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train · 793k rows

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fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-74794049	fine-tuned	2024-06-06T07:54:37Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-74794049", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-06-06T07:54:17Z	--- license: apache-2.0 datasets: - fine-tuned/FiQA2018-32000-384-gpt-4o-2024-05-13-74794049 - 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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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/FiQA2018-32000-384-gpt-4o-2024-05-13-74794049', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
nluai/phogpt-ft-test3	nluai	2024-06-06T07:53:48Z	5	0	transformers	[ "transformers", "safetensors", "mpt", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T06:15:55Z	--- library_name: transformers tags: [] widget: - text: >- sinh viên được quyền yêu cầu phúc khảo bài thi trong thời gian bao lâu kể từ ngày Phòng Đào tạo công bố điểm --- # 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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dioday45/sft_cot2	dioday45	2024-06-06T07:40:16Z	107	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-06T07:23:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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IneG/BERT_pretrained_litcov10K_paraphrased_all-shuffled	IneG	2024-06-06T07:27:50Z	128	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-06-06T07:26:19Z	--- tags: - generated_from_trainer model-index: - name: BERT_pretrained_litcov10K_paraphrased_all-shuffled results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_pretrained_litcov10K_paraphrased_all-shuffled This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
fine-tuned/before-finetuning-32000-384-gpt-4o-2024-05-13-67199932	fine-tuned	2024-06-06T07:21:41Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/before-finetuning-32000-384-gpt-4o-2024-05-13-67199932", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-06-06T07:21:37Z	--- license: apache-2.0 datasets: - fine-tuned/before-finetuning-32000-384-gpt-4o-2024-05-13-67199932 - 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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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-32000-384-gpt-4o-2024-05-13-67199932', 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-32000-384-gpt-4o-2024-05-13-55567015	fine-tuned	2024-06-06T07:20:25Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/before-finetuning-32000-384-gpt-4o-2024-05-13-55567015", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-06-06T07:20:20Z	--- license: apache-2.0 datasets: - fine-tuned/before-finetuning-32000-384-gpt-4o-2024-05-13-55567015 - 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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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-32000-384-gpt-4o-2024-05-13-55567015', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
bhargavrepaka/tiny_bin	bhargavrepaka	2024-06-06T07:13:49Z	0	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T07:13:25Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
bhargavrepaka/tiny_tensor	bhargavrepaka	2024-06-06T07:12:41Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T07:11:16Z	--- library_name: transformers tags: - unsloth --- # 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]
IneG/BERT_pretrained_litcov10K_paraphrased	IneG	2024-06-06T07:11:12Z	125	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-06-06T07:10:32Z	--- tags: - generated_from_trainer model-index: - name: BERT_pretrained_litcov10K_paraphrased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_pretrained_litcov10K_paraphrased This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
kuanhoong/gemma-2b-mt-German-to-English	kuanhoong	2024-06-06T07:10:55Z	144	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T07:04:50Z	--- 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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Kame1024/TinyLlama_1.1b_test	Kame1024	2024-06-06T07:08:14Z	148	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "arxiv:2203.05482", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:merge:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "base_model:merge:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T06:16:58Z	--- base_model: - TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T - TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: transformers tags: - mergekit - merge inference: parameters: temperature: 0.0 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # storage This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) * [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 parameters: weight: 1.0 - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T parameters: weight: 1.0 merge_method: linear dtype: float16 ```
