneme model-index: - name: Wav2Vec2-base French finetuned for Speech-to-Phoneme by LMSSC results: - task: name: Speech-to-Phoneme type: automatic-speech-recognition dataset: name: Vibravox["temple_vibration_pickup"] type: Cnam-LMSSC/vibravox args: fr metrics: - name: Test PER, in-domain training \| type: per value: 14.2 --- <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65302a613ecbe51d6a6ddcec/zhB1fh-c0pjlj-Tr4Vpmr.png" style="object-fit:contain; width:280px; height:280px;" > </p> # Model Card - Developed by: [Cnam-LMSSC](https://huggingface.co/Cnam-LMSSC) - Model type: [Wav2Vec2ForCTC](https://huggingface.co/transformers/v4.9.2/model_doc/wav2vec2.html#transformers.Wav2Vec2ForCTC) - Language: French - License: MIT - Finetuned from model: [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2) - Finetuned dataset: `temple_vibration_pickup` audio of the `speech_clean` subset of [Cnam-LMSSC/vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) (see [VibraVox paper on arXiV](https://arxiv.org/abs/2407.11828)) - Samplerate for usage: 16kHz ## Output As this model is specifically trained for a speech-to-phoneme task, the output is sequence of [IPA-encoded](https://en.wikipedia.org/wiki/International_Phonetic_Alphabet) words, without punctuation. If you don't read the phonetic alphabet fluently, you can use this excellent [IPA reader website](http://ipa-reader.xyz) to convert the transcript back to audio synthetic speech in order to check the quality of the phonetic transcription. ## Link to phonemizer models trained on other body conducted sensors : An entry point to all phonemizers models trained on different sensor data from the [Vibravox dataset](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) is available at [https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers](https://huggingface.co/Cnam-LMSSC/vibravox_phonemizers). ### Disclaimer Each of these models has been trained for a specific non-conventional speech sensor and is intended to be used with in-domain data. The only exception is the headset microphone phonemizer, which can certainly be used for many applications using audio data captured by airborne microphones. Please be advised that using these models outside their intended sensor data may result in suboptimal performance. ## Training procedure The model has been finetuned for 10 epochs with a constant learning rate of 1e-5. To reproduce experiment please visit [jhauret/vibravox](https://github.com/jhauret/vibravox). ## Inference script : ```python import torch, torchaudio from transformers import AutoProcessor, AutoModelForCTC from datasets import load_dataset processor = AutoProcessor.from_pretrained("Cnam-LMSSC/phonemizer_temple_vibration_pickup") model = AutoModelForCTC.from_pretrained("Cnam-LMSSC/phonemizer_temple_vibration_pickup") test_dataset = load_dataset("Cnam-LMSSC/vibravox", "speech_clean", split="test", streaming=True) audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.temple_vibration_pickup"]["array"]) audio_16kHz = torchaudio.functional.resample(audio_48kHz, orig_freq=48_000, new_freq=16_000) inputs = processor(audio_16kHz, sampling_rate=16_000, return_tensors="pt") logits = model(inputs.input_values).logits predicted_ids = torch.argmax(logits,dim = -1) transcription = processor.batch_decode(predicted_ids) print("Phonetic transcription : ", transcription) ```
PrunaAI/Vikhrmodels-it-5.2-fp16-cp-bnb-8bit-smashed	PrunaAI	2024-07-17T12:01:10Z	81	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:Vikhrmodels/it-5.2-fp16-cp", "base_model:quantized:Vikhrmodels/it-5.2-fp16-cp", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T11:57:31Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: Vikhrmodels/it-5.2-fp16-cp metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo Vikhrmodels/it-5.2-fp16-cp installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/Vikhrmodels-it-5.2-fp16-cp-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/it-5.2-fp16-cp") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model Vikhrmodels/it-5.2-fp16-cp before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
PrunaAI/Vikhrmodels-it-5.2-fp16-cp-bnb-4bit-smashed	PrunaAI	2024-07-17T11:59:48Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:Vikhrmodels/it-5.2-fp16-cp", "base_model:quantized:Vikhrmodels/it-5.2-fp16-cp", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T11:57:29Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: Vikhrmodels/it-5.2-fp16-cp metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo Vikhrmodels/it-5.2-fp16-cp installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/Vikhrmodels-it-5.2-fp16-cp-bnb-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/it-5.2-fp16-cp") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model Vikhrmodels/it-5.2-fp16-cp before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
jonathanjordan21/mos-mamba-18x130m-trainer-dgx	jonathanjordan21	2024-07-17T11:54:51Z	135	0	transformers	[ "transformers", "tensorboard", "safetensors", "MoSMamba", "text-generation", "generated_from_trainer", "conversational", "custom_code", "base_model:jonathanjordan21/mos-mamba-6x130m-hf", "base_model:finetune:jonathanjordan21/mos-mamba-6x130m-hf", "autotrain_compatible", "region:us" ]	text-generation	2024-07-17T10:40:17Z	--- base_model: jonathanjordan21/mos-mamba-6x130m-hf tags: - generated_from_trainer model-index: - name: mos-mamba-18x130m-trainer-dgx results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>]() # mos-mamba-18x130m-trainer-dgx This model is a fine-tuned version of [jonathanjordan21/mos-mamba-6x130m-hf](https://huggingface.co/jonathanjordan21/mos-mamba-6x130m-hf) 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-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 6 ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
PrunaAI/HODACHI-EZO-Common-9B-gemma-2-it-HQQ-4bit-smashed	PrunaAI	2024-07-17T11:53:10Z	4	0	transformers	[ "transformers", "gemma2", "text-generation", "pruna-ai", "conversational", "base_model:AXCXEPT/EZO-Common-9B-gemma-2-it", "base_model:finetune:AXCXEPT/EZO-Common-9B-gemma-2-it", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T11:50:16Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: HODACHI/EZO-Common-9B-gemma-2-it metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo HODACHI/EZO-Common-9B-gemma-2-it installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/HODACHI-EZO-Common-9B-gemma-2-it-HQQ-4bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/HODACHI-EZO-Common-9B-gemma-2-it-HQQ-4bit-smashed") tokenizer = AutoTokenizer.from_pretrained("HODACHI/EZO-Common-9B-gemma-2-it") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model HODACHI/EZO-Common-9B-gemma-2-it before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
twright8/gemma-9b-4bit-vllm	twright8	2024-07-17T11:51:54Z	75	0	transformers	[ "transformers", "pytorch", "gemma2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/gemma-2-9b-it-bnb-4bit", "base_model:quantized:unsloth/gemma-2-9b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T11:45:45Z	--- base_model: unsloth/gemma-2-9b-it-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma2 - trl - sft --- # Uploaded model - Developed by: twright8 - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-9b-it-bnb-4bit This gemma2 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)
PrunaAI/Weyaxi-Einstein-v7-Qwen2-7B-AWQ-4bit-smashed	PrunaAI	2024-07-17T11:46:38Z	81	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "pruna-ai", "conversational", "base_model:Weyaxi/Einstein-v7-Qwen2-7B", "base_model:quantized:Weyaxi/Einstein-v7-Qwen2-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-07-17T11:44:07Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: Weyaxi/Einstein-v7-Qwen2-7B metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with awq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo Weyaxi/Einstein-v7-Qwen2-7B installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install autoawq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from awq import AutoAWQForCausalLM model = AutoAWQForCausalLM.from_quantized("PrunaAI/Weyaxi-Einstein-v7-Qwen2-7B-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("Weyaxi/Einstein-v7-Qwen2-7B") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model Weyaxi/Einstein-v7-Qwen2-7B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
BrundaL/struc_pruned-bert-mrpc	BrundaL	2024-07-17T11:44:14Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T11:44:03Z	--- 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. 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]
BrundaL/bert_mrpc_trained	BrundaL	2024-07-17T11:44:02Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T11:43:48Z	--- 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]
PrunaAI/Weyaxi-Einstein-v7-Qwen2-7B-HQQ-1bit-smashed	PrunaAI	2024-07-17T11:31:55Z	6	0	transformers	[ "transformers", "qwen2", "text-generation", "pruna-ai", "conversational", "base_model:Weyaxi/Einstein-v7-Qwen2-7B", "base_model:finetune:Weyaxi/Einstein-v7-Qwen2-7B", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T11:30:17Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: Weyaxi/Einstein-v7-Qwen2-7B metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo Weyaxi/Einstein-v7-Qwen2-7B installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/Weyaxi-Einstein-v7-Qwen2-7B-HQQ-1bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/Weyaxi-Einstein-v7-Qwen2-7B-HQQ-1bit-smashed") tokenizer = AutoTokenizer.from_pretrained("Weyaxi/Einstein-v7-Qwen2-7B") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model Weyaxi/Einstein-v7-Qwen2-7B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
