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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-24 12:28:46	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 493 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-24 12:27:57	card stringlengths 11 1.01M
lsmille/lora_evo_ta_all_layers_2	lsmille	2024-05-28T19:10:07Z	6	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:togethercomputer/evo-1-8k-base", "base_model:adapter:togethercomputer/evo-1-8k-base", "license:apache-2.0", "region:us" ]	null	2024-05-28T04:19:32Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: togethercomputer/evo-1-8k-base model-index: - name: lora_evo_ta_all_layers_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. --> # lora_evo_ta_all_layers_2 This model is a fine-tuned version of [togethercomputer/evo-1-8k-base](https://huggingface.co/togethercomputer/evo-1-8k-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1660 ## Model description lora_alpha = 32 lora_dropout = 0.05 lora_r = 16 epochs = 9 <--------------- learning rate = 3e-4 warmup_steps=0.5 gradient_accumulation_steps = 8 train_batch = 1 eval_batch = 1 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.5 - num_epochs: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.0681 \| 0.9925 \| 33 \| 2.9815 \| \| 2.9165 \| 1.9850 \| 66 \| 2.9530 \| \| 2.8091 \| 2.9774 \| 99 \| 2.9446 \| \| 2.6361 \| 4.0 \| 133 \| 2.9406 \| \| 2.6312 \| 4.9925 \| 166 \| 2.9409 \| \| 2.57 \| 5.9850 \| 199 \| 2.9978 \| \| 2.5215 \| 6.9774 \| 232 \| 3.0450 \| \| 2.4107 \| 8.0 \| 266 \| 3.0763 \| \| 2.4272 \| 8.9323 \| 297 \| 3.1660 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
lsmille/lora_evo_ta_all_layers_3	lsmille	2024-05-28T19:09:17Z	3	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:togethercomputer/evo-1-8k-base", "base_model:adapter:togethercomputer/evo-1-8k-base", "license:apache-2.0", "region:us" ]	null	2024-05-28T05:08:39Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: togethercomputer/evo-1-8k-base model-index: - name: lora_evo_ta_all_layers_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. --> # lora_evo_ta_all_layers_3 This model is a fine-tuned version of [togethercomputer/evo-1-8k-base](https://huggingface.co/togethercomputer/evo-1-8k-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9530 ## Model description lora_alpha = 16 <-------- lora_dropout = 0.05 lora_r = 8 <-------- epochs = 3 learning rate = 3e-4 warmup_steps=0.5 gradient_accumulation_steps = 8 train_batch = 1 eval_batch = 1 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.5 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.0867 \| 0.9925 \| 33 \| 3.0207 \| \| 2.9359 \| 1.9850 \| 66 \| 2.9592 \| \| 2.7604 \| 2.9774 \| 99 \| 2.9530 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
amiguel/lightining_studio	amiguel	2024-05-28T19:01:17Z	0	0	adapter-transformers	[ "adapter-transformers", "medical", "text-classification", "dataset:HuggingFaceFW/fineweb", "license:apache-2.0", "region:us" ]	text-classification	2024-05-22T06:09:31Z	--- license: apache-2.0 datasets: - HuggingFaceFW/fineweb metrics: - accuracy library_name: adapter-transformers pipeline_tag: text-classification tags: - medical ---
dtorber/BioNLP-conditional-tokens-decoder-eLife	dtorber	2024-05-28T18:59:39Z	97	0	transformers	[ "transformers", "safetensors", "led", "text2text-generation", "summarization", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2024-05-28T10:54:52Z	--- tags: - summarization - generated_from_trainer model-index: - name: BioNLP-conditional-tokens-decoder-eLife 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. --> # BioNLP-conditional-tokens-decoder-eLife This model was trained from scratch 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: 1.3739167643078955e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 1.13.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.2
marian-nmt/bleurt-20	marian-nmt	2024-05-28T18:58:21Z	0	0	null	[ "region:us" ]	null	2024-05-28T18:24:56Z	#BLEURT-20 This repository hosts checkpoints compatible with Marian NMT.
phongtintruong/misjava-api-052924	phongtintruong	2024-05-28T18:56:47Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T18:26:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
luciorramos/llm_tcc_sp90_ep90_ds1000	luciorramos	2024-05-28T18:56:46Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T18:49:35Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: luciorramos - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-20151707	fine-tuned	2024-05-28T18:55:56Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-20151707", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T18:55:25Z	--- license: apache-2.0 datasets: - fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-20151707 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-20151707', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-34917964	fine-tuned	2024-05-28T18:55:41Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-34917964", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T18:55:04Z	--- license: apache-2.0 datasets: - fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-34917964 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-34917964', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
mago18/donut-demo	mago18	2024-05-28T18:55:34Z	49	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-28T18:55:04Z	--- 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]
fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-60453771	fine-tuned	2024-05-28T18:55:34Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-60453771", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T18:55:03Z	--- license: apache-2.0 datasets: - fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-60453771 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-60453771', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-10552781	fine-tuned	2024-05-28T18:55:10Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-10552781", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T18:54:33Z	--- license: apache-2.0 datasets: - fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-10552781 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-10552781', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-89836585	fine-tuned	2024-05-28T18:54:24Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-89836585", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T18:53:55Z	--- license: apache-2.0 datasets: - fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-89836585 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-89836585', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-83930416	fine-tuned	2024-05-28T18:53:40Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-83930416", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T18:53:06Z	--- license: apache-2.0 datasets: - fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-83930416 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-83930416', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/NFCorpus-512-192-gpt-4o-2024-05-13-89953157	fine-tuned	2024-05-28T18:52:41Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/NFCorpus-512-192-gpt-4o-2024-05-13-89953157", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T18:52:06Z	--- license: apache-2.0 datasets: - fine-tuned/NFCorpus-512-192-gpt-4o-2024-05-13-89953157 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/NFCorpus-512-192-gpt-4o-2024-05-13-89953157', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/SCIDOCS-512-192-gpt-4o-2024-05-13-93651135	fine-tuned	2024-05-28T18:52:08Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SCIDOCS-512-192-gpt-4o-2024-05-13-93651135", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T18:51:30Z	--- license: apache-2.0 datasets: - fine-tuned/SCIDOCS-512-192-gpt-4o-2024-05-13-93651135 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SCIDOCS-512-192-gpt-4o-2024-05-13-93651135', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
odicem/tinyllama-cleantech-v1	odicem	2024-05-28T18:50:08Z	136	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T18:47:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BehradG/vit-base-patch16-224-in21k-finetuned-lora-food101	BehradG	2024-05-28T18:47:23Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T18:04:31Z	--- 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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ds28/llama2-causal	ds28	2024-05-28T18:47:20Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T14:40:19Z	--- license: apache-2.0 ---
dtorber/BioNLP-conditional-tokens-encoder-eLife	dtorber	2024-05-28T18:47:18Z	97	0	transformers	[ "transformers", "safetensors", "led", "text2text-generation", "summarization", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2024-05-28T10:44:20Z	--- tags: - summarization - generated_from_trainer model-index: - name: BioNLP-conditional-tokens-encoder-eLife 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. --> # BioNLP-conditional-tokens-encoder-eLife This model was trained from scratch 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: 1.3739167643078955e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 1.13.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.2
MrezaPRZ/codellama_synthetic_create_context_bigquery	MrezaPRZ	2024-05-28T18:32:29Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T18:29:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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SecondNan/ppo-LunaLander-v2	SecondNan	2024-05-28T18:31:38Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-28T18:31:19Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.38 +/- 19.59 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
lmbelo/Phi-3-mini-4k-instruct	lmbelo	2024-05-28T18:28:35Z	6	0	mlx	[ "mlx", "safetensors", "phi3", "nlp", "code", "text-generation", "conversational", "custom_code", "en", "license:mit", "region:us" ]	text-generation	2024-05-27T11:50:17Z	--- language: - en license: mit tags: - nlp - code - mlx license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE pipeline_tag: text-generation inference: parameters: temperature: 0.0 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # lmbelo/Phi-3-mini-4k-instruct The Model [lmbelo/Phi-3-mini-4k-instruct](https://huggingface.co/lmbelo/Phi-3-mini-4k-instruct) was converted to MLX format from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) using mlx-lm version 0.13.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("lmbelo/Phi-3-mini-4k-instruct") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
irenepap/results	irenepap	2024-05-28T18:28:32Z	183	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T18:28:19Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2919 - Precision: 0.8957 - Recall: 0.8226 - F1: 0.8576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.3611 \| 0.2 \| 500 \| 0.3194 \| 0.8640 \| 0.8324 \| 0.8479 \| \| 0.3106 \| 0.4 \| 1000 \| 0.3039 \| 0.8905 \| 0.8013 \| 0.8435 \| \| 0.3027 \| 0.6 \| 1500 \| 0.2954 \| 0.9022 \| 0.7927 \| 0.8439 \| \| 0.2952 \| 0.81 \| 2000 \| 0.2864 \| 0.8966 \| 0.8185 \| 0.8558 \| \| 0.2905 \| 1.01 \| 2500 \| 0.2875 \| 0.8973 \| 0.8150 \| 0.8542 \| \| 0.2605 \| 1.21 \| 3000 \| 0.2841 \| 0.8924 \| 0.8369 \| 0.8637 \| \| 0.2591 \| 1.41 \| 3500 \| 0.2820 \| 0.8926 \| 0.8444 \| 0.8678 \| \| 0.2574 \| 1.61 \| 4000 \| 0.2826 \| 0.8916 \| 0.8359 \| 0.8629 \| \| 0.2602 \| 1.81 \| 4500 \| 0.2764 \| 0.8989 \| 0.8291 \| 0.8626 \| \| 0.2561 \| 2.01 \| 5000 \| 0.2813 \| 0.8891 \| 0.8454 \| 0.8667 \| \| 0.2195 \| 2.22 \| 5500 \| 0.2869 \| 0.9072 \| 0.8110 \| 0.8564 \| \| 0.2209 \| 2.42 \| 6000 \| 0.2845 \| 0.9002 \| 0.8216 \| 0.8591 \| \| 0.2178 \| 2.62 \| 6500 \| 0.2827 \| 0.8991 \| 0.8285 \| 0.8624 \| \| 0.22 \| 2.82 \| 7000 \| 0.2919 \| 0.8957 \| 0.8226 \| 0.8576 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