manbull/Qwen-Qwen1.5-0.5B-1717657333	manbull	2024-06-06T07:03:01Z	146	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T07:02:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jenniellama/carbide-tebet-loki	jenniellama	2024-06-06T07:02:07Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T06:59:40Z	--- 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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nluai/results_modified	nluai	2024-06-06T07:00:25Z	5	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:EleutherAI/gpt-neo-125m", "base_model:adapter:EleutherAI/gpt-neo-125m", "license:mit", "region:us" ]	null	2024-06-06T07:00:23Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: EleutherAI/gpt-neo-125m model-index: - name: results_modified 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_modified This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
chainup244/Qwen-Qwen1.5-0.5B-1717656956	chainup244	2024-06-06T06:56:56Z	148	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T06:56:01Z	--- 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. 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chainatao/q-Taxi-v3	chainatao	2024-06-06T06:55:51Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-06T06:36:33Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="chainatao/q-Taxi-v3", 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"]) ```
Sorour/llama3-base-ft-finred	Sorour	2024-06-06T06:51:17Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T06:45:55Z	--- 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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coconana/google-gemma-7b-1717652801	coconana	2024-06-06T06:49:10Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T05:46:42Z	--- 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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SoorajK1/e2_test01	SoorajK1	2024-06-06T06:45:32Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-06T06:44:55Z	--- 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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Chijioke-Mgbahurike/wav2vec2-base-960h-ft	Chijioke-Mgbahurike	2024-06-06T06:43:22Z	38	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-05T12:32:31Z	--- 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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podonamu/blip-image-captioning-base-insta-test	podonamu	2024-06-06T06:42:44Z	62	0	transformers	[ "transformers", "pytorch", "tensorboard", "blip", "image-text-to-text", "generated_from_trainer", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-06-06T04:05:57Z	--- license: bsd-3-clause tags: - generated_from_trainer model-index: - name: blip-image-captioning-base-insta-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # blip-image-captioning-base-insta-test This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - 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: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.30.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.13.3
shivanikerai/Llama-2-7b-chat-hf-adapter-title-ner-v2.0	shivanikerai	2024-06-06T06:42:20Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-06-06T06:41:53Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.2.dev0
no-supido-de/Tekken-8-Throw_Detector	no-supido-de	2024-06-06T06:41:34Z	0	0	null	[ "game", "tekken", "tekken 8", "throw", "detection", "object-detection", "license:mit", "region:us" ]	object-detection	2024-03-28T11:15:23Z	--- license: mit metrics: - accuracy pipeline_tag: object-detection tags: - game - tekken - tekken 8 - throw - detection --- This model is designed for detecting throw capture moments in Tekken 8 gameplay.\ It is based on the VGG16 architecture, modified by removing the top layer to serve as a feature extractor.\ The model was trained using Keras on a dataset comprising video compilations from Tekken 8 fights, resulting in a total of 701,990 images at a resolution of 640x360. Approximately 5,000 of these images feature throw captures. Training involved augmentation techniques such as slight color shifting and the addition of mild color or black-and-white noise to enhance model robustness. The model underwent 65 training cycles, each consisting of 13 epochs.\ In each cycle, a batch of 250 randomly selected images from the dataset was used, with at least 40 images depicting throw captures. The batch size was set to 20.\ The custom top layer added for this task includes a Flatten layer followed by a Dense layer with 128 units and 'relu' activation, a Dropout layer with a rate of 0.4, and a final Dense layer with 1 unit and 'sigmoid' activation to predict throw captures. Processing 640x360 image took around 14.5ms on 3060 Ti with opened Tekken 8
denru/L3-MS-Astoria-70b-4_65bpw-h6-exl2-pippa	denru	2024-06-06T06:30:08Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "conversational", "base_model:NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt", "base_model:merge:NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt", "base_model:abacusai/Llama-3-Giraffe-70B", "base_model:merge:abacusai/Llama-3-Giraffe-70B", "base_model:failspy/llama-3-70B-Instruct-abliterated", "base_model:merge:failspy/llama-3-70B-Instruct-abliterated", "base_model:migtissera/Tess-2.0-Llama-3-70B-v0.2", "base_model:merge:migtissera/Tess-2.0-Llama-3-70B-v0.2", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-06-06T06:23:24Z	--- base_model: - failspy/llama-3-70B-Instruct-abliterated - migtissera/Tess-2.0-Llama-3-70B-v0.2 - NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt - abacusai/Llama-3-Giraffe-70B library_name: transformers tags: - merge license: llama3 --- <!DOCTYPE html> <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #2E3440 