PrunaAI/Weyaxi-Einstein-v7-Qwen2-7B-bnb-4bit-smashed	PrunaAI	2024-07-17T11:30:11Z	78	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "pruna-ai", "conversational", "base_model:Weyaxi/Einstein-v7-Qwen2-7B", "base_model:quantized:Weyaxi/Einstein-v7-Qwen2-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T11:27:28Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: Weyaxi/Einstein-v7-Qwen2-7B metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo Weyaxi/Einstein-v7-Qwen2-7B installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/Weyaxi-Einstein-v7-Qwen2-7B-bnb-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("Weyaxi/Einstein-v7-Qwen2-7B") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model Weyaxi/Einstein-v7-Qwen2-7B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
mradermacher/llama3_Loradent-GGUF	mradermacher	2024-07-17T11:30:00Z	24	1	transformers	[ "transformers", "gguf", "en", "base_model:ckyip/llama3_Loradent", "base_model:quantized:ckyip/llama3_Loradent", "endpoints_compatible", "region:us", "conversational" ]	null	2024-07-17T11:03:35Z	--- base_model: ckyip/llama3_Loradent language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ckyip/llama3_Loradent <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/llama3_Loradent-GGUF/resolve/main/llama3_Loradent.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Autsadin/SeaLLM-7B-v2.5_16bit	Autsadin	2024-07-17T11:29:45Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-16T10:16:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
tsavage68/Summary4500_L3_1000steps_1e8rate_01beta_CSFTDPO	tsavage68	2024-07-17T11:22:02Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/Summary4500_L3_100steps_1e6rate_SFT", "base_model:finetune:tsavage68/Summary4500_L3_100steps_1e6rate_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T11:15:42Z	--- license: llama3 base_model: tsavage68/Summary4500_L3_100steps_1e6rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: Hyponatremia_L3_1000steps_1e8rate_01beta_CSFTDPO 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. --> # Hyponatremia_L3_1000steps_1e8rate_01beta_CSFTDPO This model is a fine-tuned version of [tsavage68/Summary4500_L3_100steps_1e6rate_SFT](https://huggingface.co/tsavage68/Summary4500_L3_100steps_1e6rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6901 - Rewards/chosen: 0.0028 - Rewards/rejected: -0.0045 - Rewards/accuracies: 0.5440 - Rewards/margins: 0.0073 - Logps/rejected: -133.2426 - Logps/chosen: -84.1618 - Logits/rejected: -1.0994 - Logits/chosen: -1.0693 ## 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-08 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.7202 \| 0.0112 \| 50 \| 0.6920 \| 0.0038 \| 0.0005 \| 0.5100 \| 0.0034 \| -133.1928 \| -84.1516 \| -1.0987 \| -1.0683 \| \| 0.6983 \| 0.0224 \| 100 \| 0.6938 \| 0.0029 \| 0.0030 \| 0.4940 \| -0.0001 \| -133.1671 \| -84.1607 \| -1.0980 \| -1.0678 \| \| 0.6799 \| 0.0336 \| 150 \| 0.6932 \| 0.0057 \| 0.0046 \| 0.5060 \| 0.0010 \| -133.1511 \| -84.1332 \| -1.0980 \| -1.0676 \| \| 0.6921 \| 0.0448 \| 200 \| 0.6896 \| 0.0039 \| -0.0043 \| 0.5800 \| 0.0081 \| -133.2399 \| -84.1511 \| -1.0984 \| -1.0683 \| \| 0.6904 \| 0.0559 \| 250 \| 0.6923 \| 0.0024 \| -0.0007 \| 0.5280 \| 0.0030 \| -133.2041 \| -84.1661 \| -1.0985 \| -1.0684 \| \| 0.6725 \| 0.0671 \| 300 \| 0.6877 \| 0.0016 \| -0.0105 \| 0.5980 \| 0.0121 \| -133.3022 \| -84.1739 \| -1.0990 \| -1.0689 \| \| 0.6848 \| 0.0783 \| 350 \| 0.6888 \| 0.0057 \| -0.0041 \| 0.5500 \| 0.0099 \| -133.2388 \| -84.1326 \| -1.0992 \| -1.0690 \| \| 0.7158 \| 0.0895 \| 400 \| 0.6916 \| 0.0032 \| -0.0012 \| 0.5400 \| 0.0044 \| -133.2096 \| -84.1577 \| -1.0988 \| -1.0687 \| \| 0.6992 \| 0.1007 \| 450 \| 0.6912 \| 0.0007 \| -0.0043 \| 0.5260 \| 0.0050 \| -133.2402 \| -84.1823 \| -1.0988 \| -1.0686 \| \| 0.6827 \| 0.1119 \| 500 \| 0.6885 \| 0.0048 \| -0.0057 \| 0.5600 \| 0.0105 \| -133.2546 \| -84.1417 \| -1.0988 \| -1.0687 \| \| 0.6949 \| 0.1231 \| 550 \| 0.6903 \| 0.0025 \| -0.0045 \| 0.5440 \| 0.0069 \| -133.2422 \| -84.1652 \| -1.0988 \| -1.0687 \| \| 0.7093 \| 0.1343 \| 600 \| 0.6915 \| 0.0015 \| -0.0031 \| 0.5300 \| 0.0046 \| -133.2279 \| -84.1744 \| -1.0988 \| -1.0687 \| \| 0.7026 \| 0.1454 \| 650 \| 0.6894 \| 0.0048 \| -0.0038 \| 0.5480 \| 0.0086 \| -133.2351 \| -84.1415 \| -1.0992 \| -1.0691 \| \| 0.6781 \| 0.1566 \| 700 \| 0.6896 \| 0.0052 \| -0.0030 \| 0.5400 \| 0.0082 \| -133.2273 \| -84.1380 \| -1.0992 \| -1.0691 \| \| 0.7174 \| 0.1678 \| 750 \| 0.6888 \| 0.0036 \| -0.0063 \| 0.5780 \| 0.0099 \| -133.2603 \| -84.1535 \| -1.0992 \| -1.0690 \| \| 0.7065 \| 0.1790 \| 800 \| 0.6895 \| 0.0071 \| -0.0013 \| 0.5580 \| 0.0084 \| -133.2102 \| -84.1191 \| -1.0992 \| -1.0691 \| \| 0.7018 \| 0.1902 \| 850 \| 0.6904 \| 0.0027 \| -0.0042 \| 0.5280 \| 0.0069 \| -133.2389 \| -84.1626 \| -1.0994 \| -1.0693 \| \| 0.6894 \| 0.2014 \| 900 \| 0.6901 \| 0.0028 \| -0.0045 \| 0.5440 \| 0.0073 \| -133.2426 \| -84.1618 \| -1.0994 \| -1.0693 \| \| 0.686 \| 0.2126 \| 950 \| 0.6901 \| 0.0028 \| -0.0045 \| 0.5440 \| 0.0073 \| -133.2426 \| -84.1618 \| -1.0994 \| -1.0693 \| \| 0.6778 \| 0.2238 \| 1000 \| 0.6901 \| 0.0028 \| -0.0045 \| 0.5440 \| 0.0073 \| -133.2426 \| -84.1618 \| -1.0994 \| -1.0693 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.0.0+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1
tej11iit/fine_tuned_colbertv2.0	tej11iit	2024-07-17T11:18:08Z	109	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-07-17T07:59:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
PrunaAI/nyu-visionx-cambrian-phi3-3b-bnb-4bit-smashed	PrunaAI	2024-07-17T11:16:30Z	112	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "pruna-ai", "conversational", "custom_code", "base_model:nyu-visionx/cambrian-phi3-3b", "base_model:quantized:nyu-visionx/cambrian-phi3-3b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T11:15:21Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: nyu-visionx/cambrian-phi3-3b metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo nyu-visionx/cambrian-phi3-3b installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/nyu-visionx-cambrian-phi3-3b-bnb-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("nyu-visionx/cambrian-phi3-3b") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model nyu-visionx/cambrian-phi3-3b before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
PrunaAI/nyu-visionx-cambrian-phi3-3b-HQQ-1bit-smashed	PrunaAI	2024-07-17T11:15:12Z	7	1	transformers	[ "transformers", "phi3", "text-generation", "pruna-ai", "conversational", "custom_code", "base_model:nyu-visionx/cambrian-phi3-3b", "base_model:finetune:nyu-visionx/cambrian-phi3-3b", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T11:14:43Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: nyu-visionx/cambrian-phi3-3b metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo nyu-visionx/cambrian-phi3-3b installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/nyu-visionx-cambrian-phi3-3b-HQQ-1bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/nyu-visionx-cambrian-phi3-3b-HQQ-1bit-smashed") tokenizer = AutoTokenizer.from_pretrained("nyu-visionx/cambrian-phi3-3b") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model nyu-visionx/cambrian-phi3-3b before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
mradermacher/Worktro-Small-v0.2-GGUF	mradermacher	2024-07-17T11:06:42Z	26	0	transformers	[ "transformers", "gguf", "text-generation", "ko", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-07-17T10:40:26Z	--- base_model: Edentns/Worktro-Small-v0.2 language: - ko library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - text-generation --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Edentns/Worktro-Small-v0.2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.IQ3_S.gguf) \| IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.IQ3_M.gguf) \| IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Worktro-Small-v0.2-GGUF/resolve/main/Worktro-Small-v0.2.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
jeggers/galactica-125m-cot-only	jeggers	2024-07-17T10:55:07Z	112	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T10:54:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
fosaber/feifeisha7_LoRA	fosaber	2024-07-17T10:53:17Z	4	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "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	2024-07-17T10:53:12Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of TOK cute cartoon shark 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 - fosaber/feifeisha7_LoRA <Gallery /> ## Model description These are fosaber/feifeisha7_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 cute cartoon shark to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](fosaber/feifeisha7_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]
Piotrasz/Llama-2-7b-hf-R_ROME-100-pl-3	Piotrasz	2024-07-17T10:48:03Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T10:45:02Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Shaleen123/yi-1.5-9b-maths	Shaleen123	2024-07-17T10:46:16Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T10:43:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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PrunaAI/Replete-AI-Llama-3-11.5B-V2-bnb-8bit-smashed	PrunaAI	2024-07-17T10:35:45Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T10:29:51Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: Replete-AI/Llama-3-11.5B-V2 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo Replete-AI/Llama-3-11.5B-V2 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/Replete-AI-Llama-3-11.5B-V2-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("Replete-AI/Llama-3-11.5B-V2") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model Replete-AI/Llama-3-11.5B-V2 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
ZidanAf/Zidan_model_output_v13	ZidanAf	2024-07-17T10:34:14Z	114	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T09:59:59Z	--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: Zidan_model_output_v13 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. --> # Zidan_model_output_v13 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8657 - Accuracy: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 248 \| 1.1254 \| 0.6909 \| \| No log \| 2.0 \| 496 \| 1.0021 \| 0.5636 \| \| 0.9773 \| 3.0 \| 744 \| 1.1986 \| 0.5091 \| \| 0.9773 \| 4.0 \| 992 \| 0.9418 \| 0.6364 \| \| 0.8463 \| 5.0 \| 1240 \| 0.9185 \| 0.5091 \| \| 0.8463 \| 6.0 \| 1488 \| 0.8848 \| 0.5273 \| \| 0.7379 \| 7.0 \| 1736 \| 0.9933 \| 0.5091 \| \| 0.7379 \| 8.0 \| 1984 \| 0.9484 \| 0.5273 \| \| 0.6888 \| 9.0 \| 2232 \| 0.9290 \| 0.7636 \| \| 0.6888 \| 10.0 \| 2480 \| 0.9072 \| 0.7636 \| \| 0.5859 \| 11.0 \| 2728 \| 0.8754 \| 0.7818 \| \| 0.5859 \| 12.0 \| 2976 \| 0.8657 \| 0.8 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