isaacchung/QwenPhi-7B-slerp	isaacchung	2024-05-28T18:26:50Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "Qwen/Qwen1.5-7B-Chat", "microsoft/Phi-3-mini-128k-instruct", "base_model:Qwen/Qwen1.5-7B-Chat", "base_model:merge:Qwen/Qwen1.5-7B-Chat", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:merge:microsoft/Phi-3-mini-128k-instruct", "region:us" ]	null	2024-05-28T18:26:49Z	--- tags: - merge - mergekit - lazymergekit - Qwen/Qwen1.5-7B-Chat - microsoft/Phi-3-mini-128k-instruct base_model: - Qwen/Qwen1.5-7B-Chat - microsoft/Phi-3-mini-128k-instruct --- # QwenPhi-7B-slerp QwenPhi-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) * [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ## 🧩 Configuration ```yaml slices: - sources: - model: Qwen/Qwen1.5-7B-Chat layer_range: [0, 32] - model: microsoft/Phi-3-mini-128k-instruct layer_range: [0, 32] merge_method: slerp base_model: microsoft/Phi-3-mini-128k-instruct parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "isaacchung/QwenPhi-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
imdatta0/meta_llama_3_MetaMathQA_40K_ortho	imdatta0	2024-05-28T18:21:37Z	5	0	peft	[ "peft", "safetensors", "unsloth", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	2024-05-28T18:21:33Z	--- license: llama3 library_name: peft tags: - unsloth - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: meta_llama_3_MetaMathQA_40K_ortho 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. --> # meta_llama_3_MetaMathQA_40K_ortho This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5219 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.02 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8807 \| 0.0211 \| 13 \| 0.6706 \| \| 0.6201 \| 0.0421 \| 26 \| 0.6389 \| \| 0.605 \| 0.0632 \| 39 \| 0.6211 \| \| 0.5929 \| 0.0842 \| 52 \| 0.6119 \| \| 0.5555 \| 0.1053 \| 65 \| 0.6045 \| \| 0.5689 \| 0.1264 \| 78 \| 0.5980 \| \| 0.5767 \| 0.1474 \| 91 \| 0.5914 \| \| 0.5584 \| 0.1685 \| 104 \| 0.5886 \| \| 0.5411 \| 0.1896 \| 117 \| 0.5847 \| \| 0.5417 \| 0.2106 \| 130 \| 0.5829 \| \| 0.5388 \| 0.2317 \| 143 \| 0.5787 \| \| 0.5473 \| 0.2527 \| 156 \| 0.5748 \| \| 0.5432 \| 0.2738 \| 169 \| 0.5701 \| \| 0.5402 \| 0.2949 \| 182 \| 0.5677 \| \| 0.5318 \| 0.3159 \| 195 \| 0.5655 \| \| 0.5155 \| 0.3370 \| 208 \| 0.5627 \| \| 0.5231 \| 0.3580 \| 221 \| 0.5584 \| \| 0.528 \| 0.3791 \| 234 \| 0.5578 \| \| 0.5372 \| 0.4002 \| 247 \| 0.5545 \| \| 0.5145 \| 0.4212 \| 260 \| 0.5517 \| \| 0.5246 \| 0.4423 \| 273 \| 0.5487 \| \| 0.5299 \| 0.4633 \| 286 \| 0.5473 \| \| 0.5297 \| 0.4844 \| 299 \| 0.5445 \| \| 0.5089 \| 0.5055 \| 312 \| 0.5425 \| \| 0.5208 \| 0.5265 \| 325 \| 0.5409 \| \| 0.5114 \| 0.5476 \| 338 \| 0.5398 \| \| 0.5092 \| 0.5687 \| 351 \| 0.5384 \| \| 0.4886 \| 0.5897 \| 364 \| 0.5359 \| \| 0.5121 \| 0.6108 \| 377 \| 0.5337 \| \| 0.5079 \| 0.6318 \| 390 \| 0.5324 \| \| 0.4996 \| 0.6529 \| 403 \| 0.5310 \| \| 0.505 \| 0.6740 \| 416 \| 0.5301 \| \| 0.5039 \| 0.6950 \| 429 \| 0.5288 \| \| 0.5073 \| 0.7161 \| 442 \| 0.5275 \| \| 0.4988 \| 0.7371 \| 455 \| 0.5264 \| \| 0.4857 \| 0.7582 \| 468 \| 0.5260 \| \| 0.4889 \| 0.7793 \| 481 \| 0.5252 \| \| 0.4836 \| 0.8003 \| 494 \| 0.5244 \| \| 0.5181 \| 0.8214 \| 507 \| 0.5237 \| \| 0.5052 \| 0.8424 \| 520 \| 0.5231 \| \| 0.4908 \| 0.8635 \| 533 \| 0.5228 \| \| 0.5136 \| 0.8846 \| 546 \| 0.5225 \| \| 0.493 \| 0.9056 \| 559 \| 0.5223 \| \| 0.4908 \| 0.9267 \| 572 \| 0.5222 \| \| 0.5066 \| 0.9478 \| 585 \| 0.5221 \| \| 0.5116 \| 0.9688 \| 598 \| 0.5219 \| \| 0.5073 \| 0.9899 \| 611 \| 0.5219 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
legraphista/AutoCoder-IMat-GGUF	legraphista	2024-05-28T18:20:10Z	371	1	gguf	[ "gguf", "quantized", "GGUF", "imatrix", "quantization", "imat", "static", "text-generation", "base_model:Bin12345/AutoCoder", "base_model:quantized:Bin12345/AutoCoder", "license:apache-2.0", "region:us", "conversational" ]	text-generation	2024-05-28T15:04:54Z	--- base_model: Bin12345/AutoCoder inference: false library_name: gguf license: apache-2.0 pipeline_tag: text-generation quantized_by: legraphista tags: - quantized - GGUF - imatrix - quantization - imat - imatrix - static --- # AutoCoder-IMat-GGUF _Llama.cpp imatrix quantization of Bin12345/AutoCoder_ Original Model: [Bin12345/AutoCoder](https://huggingface.co/Bin12345/AutoCoder) Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp [b3010](https://github.com/ggerganov/llama.cpp/releases/tag/b3010) IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw) - [AutoCoder-IMat-GGUF](#autocoder-imat-gguf) - [Files](#files) - [IMatrix](#imatrix) - [Common Quants](#common-quants) - [All Quants](#all-quants) - [Downloading using huggingface-cli](#downloading-using-huggingface-cli) - [Inference](#inference) - [Simple chat template](#simple-chat-template) - [Chat template with system prompt](#chat-template-with-system-prompt) - [Llama.cpp](#llama-cpp) - [FAQ](#faq) - [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere) - [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf) --- ## Files ### IMatrix Status: ✅ Available Link: [here](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/imatrix.dat) ### Common Quants \| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \| \| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \| \| [AutoCoder.Q8_0.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q8_0.gguf) \| Q8_0 \| 35.43GB \| ✅ Available \| ⚪ Static \| 📦 No \| [AutoCoder.Q6_K.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q6_K.gguf) \| Q6_K \| 27.36GB \| ✅ Available \| ⚪ Static \| 📦 No \| [AutoCoder.Q4_K.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q4_K.gguf) \| Q4_K \| 19.94GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.Q3_K.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q3_K.gguf) \| Q3_K \| 16.09GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.Q2_K.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q2_K.gguf) \| Q2_K \| 12.36GB \| ✅ Available \| 🟢 IMatrix \| 📦 No ### All Quants \| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \| \| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \| \| [AutoCoder.BF16/](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/tree/main/AutoCoder.BF16) \| BF16 \| 66.69GB \| ✅ Available \| ⚪ Static \| ✂ Yes \| [AutoCoder.FP16/](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/tree/main/AutoCoder.FP16) \| F16 \| 66.69GB \| ✅ Available \| ⚪ Static \| ✂ Yes \| [AutoCoder.Q5_K.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q5_K.gguf) \| Q5_K \| 23.54GB \| ✅ Available \| ⚪ Static \| 📦 No \| [AutoCoder.Q5_K_S.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q5_K_S.gguf) \| Q5_K_S \| 22.96GB \| ✅ Available \| ⚪ Static \| 📦 No \| [AutoCoder.Q4_K_S.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q4_K_S.gguf) \| Q4_K_S \| 18.94GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.Q3_K_L.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q3_K_L.gguf) \| Q3_K_L \| 17.56GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.Q3_K_S.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q3_K_S.gguf) \| Q3_K_S \| 14.42GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.Q2_K_S.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.Q2_K_S.gguf) \| Q2_K_S \| 11.39GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ4_NL.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ4_NL.gguf) \| IQ4_NL \| 18.88GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ4_XS.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ4_XS.gguf) \| IQ4_XS \| 17.86GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ3_M.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ3_M.gguf) \| IQ3_M \| 15.03GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ3_S.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ3_S.gguf) \| IQ3_S \| 14.48GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ3_XS.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ3_XS.gguf) \| IQ3_XS \| 13.71GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ3_XXS.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ3_XXS.gguf) \| IQ3_XXS \| 12.85GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ2_M.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ2_M.gguf) \| IQ2_M \| 11.36GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ2_S.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ2_S.gguf) \| IQ2_S \| 10.48GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ2_XS.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ2_XS.gguf) \| IQ2_XS \| 9.91GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ2_XXS.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ2_XXS.gguf) \| IQ2_XXS \| 8.92GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ1_M.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ1_M.gguf) \| IQ1_M \| 7.82GB \| ✅ Available \| 🟢 IMatrix \| 📦 No \| [AutoCoder.IQ1_S.gguf](https://huggingface.co/legraphista/AutoCoder-IMat-GGUF/blob/main/AutoCoder.IQ1_S.gguf) \| IQ1_S \| 7.16GB \| ✅ Available \| 🟢 IMatrix \| 📦 No ## Downloading using huggingface-cli If you do not have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Download the specific file you want: ``` huggingface-cli download legraphista/AutoCoder-IMat-GGUF --include "AutoCoder.Q8_0.gguf" --local-dir ./ ``` If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download legraphista/AutoCoder-IMat-GGUF --include "AutoCoder.Q8_0/*" --local-dir ./ # see FAQ for merging GGUF's ``` --- ## Inference ### Simple chat template ``` Human: Can you provide ways to eat combinations of bananas and dragonfruits? Assistant: Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<\|EOT\|> Human: What about solving an 2x + 3 = 7 equation? Assistant: ``` ### Chat template with system prompt ``` You are a helpful AI. Human: Can you provide ways to eat combinations of bananas and dragonfruits? Assistant: Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<\|EOT\|> Human: What about solving an 2x + 3 = 7 equation? Assistant: ``` ### Llama.cpp ``` llama.cpp/main -m AutoCoder.Q8_0.gguf --color -i -p "prompt here (according to the chat template)" ``` --- ## FAQ ### Why is the IMatrix not applied everywhere? According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results). ### How do I merge a split GGUF? 1. Make sure you have `gguf-split` available - To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases - Download the appropriate zip for your system from the latest release - Unzip the archive and you should be able to find `gguf-split` 2. Locate your GGUF chunks folder (ex: `AutoCoder.Q8_0`) 3. Run `gguf-split --merge AutoCoder.Q8_0/AutoCoder.Q8_0-00001-of-XXXXX.gguf AutoCoder.Q8_0.gguf` - Make sure to point `gguf-split` to the first chunk of the split. --- Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!