0%, #1A202C 100%); color: #D8DEE9; margin: 0; padding: 0; font-size: 16px; } .container { width: 80% auto; max-width: 1080px auto; margin: 20px auto; background-color: rgba(255, 255, 255, 0.02); padding: 20px; border-radius: 12px; box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2); backdrop-filter: blur(10px); border: 1px solid rgba(255, 255, 255, 0.1); } .header h1 { font-size: 28px; color: #ECEFF4; margin: 0 0 20px 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); } .update-section { margin-top: 30px; } .update-section h2 { font-size: 24px; color: #88C0D0; } .update-section p { font-size: 16px; line-height: 1.6; color: #ECEFF4; } .info img { width: 100%; border-radius: 10px; margin-bottom: 15px; } a { color: #88C0D0; text-decoration: none; } a:hover { color: #A3BE8C; } .button { display: inline-block; background-color: #5E81AC; color: #E5E9F0; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; } .button:hover { background-color: #81A1C1; } pre { background-color: #2E3440; padding: 10px; border-radius: 5px; overflow-x: auto; } code { font-family: 'Courier New', monospace; color: #D8DEE9; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>L3-MS-Astoria-70b Data Card</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>L3-MS-Astoria-70b</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/HU5Zz7mb4X0wK3cZM2M9E.png"> <p>Now that the cute anime girl has your attention.</p> <p><strong>Creator:</strong> <a href="https://huggingface.co/Steelskull" target="_blank">SteelSkull</a></p> <h1>About L3-MS-Astoria-70b:</h1> <p>L3 = Llama-3 <p/> <p>MS = Model Stock <p/> <p>This is my first foray into 70b models, so this is more or less an experiment, please let me know your thoughts on the model and where their can be improvements.<br> L3-MS-Astoria-70b combines the strengths of multiple models to deliver a well-rounded, capable assistant. It is aimed at performing general tasks, storytelling, roleplay, and more mature content.<br> The model stock merging method attempts to make the model remain focused, tailored, and high-quality. <h2>Quants:</h2> <p>(Thanks to <a href="https://huggingface.co/mradermacher">@Mradermacher!</a>, please send them likes and follows!)</p> <p><a href="https://huggingface.co/mradermacher/L3-MS-Astoria-70b-GGUF">L3-MS-Astoria-70b-GGUF (GGUFs)</a></p> <p></p> <h3>Config:</h3> <pre><code>MODEL_NAME = "L3-MS-Astoria-70b" yaml_config = """ base_model: failspy/llama-3-70B-Instruct-abliterated merge_method: model_stock dtype: bfloat16 models: - model: migtissera/Tess-2.0-Llama-3-70B-v0.2 - model: abacusai/Llama-3-Giraffe-70B - model: NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt """ </code></pre> <h4>Source Model Details:</h4> <p><strong>migtissera/Tess-2.0-Llama-3-70B-v0.2:</strong><br> Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-70B-v0.2 was trained on the meta-llama/Meta-Llama-3-70B base. The change between v0.1 and this version, v0.2 is that v0.2 has undergone an additional step of uncensoring. </p> <p><strong>abacusai/Llama-3-Giraffe-70B:</strong><br> General model trained on 1b tokens, up to 128k ctx </p> <p><strong>NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt:</strong><br> Llama3 trained on our RP datasets, NeverSleep tried to have a balance between the ERP and the RP, not too horny, but just enough.<br> NeverSleep also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. </p> <p><strong>Base model failspy/llama-3-70B-Instruct-abliterated:</strong><br> This is meta-llama/Llama-3-70B-Instruct with orthogonalized bfloat16 safetensor weights, generated with the methodology that was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more.<br> TL;DR: this model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal direction orthogonalized out. </p> </div> </div> </body> </html>
miles0825/google-gemma-7b-1717653670	miles0825	2024-06-06T06:29:53Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T06:01:12Z	--- 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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Mlteamnc/distilbert-base-uncased-finetuned-ner	Mlteamnc	2024-06-06T06:29:31Z	8	0	keras	[ "keras", "tf", "tensorboard", "distilbert", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-05-25T21:00:11Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: Mlteamnc/distilbert-base-uncased-finetuned-ner 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. --> # Mlteamnc/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: nan - Validation Loss: nan - Train Precision: 0.0275 - Train Recall: 0.0668 - Train F1: 0.0389 - Train Accuracy: 0.0566 - Epoch: 2 ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 0, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| nan \| nan \| 0.0275 \| 0.0668 \| 0.0389 \| 0.0566 \| 0 \| \| nan \| nan \| 0.0275 \| 0.0668 \| 0.0389 \| 0.0566 \| 1 \| \| nan \| nan \| 0.0275 \| 0.0668 \| 0.0389 \| 0.0566 \| 2 \| ### Framework versions - Transformers 4.41.2 - TensorFlow 2.15.0 - Datasets 2.19.2 - Tokenizers 0.19.1