jrcastropy/mt0-small-query-extraction-v4	jrcastropy	2024-07-17T10:27:58Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:bigscience/mt0-small", "base_model:finetune:bigscience/mt0-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-07-17T10:27:14Z	--- license: apache-2.0 base_model: bigscience/mt0-small tags: - generated_from_trainer metrics: - rouge model-index: - name: mt0-small-query-extraction-v4 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. --> # mt0-small-query-extraction-v4 This model is a fine-tuned version of [bigscience/mt0-small](https://huggingface.co/bigscience/mt0-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0016 - Rouge1: 55.6206 - Rouge2: 48.0808 - Rougel: 55.6125 - Rougelsum: 55.6119 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.0121 \| 1.0 \| 110364 \| 0.0040 \| 55.607 \| 48.038 \| 55.5908 \| 55.5903 \| 19.0 \| \| 0.0045 \| 2.0 \| 220728 \| 0.0021 \| 55.6226 \| 48.0812 \| 55.6123 \| 55.6125 \| 19.0 \| \| 0.003 \| 3.0 \| 331092 \| 0.0016 \| 55.6206 \| 48.0808 \| 55.6125 \| 55.6119 \| 19.0 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
mradermacher/Mixtral-seed-pollux-v0.1-GGUF	mradermacher	2024-07-17T10:22:42Z	12	0	transformers	[ "transformers", "gguf", "en", "base_model:TeamDelta/Mixtral-seed-pollux-v0.1", "base_model:quantized:TeamDelta/Mixtral-seed-pollux-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-07-17T10:19:56Z	--- base_model: TeamDelta/Mixtral-seed-pollux-v0.1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/TeamDelta/Mixtral-seed-pollux-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.IQ3_S.gguf) \| IQ3_S \| 0.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.IQ3_M.gguf) \| IQ3_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 0.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 0.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q6_K.gguf) \| Q6_K \| 0.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.Q8_0.gguf) \| Q8_0 \| 0.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral-seed-pollux-v0.1-GGUF/resolve/main/Mixtral-seed-pollux-v0.1.f16.gguf) \| f16 \| 1.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Piotrasz/Llama-2-7b-hf-R_ROME-50-pl-2	Piotrasz	2024-07-17T10:17:50Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T10:14:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
morturr/flan-t5-base-headlines-text-classification-2024-07-17-seed-42	morturr	2024-07-17T10:13:20Z	50	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T09:48:52Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: flan-t5-base-headlines-text-classification-2024-07-17-seed-42 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. --> # flan-t5-base-headlines-text-classification-2024-07-17-seed-42 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
Aharneish/hindi-llama	Aharneish	2024-07-17T10:10:57Z	34	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-07-05T05:49:11Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: hindi-llama 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. --> # hindi-llama This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1632 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 1.5858 \| 0.0188 \| 1000 \| 1.4610 \| \| 1.3662 \| 0.0375 \| 2000 \| 1.3469 \| \| 1.3174 \| 0.0563 \| 3000 \| 1.3143 \| \| 1.3003 \| 0.0750 \| 4000 \| 1.2895 \| \| 1.2931 \| 0.0938 \| 5000 \| 1.2762 \| \| 1.2786 \| 0.1125 \| 6000 \| 1.2649 \| \| 1.2541 \| 0.1313 \| 7000 \| 1.2556 \| \| 1.2594 \| 0.1500 \| 8000 \| 1.2481 \| \| 1.2523 \| 0.1688 \| 9000 \| 1.2415 \| \| 1.244 \| 0.1876 \| 10000 \| 1.2348 \| \| 1.2274 \| 0.2063 \| 11000 \| 1.2309 \| \| 1.2167 \| 0.2251 \| 12000 \| 1.2257 \| \| 1.2359 \| 0.2438 \| 13000 \| 1.2225 \| \| 1.2156 \| 0.2626 \| 14000 \| 1.2191 \| \| 1.204 \| 0.2813 \| 15000 \| 1.2146 \| \| 1.2203 \| 0.3001 \| 16000 \| 1.2109 \| \| 1.2016 \| 0.3188 \| 17000 \| 1.2094 \| \| 1.2117 \| 0.3376 \| 18000 \| 1.2057 \| \| 1.2183 \| 0.3563 \| 19000 \| 1.2038 \| \| 1.2108 \| 0.3751 \| 20000 \| 1.2005 \| \| 1.2153 \| 0.3939 \| 21000 \| 1.1981 \| \| 1.189 \| 0.4126 \| 22000 \| 1.1968 \| \| 1.1857 \| 0.4314 \| 23000 \| 1.1947 \| \| 1.1688 \| 0.4501 \| 24000 \| 1.1914 \| \| 1.2028 \| 0.4689 \| 25000 \| 1.1907 \| \| 1.1916 \| 0.4876 \| 26000 \| 1.1893 \| \| 1.1797 \| 0.5064 \| 27000 \| 1.1873 \| \| 1.1897 \| 0.5251 \| 28000 \| 1.1848 \| \| 1.1817 \| 0.5439 \| 29000 \| 1.1837 \| \| 1.1837 \| 0.5627 \| 30000 \| 1.1826 \| \| 1.1889 \| 0.5814 \| 31000 \| 1.1808 \| \| 1.1754 \| 0.6002 \| 32000 \| 1.1798 \| \| 1.1868 \| 0.6189 \| 33000 \| 1.1790 \| \| 1.1792 \| 0.6377 \| 34000 \| 1.1780 \| \| 1.1772 \| 0.6564 \| 35000 \| 1.1766 \| \| 1.1763 \| 0.6752 \| 36000 \| 1.1755 \| \| 1.1719 \| 0.6939 \| 37000 \| 1.1746 \| \| 1.1804 \| 0.7127 \| 38000 \| 1.1724 \| \| 1.1763 \| 0.7314 \| 39000 \| 1.1717 \| \| 1.1715 \| 0.7502 \| 40000 \| 1.1717 \| \| 1.1732 \| 0.7690 \| 41000 \| 1.1701 \| \| 1.1808 \| 0.7877 \| 42000 \| 1.1692 \| \| 1.1713 \| 0.8065 \| 43000 \| 1.1688 \| \| 1.175 \| 0.8252 \| 44000 \| 1.1678 \| \| 1.1604 \| 0.8440 \| 45000 \| 1.1668 \| \| 1.1619 \| 0.8627 \| 46000 \| 1.1658 \| \| 1.1686 \| 0.8815 \| 47000 \| 1.1650 \| \| 1.1541 \| 0.9002 \| 48000 \| 1.1647 \| \| 1.1776 \| 0.9190 \| 49000 \| 1.1641 \| \| 1.1675 \| 0.9378 \| 50000 \| 1.1640 \| \| 1.1727 \| 0.9565 \| 51000 \| 1.1636 \| \| 1.1566 \| 0.9753 \| 52000 \| 1.1633 \| \| 1.1657 \| 0.9940 \| 53000 \| 1.1632 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
vwxyzjn/online_dpo_llmjudge	vwxyzjn	2024-07-17T10:09:59Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-16T19:52:34Z	--- tags: - generated_from_trainer model-index: - name: online_dpo_llmjudge results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/costa-huang/huggingface/runs/e52d1ezu) # online_dpo_llmjudge This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
morturr/flan-t5-base-one_liners-text-classification-2024-07-17-seed-42	morturr	2024-07-17T10:07:32Z	48	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T09:48:43Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: flan-t5-base-one_liners-text-classification-2024-07-17-seed-42 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. --> # flan-t5-base-one_liners-text-classification-2024-07-17-seed-42 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
tekloon/autotrain-agent-experience	tekloon	2024-07-17T10:06:53Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T01:52:55Z	--- base_model: meta-llama/Meta-Llama-3-8B-Instruct library_name: transformers license: other tags: - autotrain - text-generation-inference - text-generation - peft widget: - messages: - role: user content: What is your favorite condiment? --- # 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) ```
zhoukz/qwen1.5-14b-a2.7b-chat-4bit	zhoukz	2024-07-17T10:04:43Z	74	0	transformers	[ "transformers", "safetensors", "qwen2_moe", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T09:48:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Frinkles/RiPPL-Alpha	Frinkles	2024-07-17T09:57:22Z	4	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-08T05:36:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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apwic/summarization-base-0	apwic	2024-07-17T09:51:49Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "generated_from_trainer", "base_model:LazarusNLP/IndoNanoT5-base", "base_model:finetune:LazarusNLP/IndoNanoT5-base", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2024-06-30T18:04:33Z	--- license: apache-2.0 base_model: LazarusNLP/IndoNanoT5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: summarization-base-0 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. --> # summarization-base-0 This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7149 - Rouge1: 0.4331 - Rouge2: 0.0 - Rougel: 0.4333 - Rougelsum: 0.4319 - Gen Len: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 1.5249 \| 1.0 \| 3566 \| 0.8858 \| 0.417 \| 0.0 \| 0.4187 \| 0.414 \| 1.0 \| \| 0.8202 \| 2.0 \| 7132 \| 0.7492 \| 0.4125 \| 0.0 \| 0.412 \| 0.4102 \| 1.0 \| \| 0.6232 \| 3.0 \| 10698 \| 0.6953 \| 0.4015 \| 0.0 \| 0.3971 \| 0.3965 \| 1.0 \| \| 0.4728 \| 4.0 \| 14264 \| 0.6717 \| 0.4319 \| 0.0 \| 0.4307 \| 0.4292 \| 1.0 \| \| 0.3238 \| 5.0 \| 17830 \| 0.7149 \| 0.4331 \| 0.0 \| 0.4333 \| 0.4319 \| 1.0 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