yh1306/l1	yh1306	2024-05-28T18:19:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-28T18:16:53Z	--- license: apache-2.0 ---
chirbard/ppo-Pyramids	chirbard	2024-05-28T18:17:58Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2024-04-28T10:39:23Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: chirbard/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jayashreedevi2020/wav2vec2-large-xls-r-300m-assamese_speech_to_IPA_js	jayashreedevi2020	2024-05-28T18:15:46Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_11_0", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-28T17:18:46Z	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-assamese_speech_to_IPA_js results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: as split: test args: as metrics: - name: Wer type: wer value: 1.0 --- <!-- 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. --> # wav2vec2-large-xls-r-300m-assamese_speech_to_IPA_js This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7838 - Wer: 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: 0.0003 - 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: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---:\| \| 2.4782 \| 9.8765 \| 400 \| 1.6148 \| 1.0 \| \| 0.69 \| 19.7531 \| 800 \| 0.7838 \| 1.0 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
surya-ravindra/calvin_finetuning	surya-ravindra	2024-05-28T18:11:13Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-28T18:04:29Z	--- 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. (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]
LucyintheSky/24-5-10_24-5-17-2000-pred1	LucyintheSky	2024-05-28T18:08:18Z	0	0	null	[ "safetensors", "Image Regression", "dataset:LucyintheSky/24-5-10_24-5-17-2000", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "region:us" ]	null	2024-05-28T18:06:44Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - Image Regression datasets: - "LucyintheSky/24-5-10_24-5-17-2000" metrics: - accuracy model-index: - name: "24-5-10_24-5-17-2000-pred1" results: [] --- # 24-5-10_24-5-17-2000-pred1 ## Image Regression Model This model was trained with [Image Regression Model Trainer](https://github.com/TonyAssi/ImageRegression/tree/main). It takes an image as input and outputs a float value. ```python from ImageRegression import predict predict(repo_id='LucyintheSky/24-5-10_24-5-17-2000-pred1',image_path='image.jpg') ``` --- ## Dataset Dataset: LucyintheSky/24-5-10_24-5-17-2000\ Value Column: 'sales_index'\ Train Test Split: 0.2 --- ## Training Base Model: [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)\ Epochs: 10\ Learning Rate: 0.0001 --- ## Usage ### Download ```bash git clone https://github.com/TonyAssi/ImageRegression.git cd ImageRegression ``` ### Installation ```bash pip install -r requirements.txt ``` ### Import ```python from ImageRegression import train_model, upload_model, predict ``` ### Inference (Prediction) - repo_id 🤗 repo id of the model - image_path path to image ```python predict(repo_id='LucyintheSky/24-5-10_24-5-17-2000-pred1', image_path='image.jpg') ``` The first time this function is called it'll download the safetensor model. Subsequent function calls will run faster. ### Train Model - dataset_id 🤗 dataset id - value_column_name column name of prediction values in dataset - test_split test split of the train/test split - output_dir the directory where the checkpoints will be saved - num_train_epochs training epochs - learning_rate learning rate ```python train_model(dataset_id='LucyintheSky/24-5-10_24-5-17-2000', value_column_name='sales_index', test_split=0.2, output_dir='./results', num_train_epochs=10, learning_rate=0.0001) ``` The trainer will save the checkpoints in the output_dir location. The model.safetensors are the trained weights you'll use for inference (predicton). ### Upload Model This function will upload your model to the 🤗 Hub. - model_id the name of the model id - token go [here](https://huggingface.co/settings/tokens) to create a new 🤗 token - checkpoint_dir checkpoint folder that will be uploaded ```python upload_model(model_id='24-5-10_24-5-17-2000-pred1', token='YOUR_HF_TOKEN', checkpoint_dir='./results/checkpoint-940') ```
chirbard/poca-SoccerTwos	chirbard	2024-05-28T18:07:45Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-05-17T07:45:06Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos 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: chirbard/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
DiederikMartens/eBERT_sa_cv_13_fold9	DiederikMartens	2024-05-28T18:04:33Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T17:52:49Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_13_fold9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eBERT_sa_cv_13_fold9 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: 0.6852 - F1: 0.5593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.6179 \| 0.4328 \| \| 0.6082 \| 2.0 \| 650 \| 0.5883 \| 0.4874 \| \| 0.6082 \| 3.0 \| 975 \| 0.6852 \| 0.5593 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
enithgma/asogrocaima	enithgma	2024-05-28T17:59:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-28T17:59:01Z	--- license: apache-2.0 ---
MLP-SEMO/semo_stage1	MLP-SEMO	2024-05-28T17:55:06Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2024-05-27T07:17:29Z	--- {} --- Trained : Reconstruction tokens ```python import torch from safetensors.torch import load_file from huggingface_hub import hf_hub_download from semo_lm.model import SemoLlama from semo_lm.semo_utils.prefix_vars import PAD_TOKEN_ID model = SemoLlama.from_pretrained( "meta-llama/Meta-Llama-3-8B-Instruct", torch_dtype=torch.bfloat16, pad_token_id=PAD_TOKEN_ID ) model.init_sentence_encoder_weights() repo_id = "MLP-SEMO/Llama-Reconstruction-embedding" filename = "embed_tokens.safetensors" downloaded_file = hf_hub_download(repo_id=repo_id, filename=filename) embedding_weights = load_file(downloaded_file) model.model.embed_tokens.load_state_dict(embedding_weights) ```
lukarape/w2v-bert-2.0-acoustic-erebuni-commonvoice-v23-hyper2	lukarape	2024-05-28T17:54:27Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T17:54:26Z	--- 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]
TopicNavi/Wikipedia-example-topic-model	TopicNavi	2024-05-28T17:54:15Z	4	0	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-05-28T17:54:11Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # Wikipedia-example-topic-model This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("TopicNavi/Wikipedia-example-topic-model") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 227 * Number of training documents: 25000 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| -1 \| of - the - to - and - in \| 10 \| -1_of_the_to_and \| \| 0 \| actor - he - award - his - born \| 7457 \| 0_actor_he_award_his \| \| 1 \| film - directed - stars - written - by \| 1494 \| 1_film_directed_stars_written \| \| 2 \| actress - she - her - award - born \| 1487 \| 2_actress_she_her_award \| \| 3 \| series - premiered - created - season - television \| 1339 \| 3_series_premiered_created_season \| \| 4 \| band - rock - guitarist - formed - lead \| 740 \| 4_band_rock_guitarist_formed \| \| 5 \| species - are - genus - breed - dog \| 501 \| 5_species_are_genus_breed \| \| 6 \| indian - hindi - filmfare - cinema - tamil \| 428 \| 6_indian_hindi_filmfare_cinema \| \| 7 \| footballer - club - professional - plays - midfielder \| 395 \| 7_footballer_club_professional_plays \| \| 8 \| king - queen - prince - duke - throne \| 372 \| 8_king_queen_prince_duke \| \| 9 \| symptoms - disease - may - disorder - pain \| 310 \| 9_symptoms_disease_may_disorder \| \| 10 \| war - battle - fought - empire - german \| 299 \| 10_war_battle_fought_empire \| \| 11 \| sexual - sex - or - gender - activity \| 284 \| 11_sexual_sex_or_gender \| \| 12 \| singer - songwriter - album - music - albums \| 268 \| 12_singer_songwriter_album_music \| \| 13 \| language - spoken - languages - ethnic - speakers \| 262 \| 13_language_spoken_languages_ethnic \| \| 14 \| company - multinational - headquartered - corporation - technology \| 204 \| 14_company_multinational_headquartered_corporation \| \| 15 \| species - plant - genus - fruit - plants \| 203 \| 15_species_plant_genus_fruit \| \| 16 \| poet - philosopher - his - writer - novelist \| 197 \| 16_poet_philosopher_his_writer \| \| 17 \| aircraft - boeing - fighter - air - designed \| 185 \| 17_aircraft_boeing_fighter_air \| \| 18 \| game - xbox - playstation - developed - windows \| 183 \| 18_game_xbox_playstation_developed \| \| 19 \| city - capital - population - area - largest \| 175 \| 19_city_capital_population_area \| \| 20 \| manga - anime - aired - adaptation - japanese \| 164 \| 20_manga_anime_aired_adaptation \| \| 21 \| hindilanguage - indian - stars - film - produced \| 156 \| 21_hindilanguage_indian_stars_film \| \| 22 \| bible - jesus - god - hebrew - testament \| 156 \| 22_bible_jesus_god_hebrew \| \| 23 \| mathematics - probability - function - distribution - numbers \| 151 \| 23_mathematics_probability_function_distribution \| \| 24 \| nba - basketball - player - association - allstar \| 150 \| 24_nba_basketball_player_association \| \| 25 \| killer - convicted - serial - murders - murder \| 148 \| 25_killer_convicted_serial_murders \| \| 26 \| rapper - album - records - released - professionally \| 141 \| 26_rapper_album_records_released \| \| 27 \| wrestling - wwe - wrestler - ring - professional \| 140 \| 27_wrestling_wwe_wrestler_ring \| \| 28 \| forces - armed - military - force - air \| 133 \| 28_forces_armed_military_force \| \| 29 \| toyota - car - honda - manufactured - model \| 124 \| 29_toyota_car_honda_manufactured \| \| 30 \| nfl - football - quarterback - college - played \| 116 \| 30_nfl_football_quarterback_college \| \| 31 \| greek - mythology - goddess - ancient - roman \| 115 \| 31_greek_mythology_goddess_ancient \| \| 32 \| disney - walt - entertainment - studios - company \| 110 \| 32_disney_walt_entertainment_studios \| \| 33 \| team - compete - division - conference - league \| 106 \| 33_team_compete_division_conference \| \| 34 \| medication - treat - used - mouth - taken \| 105 \| 34_medication_treat_used_mouth \| \| 35 \| political - social - economic - democracy - government \| 100 \| 35_political_social_economic_democracy \| \| 36 \| football - club - league - bundesliga - professional \| 96 \| 36_football_club_league_bundesliga \| \| 37 \| dish - sauce - cheese - meat - vegetables \| 92 \| 37_dish_sauce_cheese_meat \| \| 38 \| element - chemical - atomic - symbol - metal \| 92 \| 38_element_chemical_atomic_symbol \| \| 39 \| mind - psychology - or - that - philosophical \| 91 \| 39_mind_psychology_or_that \| \| 40 \| novel - published - author - story - book \| 89 \| 40_novel_published_author_story \| \| 41 \| rifle - cartridge - pistol - gun - sig \| 85 \| 41_rifle_cartridge_pistol_gun \| \| 42 \| cup - fifa - tournament - world - teams \| 84 \| 42_cup_fifa_tournament_world \| \| 43 \| cofounder - ceo - entrepreneur - investor - facebook \| 82 \| 43_cofounder_ceo_entrepreneur_investor \| \| 44 \| computer - programming - data - software - language \| 82 \| 44_computer_programming_data_software \| \| 45 \| marvel - comics - comic - character - books \| 81 \| 45_marvel_comics_comic_character \| \| 46 \| ufc - mixed - martial - fighting - champion \| 77 \| 46_ufc_mixed_martial_fighting \| \| 47 \| korean - south - kim - roles - my \| 76 \| 47_korean_south_kim_roles \| \| 48 \| korean - south - entertainment - group - girl \| 75 \| 48_korean_south_entertainment_group \| \| 49 \| president - served - vice - states - bush \| 74 \| 49_president_served_vice_states \| \| 50 \| mafia - crime - cartel - organized - drug \| 73 \| 50_mafia_crime_cartel_organized \| \| 51 \| islands - island - australia - ocean - pacific \| 73 \| 