richardkelly/Qwen-Qwen1.5-0.5B-1717653824	richardkelly	2024-06-06T06:28:43Z	147	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T06:03:45Z	--- 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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ioseff/falcon_cs	ioseff	2024-06-06T06:28:32Z	2	0	peft	[ "peft", "safetensors", "text-generation", "en", "arxiv:1910.09700", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "license:apache-2.0", "region:us" ]	text-generation	2024-06-06T06:25:37Z	--- library_name: peft base_model: tiiuae/falcon-7b license: apache-2.0 language: - en pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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anonymous-guest/QiangGuoAI-V1.1-1.8B	anonymous-guest	2024-06-06T06:20:33Z	149	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T05:20:09Z	--- library_name: transformers tags: - llama-factory --- # 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 #### Example1: [Enter some positive ideas] Input： ``` 坚决清除低俗庸俗媚俗之风 ``` Output： ``` 阻止庸俗的东西侵蚀人们的精神世界，关键还是要着力引导人们牢固树立时代精神追求、增强道德自律意识、提升精神境界。正所谓“欲知得失，穷变通移”。为此，要坚定不移深化文化体制改革，坚定不移实施文化惠民工程，深入开展中国特色社会主义宣传教育，弘扬民族精神和时代精神，推动文化事业全面繁荣、文化产业快速发展，不断丰富人民精神世界、增强人民精神力量，切实提高文化软实力，不断增强中华民族的凝聚力，不断为... ``` #### Example2: [Input part of news from offical sources] Input： ``` 国际观察｜欧盟在多重挑战中寻找方向新一届欧洲议会选举将于6日至9日举行。这是英国“脱欧”后的首场欧洲议会选举，选后欧洲领导层也将迎来新一轮换届，本次选举因此被视为欧洲政治生态的“风向标”。欧洲目前深受乌克兰危机、巴以冲突外溢效应等影响，内外矛盾与挑战相互叠加。分析人士指出，选民的意志立场将在此次选举中得到集中体现，欧洲政治版图或面临新一轮洗牌，右翼政治势力可能“异军突起”，进一步扩大在欧洲议会的影响力，这或将深刻影响未来五年欧洲政策走向。 ``` Output： ``` 团结合作是欧盟发展的不竭动力。在百年变局和世纪疫情交织的复杂局面下，欧盟在抗疫和经济复苏、防务和安全、数字化和就业等领域面临诸多挑战，需要在加强团结合作方面多些担当、多些行动。中欧作为推动多极化的两大力量，应该携手走出一条平等、合作、共赢的新时代之路。双方应该继续坚定支持彼此核心利益和重大关切，坚定支持彼此践行真正的多边主义，坚定支持彼此维护国际公平正义，为世界注入更多稳定性和正能量。我愿同欧方一道努力，把握好新一轮欧中合作的时机和空间，使中欧关系在危机和挑战中更加广阔、更加稳固、更加富有成果。中方高度重视中欧关系，愿同欧方共同努力，将中欧一系列高层和战略层面的沟通和协调机制化、常态化，确保中欧关系不偏航、不走调。中方愿同欧方一道努力，推动中欧关系迈上新台阶，更好造福双方人民。 ``` <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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dewifaj/text-classification	dewifaj	2024-06-06T06:18:43Z	15	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-06T05:30:25Z	--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: 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. --> # text-classification This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4783 - Balanced Accuracy: 0.5328 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Balanced Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------------:\| \| 0.2974 \| 1.0 \| 150 \| 1.4783 \| 0.5328 \| \| 0.2406 \| 2.0 \| 300 \| 1.6163 \| 0.5288 \| \| 0.3334 \| 3.0 \| 450 \| 1.7306 \| 0.5253 \| \| 0.2145 \| 4.0 \| 600 \| 1.6787 \| 0.5254 \| \| 0.1358 \| 5.0 \| 750 \| 1.9275 \| 0.5482 \| \| 0.081 \| 6.0 \| 900 \| 1.8528 \| 0.5372 \| \| 0.2184 \| 7.0 \| 1050 \| 1.9453 \| 0.5604 \| \| 0.0593 \| 8.0 \| 1200 \| 1.9722 \| 0.5558 \| \| 0.0068 \| 9.0 \| 1350 \| 2.0153 \| 0.5561 \| \| 0.0531 \| 10.0 \| 1500 \| 2.0004 \| 0.5429 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
Nandita01/Taxi-v3	Nandita01	2024-06-06T06:16:04Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-06T06:16:00Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.76 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Nandita01/Taxi-v3", 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"]) ```
srikar-v05/llama3-Medical-Chat	srikar-v05	2024-06-06T06:14:52Z	132	1	transformers	[ "transformers", "pytorch", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-06T05:32:51Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: srikar-v05 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sanjib01/q-FrozenLake-v1-4x4-noSlippery	sanjib01	2024-06-06T06:11:18Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-06T06:11:15Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="sanjib01/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"]) ```
tyzhu/lmind_nq_train6000_eval6489_v1_qa_Qwen_Qwen1.5-4B_lora2	tyzhu	2024-06-06T06:05:19Z	5	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_nq_train6000_eval6489_v1_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-04T13:45:39Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_nq_train6000_eval6489_v1_qa metrics: - accuracy model-index: - name: lmind_nq_train6000_eval6489_v1_qa_Qwen_Qwen1.5-4B_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_nq_train6000_eval6489_v1_qa type: tyzhu/lmind_nq_train6000_eval6489_v1_qa metrics: - name: Accuracy type: accuracy value: 0.5578974358974359 library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lmind_nq_train6000_eval6489_v1_qa_Qwen_Qwen1.5-4B_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_nq_train6000_eval6489_v1_qa dataset. It achieves the following results on the evaluation set: - Loss: 2.4726 - Accuracy: 0.5579 ## 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: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:--------:\|:---------------:\| \| 1.7657 \| 0.9973 \| 187 \| 0.5738 \| 1.6215 \| \| 1.497 \| 2.0 \| 375 \| 0.5742 \| 1.6180 \| \| 1.2345 \| 2.9973 \| 562 \| 0.5713 \| 1.6951 \| \| 1.0084 \| 4.0 \| 750 \| 0.5659 \| 1.8059 \| \| 0.8397 \| 4.9973 \| 937 \| 0.5647 \| 1.9245 \| \| 0.7186 \| 6.0 \| 1125 \| 0.5614 \| 2.0345 \| \| 0.6421 \| 6.9973 \| 1312 \| 0.5608 \| 2.1148 \| \| 0.5968 \| 8.0 \| 1500 \| 0.5585 \| 2.1779 \| \| 0.5417 \| 8.9973 \| 1687 \| 0.5568 \| 2.2654 \| \| 0.5356 \| 9.9733 \| 1870 \| 0.5594 \| 2.2527 \| \| 0.5261 \| 10.9973 \| 2057 \| 2.3376 \| 0.5585 \| \| 0.5179 \| 12.0 \| 2245 \| 2.3704 \| 0.5595 \| \| 0.5116 \| 12.9973 \| 2432 \| 2.3617 \| 0.5589 \| \| 0.5056 \| 14.0 \| 2620 \| 2.4022 \| 0.5581 \| \| 0.5063 \| 14.9973 \| 2807 \| 2.3861 \| 0.5587 \| \| 0.4796 \| 16.0 \| 2995 \| 2.3658 \| 0.5585 \| \| 0.4757 \| 16.9973 \| 3182 \| 2.4195 \| 0.5577 \| \| 0.4779 \| 18.0 \| 3370 \| 2.4573 \| 0.5573 \| \| 0.4782 \| 18.9973 \| 3557 \| 2.4896 \| 0.5589 \| \| 0.4784 \| 19.9733 \| 3740 \| 2.4726 \| 0.5579 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