pranavSIT/F2-TB-activities-20240717_093325	pranavSIT	2024-07-17T09:43:10Z	105	0	transformers	[ "transformers", "safetensors", "florence2", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2024-07-17T09:33:25Z	--- 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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ABHIiiii1/LaBSE-Fine-Tuned-EN-KHA	ABHIiiii1	2024-07-17T09:42:00Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:23999", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/LaBSE", "base_model:finetune:sentence-transformers/LaBSE", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-07-17T09:30:23Z	--- base_model: sentence-transformers/LaBSE datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:23999 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Who led thee through that great and terrible wilderness , wherein were fiery serpents , and scorpions , and drought , where there was no water ; who brought thee forth water out of the rock of flint ; sentences: - bad u ai ïa ki ha u Aaron bad ki khun shynrang jong u . - U la ïalam ïa phi lyngba ka ri shyiap kaba ïar bad kaba ishyrkhei eh , ha kaba la don ki bseiñ kiba don bih bad ki ñianglartham . Ha kata ka ri kaba tyrkhong bad ka bym don um , u la pynmih um na u mawsiang na ka bynta jong phi . - Ki paidbah na ki jait ba na shatei ki phah khot ïa u , bad nangta ma ki baroh ki ïaleit lang sha u Rehoboam bad ki ong ha u , - source_sentence: And , behold , Boaz came from Beth–lehem , and said unto the reapers , The Lord be with you . And they answered him , The Lord bless thee . sentences: - Ko ki briew bymïaineh , to wan noh ; phi long ki jong nga . Ngan shim iwei na phi na kawei kawei ka shnong bad ar ngut na kawei kawei ka kur , bad ngan wallam pat ïa phi sha u lum Seïon . - Hadien katto katne por u Boas da lade hi u wan poi na Bethlehem bad u ai khublei ïa ki nongtrei . To U Trai un long ryngkat bad phi ! u ong . U Trai u kyrkhu ïa phi ! ki jubab . - U Trai u la ong ha u , Khreh bad leit sha ‘ Ka Lynti Ba-beit ,’ bad ha ka ïing jong u Judas kylli ïa u briew na Tarsos uba kyrteng u Saul . - source_sentence: Jehovah used the prehuman Jesus as his “master worker” in creating all other things in heaven and on earth . sentences: - Shuwa ba un wan long briew U Jehobah u la pyndonkam ïa u Jisu kum u “rangbah nongtrei” ha kaba thaw ïa kiei kiei baroh kiba don ha bneng bad ha khyndew . - Shisien la don u briew uba la leit ban bet symbai . Katba u dang bet ïa u symbai , katto katne na u , ki la hap ha shi lynter ka lynti ïaid kjat , ha kaba ki la shah ïuh , bad ki sim ki la bam lut . - Ngan ïathuh ïa ka shatei ban shah ïa ki ban leit bad ïa ka shathie ban ym bat noh ïa ki . Ai ba ki briew jong nga ki wan phai na ki ri bajngai , na man la ki bynta baroh jong ka pyrthei . - source_sentence: 'The like figure whereunto even baptism doth also now save us ( not the putting away of the filth of the flesh , but the answer of a good conscience toward God , ) by the resurrection of Jesus Christ :' sentences: - kaba long ka dak kaba kdew sha ka jingpynbaptis , kaba pyllait im ïa phi mynta . Kam dei ka jingsait noh ïa ka jakhlia na ka met , hynrei ka jingkular ba la pynlong sha U Blei na ka jingïatiplem babha . Ka pynim ïa phi da ka jingmihpat jong U Jisu Khrist , - Ki briew kiba sniew kin ïoh ïa kaei kaba ki dei ban ïoh . Ki briew kiba bha kin ïoh bainong na ka bynta ki kam jong ki . - Nangta nga la ïohi ïa ka bneng bathymmai bad ïa ka pyrthei bathymmai . Ka bneng banyngkong bad ka pyrthei banyngkong ki la jah noh , bad ka duriaw kam don shuh . - source_sentence: On that day they read in the book of Moses in the audience of the people ; and therein was found written , that the Ammonite and the Moabite should not come into the congregation of God for ever ; sentences: - U Elisha u la ïap bad la tep ïa u . Man la ka snem ki kynhun jong ki Moab ki ju wan tur thma ïa ka ri Israel . - Katba dang pule jam ïa ka Hukum u Moses ha u paidbah , ki poi ha ka bynta kaba ong ba ym dei ban shah ïa u nong Amon ne u nong Moab ban ïasnohlang bad ki briew jong U Blei . - U angel u la jubab , U Mynsiem Bakhuid un sa wan ha pha , bad ka bor jong U Blei kan shong halor jong pha . Na kane ka daw , ïa i khunlung bakhuid yn khot U Khun U Blei . --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision e34fab64a3011d2176c99545a93d5cbddc9a91b7 --> - Maximum Sequence Length: 256 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("ABHIiiii1/LaBSE-Fine-Tuned-EN-KHA") # Run inference sentences = [ 'On that day they read in the book of Moses in the audience of the people ; and therein was found written , that the Ammonite and the Moabite should not come into the congregation of God for ever ;', 'Katba dang pule jam ïa ka Hukum u Moses ha u paidbah , ki poi ha ka bynta kaba ong ba ym dei ban shah ïa u nong Amon ne u nong Moab ban ïasnohlang bad ki briew jong U Blei .', 'U Elisha u la ïap bad la tep ïa u . Man la ka snem ki kynhun jong ki Moab ki ju wan tur thma ïa ka ri Israel .', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 23,999 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 6 tokens</li><li>mean: 34.89 tokens</li><li>max: 87 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 51.51 tokens</li><li>max: 127 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>And Moses went out from Pharaoh , and entreated the Lord .</code> \| <code>U Moses u mihnoh na u Pharaoh , bad u kyrpad ïa U Trai ,</code> \| \| <code>In the ninth year of Hoshea the king of Assyria took Samaria , and carried Israel away into Assyria , and placed them in Halah and in Habor by the river of Gozan , and in the cities of the Medes .</code> \| <code>kaba long ka snem kaba khyndai jong ka jingsynshar u Hoshea , u patsha ka Assyria u kurup ïa ka Samaria , u rah ïa ki Israel sha Assyria kum ki koidi , bad pynsah katto katne ngut na ki ha ka nongbah Halah , katto katne pat hajan ka wah Habor ha ka distrik Gosan , bad katto katne ha ki nongbah jong ka Media .</code> \| \| <code>And the king said unto Cushi , Is the young man Absalom safe ? And Cushi answered , The enemies of my lord the king , and all that rise against thee to do thee hurt , be as that young man is .</code> \| <code>Hato u samla Absalom u dang im ? u syiem u kylli . U mraw u jubab , Ko Kynrad , nga sngew ba kaei kaba la jia ha u kan jin da la jia ha baroh ki nongshun jong ngi , bad ha baroh kiba ïaleh pyrshah ïa phi .</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:----:\|:-------------:\| \| 0.3333 \| 500 \| 0.542 \| \| 0.6667 \| 1000 \| 0.135 \| \| 1.0 \| 1500 \| 0.0926 \| \| 1.3333 \| 2000 \| 0.0535 \| \| 1.6667 \| 2500 \| 0.0226 \| \| 2.0 \| 3000 \| 0.018 \| \| 2.3333 \| 3500 \| 0.0124 \| \| 2.6667 \| 4000 \| 0.0057 \| \| 3.0 \| 4500 \| 0.0053 \| ### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.42.3 - PyTorch: 2.1.2 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
vrclc/W2V2-BERT-Malayalam-studio	vrclc	2024-07-17T09:39:27Z	8	1	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/w2v-bert-2.0", "base_model:finetune:facebook/w2v-bert-2.0", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-17T04:27:27Z	--- license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: w2v-bert-studio 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. --> # w2v-bert-studio This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1587 - Wer: 0.1157 ## 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 \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 1.0335 \| 0.4932 \| 600 \| 0.3654 \| 0.4387 \| \| 0.1531 \| 0.9864 \| 1200 \| 0.2373 \| 0.3332 \| \| 0.1074 \| 1.4797 \| 1800 \| 0.2069 \| 0.2953 \| \| 0.0928 \| 1.9729 \| 2400 \| 0.2146 \| 0.2814 \| \| 0.0734 \| 2.4661 \| 3000 \| 0.1947 \| 0.2433 \| \| 0.0678 \| 2.9593 \| 3600 \| 0.1938 \| 0.2406 \| \| 0.0522 \| 3.4525 \| 4200 \| 0.1566 \| 0.2053 \| \| 0.0493 \| 3.9457 \| 4800 \| 0.1649 \| 0.1988 \| \| 0.0366 \| 4.4390 \| 5400 \| 0.1417 \| 0.1834 \| \| 0.0372 \| 4.9322 \| 6000 \| 0.1542 \| 0.1749 \| \| 0.028 \| 5.4254 \| 6600 \| 0.1476 \| 0.1620 \| \| 0.0263 \| 5.9186 \| 7200 \| 0.1388 \| 0.1622 \| \| 0.0195 \| 6.4118 \| 7800 \| 0.1384 \| 0.1495 \| \| 0.0185 \| 6.9051 \| 8400 \| 0.1351 \| 0.1383 \| \| 0.0136 \| 7.3983 \| 9000 \| 0.1404 \| 0.1344 \| \| 0.0119 \| 7.8915 \| 9600 \| 0.1253 \| 0.1276 \| \| 0.0087 \| 8.3847 \| 10200 \| 0.1443 \| 0.1284 \| \| 0.0066 \| 8.8779 \| 10800 \| 0.1475 \| 0.1252 \| \| 0.0049 \| 9.3711 \| 11400 \| 0.1577 \| 0.1227 \| \| 0.0038 \| 9.8644 \| 12000 \| 0.1587 \| 0.1157 \| ### Framework versions - Transformers 4.42.2 - Pytorch 2.1.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
quarkss/indobert-large-stsb	quarkss	2024-07-17T09:38:18Z	35	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:5749", "loss:CosineSimilarityLoss", "dataset:quarkss/stsb-indo-mt", "arxiv:1908.10084", "base_model:indobenchmark/indobert-large-p2", "base_model:finetune:indobenchmark/indobert-large-p2", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-07-16T09:14:25Z	--- base_model: indobenchmark/indobert-large-p2 datasets: - quarkss/stsb-indo-mt library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:5749 - loss:CosineSimilarityLoss widget: - source_sentence: Dua ekor anjing berenang di kolam renang. sentences: - Anjing-anjing sedang berenang di kolam renang. - Seekor binatang sedang berjalan di atas tanah. - Seorang pria sedang menyeka pinggiran mangkuk. - source_sentence: Seorang anak perempuan sedang mengiris mentega menjadi dua bagian. sentences: - Seorang wanita sedang mengiris tahu. - Dua orang berkelahi. - Seorang pria sedang menari. - source_sentence: Seorang gadis sedang makan kue mangkuk. sentences: - Seorang pria sedang mengiris bawang putih dengan alat pengiris mandolin. - Seorang pria sedang memotong dan memotong bawang. - Seorang wanita sedang makan kue mangkuk. - source_sentence: Sebuah helikopter mendarat di landasan helikopter. sentences: - Seorang pria sedang mengiris mentimun. - Seorang pria sedang memotong batang pohon dengan kapak. - Sebuah helikopter mendarat. - source_sentence: Seorang pria sedang berjalan dengan seekor kuda. sentences: - Seorang pria sedang menuntun seekor kuda dengan tali kekang. - Seorang pria sedang menembakkan pistol. - Seorang wanita sedang memetik tomat. model-index: - name: SentenceTransformer based on indobenchmark/indobert-large-p2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.8691840566814281 name: Pearson Cosine - type: spearman_cosine value: 0.8676618157111291 name: Spearman Cosine - type: pearson_manhattan value: 0.8591936899214765 name: Pearson Manhattan - type: spearman_manhattan value: 0.8625729388794413 name: Spearman Manhattan - type: pearson_euclidean value: 0.8599101625523397 name: Pearson Euclidean - type: spearman_euclidean value: 0.8632992102966184 name: Spearman Euclidean - type: pearson_dot value: 0.8440663965451926 name: Pearson Dot - type: spearman_dot value: 0.8392116432595296 name: Spearman Dot - type: pearson_max value: 0.8691840566814281 name: Pearson Max - type: spearman_max value: 0.8676618157111291 name: Spearman Max - type: pearson_cosine value: 0.8401688802461491 name: Pearson Cosine - type: spearman_cosine value: 0.8365597846163649 name: Spearman Cosine - type: pearson_manhattan value: 0.8276067064758832 name: Pearson Manhattan - type: spearman_manhattan value: 0.8315689286193226 name: Spearman Manhattan - type: pearson_euclidean value: 0.8277930159560367 name: Pearson Euclidean - type: spearman_euclidean value: 0.831557090168861 name: Spearman Euclidean - type: pearson_dot value: 0.8170329546065831 name: Pearson Dot - type: spearman_dot value: 0.8083098402255348 name: Spearman Dot - type: pearson_max value: 0.8401688802461491 name: Pearson Max - type: spearman_max value: 0.8365597846163649 name: Spearman Max --- # SentenceTransformer based on indobenchmark/indobert-large-p2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## STSB Test \| Model \| Spearman Correlation \| \|:----------------------------------------\|-----------------------:\| \| quarkss/indobert-large-stsb \| 0.8366 \| \| quarkss/indobert-base-stsb \| 0.8123 \| \| sentence-transformers/all-MiniLM-L6-v2 \| 0.5952 \| \| indobenchmark/indobert-large-p2 \| 0.5673 \| \| sentence-transformers/all-mpnet-base-v2 \| 0.5531 \| \| sentence-transformers/stsb-bert-base \| 0.5349 \| \| indobenchmark/indobert-base-p2 \| 0.5309 \| ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) <!