51_islands_island_australia_ocean \| \| 52 \| state - india - pradesh - capital - region \| 72 \| 52_state_india_pradesh_capital \| \| 53 \| politician - president - served - minister - since \| 69 \| 53_politician_president_served_minister \| \| 54 \| city - county - populous - metropolitan - population \| 69 \| 54_city_county_populous_metropolitan \| \| 55 \| africa - country - republic - officially - west \| 69 \| 55_africa_country_republic_officially \| \| 56 \| university - research - college - private - universities \| 66 \| 56_university_research_college_private \| \| 57 \| ceremony - presented - awards - academy - ampas \| 65 \| 57_ceremony_presented_awards_academy \| \| 58 \| tennis - open - titles - singles - atp \| 64 \| 58_tennis_open_titles_singles \| \| 59 \| korean - kim - kst - aired - south \| 64 \| 59_korean_kim_kst_aired \| \| 60 \| music - rock - genre - pop - punk \| 63 \| 60_music_rock_genre_pop \| \| 61 \| caribbean - islands - island - country - antilles \| 61 \| 61_caribbean_islands_island_country \| \| 62 \| politician - senator - republican - democratic - party \| 60 \| 62_politician_senator_republican_democratic \| \| 63 \| electric - electromagnetic - radiation - energy - magnetic \| 57 \| 63_electric_electromagnetic_radiation_energy \| \| 64 \| wars - star - jedi - skywalker - trilogy \| 55 \| 64_wars_star_jedi_skywalker \| \| 65 \| planet - solar - sun - earth - jupiter \| 54 \| 65_planet_solar_sun_earth \| \| 66 \| class - ship - navy - ships - submarines \| 54 \| 66_class_ship_navy_ships \| \| 67 \| president - sabha - house - government - chief \| 53 \| 67_president_sabha_house_government \| \| 68 \| alphabet - letter - alphabets - languages - english \| 49 \| 68_alphabet_letter_alphabets_languages \| \| 69 \| football - team - represents - mens - governing \| 48 \| 69_football_team_represents_mens \| \| 70 \| club - football - stadium - league - tier \| 48 \| 70_club_football_stadium_league \| \| 71 \| empire - ancient - egypt - bc - civilization \| 45 \| 71_empire_ancient_egypt_bc \| \| 72 \| manufacturer - automobile - automotive - stellantis - company \| 45 \| 72_manufacturer_automobile_automotive_stellantis \| \| 73 \| flag - flags - national - tricolour - anthem \| 44 \| 73_flag_flags_national_tricolour \| \| 74 \| church - religious - christianity - religion - movement \| 43 \| 74_church_religious_christianity_religion \| \| 75 \| minister - prime - conservative - mp - served \| 42 \| 75_minister_prime_conservative_mp \| \| 76 \| wine - drink - sugar - alcoholic - cocktail \| 41 \| 76_wine_drink_sugar_alcoholic \| \| 77 \| hindu - hinduism - shiva - vishnu - goddess \| 41 \| 77_hindu_hinduism_shiva_vishnu \| \| 78 \| batman - dc - comics - gotham - superhero \| 41 \| 78_batman_dc_comics_gotham \| \| 79 \| formula - racing - driver - prix - championship \| 41 \| 79_formula_racing_driver_prix \| \| 80 \| airline - airlines - airport - carrier - destinations \| 41 \| 80_airline_airlines_airport_carrier \| \| 81 \| compound - acid - organic - chemical - formula \| 40 \| 81_compound_acid_organic_chemical \| \| 82 \| nazi - german - hitler - adolf - germany \| 40 \| 82_nazi_german_hitler_adolf \| \| 83 \| bond - james - eon - spy - mi6 \| 39 \| 83_bond_james_eon_spy \| \| 84 \| belief - god - religious - existence - atheism \| 39 \| 84_belief_god_religious_existence \| \| 85 \| energy - constant - force - heat - unit \| 39 \| 85_energy_constant_force_heat \| \| 86 \| minister - prime - indian - pakistan - india \| 39 \| 86_minister_prime_indian_pakistan \| \| 87 \| roman - emperor - bc - augustus - caesar \| 38 \| 87_roman_emperor_bc_augustus \| \| 88 \| asia - gulf - sea - east - oman \| 38 \| 88_asia_gulf_sea_east \| \| 89 \| boxer - heavyweight - title - wba - ibf \| 37 \| 89_boxer_heavyweight_title_wba \| \| 90 \| county - england - city - ceremonial - london \| 36 \| 90_county_england_city_ceremonial \| \| 91 \| data - learning - algorithm - machine - neural \| 36 \| 91_data_learning_algorithm_machine \| \| 92 \| day - holiday - celebrated - thanksgiving - celebration \| 35 \| 92_day_holiday_celebrated_thanksgiving \| \| 93 \| saul - breaking - bad - call - better \| 34 \| 93_saul_breaking_bad_call \| \| 94 \| punishment - death - execution - homicide - suicide \| 34 \| 94_punishment_death_execution_homicide \| \| 95 \| degree - education - secondary - bachelor - bachelors \| 34 \| 95_degree_education_secondary_bachelor \| \| 96 \| console - nintendo - playstation - game - consoles \| 34 \| 96_console_nintendo_playstation_game \| \| 97 \| iphone - apple - ipad - pro - inc \| 34 \| 97_iphone_apple_ipad_pro \| \| 98 \| vitamin - organisms - bacteria - animals - plants \| 33 \| 98_vitamin_organisms_bacteria_animals \| \| 99 \| cells - blood - system - gland - organ \| 33 \| 99_cells_blood_system_gland \| \| 100 \| trek - star - kirk - starship - uss \| 33 \| 100_trek_star_kirk_starship \| \| 101 \| jews - nazi - camps - camp - extermination \| 33 \| 101_jews_nazi_camps_camp \| \| 102 \| space - moon - apollo - nasa - shuttle \| 33 \| 102_space_moon_apollo_nasa \| \| 103 \| roman - empire - rome - western - byzantine \| 32 \| 103_roman_empire_rome_western \| \| 104 \| marvel - studios - mcu - thor - superhero \| 32 \| 104_marvel_studios_mcu_thor \| \| 105 \| organisms - biology - genetic - genes - species \| 32 \| 105_organisms_biology_genetic_genes \| \| 106 \| fashion - gucci - designer - luxury - chanel \| 32 \| 106_fashion_gucci_designer_luxury \| \| 107 \| baseball - mlb - league - major - runs \| 32 \| 107_baseball_mlb_league_major \| \| 108 \| island - islands - ireland - isles - northern \| 31 \| 108_island_islands_ireland_isles \| \| 109 \| creature - folklore - legendary - depicted - or \| 31 \| 109_creature_folklore_legendary_depicted \| \| 110 \| empire - mughal - maratha - subcontinent - dynasty \| 31 \| 110_empire_mughal_maratha_subcontinent \| \| 111 \| social - racial - race - racism - white \| 31 \| 111_social_racial_race_racism \| \| 112 \| election - presidential - incumbent - tuesday - republican \| 30 \| 112_election_presidential_incumbent_tuesday \| \| 113 \| building - tallest - street - manhattan - york \| 29 \| 113_building_tallest_street_manhattan \| \| 114 \| bowl - super - champion - football - conference \| 29 \| 114_bowl_super_champion_football \| \| 115 \| election - elections - elect - held - general \| 29 \| 115_election_elections_elect_held \| \| 116 \| soviet - union - stalin - communist - russian \| 29 \| 116_soviet_union_stalin_communist \| \| 117 \| stock - exchange - securities - investment - companies \| 29 \| 117_stock_exchange_securities_investment \| \| 118 \| bmw - mercedesbenz - generation - sedan - marketed \| 29 \| 118_bmw_mercedesbenz_generation_sedan \| \| 119 \| currency - dollar - currencies - monetary - bank \| 29 \| 119_currency_dollar_currencies_monetary \| \| 120 \| dynasty - emperor - china - qin - chinese \| 28 \| 120_dynasty_emperor_china_qin \| \| 121 \| internet - protocol - ip - networks - network \| 28 \| 121_internet_protocol_ip_networks \| \| 122 \| tropical - cyclones - cyclone - hurricane - hemisphere \| 28 \| 122_tropical_cyclones_cyclone_hurricane \| \| 123 \| anthropomorphic - cartoon - character - peanuts - bugs \| 28 \| 123_anthropomorphic_cartoon_character_peanuts \| \| 124 \| elections - election - senate - elect - governor \| 28 \| 124_elections_election_senate_elect \| \| 125 \| windows - operating - microsoft - macos - server \| 28 \| 125_windows_operating_microsoft_macos \| \| 126 \| san - county - california - los - angeles \| 27 \| 126_san_county_california_los \| \| 127 \| potter - harry - hogwarts - rowling - rowlings \| 27 \| 127_potter_harry_hogwarts_rowling \| \| 128 \| tank - soviet - tanks - t72 - armoured \| 27 \| 128_tank_soviet_tanks_t72 \| \| 129 \| website - youtube - pornographic - videos - websites \| 26 \| 129_website_youtube_pornographic_videos \| \| 130 \| missile - missiles - surfacetoair - ballistic - system \| 26 \| 130_missile_missiles_surfacetoair_ballistic \| \| 131 \| formula - championship - fia - racing - drivers \| 26 \| 131_formula_championship_fia_racing \| \| 132 \| mario - game - nintendo - super - games \| 26 \| 132_mario_game_nintendo_super \| \| 133 \| composer - composers - symphony - music - pianist \| 26 \| 133_composer_composers_symphony_music \| \| 134 \| music - theatre - musical - art - or \| 26 \| 134_music_theatre_musical_art \| \| 135 \| party - political - democratic - liberal - labour \| 25 \| 135_party_political_democratic_liberal \| \| 136 \| province - canada - provinces - territories - city \| 25 \| 136_province_canada_provinces_territories \| \| 137 \| airport - busiest - international - passenger - traffic \| 25 \| 137_airport_busiest_international_passenger \| \| 138 \| china - shanghai - province - guangzhou - populous \| 24 \| 138_china_shanghai_province_guangzhou \| \| 139 \| flight - airport - airlines - accident - crashed \| 24 \| 139_flight_airport_airlines_accident \| \| 140 \| expedition - spanish - america - explorer - americas \| 24 \| 140_expedition_spanish_america_explorer \| \| 141 \| economy - gdp - capita - ppp - countries \| 24 \| 141_economy_gdp_capita_ppp \| \| 142 \| ball - sport - players - teams - team \| 24 \| 142_ball_sport_players_teams \| \| 143 \| thrones - fire - ice - hbo - fantasy \| 23 \| 143_thrones_fire_ice_hbo \| \| 144 \| uefa - champions - league - cup - organised \| 23 \| 144_uefa_champions_league_cup \| \| 145 \| terminator - transformers - fiction - science - action \| 23 \| 145_terminator_transformers_fiction_science \| \| 146 \| time - calendar - zone - year - daylight \| 23 \| 146_time_calendar_zone_year \| \| 147 \| caliphate - muhammad - ibn - islam - islamic \| 22 \| 147_caliphate_muhammad_ibn_islam \| \| 148 \| holmes - sherlock - dracula - conan - watson \| 22 \| 148_holmes_sherlock_dracula_conan \| \| 149 \| games - multisport - olympic - olympics - winter \| 21 \| 149_games_multisport_olympic_olympics \| \| 150 \| web - google - search - pages - users \| 21 \| 150_web_google_search_pages \| \| 151 \| google - chat - messaging - users - torrent \| 21 \| 151_google_chat_messaging_users \| \| 152 \| renaissance - italian - leonardo - michelangelo - vinci \| 21 \| 152_renaissance_italian_leonardo_michelangelo \| \| 153 \| amendment - court - constitution - rights - abortion \| 21 \| 153_amendment_court_constitution_rights \| \| 154 \| marvel - continuity - comics - mcu - cinematic \| 21 \| 154_marvel_continuity_comics_mcu \| \| 155 \| draft - players - nba - lottery - eligible \| 20 \| 155_draft_players_nba_lottery \| \| 156 \| kennedy - clinton - president - jacqueline - lewinsky \| 20 \| 156_kennedy_clinton_president_jacqueline \| \| 157 \| shooting - school - injured - killed - mass \| 20 \| 157_shooting_school_injured_killed \| \| 158 \| greys - anatomy - abc - medical - rhimes \| 20 \| 158_greys_anatomy_abc_medical \| \| 159 \| kardashian - kardashians - jenner - keeping - kourtney \| 19 \| 159_kardashian_kardashians_jenner_keeping \| \| 160 \| godfather - corleone - coppola - vito - pacino \| 19 \| 160_godfather_corleone_coppola_vito \| \| 161 \| script - alphabet - chinese - writing - write \| 19 \| 161_script_alphabet_chinese_writing \| \| 162 \| beatles - album - parlophone - studio - songs \| 19 \| 162_beatles_album_parlophone_studio \| \| 163 \| martial - boxing - combat - aikido - wrestling \| 18 \| 163_martial_boxing_combat_aikido \| \| 164 \| york - island - new - borough - county \| 18 \| 164_york_island_new_borough \| \| 165 \| court - supreme - justice - associate - jurist \| 18 \| 165_court_supreme_justice_associate \| \| 166 \| hamlet - shakespeare - shakespeares - tragedy - william \| 18 \| 166_hamlet_shakespeare_shakespeares_tragedy \| \| 167 \| hong - kong - martial - yen - chow \| 18 \| 167_hong_kong_martial_yen \| \| 168 \| rocky - stallone - rambo - sylvester - balboa \| 18 \| 168_rocky_stallone_rambo_sylvester \| \| 169 \| nobel - prize - physics - prizes - physicist \| 18 \| 169_nobel_prize_physics_prizes \| \| 170 \| thrones - hbo - game - 20112019 - fantasy \| 18 \| 170_thrones_hbo_game_20112019 \| \| 171 \| cricket - cricketer - indian - captain - righthanded \| 17 \| 171_cricket_cricketer_indian_captain \| \| 172 \| art - architecture - movement - style - baroque \| 17 \| 172_art_architecture_movement_style \| \| 173 \| nuclear - bomb - weapons - weapon - thermonuclear \| 17 \| 173_nuclear_bomb_weapons_weapon \| \| 174 \| amphetamine - enhancer - stimulant - drug - adhd \| 16 \| 174_amphetamine_enhancer_stimulant_drug \| \| 175 \| walking - dead - kirkman - adlard - amc \| 16 \| 175_walking_dead_kirkman_adlard \| \| 176 \| snuff - genre - comedy - laughter - films \| 16 \| 176_snuff_genre_comedy_laughter \| \| 177 \| superman - dc - aquaman - dceu - warner \| 16 \| 177_superman_dc_aquaman_dceu \| \| 178 \| health - care - medical - medicine - hospitals \| 16 \| 178_health_care_medical_medicine \| \| 179 \| color - colors - rgb - red - blue \| 16 \| 179_color_colors_rgb_red \| \| 180 \| smiley - bokeh - clothing - meme - face \| 16 \| 180_smiley_bokeh_clothing_meme \| \| 181 \| metallica - metal - band - ulrich - thrash \| 15 \| 181_metallica_metal_band_ulrich \| \| 182 \| economic - prices - inflation - price - crisis \| 15 \| 182_economic_prices_inflation_price \| \| 183 \| doctor - incarnation - thirteenth - bbc - specials \| 15 \| 183_doctor_incarnation_thirteenth_bbc \| \| 184 \| rings - tolkiens - tolkien - hobbit - lord \| 15 \| 184_rings_tolkiens_tolkien_hobbit \| \| 185 \| pope - church - vatican - catholic - roncalli \| 15 \| 185_pope_church_vatican_catholic \| \| 186 \| rockefeller - miss - oil - rothschild - family \| 15 \| 186_rockefeller_miss_oil_rothschild \| \| 187 \| seinfeld - comedian - sitcom - kramer - jerry \| 14 \| 187_seinfeld_comedian_sitcom_kramer \| \| 188 \| ottoman - sultan - selim - empire - erturul \| 14 \| 188_ottoman_sultan_selim_empire \| \| 189 \| chinese - china - ccp - mao - communist \| 14 \| 189_chinese_china_ccp_mao \| \| 190 \| philosopher - philosophy - greek - treatise - mathematician \| 14 \| 190_philosopher_philosophy_greek_treatise \| \| 191 \| mark - punctuation - exclamation - bracket - marks \| 14 \| 191_mark_punctuation_exclamation_bracket \| \| 192 \| event - wrestlemania - wwe - payperview - livestreaming \| 14 \| 192_event_wrestlemania_wwe_payperview \| \| 193 \| norse - mythology - old - loki - odin \| 14 \| 193_norse_mythology_old_loki \| \| 194 \| dre - hop - hip - wutang - group \| 14 \| 194_dre_hop_hip_wutang \| \| 195 \| newspaper - daily - guardian - times - news \| 14 \| 195_newspaper_daily_guardian_times \| \| 196 \| theft - kratos - auto - rockstar - god \| 13 \| 196_theft_kratos_auto_rockstar \| \| 197 \| drag - rupauls - race - vh1 - season \| 13 \| 197_drag_rupauls_race_vh1 \| \| 198 \| polyethylene - polymers - silk - plastics - synthetic \| 13 \| 198_polyethylene_polymers_silk_plastics \| \| 199 \| strings - instrument - instruments - guitar - electronic \| 13 \| 199_strings_instrument_instruments_guitar \| \| 200 \| population - census - rate - growth - increase \| 13 \| 200_population_census_rate_growth \| \| 201 \| resolution - hdtv - display - hd - pixels \| 13 \| 201_resolution_hdtv_display_hd \| \| 202 \| athletic - hockey - ncaa - conference - university \| 13 \| 202_athletic_hockey_ncaa_conference \| \| 203 \| nervous - spinal - brain - nerves - cord \| 13 \| 203_nervous_spinal_brain_nerves \| \| 204 \| peppers - chili - hot - rock - red \| 13 \| 204_peppers_chili_hot_rock \| \| 205 \| accounting - tax - financial - nonprofit - entity \| 12 \| 205_accounting_tax_financial_nonprofit \| \| 206 \| swift - album - studio - taylor - singersongwriter \| 12 \| 206_swift_album_studio_taylor \| \| 207 \| sheldon - bang - theory - big - parsons \| 12 \| 207_sheldon_bang_theory_big \| \| 208 \| conjuring - wan - annabelle - lorraine - dauberman \| 12 \| 208_conjuring_wan_annabelle_lorraine \| \| 209 \| karate - kid - miyagi - macchio - kai \| 12 \| 209_karate_kid_miyagi_macchio \| \| 210 \| earthquake - eruption - tsunami - fault - occurred \| 12 \| 210_earthquake_eruption_tsunami_fault \| \| 211 \| guard - guards - ball - positions - midfielders \| 12 \| 211_guard_guards_ball_positions \| \| 212 \| geologic - planets - earth - how - earths \| 12 \| 212_geologic_planets_earth_how \| \| 213 \| card - game - cards - chess - baccarat \| 12 \| 213_card_game_cards_chess \| \| 214 \| zodiac - sign - astrological - transits - spans \| 12 \| 214_zodiac_sign_astrological_transits \| \| 215 \| gandhi - singh - godse - india - bhindranwale \| 12 \| 215_gandhi_singh_godse_india \| \| 216 \| cannabis - cigarette - thc - tobacco - cocaine \| 11 \| 216_cannabis_cigarette_thc_tobacco \| \| 217 \| xmen - wolverine - installment - jackman - superhero \| 11 \| 217_xmen_wolverine_installment_jackman \| \| 218 \| caucasus - azerbaijan - baku - sea - caspian \| 11 \| 218_caucasus_azerbaijan_baku_sea \| \| 219 \| draft - nfl - meeting - select - eligible \| 11 \| 219_draft_nfl_meeting_select \| \| 220 \| nobility - royalty - rank - knighthood - dukes \| 11 \| 220_nobility_royalty_rank_knighthood \| \| 221 \| saudi - arabia - saud - abdulaziz - bin \| 11 \| 221_saudi_arabia_saud_abdulaziz \| \| 222 \| jolyne - jotaro - her - school - stand \| 11 \| 222_jolyne_jotaro_her_school \| \| 223 \| prefecture - kon - mifune - ueno - hachik \| 11 \| 223_prefecture_kon_mifune_ueno \| \| 224 \| guru - granth - baba - gobind - das \| 10 \| 224_guru_granth_baba_gobind \| \| 225 \| un - nations - intergovernmental - organisation - organization \| 10 \| 225_un_nations_intergovernmental_organisation \| </details> ## Training hyperparameters * calculate_probabilities: False * language: english * low_memory: False * min_topic_size: 10 * n_gram_range: (1, 1) * nr_topics: None * seed_topic_list: None * top_n_words: 10 * verbose: False * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.26.4 * HDBSCAN: 0.8.33 * UMAP: 0.5.6 * Pandas: 2.2.2 * Scikit-Learn: 1.4.2 * Sentence-transformers: 2.7.0 * Transformers: 4.40.2 * Numba: 0.59.1 * Plotly: 5.22.0 * Python: 3.11.9
doubledsbv/KafkaLM-Mixtral-8x7B-V0.2_DPO-AWQ	doubledsbv	2024-05-28T17:53:24Z	8	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-28T17:47:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
DiederikMartens/eBERT_sa_cv_13_fold8	DiederikMartens	2024-05-28T17:52:42Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T16:27:46Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_13_fold8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eBERT_sa_cv_13_fold8 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: 0.5854 - F1: 0.5584 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.5765 \| 0.4529 \| \| 0.6339 \| 2.0 \| 650 \| 0.5104 \| 0.5005 \| \| 0.6339 \| 3.0 \| 975 \| 0.5854 \| 0.5584 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
yqelz/wsd-rubert-cased	yqelz	2024-05-28T17:49:09Z	110	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-27T08:52:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID Resolve WSD problem based on CoBaLD Rus ## Model Details ### Model Description - Developed by: Sergey Biryukov - Model type: WSD - Language(s) (NLP): Russian - Finetuned from model [optional]: rubert-base-cased
zakaria99/Gptmodel	zakaria99	2024-05-28T17:39:49Z	141	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T17:39:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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intone/unaligned-llama3-8b-v0.1-16bit	intone	2024-05-28T17:39:36Z	4	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T16:21:36Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - llama - trl --- unaligned llama 8b, 16bit. <br> Not DPOd, just SFT trained. Horrific model (THIS IS A TEXT GENERATION MODEL)
datek/Qwen-Qwen1.5-1.8B-1716917636	datek	2024-05-28T17:36:00Z	138	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T17:33:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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QuietImpostor/Llama-3-Refueled-Pruned	QuietImpostor	2024-05-28T17:31:08Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "dataset:yahma/alpaca-cleaned", "base_model:refuelai/Llama-3-Refueled", "base_model:finetune:refuelai/Llama-3-Refueled", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T19:26:24Z	--- base_model: - refuelai/Llama-3-Refueled library_name: transformers tags: - mergekit - merge license: llama3 datasets: - yahma/alpaca-cleaned language: - en --- ### Pruning Details This is a prune of [Llama 3 Refueled](https://www.huggingface.co/refuelai/llama-3-refueled) using [mergekit](https://github.com/cg123/mergekit) and [PruneMe](https://www.github.com/arcee-ai/PruneMe) The model is semi-tested, but still needs some debugging, namely with converting to GGUF, though I am working on that. Note: the [dataset](https://www.huggingface.co/yahma/alpaca-cleaned) was used for evaluating what layers should be pruned. This model was NOT finetuned. ### Performance After only 1 test because of lack of compute and for stupid long inference times on my 3060ti (8GB), it does show some interesting results. Here's the response after being prompted "Hi!" using the [example from Meta](https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3). ```model_response vel tips and recommendations.user Hi!assistant Hi! I can help you find the best travel tips and recommendations for your next trip. Where you most interested to travel and what kind of activities you most to to the 9e sure, we can start and letiing 10e 11e 12e 13e 14e 15e 16e 17e 18e 19e 20e 21e 23e 24e 5e 6e 7e 8e 9e 10e 11e 12e 13e 14e 15e ``` Even without finetuning, the model still exhibits some extent of instruction following. And fine-tuning is a WIP and I will update this when it's ready. Finetuning is no longer in progress due to issues with unsloth. However, I am working on a project that will hopefully make pruning models easier. ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: refuelai/Llama-3-Refueled layer_range: [0, 19] - sources: - model: refuelai/Llama-3-Refueled layer_range: [29, 32] merge_method: passthrough dtype: bfloat16 ```
DiederikMartens/tsBERT_sa_cv_13_fold9	DiederikMartens	2024-05-28T17:29:56Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T17:07:02Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_13_fold9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tsBERT_sa_cv_13_fold9 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6597 - F1: 0.6462 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4657 \| 0.6034 \| \| 0.4337 \| 2.0 \| 650 \| 0.4886 \| 0.5960 \| \| 0.4337 \| 3.0 \| 975 \| 0.6597 \| 0.6462 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/mBERT_sa_cv_13_fold9	DiederikMartens	2024-05-28T17:29:47Z	115	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T17:06:58Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_13_fold9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mBERT_sa_cv_13_fold9 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5084 - F1: 0.5983 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.5642 \| 0.4782 \| \| 0.5411 \| 2.0 \| 650 \| 0.5084 \| 0.5983 \| \| 0.5411 \| 3.0 \| 975 \| 0.6772 \| 0.5917 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
cs552-mlp/phi3-dpo	cs552-mlp	2024-05-28T17:28:04Z	2	0	peft	[ "peft", "safetensors", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "region:us" ]	null	2024-05-28T17:08:06Z	--- library_name: peft base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Model Card for Model ID DPO finetuned version of `phi3-instruct-4k` on student annotated preference data focusing on course content questions from EPFL curriculum (physics, math, cs).