nc33/bartpho-word-base-finetuned-ee	nc33	2024-06-06T06:04:26Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:vinai/bartpho-word-base", "base_model:finetune:vinai/bartpho-word-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-30T09:23:10Z	--- base_model: vinai/bartpho-word-base tags: - generated_from_trainer metrics: - rouge model-index: - name: bartpho-word-base-finetuned-ee 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. --> # bartpho-word-base-finetuned-ee This model is a fine-tuned version of [vinai/bartpho-word-base](https://huggingface.co/vinai/bartpho-word-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0116 - Rouge1: 88.7018 - Rouge2: 79.3922 - Rougel: 88.6978 - Rougelsum: 88.7065 - Gen Len: 9.733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.0184 \| 1.0 \| 1492 \| 0.0143 \| 87.5521 \| 76.9727 \| 87.5302 \| 87.5409 \| 9.779 \| \| 0.0124 \| 2.0 \| 2984 \| 0.0125 \| 87.4254 \| 76.7809 \| 87.4381 \| 87.4401 \| 9.9261 \| \| 0.0093 \| 3.0 \| 4476 \| 0.0116 \| 88.7018 \| 79.3922 \| 88.6978 \| 88.7065 \| 9.733 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
minmingzhu02/Mistral-7B-v0.1-habana-deepspeed-lora	minmingzhu02	2024-06-06T06:02:01Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "optimum_habana", "region:us" ]	null	2024-06-06T01:47:18Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
HyperdustProtocol/ImHyperAGI_llama3	HyperdustProtocol	2024-06-06T05:57:43Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-06-06T05:46:08Z	--- license: apache-2.0 ---
Schila/openai-whisper-small-lr4-ep3-LORA-colab	Schila	2024-06-06T05:53:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T05:53:49Z	--- 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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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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bella05/pogny-64-0.000001	bella05	2024-06-06T05:46:13Z	109	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-06T05:20:36Z	--- base_model: klue/roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: pogny-64-0.000001 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. --> # pogny-64-0.000001 This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2381 - Accuracy: 0.7685 - F1: 0.7667 ## 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-06 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.0912 \| 1.0 \| 1205 \| 1.2147 \| 0.7687 \| 0.7677 \| \| 0.0828 \| 2.0 \| 2410 \| 1.2212 \| 0.7681 \| 0.7667 \| \| 0.0787 \| 3.0 \| 3615 \| 1.2381 \| 0.7685 \| 0.7667 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0a0+b5021ba - Datasets 2.6.2 - Tokenizers 0.14.1
mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF	mradermacher	2024-06-06T05:45:46Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:sophosympatheia/Wizard-Tulu-Dolphin-70B-v1.0", "base_model:quantized:sophosympatheia/Wizard-Tulu-Dolphin-70B-v1.0", "endpoints_compatible", "region:us" ]	null	2024-06-05T10:04:18Z	--- base_model: sophosympatheia/Wizard-Tulu-Dolphin-70B-v1.0 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/sophosympatheia/Wizard-Tulu-Dolphin-70B-v1.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q2_K.gguf) \| Q2_K \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.IQ3_XS.gguf) \| IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.IQ3_S.gguf) \| IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q3_K_S.gguf) \| Q3_K_S \| 30.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.IQ3_M.gguf) \| IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q3_K_M.gguf) \| Q3_K_M \| 33.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q3_K_L.gguf) \| Q3_K_L \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.IQ4_XS.gguf) \| IQ4_XS \| 37.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q4_K_S.gguf) \| Q4_K_S \| 39.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q4_K_M.gguf) \| Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q5_K_S.gguf) \| Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q5_K_M.gguf) \| Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q6_K.gguf.part2of2) \| Q6_K \| 56.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Wizard-Tulu-Dolphin-70B-v1.0-GGUF/resolve/main/Wizard-Tulu-Dolphin-70B-v1.0.Q8_0.gguf.part2of2) \| Q8_0 \| 73.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
chihhh/attack-llama-chat	chihhh	2024-06-06T05:42:27Z	6	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-06-04T11:49:11Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: attack-llama-chat 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. --> # attack-llama-chat This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 8 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.1.0+cu118 - Datasets 2.19.2 - Tokenizers 0.19.1
magnifi/parser_user_v1-0605-epoch10-0.002_user_only	magnifi	2024-06-06T05:41:27Z	76	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T05:39:41Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MatouK98/test_1	MatouK98	2024-06-06T05:36:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-06T05:36:37Z	--- license: apache-2.0 ---