-- at revision 4b280c3bfcc1ed2d6b4589be5c876076b7d73568 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': 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}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("quarkss/indobert-large-stsb") # Run inference sentences = [ 'Seorang pria sedang berjalan dengan seekor kuda.', 'Seorang pria sedang menuntun seekor kuda dengan tali kekang.', 'Seorang pria sedang menembakkan pistol.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8692 \| \| spearman_cosine \| 0.8677 \| \| pearson_manhattan \| 0.8592 \| \| spearman_manhattan \| 0.8626 \| \| pearson_euclidean \| 0.8599 \| \| spearman_euclidean \| 0.8633 \| \| pearson_dot \| 0.8441 \| \| spearman_dot \| 0.8392 \| \| pearson_max \| 0.8692 \| \| spearman_max \| 0.8677 \| #### Semantic Similarity * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:-------------------\|:-----------\| \| pearson_cosine \| 0.8402 \| \| spearman_cosine \| 0.8366 \| \| pearson_manhattan \| 0.8276 \| \| spearman_manhattan \| 0.8316 \| \| pearson_euclidean \| 0.8278 \| \| spearman_euclidean \| 0.8316 \| \| pearson_dot \| 0.817 \| \| spearman_dot \| 0.8083 \| \| pearson_max \| 0.8402 \| \| spearman_max \| 0.8366 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 5,749 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: \| \| sentence1 \| sentence2 \| score \| \|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 6 tokens</li><li>mean: 9.65 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 9.59 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \| * Samples: \| sentence1 \| sentence2 \| score \| \|:-----------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------\|:------------------\| \| <code>Sebuah pesawat sedang lepas landas.</code> \| <code>Sebuah pesawat terbang sedang lepas landas.</code> \| <code>1.0</code> \| \| <code>Seorang pria sedang memainkan seruling besar.</code> \| <code>Seorang pria sedang memainkan seruling.</code> \| <code>0.76</code> \| \| <code>Seorang pria sedang mengoleskan keju parut di atas pizza.</code> \| <code>Seorang pria sedang mengoleskan keju parut di atas pizza yang belum matang.</code> \| <code>0.76</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `weight_decay`: 0.01 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| spearman_cosine \| spearman_max \| \|:------:\|:----:\|:-------------:\|:---------------:\|:------------:\| \| 0.2778 \| 100 \| 0.0867 \| - \| - \| \| 0.5556 \| 200 \| 0.0351 \| - \| - \| \| 0.8333 \| 300 \| 0.0303 \| - \| - \| \| 1.1111 \| 400 \| 0.0202 \| - \| - \| \| 1.3889 \| 500 \| 0.0154 \| 0.8612 \| - \| \| 1.6667 \| 600 \| 0.0136 \| - \| - \| \| 1.9444 \| 700 \| 0.0145 \| - \| - \| \| 2.2222 \| 800 \| 0.0082 \| - \| - \| \| 2.5 \| 900 \| 0.0072 \| - \| - \| \| 2.7778 \| 1000 \| 0.0068 \| 0.8660 \| - \| \| 3.0556 \| 1100 \| 0.0065 \| - \| - \| \| 3.3333 \| 1200 \| 0.0044 \| - \| - \| \| 3.6111 \| 1300 \| 0.0044 \| - \| - \| \| 3.8889 \| 1400 \| 0.0045 \| - \| - \| \| 4.1667 \| 1500 \| 0.0038 \| 0.8677 \| - \| \| 4.4444 \| 1600 \| 0.0038 \| - \| - \| \| 4.7222 \| 1700 \| 0.0035 \| - \| - \| \| 5.0 \| 1800 \| 0.0034 \| - \| 0.8366 \| ### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.0.1+cu117 - Accelerate: 0.32.1 - Datasets: 2.17.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
quarkss/indobert-base-stsb	quarkss	2024-07-17T09:37:35Z	11	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:5749", "loss:CosineSimilarityLoss", "dataset:quarkss/stsb-indo-mt", "arxiv:1908.10084", "base_model:indobenchmark/indobert-base-p2", "base_model:finetune:indobenchmark/indobert-base-p2", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-07-16T08:47:08Z	--- base_model: indobenchmark/indobert-base-p2 datasets: - quarkss/stsb-indo-mt library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:5749 - loss:CosineSimilarityLoss widget: - source_sentence: Dua ekor anjing berenang di kolam renang. sentences: - Anjing-anjing sedang berenang di kolam renang. - Seekor binatang sedang berjalan di atas tanah. - Seorang pria sedang menyeka pinggiran mangkuk. - source_sentence: Seorang anak perempuan sedang mengiris mentega menjadi dua bagian. sentences: - Seorang wanita sedang mengiris tahu. - Dua orang berkelahi. - Seorang pria sedang menari. - source_sentence: Seorang gadis sedang makan kue mangkuk. sentences: - Seorang pria sedang mengiris bawang putih dengan alat pengiris mandolin. - Seorang pria sedang memotong dan memotong bawang. - Seorang wanita sedang makan kue mangkuk. - source_sentence: Sebuah helikopter mendarat di landasan helikopter. sentences: - Seorang pria sedang mengiris mentimun. - Seorang pria sedang memotong batang pohon dengan kapak. - Sebuah helikopter mendarat. - source_sentence: Seorang pria sedang berjalan dengan seekor kuda. sentences: - Seorang pria sedang menuntun seekor kuda dengan tali kekang. - Seorang pria sedang menembakkan pistol. - Seorang wanita sedang memetik tomat. model-index: - name: SentenceTransformer based on indobenchmark/indobert-base-p2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.8577280779646681 name: Pearson Cosine - type: spearman_cosine value: 0.8588776334781149 name: Spearman Cosine - type: pearson_manhattan value: 0.8315261521874587 name: Pearson Manhattan - type: spearman_manhattan value: 0.8355406849443783 name: Spearman Manhattan - type: pearson_euclidean value: 0.8318083198603524 name: Pearson Euclidean - type: spearman_euclidean value: 0.8359194889385243 name: Spearman Euclidean - type: pearson_dot value: 0.7767060276322824 name: Pearson Dot - type: spearman_dot value: 0.783607744137448 name: Spearman Dot - type: pearson_max value: 0.8577280779646681 name: Pearson Max - type: spearman_max value: 0.8588776334781149 name: Spearman Max - type: pearson_cosine value: 0.8122790124383042 name: Pearson Cosine - type: spearman_cosine value: 0.8123119892530147 name: Spearman Cosine - type: pearson_manhattan value: 0.7987643661729152 name: Pearson Manhattan - type: spearman_manhattan value: 0.7966661480553803 name: Spearman Manhattan - type: pearson_euclidean value: 0.7992882233155829 name: Pearson Euclidean - type: spearman_euclidean value: 0.797227936168015 name: Spearman Euclidean - type: pearson_dot value: 0.712195542080357 name: Pearson Dot - type: spearman_dot value: 0.7014898656834544 name: Spearman Dot - type: pearson_max value: 0.8122790124383042 name: Pearson Max - type: spearman_max value: 0.8123119892530147 name: Spearman Max --- # SentenceTransformer based on indobenchmark/indobert-base-p2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## STSB Test \| Model \| Spearman Correlation \| \|:----------------------------------------\|-----------------------:\| \| quarkss/indobert-large-stsb \| 0.8366 \| \| quarkss/indobert-base-stsb \| 0.8123 \| \| sentence-transformers/all-MiniLM-L6-v2 \| 0.5952 \| \| indobenchmark/indobert-large-p2 \| 0.5673 \| \| sentence-transformers/all-mpnet-base-v2 \| 0.5531 \| \| sentence-transformers/stsb-bert-base \| 0.5349 \| \| indobenchmark/indobert-base-p2 \| 0.5309 \| ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (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}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("quarkss/indobert-base-stsb") # Run inference sentences = [ 'Seorang pria sedang berjalan dengan seekor kuda.', 'Seorang pria sedang menuntun seekor kuda dengan tali kekang.', 'Seorang pria sedang menembakkan pistol.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8577 \| \| spearman_cosine \| 0.8589 \| \| pearson_manhattan \| 0.8315 \| \| spearman_manhattan \| 0.8355 \| \| pearson_euclidean \| 0.8318 \| \| spearman_euclidean \| 0.8359 \| \| pearson_dot \| 0.7767 \| \| spearman_dot \| 0.7836 \| \| pearson_max \| 0.8577 \| \| spearman_max \| 0.8589 \| #### Semantic Similarity * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:-------------------\|:-----------\| \| pearson_cosine \| 0.8123 \| \| spearman_cosine \| 0.8123 \| \| pearson_manhattan \| 0.7988 \| \| spearman_manhattan \| 0.7967 \| \| pearson_euclidean \| 0.7993 \| \| spearman_euclidean \| 0.7972 \| \| pearson_dot \| 0.7122 \| \| spearman_dot \| 0.7015 \| \| pearson_max \| 0.8123 \| \| spearman_max \| 0.8123 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 5,749 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: \| \| sentence1 \| sentence2 \| score \| \|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 6 tokens</li><li>mean: 9.65 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 9.59 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \| * Samples: \| sentence1 \| sentence2 \| score \| \|:-----------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------\|:------------------\| \| <code>Sebuah pesawat sedang lepas landas.</code> \| <code>Sebuah pesawat terbang sedang lepas landas.</code> \| <code>1.0</code> \| \| <code>Seorang pria sedang memainkan seruling besar.</code> \| <code>Seorang pria sedang memainkan seruling.</code> \| <code>0.76</code> \| \| <code>Seorang pria sedang mengoleskan keju parut di atas pizza.</code> \| <code>Seorang pria sedang mengoleskan keju parut di atas pizza yang belum matang.</code> \| <code>0.76</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `weight_decay`: 0.01 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| spearman_cosine \| spearman_max \| \|:------:\|:----:\|:-------------:\|:---------------:\|:------------:\| \| 0.2778 \| 100 \| 0.0615 \| - \| - \| \| 0.5556 \| 200 \| 0.0336 \| - \| - \| \| 0.8333 \| 300 \| 0.0331 \| - \| - \| \| 1.1111 \| 400 \| 0.0235 \| - \| - \| \| 1.3889 \| 500 \| 0.018 \| 0.8472 \| - \| \| 1.6667 \| 600 \| 0.0164 \| - \| - \| \| 1.9444 \| 700 \| 0.0159 \| - \| - \| \| 2.2222 \| 800 \| 0.0097 \| - \| - \| \| 2.5 \| 900 \| 0.0085 \| - \| - \| \| 2.7778 \| 1000 \| 0.0084 \| 0.8563 \| - \| \| 3.0556 \| 1100 \| 0.0076 \| - \| - \| \| 3.3333 \| 1200 \| 0.0056 \| - \| - \| \| 3.6111 \| 1300 \| 0.0054 \| - \| - \| \| 3.8889 \| 1400 \| 0.0052 \| - \| - \| \| 4.1667 \| 1500 \| 0.0047 \| 0.8589 \| - \| \| 4.4444 \| 1600 \| 0.0045 \| - \| - \| \| 4.7222 \| 1700 \| 0.004 \| - \| - \| \| 5.0 \| 1800 \| 0.0042 \| - \| 0.8123 \| ### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.0.1+cu117 - Accelerate: 0.32.1 - Datasets: 2.17.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