nisar2424/Nisar__	nisar2424	2024-05-28T17:23:47Z	0	0	null	[ "license:other", "region:us" ]	null	2024-05-28T17:23:47Z	--- license: other license_name: other license_link: LICENSE ---
dwb2023/paligemma_rlaifv-V-1	dwb2023	2024-05-28T17:23:21Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "paligemma", "generated_from_trainer", "base_model:google/paligemma-3b-pt-224", "base_model:adapter:google/paligemma-3b-pt-224", "license:gemma", "region:us" ]	null	2024-05-28T05:30:29Z	--- license: gemma library_name: peft tags: - generated_from_trainer base_model: google/paligemma-3b-pt-224 model-index: - name: paligemma_rlaifv-V-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. --> # paligemma_rlaifv-V-1 This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 8 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
MrezaPRZ/codellama_synthetic_gretel_bigquery	MrezaPRZ	2024-05-28T17:23:09Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T17:20:48Z	--- 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]
amosp5/llama3-8b-instruct-scrum	amosp5	2024-05-28T17:21:11Z	3	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-28T17:15:03Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct datasets: - generator model-index: - name: llama3-8b-instruct-scrum 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. --> # llama3-8b-instruct-scrum This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.38.2 - Pytorch 2.3.0a0+40ec155e58.nv24.03 - Datasets 2.19.1 - Tokenizers 0.15.2
vuongnhathien/test-wrong-label	vuongnhathien	2024-05-28T17:14:29Z	192	0	transformers	[ "transformers", "tensorboard", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "base_model:facebook/convnextv2-base-22k-384", "base_model:finetune:facebook/convnextv2-base-22k-384", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T17:06:36Z	--- license: apache-2.0 base_model: facebook/convnextv2-base-22k-384 tags: - generated_from_trainer model-index: - name: test-wrong-label 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. --> # test-wrong-label This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) 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: 4e-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: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.9315 \| 0.7625 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
hchcsuim/batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned_unaugmentation	hchcsuim	2024-05-28T17:13:29Z	215	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T17:00:08Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned_unaugmentation results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.9837432499886555 - name: Precision type: precision value: 0.9830542407298831 - name: Recall type: recall value: 0.9964053803339518 - name: F1 type: f1 value: 0.9896847848777363 --- <!-- 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. --> # batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned_unaugmentation This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0442 - Accuracy: 0.9837 - Precision: 0.9831 - Recall: 0.9964 - F1: 0.9897 - Roc Auc: 0.9991 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Roc Auc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:-------:\| \| 0.0483 \| 1.0 \| 1377 \| 0.0442 \| 0.9837 \| 0.9831 \| 0.9964 \| 0.9897 \| 0.9991 \| ### Framework versions - Transformers 4.39.2 - Pytorch 2.3.0 - Datasets 2.18.0 - Tokenizers 0.15.2
malerbe/q-FrozenLake-v1-4x4-noSlippery	malerbe	2024-05-28T17:11:50Z	0	0	null	[ "FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-24T09:57:19Z	--- tags: - FrozenLake-v1-8x8-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-8x8-no_slippery type: FrozenLake-v1-8x8-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage model = load_from_hub(repo_id="malerbe/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])
momina296/flan-t5-base-imdb-text-classification	momina296	2024-05-28T17:09:18Z	13	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "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" ]	text2text-generation	2024-02-11T16:54:00Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - f1 model-index: - name: flan-t5-base-imdb-text-classification 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-imdb-text-classification This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5742 - F1: 54.5455 - Gen Len: 2.5 ## 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: 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: 10 ### Training results ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
Shiv1143/corgy_dog_LoRA	Shiv1143	2024-05-28T17:09:03Z	1	1	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	2024-05-28T16:52:17Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK dog widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Shiv1143/corgy_dog_LoRA <Gallery /> ## Model description These are Shiv1143/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Shiv1143/corgy_dog_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
aknaraya/summarization_fine_tune_bbc_summary	aknaraya	2024-05-28T17:08:38Z	10	0	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-28T09:52:46Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: aknaraya/summarization_fine_tune_bbc_summary results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # aknaraya/summarization_fine_tune_bbc_summary This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5873 - Validation Loss: 0.3274 - Train Lr: 2e-05 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Lr \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.7762 \| 0.4048 \| 2e-05 \| 0 \| \| 0.7113 \| 0.3899 \| 2e-05 \| 1 \| \| 0.6596 \| 0.3765 \| 2e-05 \| 2 \| \| 0.6524 \| 0.3654 \| 2e-05 \| 3 \| \| 0.6652 \| 0.3553 \| 2e-05 \| 4 \| \| 0.6315 \| 0.3476 \| 2e-05 \| 5 \| \| 0.5763 \| 0.3411 \| 2e-05 \| 6 \| \| 0.5952 \| 0.3358 \| 2e-05 \| 7 \| \| 0.5940 \| 0.3309 \| 2e-05 \| 8 \| \| 0.5873 \| 0.3274 \| 2e-05 \| 9 \| ### Framework versions - Transformers 4.41.0 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/tsBERT_sa_cv_13_fold8	DiederikMartens	2024-05-28T17:06:54Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T16:23:17Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_13_fold8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tsBERT_sa_cv_13_fold8 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5081 - F1: 0.6678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4193 \| 0.6050 \| \| 0.45 \| 2.0 \| 650 \| 0.4256 \| 0.6563 \| \| 0.45 \| 3.0 \| 975 \| 0.5081 \| 0.6678 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
bellge/cw3_trained_model_smaller	bellge	2024-05-28T16:57:00Z	120	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T16:56:17Z	--- license: mit base_model: roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: cw3_trained_model_smaller 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. --> # cw3_trained_model_smaller This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7497 - Accuracy: 0.7379 - F1: 0.7372 - Precision: 0.7388 - Recall: 0.7379 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.5662 \| 3.11 \| 500 \| 0.7752 \| 0.6552 \| 0.6330 \| 0.7420 \| 0.6552 \| \| 0.2541 \| 6.21 \| 1000 \| 0.7497 \| 0.7379 \| 0.7372 \| 0.7388 \| 0.7379 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Fawazzx/Saul-semantic.v3	Fawazzx	2024-05-28T16:54:31Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-28T08:28:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Milad1b/Clinical_BERT_CL_DRugcomb_FT	Milad1b	2024-05-28T16:53:37Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T20:10:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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Essacheez/gemma-7b-it-finetune-code-10k-gemma-style	Essacheez	2024-05-28T16:50:15Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T15:34:12Z	--- 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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straenyagun/akilvedavranisbozukluklari-classification	straenyagun	2024-05-28T16:48:58Z	111	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T16:48:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yetanotherhif/jmg_starcoder2-7b-100k	yetanotherhif	2024-05-28T16:48:41Z	7	0	transformers	[ "transformers", "safetensors", "starcoder2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T12:20:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nicholasb00/llama3_newds	nicholasb00	2024-05-28T16:47:16Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:adapter:NousResearch/Meta-Llama-3-8B", "license:other", "region:us" ]	null	2024-05-28T16:47:09Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: NousResearch/Meta-Llama-3-8B model-index: - name: llama3_newds 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/nicholas-bianchini-unipr/huggingface/runs/1lao0bjw) # llama3_newds This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - num_epochs: 20 ### Training results ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
vonvolous/tattoo_realism_before_LoRA	vonvolous	2024-05-28T16:46:35Z	9	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	2024-05-25T04:12:15Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: In the style of TOK tattoo 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 - vonvolous/tattoo_realism_LoRA <Gallery /> ## Model description These are vonvolous/tattoo_realism_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use In the style of TOK tattoo to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](vonvolous/tattoo_realism_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]
fine-tuned/SCIDOCS-512-192-gpt-4o-2024-05-13-591725	fine-tuned	2024-05-28T16:46:08Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SCIDOCS-512-192-gpt-4o-2024-05-13-591725", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T16:45:17Z	--- license: apache-2.0 datasets: - fine-tuned/SCIDOCS-512-192-gpt-4o-2024-05-13-591725 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SCIDOCS-512-192-gpt-4o-2024-05-13-591725', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf	RichardErkhov	2024-05-28T16:46:04Z	80	0	null	[ "gguf", "arxiv:2402.06332", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-28T04:54:44Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) internlm2-math-plus-7b - GGUF - Model creator: https://huggingface.co/internlm/ - Original model: https://huggingface.co/internlm/internlm2-math-plus-7b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [internlm2-math-plus-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q2_K.gguf) \| Q2_K \| 2.8GB \| \| [internlm2-math-plus-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.1GB \| \| [internlm2-math-plus-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.IQ3_S.gguf) \| IQ3_S \| 3.25GB \| \| [internlm2-math-plus-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.24GB \| \| [internlm2-math-plus-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.IQ3_M.gguf) \| IQ3_M \| 3.35GB \| \| [internlm2-math-plus-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q3_K.gguf) \| Q3_K \| 3.57GB \| \| [internlm2-math-plus-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.57GB \| \| [internlm2-math-plus-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.85GB \| \| [internlm2-math-plus-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.IQ4_XS.gguf) \| IQ4_XS \| 3.99GB \| \| [internlm2-math-plus-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q4_0.gguf) \| Q4_0 \| 4.15GB \| \| [internlm2-math-plus-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.IQ4_NL.gguf) \| IQ4_NL \| 4.19GB \| \| [internlm2-math-plus-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.18GB \| \| [internlm2-math-plus-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q4_K.gguf) \| Q4_K \| 4.39GB \| \| [internlm2-math-plus-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.39GB \| \| [internlm2-math-plus-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q4_1.gguf) \| Q4_1 \| 4.58GB \| \| [internlm2-math-plus-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q5_0.gguf) \| Q5_0 \| 5.0GB \| \| [internlm2-math-plus-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.0GB \| \| [internlm2-math-plus-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q5_K.gguf) \| Q5_K \| 5.13GB \| \| [internlm2-math-plus-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.13GB \| \| [internlm2-math-plus-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q5_1.gguf) \| Q5_1 \| 5.43GB \| \| [internlm2-math-plus-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q6_K.gguf) \| Q6_K \| 5.91GB \| \| [internlm2-math-plus-7b.Q8_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-math-plus-7b-gguf/blob/main/internlm2-math-plus-7b.Q8_0.gguf) \| Q8_0 \| 7.66GB \| Original model description: --- pipeline_tag: text-generation license: other language: - en - zh tags: - math --- # InternLM-Math-Plus <div align="center"> <img src="https://raw.githubusercontent.com/InternLM/InternLM/main/assets/logo.svg" width="200"/> <div> </div> <div align="center"> <b><font size="5">InternLM-Math</font></b> <sup> <a href="https://internlm.intern-ai.org.cn/"> <i><font size="4">Plus</font></i> </a> </sup> <div> </div> </div> State-of-the-art bilingual open-sourced Math reasoning LLMs. A solver, prover, verifier, augmentor. [💻 Github](https://github.com/InternLM/InternLM-Math) [🤗 Demo](https://huggingface.co/spaces/internlm/internlm2-math-7b) </div> # News - [2024.05.24] We release updated version InternLM2-Math-Plus with 4 sizes and state-of-the-art performances including 1.8B, 7B, 20B, and 8x22B. We improve informal math reasoning performance (chain-of-thought and code-intepreter) and formal math reasoning performance (LEAN 4 translation and LEAN 4 theorem proving) significantly. - [2024.02.10] We add tech reports and citation reference. - [2024.01.31] We add MiniF2F results with evaluation codes! - [2024.01.29] We add checkpoints from ModelScope. Update results about majority voting and Code Intepreter. Tech report is on the way! - [2024.01.26] We add checkpoints from OpenXLab, which