PhillipGuo/hp-lat-llama-PCA-epsilon0.0-pgd_layer8_16_24-def_layer8-wikitext-33	PhillipGuo	2024-06-06T05:36:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T05:36:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
RichardErkhov/bigcode_-_tiny_starcoder_py-gguf	RichardErkhov	2024-06-06T05:28:50Z	360	1	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-06-06T05:11:37Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) tiny_starcoder_py - GGUF - Model creator: https://huggingface.co/bigcode/ - Original model: https://huggingface.co/bigcode/tiny_starcoder_py/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [tiny_starcoder_py.Q2_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q2_K.gguf) \| Q2_K \| 0.1GB \| \| [tiny_starcoder_py.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.IQ3_XS.gguf) \| IQ3_XS \| 0.1GB \| \| [tiny_starcoder_py.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.IQ3_S.gguf) \| IQ3_S \| 0.1GB \| \| [tiny_starcoder_py.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q3_K_S.gguf) \| Q3_K_S \| 0.1GB \| \| [tiny_starcoder_py.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.IQ3_M.gguf) \| IQ3_M \| 0.11GB \| \| [tiny_starcoder_py.Q3_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q3_K.gguf) \| Q3_K \| 0.11GB \| \| [tiny_starcoder_py.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q3_K_M.gguf) \| Q3_K_M \| 0.11GB \| \| [tiny_starcoder_py.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q3_K_L.gguf) \| Q3_K_L \| 0.12GB \| \| [tiny_starcoder_py.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.IQ4_XS.gguf) \| IQ4_XS \| 0.11GB \| \| [tiny_starcoder_py.Q4_0.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q4_0.gguf) \| Q4_0 \| 0.12GB \| \| [tiny_starcoder_py.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.IQ4_NL.gguf) \| IQ4_NL \| 0.12GB \| \| [tiny_starcoder_py.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q4_K_S.gguf) \| Q4_K_S \| 0.12GB \| \| [tiny_starcoder_py.Q4_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q4_K.gguf) \| Q4_K \| 0.12GB \| \| [tiny_starcoder_py.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q4_K_M.gguf) \| Q4_K_M \| 0.12GB \| \| [tiny_starcoder_py.Q4_1.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q4_1.gguf) \| Q4_1 \| 0.12GB \| \| [tiny_starcoder_py.Q5_0.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q5_0.gguf) \| Q5_0 \| 0.13GB \| \| [tiny_starcoder_py.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q5_K_S.gguf) \| Q5_K_S \| 0.13GB \| \| [tiny_starcoder_py.Q5_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q5_K.gguf) \| Q5_K \| 0.14GB \| \| [tiny_starcoder_py.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q5_K_M.gguf) \| Q5_K_M \| 0.14GB \| \| [tiny_starcoder_py.Q5_1.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q5_1.gguf) \| Q5_1 \| 0.14GB \| \| [tiny_starcoder_py.Q6_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q6_K.gguf) \| Q6_K \| 0.15GB \| \| [tiny_starcoder_py.Q8_0.gguf](https://huggingface.co/RichardErkhov/bigcode_-_tiny_starcoder_py-gguf/blob/main/tiny_starcoder_py.Q8_0.gguf) \| Q8_0 \| 0.18GB \| Original model description: --- pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python license: bigcode-openrail-m datasets: - bigcode/the-stack-dedup metrics: - code_eval library_name: transformers tags: - code model-index: - name: Tiny-StarCoder-Py results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 7.84% verified: false --- # TinyStarCoderPy This is a 164M parameters model with the same architecture as [StarCoder](https://huggingface.co/bigcode/starcoder) (8k context length, MQA & FIM). It was trained on the Python data from [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata) for ~6 epochs which amounts to 100B tokens. ## Use ### Intended use The model was trained on GitHub code, to assist with some tasks like [Assisted Generation](https://huggingface.co/blog/assisted-generation). For pure code completion, we advise using our 15B models [StarCoder]() or [StarCoderBase](). ### Generation ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/tiny_starcoder_py" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Fill-in-the-middle Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: ```python input_text = "<fim_prefix>def print_one_two_three():\n print('one')\n <fim_suffix>\n print('three')<fim_middle>" inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` # Training ## Model - Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective - Pretraining steps: 50k - Pretraining tokens: 100 billion - Precision: bfloat16 ## Hardware - GPUs: 32 Tesla A100 - Training time: 18 hours ## Software - Orchestration: [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - Neural networks: [PyTorch](https://github.com/pytorch/pytorch) - BP16 if applicable: [apex](https://github.com/NVIDIA/apex) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
hdve/google-gemma-7b-1717651341	hdve	2024-06-06T05:25:10Z	8	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T05:22:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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Iyan/2024-06-05	Iyan	2024-06-06T05:21:17Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T17:53:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LimYeri/CodeMind-Llama3-8B-unsloth_v4-one-GGUF	LimYeri	2024-06-06T05:21:12Z	11	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-06T05:10:10Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: LimYeri - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