YL95/4bit_VLLM_copa_v_wright_SFT_mistral_file_folder_path_2epoch	YL95	2024-07-17T09:36:40Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T09:34:35Z	--- base_model: unsloth/mistral-7b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: YL95 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
atgarcia/wav2vec2part12	atgarcia	2024-07-17T09:28:27Z	108	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-07-17T07:30:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
firstmango/Project_02_LLAMA3_unsloth7B-gguf	firstmango	2024-07-17T09:28:27Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "base_model:quantized:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-07-17T09:21:23Z	--- base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: firstmango - License: apache-2.0 - Finetuned from model : beomi/Llama-3-Open-Ko-8B-Instruct-preview This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MaziyarPanahi/Llama-3-Groq-8B-Tool-Use-GGUF	MaziyarPanahi	2024-07-17T09:25:57Z	712,903	7	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:Groq/Llama-3-Groq-8B-Tool-Use", "base_model:quantized:Groq/Llama-3-Groq-8B-Tool-Use", "region:us", "conversational" ]	text-generation	2024-07-17T08:47:20Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: Llama-3-Groq-8B-Tool-Use-GGUF base_model: Groq/Llama-3-Groq-8B-Tool-Use inference: false model_creator: Groq pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Llama-3-Groq-8B-Tool-Use-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-Groq-8B-Tool-Use-GGUF) - Model creator: [Groq](https://huggingface.co/Groq) - Original model: [Groq/Llama-3-Groq-8B-Tool-Use](https://huggingface.co/Groq/Llama-3-Groq-8B-Tool-Use) ## Description [MaziyarPanahi/Llama-3-Groq-8B-Tool-Use-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-Groq-8B-Tool-Use-GGUF) contains GGUF format model files for [Groq/Llama-3-Groq-8B-Tool-Use](https://huggingface.co/Groq/Llama-3-Groq-8B-Tool-Use). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
Piotrasz/Llama-2-7b-hf-ROME-50-pl-3	Piotrasz	2024-07-17T09:24:26Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T09:21:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Kunalbhagat88/Mixral_7B_instruct_finetuned	Kunalbhagat88	2024-07-17T09:23:22Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T09:11:48Z	--- 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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Niggendar/WeirdInCase	Niggendar	2024-07-17T09:22:27Z	107	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-07-17T09:12:15Z	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. 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kr-manish/fine-tune-embedding-bge-base-HrPolicy_vfinal	kr-manish	2024-07-17T09:21:15Z	7	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:160", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "base_model:finetune:BAAI/bge-base-en-v1.5", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-07-17T09:20:36Z	--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: [] library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:160 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Priya Softweb has specific guidelines for managing the arrival of international shipments. To ensure smooth customs clearance, the company requires an authorization letter from the client, written on their company letterhead. This letter must clearly state that the shipment is "Not for commercial purposes" to prevent the application of duty charges by the customs office. All international shipments should be addressed to Keyur Patel at Priya Softweb Solutions Pvt. Ltd., with the company's full address and contact information clearly indicated. Employees are advised to contact the HR department for the correct format of the authorization letter and to inform Keyur Patel about the expected arrival of such shipments. These procedures streamline the handling of international shipments and help avoid potential customs-related delays or complications. sentences: - Female employees at Priya Softweb are allowed to wear:- Formal trousers/jeans and shirts- Sarees- Formal skirts- T-shirts with collars- Chudidars & Kurtis- Salwar SuitsHowever, they are not allowed to wear:- Round neck, deep neck, cold shoulder, and fancy T-shirts- Low waist jeans, short T-shirts, and short shirts- Transparent wear- Wear with deep-cut sleeves- Capris- Slippers- Visible tattoos & piercingsPriya Softweb emphasizes a professional appearance for its employees while providing flexibility in choosing appropriate attire within the defined guidelines. - Priya Softweb has specific guidelines for managing the arrival of international shipments. To ensure smooth customs clearance, the company requires an authorization letter from the client, written on their company letterhead. This letter must clearly state that the shipment is "Not for commercial purposes" to prevent the application of duty charges by the customs office. All international shipments should be addressed to Keyur Patel at Priya Softweb Solutions Pvt. Ltd., with the company's full address and contact information clearly indicated. Employees are advised to contact the HR department for the correct format of the authorization letter and to inform Keyur Patel about the expected arrival of such shipments. These procedures streamline the handling of international shipments and help avoid potential customs-related delays or complications. - Priya Softweb has a structured onboarding process for new employees. Upon joining, new hires undergo an induction program conducted by the HR department. This program introduces them to the company's culture, values, processes, and policies, ensuring they are well-acquainted with the work environment and expectations. HR also facilitates introductions to the relevant department and sends out a company-wide email announcing the new employee's arrival. Additionally, new employees are required to complete quarterly Ethics & Compliance training to familiarize themselves with the company's ethical standards and compliance requirements. This comprehensive onboarding approach helps new employees integrate seamlessly into the company and quickly become productive members of the team. - source_sentence: The sanctioning and approving authority for Casual Leave, Sick Leave, and Privilege Leave at Priya Softweb is the Leader/Manager. sentences: - Even if an employee utilizes the 'Hybrid' Work From Home model for only half a day, a full count is deducted from their monthly allowance of 4 WFH days. This clarifies that any utilization of the 'Hybrid' model, regardless of the duration, is considered a full WFH day and counts towards the monthly limit. - The sanctioning and approving authority for Casual Leave, Sick Leave, and Privilege Leave at Priya Softweb is the Leader/Manager. - To be eligible for gratuity at Priya Softweb, an employee must have completed a minimum of 5 continuous years of service. This ensures that only long-term employees are entitled to this benefit. - source_sentence: 'Priya Softweb utilizes Employee Agreements/Bonds as a mechanism to retain talent within the company. These agreements are implemented in various situations, including: * Retention: When the company seeks to retain valuable employees who have resigned, a 15-month bond may be applied based on the company''s requirements. * Freshers: New employees with 0 to 1 year of experience are generally subject to an 18-month bond. * Rejoining: When former employees are rehired, a 15-month bond is typically implemented. These bond periods vary based on the specific circumstances and aim to ensure a certain level of commitment from employees, especially in roles that require significant investment in training and development.' sentences: - To claim gratuity, employees must submit an application form to the Accounts department. This formal process ensures proper documentation and timely processing of the gratuity payment. - Priya Softweb acknowledges the efforts of employees who work late hours. Employees working more than 11 hours on weekdays are eligible for reimbursement of up to Rs. 250/- for their dinner expenses. However, this reimbursement is subject to approval from their Department Head. This policy recognizes the extra effort put in by employees working extended hours and provides some financial compensation for their meals. - 'Priya Softweb utilizes Employee Agreements/Bonds as a mechanism to retain talent within the company. These agreements are implemented in various situations, including: * Retention: When the company seeks to retain valuable employees who have resigned, a 15-month bond may be applied based on the company''s requirements. * Freshers: New employees with 0 to 1 year of experience are generally subject to an 18-month bond. * Rejoining: When former employees are rehired, a 15-month bond is typically implemented. These bond periods vary based on the specific circumstances and aim to ensure a certain level of commitment from employees, especially in roles that require significant investment in training and development.' - source_sentence: Chewing tobacco, gutka, gum, or smoking within the office premises is strictly prohibited at Priya Softweb. Bringing such substances inside the office will lead to penalties and potentially harsh decisions from management. This strict policy reflects Priya Softweb's commitment to a healthy and clean work environment. sentences: - Chewing tobacco, gutka, gum, or smoking within the office premises is strictly prohibited at Priya Softweb. Bringing such substances inside the office will lead to penalties and potentially harsh decisions from management. This strict policy reflects Priya Softweb's commitment to a healthy and clean work environment. - In situations of 'Bad Weather', the HR department at Priya Softweb will enable the 'Work From Home' option within the OMS system based on the severity of the weather and potential safety risks for employees commuting to the office. This proactive approach prioritizes employee safety and allows for flexible work arrangements during adverse weather events. - Priya Softweb employees are entitled to 5 Casual Leaves (CL) per year. - source_sentence: Priya Softweb prioritizes the health and wellness of its employees. The company strongly prohibits chewing tobacco, gutka, gum, or smoking within the office premises. Penalties and harsh decisions from management await anyone found bringing such substances into the office. Furthermore, carrying food to the desk is not permitted. Employees are encouraged to use the terrace dining facility for lunch, snacks, and dinner. Priya Softweb also emphasizes cleanliness and orderliness in the workspace. Employees are responsible for maintaining their designated work areas, keeping them clean, organized, and free from unnecessary items. Spitting gutka, gum, or tobacco in the washrooms is strictly prohibited. These policies contribute to a healthier and more pleasant work environment for everyone. sentences: - Priya Softweb prioritizes the health and wellness of its employees. The company strongly prohibits chewing tobacco, gutka, gum, or smoking within the office premises. Penalties and harsh decisions from management await anyone found bringing such substances into the office. Furthermore, carrying food to the desk is not permitted. Employees are encouraged to use the terrace dining facility for lunch, snacks, and dinner. Priya Softweb also emphasizes cleanliness and orderliness in the workspace. Employees are responsible for maintaining their designated work areas, keeping them clean, organized, and free from unnecessary items. Spitting gutka, gum, or tobacco in the washrooms is strictly prohibited. These policies contribute to a healthier and more pleasant work environment for everyone. - The Performance Appraisal at Priya Softweb is solely based on the employee's performance evaluation. The evaluation score is compiled by the Team Leader/Project Manager, who also gives the final rating to the team member. Detailed recommendations are provided by the TL/PM, and increment or promotion is granted accordingly. This process ensures that performance is the primary factor driving salary revisions and promotions. - Priya Softweb actively promotes diversity in its hiring practices. The company focuses on recruiting individuals from a wide range of backgrounds, including different races, ethnicities, religions, political beliefs, education levels, socio-economic backgrounds, geographical locations, languages, and cultures. This commitment to diversity enriches the company culture and brings in a variety of perspectives and experiences. model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.33333333333333326 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.33333333333333326 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.33333333333333326 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.33333333333333326 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.33333333333333326 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("kr-manish/fine-tune-embedding-bge-base-HrPolicy_vfinal") # Run inference sentences = [ 'Priya Softweb prioritizes the health and wellness of its employees. The company strongly prohibits chewing tobacco, gutka, gum, or smoking within the office premises. Penalties and harsh decisions from management await anyone found bringing such substances into the office. Furthermore, carrying food to the desk is not permitted. Employees are encouraged to use the terrace dining facility for lunch, snacks, and dinner. Priya Softweb also emphasizes cleanliness and orderliness in the workspace. Employees are responsible for maintaining their designated work areas, keeping them clean, organized, and free from unnecessary items. Spitting gutka, gum, or tobacco in the washrooms is strictly prohibited. These policies contribute to a healthier and more pleasant work environment for everyone.', 'Priya Softweb prioritizes the health and wellness of its employees. The company strongly prohibits chewing tobacco, gutka, gum, or smoking within the office premises. Penalties and harsh decisions from management await anyone found bringing such substances into the office. Furthermore, carrying food to the desk is not permitted. Employees are encouraged to use the terrace dining facility for lunch, snacks, and dinner. Priya Softweb also emphasizes cleanliness and orderliness in the workspace. Employees are responsible for maintaining their designated work areas, keeping them clean, organized, and free from unnecessary items. Spitting gutka, gum, or tobacco in the washrooms is strictly prohibited. These policies contribute to a healthier and more pleasant work environment for everyone.', "The Performance Appraisal at Priya Softweb is solely based on the employee's performance evaluation. The evaluation score is compiled by the Team Leader/Project Manager, who also gives the final rating to the team member. Detailed recommendations are provided by the TL/PM, and increment or promotion is granted accordingly. This process ensures that performance is the primary factor driving salary revisions and promotions.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:--------\| \| cosine_accuracy@1 \| 1.0 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 1.0 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 1.0 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 1.0 \| \| cosine_mrr@10 \| 1.0 \| \| cosine_map@100 \| 1.0 \| #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:--------\| \| cosine_accuracy@1 \| 1.0 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 1.0 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 1.0 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 1.0 \| \| cosine_mrr@10 \| 1.0 \| \| cosine_map@100 \| 1.0 \| #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:--------\| \| cosine_accuracy@1 \| 1.0 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 1.0 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 1.0 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 1.0 \| \| cosine_mrr@10 \| 1.0 \| \| cosine_map@100 \| 1.0 \| #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:--------\| \| cosine_accuracy@1 \| 1.0 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 1.0 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 1.0 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 1.0 \| \| cosine_mrr@10 \| 1.0 \| \| cosine_map@100 \| 1.0 \| #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:--------\| \| cosine_accuracy@1 \| 1.0 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 1.0 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 1.0 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 1.0 \| \| cosine_mrr@10 \| 1.0 \| \| cosine_map@100 \| 1.0 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 160 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: \| \| positive \| anchor \| \|:--------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 16 tokens</li><li>mean: 90.76 tokens</li><li>max: 380 tokens</li></ul> \| <ul><li>min: 16 tokens</li><li>mean: 90.76 tokens</li><li>max: 380 tokens</li></ul> \| * Samples: \| positive \| anchor \| \|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>The general timings for the Marketing team vary: BD works from 1:00 PM to 10:00 PM or 3:00 PM to 12:00 AM, while BA/SEO works from 11:00 AM to 8:00 PM.</code> \| <code>The general timings for the Marketing team vary: BD works from 1:00 PM to 10:00 PM or 3:00 PM to 12:00 AM, while BA/SEO works from 11:00 AM to 8:00 PM.</code> \| \| <code>Priya Softweb acknowledges the efforts of employees who work late hours. Employees working more than 11 hours on weekdays are eligible for reimbursement of up to Rs. 250/- for their dinner expenses. However, this reimbursement is subject to approval from their Department Head. This policy recognizes the extra effort put in by employees working extended hours and provides some financial compensation for their meals.</code> \| <code>Priya Softweb acknowledges the efforts of employees who work late hours. Employees working more than 11 hours on weekdays are eligible for reimbursement of up to Rs. 250/- for their dinner expenses. However, this reimbursement is subject to approval from their Department Head. This policy recognizes the extra effort put in by employees working extended hours and provides some financial compensation for their meals.