ease Chinese users to download! # Performance ## Formal Math Reasoning We evaluate the performance of InternLM2-Math-Plus on formal math reasoning benchmark MiniF2F-test. The evaluation setting is same as Llemma with LEAN 4. \| Models \| MiniF2F-test \| \| -------------------------------- \| ------------ \| \| ReProver \| 26.5 \| \| LLMStep \| 27.9 \| \| GPT-F \| 36.6 \| \| HTPS \| 41.0 \| \| Llemma-7B \| 26.2 \| \| Llemma-34B \| 25.8 \| \| InternLM2-Math-7B-Base \| 30.3 \| \| InternLM2-Math-20B-Base \| 29.5 \| \| InternLM2-Math-Plus-1.8B \| 38.9 \| \| InternLM2-Math-Plus-7B \| 43.4 \| \| InternLM2-Math-Plus-20B \| 42.6 \| \| InternLM2-Math-Plus-Mixtral8x22B \| 37.3 \| ## Informal Math Reasoning We evaluate the performance of InternLM2-Math-Plus on informal math reasoning benchmark MATH and GSM8K. InternLM2-Math-Plus-1.8B outperforms MiniCPM-2B in the smallest size setting. InternLM2-Math-Plus-7B outperforms Deepseek-Math-7B-RL which is the state-of-the-art math reasoning open source model. InternLM2-Math-Plus-Mixtral8x22B achieves 68.5 on MATH (with Python) and 91.8 on GSM8K. \| Model \| MATH \| MATH-Python \| GSM8K \| \| -------------------------------- \| -------- \| ----------- \| -------- \| \| MiniCPM-2B \| 10.2 \| - \| 53.8 \| \| InternLM2-Math-Plus-1.8B \| 37.0 \| 41.5 \| 58.8 \| \| InternLM2-Math-7B \| 34.6 \| 50.9 \| 78.1 \| \| Deepseek-Math-7B-RL \| 51.7 \| 58.8 \| 88.2 \| \| InternLM2-Math-Plus-7B \| 53.0 \| 59.7 \| 85.8 \| \| InternLM2-Math-20B \| 37.7 \| 54.3 \| 82.6 \| \| InternLM2-Math-Plus-20B \| 53.8 \| 61.8 \| 87.7 \| \| Mixtral8x22B-Instruct-v0.1 \| 41.8 \| - \| 78.6 \| \| Eurux-8x22B-NCA \| 49.0 \| - \| - \| \| InternLM2-Math-Plus-Mixtral8x22B \| 58.1 \| 68.5 \| 91.8 \| We also evaluate models on [MathBench-A](https://github.com/open-compass/MathBench). InternLM2-Math-Plus-Mixtral8x22B has comparable performance compared to Claude 3 Opus. \| Model \| Arithmetic \| Primary \| Middle \| High \| College \| Average \| \| -------------------------------- \| ---------- \| ------- \| ------ \| ---- \| ------- \| ------- \| \| GPT-4o-0513 \| 77.7 \| 87.7 \| 76.3 \| 59.0 \| 54.0 \| 70.9 \| \| Claude 3 Opus \| 85.7 \| 85.0 \| 58.0 \| 42.7 \| 43.7 \| 63.0 \| \| Qwen-Max-0428 \| 72.3 \| 86.3 \| 65.0 \| 45.0 \| 27.3 \| 59.2 \| \| Qwen-1.5-110B \| 70.3 \| 82.3 \| 64.0 \| 47.3 \| 28.0 \| 58.4 \| \| Deepseek-V2 \| 82.7 \| 89.3 \| 59.0 \| 39.3 \| 29.3 \| 59.9 \| \| Llama-3-70B-Instruct \| 70.3 \| 86.0 \| 53.0 \| 38.7 \| 34.7 \| 56.5 \| \| InternLM2-Math-Plus-Mixtral8x22B \| 77.5 \| 82.0 \| 63.6 \| 50.3 \| 36.8 \| 62.0 \| \| InternLM2-Math-20B \| 58.7 \| 70.0 \| 43.7 \| 24.7 \| 12.7 \| 42.0 \| \| InternLM2-Math-Plus-20B \| 65.8 \| 79.7 \| 59.5 \| 47.6 \| 24.8 \| 55.5 \| \| Llama3-8B-Instruct \| 54.7 \| 71.0 \| 25.0 \| 19.0 \| 14.0 \| 36.7 \| \| InternLM2-Math-7B \| 53.7 \| 67.0 \| 41.3 \| 18.3 \| 8.0 \| 37.7 \| \| Deepseek-Math-7B-RL \| 68.0 \| 83.3 \| 44.3 \| 33.0 \| 23.0 \| 50.3 \| \| InternLM2-Math-Plus-7B \| 61.4 \| 78.3 \| 52.5 \| 40.5 \| 21.7 \| 50.9 \| \| MiniCPM-2B \| 49.3 \| 51.7 \| 18.0 \| 8.7 \| 3.7 \| 26.3 \| \| InternLM2-Math-Plus-1.8B \| 43.0 \| 43.3 \| 25.4 \| 18.9 \| 4.7 \| 27.1 \| # Citation and Tech Report ``` @misc{ying2024internlmmath, title={InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning}, author={Huaiyuan Ying and Shuo Zhang and Linyang Li and Zhejian Zhou and Yunfan Shao and Zhaoye Fei and Yichuan Ma and Jiawei Hong and Kuikun Liu and Ziyi Wang and Yudong Wang and Zijian Wu and Shuaibin Li and Fengzhe Zhou and Hongwei Liu and Songyang Zhang and Wenwei Zhang and Hang Yan and Xipeng Qiu and Jiayu Wang and Kai Chen and Dahua Lin}, year={2024}, eprint={2402.06332}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
pchopalli/whisper-small-or-en	pchopalli	2024-05-28T16:44:36Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "or", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-28T16:43:31Z	--- language: - or license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Oriya Translate - Prashant C results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: or split: test args: 'config: bg, split: test' metrics: - name: Wer type: wer value: 26.790595954073265 --- <!-- 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 Oriya Translate - Prashant C This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3157 - Wer Ortho: 60.6530 - Wer: 26.7906 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.0106 \| 9.6154 \| 500 \| 0.3157 \| 60.6530 \| 26.7906 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
javidanaslanli/tiny-az-tokenizer-13k	javidanaslanli	2024-05-28T16:40:10Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T16:40:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Klevin/DECYPHERS-TEST-2.0	Klevin	2024-05-28T16:35:30Z	138	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T16:28:25Z	--- 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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Weblet/llama2-7b-hf-chat-lora-v3-turbo17169127082140281_mlabonne-guanaco-llama2-1k_train	Weblet	2024-05-28T16:34:43Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T16:30:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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GhostDragon01/rfp-questionnaires-test-01	GhostDragon01	2024-05-28T16:32:33Z	3	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:yleo/EmertonMonarch-7B", "base_model:adapter:yleo/EmertonMonarch-7B", "license:cc-by-nc-4.0", "region:us" ]	null	2024-05-28T16:00:23Z	--- license: cc-by-nc-4.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: yleo/EmertonMonarch-7B datasets: - generator model-index: - name: rfp-questionnaires-test-01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rfp-questionnaires-test-01 This model is a fine-tuned version of [yleo/EmertonMonarch-7B](https://huggingface.co/yleo/EmertonMonarch-7B) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
fimbulvntr/vllm_model_70b	fimbulvntr	2024-05-28T16:32:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-70b-bnb-4bit", "base_model:finetune:unsloth/llama-3-70b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T16:28:58Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-70b-bnb-4bit --- # Uploaded model - Developed by: fimbulvntr - License: apache-2.0 - Finetuned from model : unsloth/llama-3-70b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ClaudioItaly/TopEvolution-Q8_0-GGUF	ClaudioItaly	2024-05-28T16:30:34Z	1	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:merge:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:mergekit-community/mergekit-slerp-ebgdloh", "base_model:merge:mergekit-community/mergekit-slerp-ebgdloh", "endpoints_compatible", "region:us" ]	null	2024-05-28T16:30:15Z	--- library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - NousResearch/Hermes-2-Pro-Mistral-7B - mergekit-community/mergekit-slerp-ebgdloh --- # ClaudioItaly/TopEvolution-Q8_0-GGUF This model was converted to GGUF format from [`mergekit-community/TopEvolution`](https://huggingface.co/mergekit-community/TopEvolution) 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/mergekit-community/TopEvolution) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo ClaudioItaly/TopEvolution-Q8_0-GGUF --model topevolution-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo ClaudioItaly/TopEvolution-Q8_0-GGUF --model topevolution-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m topevolution-q8_0.gguf -n 128 ```
RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf	RichardErkhov	2024-05-28T16:29:31Z	18	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-28T12:47:10Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) OpenHermes-2.5-Nebula-v2-7B - GGUF - Model creator: https://huggingface.co/Weyaxi/ - Original model: https://huggingface.co/Weyaxi/OpenHermes-2.5-Nebula-v2-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [OpenHermes-2.5-Nebula-v2-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [OpenHermes-2.5-Nebula-v2-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [OpenHermes-2.5-Nebula-v2-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [OpenHermes-2.5-Nebula-v2-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [OpenHermes-2.5-Nebula-v2-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [OpenHermes-2.5-Nebula-v2-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [OpenHermes-2.5-Nebula-v2-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenHermes-2.5-Nebula-v2-7B-gguf/blob/main/OpenHermes-2.5-Nebula-v2-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: cc-by-nc-4.0 datasets: - garage-bAInd/Open-Platypus language: - en --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/cKySe1S5IW_KnbZpKmozQ.png) <a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> # OpenHermes-2.5-Nebula-v2-7B OpenHermes-2.5-Nebula-v2-7B is a merge of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [PulsarAI/Nebula-v2-7B-Lora](https://huggingface.co/PulsarAI/Nebula-v2-7B-Lora) # Evaluation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)) \| Metric \| Value \| \|-----------------------\|-----------\| \| Avg. \| \| \| ARC (25-shot) \| \| \| HellaSwag (10-shot) \| \| \| MMLU (5-shot) \| \| \| TruthfulQA (0-shot) \| \| \| Winogrande (5-shot) \| \| \| GSM8K (5-shot) \| \| \| DROP (3-shot) \| \|
Yoxas/autotrain-gpt2-statistical1	Yoxas	2024-05-28T16:29:12Z	137	0	transformers	[ "transformers", "pytorch", "tf", "jax", "tensorboard", "safetensors", "gpt2", "text-generation", "autotrain", "text-generation-inference", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T16:05:31Z	--- tags: - autotrain - text-generation-inference - text-generation library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
vuongnhathien/convnext-base-3e-5-wd-1e-8-raug	vuongnhathien	2024-05-28T16:26:31Z	193	0	transformers	[ "transformers", "tensorboard", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/convnextv2-base-22k-384", "base_model:finetune:facebook/convnextv2-base-22k-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T11:03:30Z	--- license: apache-2.0 base_model: facebook/convnextv2-base-22k-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnext-base-3e-5-wd-1e-8-raug results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9458333333333333 --- <!-- 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. --> # convnext-base-3e-5-wd-1e-8-raug This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2296 - Accuracy: 0.9458 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.6237 \| 1.0 \| 1099 \| 0.3587 \| 0.8994 \| \| 0.4599 \| 2.0 \| 2198 \| 0.2743 \| 0.9213 \| \| 0.359 \| 3.0 \| 3297 \| 0.2579 \| 0.9252 \| \| 0.3047 \| 4.0 \| 4396 \| 0.2404 \| 0.9388 \| \| 0.2869 \| 5.0 \| 5495 \| 0.2348 \| 0.9408 \| \| 0.2468 \| 6.0 \| 6594 \| 0.2276 \| 0.9455 \| \| 0.2098 \| 7.0 \| 7693 \| 0.2303 \| 0.9471 \| \| 0.1944 \| 8.0 \| 8792 \| 0.2244 \| 0.9495 \| \| 0.1739 \| 9.0 \| 9891 \| 0.2247 \| 0.9507 \| \| 0.1508 \| 10.0 \| 10990 \| 0.2243 \| 0.9487 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
DiederikMartens/mBERT_sa_cv_13_fold7	DiederikMartens	2024-05-28T16:24:55Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T16:03:20Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_13_fold7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mBERT_sa_cv_13_fold7 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5312 - F1: 0.6178 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.5553 \| 0.4855 \| \| 0.5476 \| 2.0 \| 650 \| 0.4588 \| 0.5491 \| \| 0.5476 \| 3.0 \| 975 \| 0.5312 \| 0.6178 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
zoedc/resume_model_3labels_final	zoedc	2024-05-28T16:24:29Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T15:47:05Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: resume_model_3labels_final 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. --> # resume_model_3labels_final This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3759 - Accuracy: 0.8333 - F1 Weighted: 0.7882 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Weighted \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-----------:\| \| 1.0074 \| 1.0 \| 60 \| 0.7552 \| 0.7667 \| 0.6835 \| \| 0.693 \| 2.0 \| 120 \| 0.6421 \| 0.7333 \| 0.6505 \| \| 0.5233 \| 3.0 \| 180 \| 0.3900 \| 0.8333 \| 0.7882 \| \| 0.3459 \| 4.0 \| 240 \| 0.3759 \| 0.8333 \| 0.7882 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/tsBERT_sa_cv_13_fold7	DiederikMartens	2024-05-28T16:23:08Z	113	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T16:01:31Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_13_fold7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tsBERT_sa_cv_13_fold7 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4860 - F1: 0.7193 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4105 \| 0.5852 \| \| 0.4368 \| 2.0 \| 650 \| 0.3952 \| 0.6444 \| \| 0.4368 \| 3.0 \| 975 \| 0.4860 \| 0.7193 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf	RichardErkhov	2024-05-28T16:22:27Z	34	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-28T12:47:11Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-beta-Nebula-v2-7B - GGUF - Model creator: https://huggingface.co/Weyaxi/ - Original model: https://huggingface.co/Weyaxi/zephyr-beta-Nebula-v2-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [zephyr-beta-Nebula-v2-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [zephyr-beta-Nebula-v2-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [zephyr-beta-Nebula-v2-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [zephyr-beta-Nebula-v2-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [zephyr-beta-Nebula-v2-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [zephyr-beta-Nebula-v2-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [zephyr-beta-Nebula-v2-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [zephyr-beta-Nebula-v2-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [zephyr-beta-Nebula-v2-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [zephyr-beta-Nebula-v2-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [zephyr-beta-Nebula-v2-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [zephyr-beta-Nebula-v2-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [zephyr-beta-Nebula-v2-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [zephyr-beta-Nebula-v2-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [zephyr-beta-Nebula-v2-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [zephyr-beta-Nebula-v2-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [zephyr-beta-Nebula-v2-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [zephyr-beta-Nebula-v2-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [zephyr-beta-Nebula-v2-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [zephyr-beta-Nebula-v2-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [zephyr-beta-Nebula-v2-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [zephyr-beta-Nebula-v2-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_zephyr-beta-Nebula-v2-7B-gguf/blob/main/zephyr-beta-Nebula-v2-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: cc-by-nc-4.0 datasets: - garage-bAInd/Open-Platypus language: - en --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/cKySe1S5IW_KnbZpKmozQ.png) <a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> # zephyr-beta-Nebula-v2-7B zephyr-beta-Nebula-v2-7B is a merge of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) and [PulsarAI/Nebula-v2-7B-Lora](https://huggingface.co/PulsarAI/Nebula-v2-7B-Lora) # Evaluation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)) \| Metric \| Value \| \|-----------------------\|-----------\| \| Avg. \| \| \| ARC (25-shot) \| \| \| HellaSwag (10-shot) \| \| \| MMLU (5-shot) \| \| \| TruthfulQA (0-shot) \| \| \| Winogrande (5-shot) \| \| \| GSM8K (5-shot) \| \| \| DROP (3-shot) \| \|
Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-5_0bpw_exl2	Zoyd	2024-05-28T16:15:57Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dpo", "dataset:mlabonne/orpo-dpo-mix-40k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-28T15:45:42Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - dpo --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4931 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6495 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6903 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8157 MB</center> \| <center>8</center> \| # NeuralDaredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) This is a DPO fine-tune of [mlabonne/Daredevil-8-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) trained on one epoch of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k). ## 🏆 Evaluation ### Open LLM Leaderboard TBD. ### Nous Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/ae0bf16936cef900b72964b33c99edbc) \| 55.87 \| 43.73 \| 73.6 \| 59.36 \| 46.8 \| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/5df2a3051dd6eb3368a77b684635dc05) \| 54.28 \| 43.9 \| 72.62 \| 56.36 \| 44.23 \| \| [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) [📄](https://gist.github.com/mlabonne/95eef8e8d26b7b17910dcb78e1c95f4a) \| 53.49 \| 44.03 \| 73.67 \| 49.78 \| 46.48 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \| ## 🌳 Model family tree ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ekwRGgnjzEOyprT8sEBFt.png)
fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-437825	fine-tuned	2024-05-28T16:15:08Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-437825", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T16:14:14Z	--- license: apache-2.0 datasets: - fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-437825 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-437825', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_5bpw_exl2	Zoyd	2024-05-28T16:14:40Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dpo", "dataset:mlabonne/orpo-dpo-mix-40k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T15:09:35Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - dpo --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4931 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6495 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6903 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8157 MB</center> \| <center>8</center> \| # NeuralDaredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) This is a DPO fine-tune of [mlabonne/Daredevil-8-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) trained on one epoch of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k). ## 🏆 Evaluation ### Open LLM Leaderboard TBD. ### Nous Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/ae0bf16936cef900b72964b33c99edbc) \| 55.87 \| 43.73 \| 73.6 \| 59.36 \| 46.8 \| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/5df2a3051dd6eb3368a77b684635dc05) \| 54.28 \| 43.9 \| 72.62 \| 56.36 \| 44.23 \| \| [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) [📄](https://gist.github.com/mlabonne/95eef8e8d26b7b17910dcb78e1c95f4a) \| 53.49 \| 44.03 \| 73.67 \| 49.78 \| 46.48 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \| ## 🌳 Model family tree ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ekwRGgnjzEOyprT8sEBFt.png)
fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-859511	fine-tuned	2024-05-28T16:14:29Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-859511", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T16:13:34Z	--- license: apache-2.0 datasets: - fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-859511 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-859511', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_25bpw_exl2	Zoyd	2024-05-28T16:13:55Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dpo", "dataset:mlabonne/orpo-dpo-mix-40k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T15:39:53Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - dpo --- Exllamav2 quant (exl2 / 4.25 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4931 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6495 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6903 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8157 MB</center> \| <center>8</center> \| # NeuralDaredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) This is a DPO fine-tune of [mlabonne/Daredevil-8-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) trained on one epoch of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k). ## 🏆 Evaluation ### Open LLM Leaderboard TBD. ### Nous Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/ae0bf16936cef900b72964b33c99edbc) \| 55.87 \| 43.73 \| 73.6 \| 59.36 \| 46.8 \| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/5df2a3051dd6eb3368a77b684635dc05) \| 54.28 \| 43.9 \| 72.62 \| 56.36 \| 44.23 \| \| [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) [📄](https://gist.github.com/mlabonne/95eef8e8d26b7b17910dcb78e1c95f4a) \| 53.49 \| 44.03 \| 73.67 \| 49.78 \| 46.48 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \| ## 🌳 Model family tree ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ekwRGgnjzEOyprT8sEBFt.png)
roscazo/vih_explainability3	roscazo	2024-05-28T16:13:39Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:PlanTL-GOB-ES/bsc-bio-ehr-es", "base_model:finetune:PlanTL-GOB-ES/bsc-bio-ehr-es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T16:13:21Z	--- license: apache-2.0 base_model: PlanTL-GOB-ES/bsc-bio-ehr-es tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: vih_explainability3 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. --> # vih_explainability3 This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3951 - Roc Auc: 0.8213 - Ap Score: 0.7049 - Precision: 0.9836 - Recall: 0.6452 - F1: 0.7792 ## 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: 16 - 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 \| Roc Auc \| Ap Score \| Precision \| Recall \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4261 \| 0.8475 \| 100 \| 0.3832 \| 0.6129 \| 0.3793 \| 1.0 \| 0.2258 \| 0.3684 \| \| 0.2405 \| 1.6949 \| 200 \| 0.4736 \| 0.6344 \| 0.4138 \| 1.0 \| 0.2688 \| 0.4237 \| \| 0.2088 \| 2.5424 \| 300 \| 0.3452 \| 0.7729 \| 0.6274 \| 0.9808 \| 0.5484 \| 0.7034 \| \| 0.2196 \| 3.3898 \| 400 \| 0.3644 \| 0.7151 \| 0.5431 \| 1.0 \| 0.4301 \| 0.6015 \| \| 0.2068 \| 4.2373 \| 500 \| 0.5156 \| 0.6344 \| 0.4138 \| 1.0 \| 0.2688 \| 0.4237 \| \| 0.1374 \| 5.0847 \| 600 \| 0.3988 \| 0.7944 \| 0.6619 \| 0.9821 \| 0.5914 \| 0.7383 \| \| 0.1098 \| 5.9322 \| 700 \| 0.3629 \| 0.8051 \| 0.6791 \| 0.9828 \| 0.6129 \| 0.7550 \| \| 0.0914 \| 6.7797 \| 800 \| 0.3394 \| 0.8240 \| 0.6934 \| 0.9531 \| 0.6559 \| 0.7771 \| \| 0.088 \| 7.6271 \| 900 \| 0.3612 \| 0.8334 \| 0.7009 \| 0.9403 \| 0.6774 \| 0.7875 \| \| 0.0787 \| 8.4746 \| 1000 \| 0.3801 \| 0.8213 \| 0.7049 \| 0.9836 \| 0.6452 \| 0.7792 \| \| 0.0588 \| 9.3220 \| 1100 \| 0.3951 \| 0.8213 \| 0.7049 \| 0.9836 \| 0.6452 \| 0.7792 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_0bpw_exl2	Zoyd	2024-05-28T16:13:37Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dpo", "dataset:mlabonne/orpo-dpo-mix-40k", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-28T15:16:51Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - dpo --- Exllamav2 quant (exl2 / 3.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4931 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6495 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6903 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_NeuralDaredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8157 MB</center> \| <center>8</center> \| # NeuralDaredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) This is a DPO fine-tune of [mlabonne/Daredevil-8-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) trained on one epoch of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k). ## 🏆 Evaluation ### Open LLM Leaderboard TBD. ### Nous Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/ae0bf16936cef900b72964b33c99edbc) \| 55.87 \| 43.73 \| 73.6 \| 59.36 \| 46.8 \| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/5df2a3051dd6eb3368a77b684635dc05) \| 54.28 \| 43.9 \| 72.62 \| 56.36 \| 44.23 \| \| [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) [📄](https://gist.github.com/mlabonne/95eef8e8d26b7b17910dcb78e1c95f4a) \| 53.49 \| 44.03 \| 73.67 \| 49.78 \| 46.48 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \| ## 🌳 Model family tree ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ekwRGgnjzEOyprT8sEBFt.png)
fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-110174	fine-tuned	2024-05-28T16:13:23Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-110174", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T16:12:27Z	--- license: apache-2.0 datasets: - fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-110174 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/before-finetuning-512-192-gpt-4o-2024-05-13-110174', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
Kovalev/aya23_8B_kazparc	Kovalev	2024-05-28T16:10:16Z	0	0	null	[ "safetensors", "license:cc-by-nc-4.0", "region:us" ]	null	2024-05-28T16:09:26Z	--- license: cc-by-nc-4.0 ---
Toshifumi/Llama3-IMDB_20240528v1	Toshifumi	2024-05-28T16:08:11Z	4	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T16:02:49Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Toshifumi - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-418918	fine-tuned	2024-05-28T16:06:47Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-418918", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T16:06:12Z	--- license: apache-2.0 datasets: - fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-418918 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-418918', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
ybelkada/tiny-random-llama-Q6_K-GGUF	ybelkada	2024-05-28T16:06:31Z	6	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us" ]	null	2024-05-28T16:06:30Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo --- # ybelkada/tiny-random-llama-Q6_K-GGUF This model was converted to GGUF format from [`ybelkada/tiny-random-llama`](https://huggingface.co/ybelkada/tiny-random-llama) 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/ybelkada/tiny-random-llama) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo ybelkada/tiny-random-llama-Q6_K-GGUF --model tiny-random-llama.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo ybelkada/tiny-random-llama-Q6_K-GGUF --model tiny-random-llama.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. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tiny-random-llama.Q6_K.gguf -n 128 ```
MoTHer-VTHR/VTHR-LoRA-V-ModelTree_4-Depth_2-Node_nuB5P4de	MoTHer-VTHR	2024-05-28T16:06:30Z	170	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T16:06:14Z	--- 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. 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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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MoTHer-VTHR/VTHR-LoRA-V-ModelTree_4-Depth_2-Node_ehobdK3q	MoTHer-VTHR	2024-05-28T16:05:59Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T15:48:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-LoRA-V-ModelTree_4-Depth_2-Node_Kb6teTEK	MoTHer-VTHR	2024-05-28T16:05:52Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T15:48:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-935443	fine-tuned	2024-05-28T16:05:51Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-935443", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T16:05:19Z	--- license: apache-2.0 datasets: - fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-935443 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-935443', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-186741	fine-tuned	2024-05-28T16:05:51Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-186741", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T16:05:17Z	--- license: apache-2.0 datasets: - fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-186741 - allenai/c4 language: - en - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/FiQA2018-512-192-gpt-4o-2024-05-13-186741', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```

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