</code> \| \| <code>While Priya Softweb allows employees to keep their cell phones during work hours for emergency purposes, excessive personal mobile phone usage and lengthy calls within the office premises are strictly prohibited. Excessive use may result in disciplinary actions. This policy aims to strike a balance between allowing accessibility for emergencies and maintaining a productive work environment free from distractions.</code> \| <code>While Priya Softweb allows employees to keep their cell phones during work hours for emergency purposes, excessive personal mobile phone usage and lengthy calls within the office premises are strictly prohibited. Excessive use may result in disciplinary actions. This policy aims to strike a balance between allowing accessibility for emergencies and maintaining a productive work environment free from distractions.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 3e-05 - `num_train_epochs`: 15 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 3e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 15 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| dim_128_cosine_map@100 \| dim_256_cosine_map@100 \| dim_512_cosine_map@100 \| dim_64_cosine_map@100 \| dim_768_cosine_map@100 \| \|:-------:\|:-----:\|:-------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|:----------------------:\| \| 0 \| 0 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 1.0 \| 1 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 2.0 \| 3 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 3.0 \| 4 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 4.0 \| 6 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 5.0 \| 8 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 6.0 \| 9 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 6.4 \| 10 \| 0.0767 \| - \| - \| - \| - \| - \| \| 7.0 \| 11 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 8.0 \| 12 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 9.0 \| 13 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| 10.0 \| 15 \| - \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.32.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
hmart824/layoutlmv3-finetuned_1.1	hmart824	2024-07-17T09:17:40Z	106	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "token-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-07-17T09:17:12Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: layoutlmv3-finetuned_1.1 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. --> # layoutlmv3-finetuned_1.1 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0191 - Precision: 0.9972 - Recall: 0.9966 - F1: 0.9968 - Accuracy: 0.9960 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.3713 \| 1.0 \| 60 \| 0.0460 \| 0.9874 \| 0.9914 \| 0.9893 \| 0.9901 \| \| 0.068 \| 2.0 \| 120 \| 0.0281 \| 0.9947 \| 0.9930 \| 0.9938 \| 0.9930 \| \| 0.0612 \| 3.0 \| 180 \| 0.0191 \| 0.9972 \| 0.9966 \| 0.9968 \| 0.9960 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
firstmango/Project_02_LLAMA3_unsloth7B	firstmango	2024-07-17T09:16:17Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-07-17T09:05:01Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF	QuantFactory	2024-07-17T09:16:04Z	233	1	transformers	[ "transformers", "gguf", "text-generation", "pytorch", "Lynx", "Patronus AI", "evaluation", "hallucination-detection", "en", "base_model:PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct", "base_model:quantized:PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-07-12T16:33:16Z	--- library_name: transformers tags: - text-generation - pytorch - Lynx - Patronus AI - evaluation - hallucination-detection license: cc-by-nc-4.0 language: - en base_model: PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct pipeline_tag: text-generation --- # QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF This is quantized version of [PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct](https://huggingface.co/PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct) created using llama.cpp # Model Description Lynx is an open-source hallucination evaluation model. Patronus-Lynx-8B-Instruct was trained on a mix of datasets including CovidQA, PubmedQA, DROP, RAGTruth. The datasets contain a mix of hand-annotated and synthetic data. The maximum sequence length is 8000 tokens. ## Model Details - Model Type: Patronus-Lynx-8B-Instruct is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct model. - Language: Primarily English - Developed by: Patronus AI - License: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [https://github.com/patronus-ai/Lynx-hallucination-detection](https://github.com/patronus-ai/Lynx-hallucination-detection) ## How to Get Started with the Model The model is fine-tuned to be used to detect hallucinations in a RAG setting. Provided a document, question and answer, the model can evaluate whether the answer is faithful to the document. To use the model, we recommend using the prompt we used for fine-tuning: ``` PROMPT = """ Given the following QUESTION, DOCUMENT and ANSWER you must analyze the provided answer and determine whether it is faithful to the contents of the DOCUMENT. The ANSWER must not offer new information beyond the context provided in the DOCUMENT. The ANSWER also must not contradict information provided in the DOCUMENT. Output your final verdict by strictly following this format: "PASS" if the answer is faithful to the DOCUMENT and "FAIL" if the answer is not faithful to the DOCUMENT. Show your reasoning. -- QUESTION (THIS DOES NOT COUNT AS BACKGROUND INFORMATION): {question} -- DOCUMENT: {context} -- ANSWER: {answer} -- Your output should be in JSON FORMAT with the keys "REASONING" and "SCORE": {{"REASONING": <your reasoning as bullet points>, "SCORE": <your final score>}} """ ``` The model will output the score as 'PASS' if the answer is faithful to the document or FAIL if the answer is not faithful to the document. ## Training Details The model was finetuned for 3 epochs using H100s on dataset of size 2400. We use [lion](https://github.com/lucidrains/lion-pytorch) optimizer with lr=5.0e-7. For more details on data generation, please check out our Github repo. ### Training Data We train on 2400 samples consisting of CovidQA, PubmedQA, DROP and RAGTruth samples. For datasets that do not contain hallucinated samples, we generate perturbations to introduce hallucinations in the data. For more details about the data generation process, refer to the paper. ## Evaluation The model was evaluated on [PatronusAI/HaluBench](https://huggingface.co/datasets/PatronusAI/HaluBench). It outperforms GPT-3.5-Turbo, GPT-4-Turbo, GPT-4o and Claude Sonnet. ## Model Card Contact [@sunitha-ravi](https://huggingface.co/sunitha-ravi)
langyatest/new_to_return_2	langyatest	2024-07-17T09:15:56Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-17T08:50:03Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: new_to_return_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. --> # new_to_return_2 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9778 - Accuracy: 0.5458 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 115 \| 1.0336 \| 0.5 \| \| No log \| 2.0 \| 230 \| 0.9290 \| 0.5430 \| \| No log \| 3.0 \| 345 \| 0.9143 \| 0.5572 \| \| No log \| 4.0 \| 460 \| 0.9428 \| 0.5496 \| \| 0.893 \| 5.0 \| 575 \| 0.9778 \| 0.5458 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
hw2942/bert-base-chinese-climate-transition-physical-risk-prediction-1	hw2942	2024-07-17T09:01:50Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "climate", "zh", "dataset:hw2942/climate-transition-risk_0-physical-risk_1", "base_model:google-bert/bert-base-chinese", "base_model:finetune:google-bert/bert-base-chinese", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-07-16T08:19:24Z	--- base_model: bert-base-chinese metrics: - accuracy tags: - generated_from_trainer - climate model-index: - name: bert-base-chinese-climate-transition-physical-risk-prediction-1 results: [] datasets: - hw2942/climate-transition-risk_0-physical-risk_1 language: - zh pipeline_tag: text-classification --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-chinese-climate-transition-physical-risk-prediction-1 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Accuracy: 1.0 ## Model description Predict the Chinese sentence to climate transition risk or physical risk ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 57 \| 0.6517 \| 0.88 \| \| No log \| 2.0 \| 114 \| 0.1019 \| 0.98 \| \| No log \| 3.0 \| 171 \| 0.0003 \| 1.0 \| \| No log \| 4.0 \| 228 \| 0.0002 \| 1.0 \| \| No log \| 5.0 \| 285 \| 0.0001 \| 1.0 \| \| No log \| 6.0 \| 342 \| 0.0001 \| 1.0 \| \| No log \| 7.0 \| 399 \| 0.0001 \| 1.0 \| \| No log \| 8.0 \| 456 \| 0.0001 \| 1.0 \| \| 0.0465 \| 9.0 \| 513 \| 0.0001 \| 1.0 \| \| 0.0465 \| 10.0 \| 570 \| 0.0001 \| 1.0 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
Niggendar/InLightMix	Niggendar	2024-07-17T08:59:09Z	95	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-07-17T08:50:37Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
cadene/2024_07_16_diffusion_koch_pick_place_lego_simple_v2_latency_040000	cadene	2024-07-17T08:51:24Z	51	0	transformers	[ "transformers", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-07-17T08:51:19Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
naserahmadi/adapt-fa-t5-small	naserahmadi	2024-07-17T08:44:17Z	162	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "fa", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-10-04T06:58:25Z	--- license: mit language: - en - fa library_name: transformers pipeline_tag: text2text-generation --- This model has been created by keeping only Persian and English token embeddings in mT5-small model. The model was created by following instruction from this [link](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90)
TopGxn/llama3_Inst_8B_septupled_json_MA	TopGxn	2024-07-17T08:43:38Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-07-17T07:16:09Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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