amenon/EthcalLLM-PPO	amenon	2024-06-06T05:16:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-05T23:19:33Z	--- 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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PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer8_16_24-def_layer8-wikitext-33	PhillipGuo	2024-06-06T05:09:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T05:09:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MG31/license_aug_380_200_	MG31	2024-06-06T05:07:50Z	192	0	transformers	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	object-detection	2024-06-06T00:58:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LimYeri/CodeMind-Llama3-8B-unsloth_v4-one-merged	LimYeri	2024-06-06T05:06:02Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T05:00:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: LimYeri - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
bella05/pogny-16-0.00001	bella05	2024-06-06T05:02:11Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-06T03:39:19Z	--- base_model: klue/roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: pogny-16-0.00001 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. --> # pogny-16-0.00001 This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3664 - Accuracy: 0.7742 - F1: 0.7716 ## 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-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.1032 \| 1.0 \| 4818 \| 1.1872 \| 0.7745 \| 0.7720 \| \| 0.0797 \| 2.0 \| 9636 \| 1.2999 \| 0.7772 \| 0.7738 \| \| 0.0601 \| 3.0 \| 14454 \| 1.3664 \| 0.7742 \| 0.7716 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0a0+b5021ba - Datasets 2.6.2 - Tokenizers 0.14.1
JessicaPimentel02/BETO-finetuned-ner-1	JessicaPimentel02	2024-06-06T04:58:16Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-04T12:33:26Z	Modelo probado con batch_size=20 y numero de epocas=30, {'eval_loss': 0.07771027088165283, 'eval_precision': 0.8296777687516924, 'eval_recall': 0.8609159876369766, 'eval_f1': 0.8450082735797021, 'eval_accuracy': 0.9806882891804902, 'eval_runtime': 4.3292, 'eval_samples_per_second': 350.642, 'eval_steps_per_second': 17.555, 'epoch': 30.0}
PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer8_16_24-def_layer8-wikitext-32	PhillipGuo	2024-06-06T04:57:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:57:20Z	--- 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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John19870907/roberta-large-peft-lora	John19870907	2024-06-06T04:56:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:56:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zodiache/unaligned	zodiache	2024-06-06T04:53:51Z	182	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-08T17:00:22Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: unaligned 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. --> # unaligned 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.0709 ## 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: 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: linear - lr_scheduler_warmup_steps: 100 - training_steps: 2048 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.1154 \| 0.1110 \| 100 \| 0.1172 \| \| 0.092 \| 0.2220 \| 200 \| 0.1028 \| \| 0.0462 \| 0.3330 \| 300 \| 0.0992 \| \| 0.0482 \| 0.4440 \| 400 \| 0.0755 \| \| 0.043 \| 0.5550 \| 500 \| 0.0794 \| \| 0.0476 \| 0.6660 \| 600 \| 0.0628 \| \| 0.0482 \| 0.7770 \| 700 \| 0.0821 \| \| 0.0484 \| 0.8880 \| 800 \| 0.0691 \| \| 0.0448 \| 0.9990 \| 900 \| 0.0829 \| \| 0.0214 \| 1.1100 \| 1000 \| 0.0720 \| \| 0.0439 \| 1.2210 \| 1100 \| 0.0635 \| \| 0.0364 \| 1.3320 \| 1200 \| 0.0713 \| \| 0.0497 \| 1.4430 \| 1300 \| 0.0669 \| \| 0.0455 \| 1.5540 \| 1400 \| 0.0672 \| \| 0.0614 \| 1.6650 \| 1500 \| 0.0805 \| \| 0.0416 \| 1.7761 \| 1600 \| 0.0669 \| \| 0.0367 \| 1.8871 \| 1700 \| 0.0716 \| \| 0.0578 \| 1.9981 \| 1800 \| 0.0684 \| \| 0.0358 \| 2.1091 \| 1900 \| 0.0705 \| \| 0.0326 \| 2.2201 \| 2000 \| 0.0709 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf	RichardErkhov	2024-06-06T04:53:38Z	85	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:20:38Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) megatron-GPT-2-345m-EvolInstruct - GGUF - Model creator: https://huggingface.co/KnutJaegersberg/ - Original model: https://huggingface.co/KnutJaegersberg/megatron-GPT-2-345m-EvolInstruct/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [megatron-GPT-2-345m-EvolInstruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q2_K.gguf) \| Q2_K \| 0.17GB \| \| [megatron-GPT-2-345m-EvolInstruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.IQ3_XS.gguf) \| IQ3_XS \| 0.18GB \| \| [megatron-GPT-2-345m-EvolInstruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.IQ3_S.gguf) \| IQ3_S \| 0.19GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q3_K_S.gguf) \| Q3_K_S \| 0.19GB \| \| [megatron-GPT-2-345m-EvolInstruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.IQ3_M.gguf) \| IQ3_M \| 0.2GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q3_K.gguf) \| Q3_K \| 0.21GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q3_K_M.gguf) \| Q3_K_M \| 0.21GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q3_K_L.gguf) \| Q3_K_L \| 0.23GB \| \| [megatron-GPT-2-345m-EvolInstruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.IQ4_XS.gguf) \| IQ4_XS \| 0.22GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q4_0.gguf) \| Q4_0 \| 0.23GB \| \| [megatron-GPT-2-345m-EvolInstruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.IQ4_NL.gguf) \| IQ4_NL \| 0.23GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q4_K_S.gguf) \| Q4_K_S \| 0.23GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q4_K.gguf) \| Q4_K \| 0.25GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q4_K_M.gguf) \| Q4_K_M \| 0.25GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q4_1.gguf) \| Q4_1 \| 0.25GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q5_0.gguf) \| Q5_0 \| 0.27GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q5_K_S.gguf) \| Q5_K_S \| 0.27GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q5_K.gguf) \| Q5_K \| 0.29GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q5_K_M.gguf) \| Q5_K_M \| 0.29GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q5_1.gguf) \| Q5_1 \| 0.29GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q6_K.gguf) \| Q6_K \| 0.32GB \| \| [megatron-GPT-2-345m-EvolInstruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_megatron-GPT-2-345m-EvolInstruct-gguf/blob/main/megatron-GPT-2-345m-EvolInstruct.Q8_0.gguf) \| Q8_0 \| 0.41GB \| Original model description: --- license: cc-by-nc-4.0 --- Fine tuned small megatron-GPT-2 on Evol-Instruct dataset of WizardLM. Megatron-GPT-2-345m is competetive with GPT-2 large in some benchmarks. Megatron-GPT-2-345m performance on Wikitext-103 is 19.31, GPT2-large (1.5B) is 17.48 and GPT2-medium (762M) is 22.05. Code example: from transformers import AutoModel, GPT2LMHeadModel, AutoTokenizer model2 = GPT2LMHeadModel.from_pretrained('KnutJaegersberg/megatron-GPT-2-345m-EvolInstruct') tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/megatron-GPT-2-345m-EvolInstruct") text = "Below is an instruction that describes a task. Write a response that appropriately completes the request. \n\n### Instruction:\n Give me a list of 5 political commentators, compare and contrast their beliefs. \n### Response:\n" encoded_input = tokenizer(text, return_tensors='pt').input_ids.to('cuda:0') from transformers import pipeline generator = pipeline('text-generation', model=model2, tokenizer=tokenizer, device=0) generator(text, penalty_alpha=0.6, top_k=4, max_length=1000) output: "1. Rush Limbaugh - Religious conservative\n2. Bernard Adjaye - African American\n3. Juan Williams - Hispanic\n4. Rachel Maddow - Journalist\n5. Bill O'Reilly - Political Commentator\nLimbaugh and Adjaye are both religious conservative politicians, but they differ in their beliefs on issues such as abortion, same-sex marriage, and immigration. Adjaye is a strong advocate for gun rights and has been criticized for his pro-choice stance on social issues. However, he has also been critical of President Trump's handling of the COVID-19 pandemic and has suggested that the country needs to focus on mental health issues rather than politics.\nMaddow, on the other hand, is a journalist and talk show host who has made a name for herself in the media. She has been critical of Trump's handling of the COVID-19 pandemic and has suggested that the country needs to focus on mental health issues rather than politics. However, she has also been critical of the government's response to the pandemic and has suggested that the government needs to do more to address the issue.\nO'Reilly, on the other hand, is a political commentary and talk show host who has made a name for himself in the media. He has been critical of Trump's handling of the COVID-19 pandemic and has suggested that the country needs to focus on mental health issues rather than politics. However, he has also been critical of the government's response to the pandemic and has suggested that the country needs to take care of its citizens who are sick.\nOverall, each of these political commentators has their own unique perspective on the political landscape, and their beliefs are shaped by a variety of factors, including their religious beliefs, political affiliations, and personal experiences. It is important to note that these opinions are not universal and may vary across different demographics and regions of the country. Therefore, it is always best to consult with a qualified political analyst or news organization for a comprehensive understanding of the political landscape. Additionally, it is important to be respectful of others' opinions and not try to influence them. By doing so, we can work together to create a more just and equitable society for all.\nSources:\nLimbaugh, R. (2020). The rise of religion in America. Christianity Today, www.cchurch.com/content/dam/2021/08/the-rise-of-religion-in-america. Retrieved from https://www. ChristianityToday.com/blog/how-religion-is-becoming-a-part-of-america/\nAdjaye, B. (2020). Black Lives Matter: A Call to Action. National Book Critics, www.nrdc.org/books/britannica/article/2020/08/black-lives-matter-a-call-to-action.html\nWright, J. (2020). Climate change and the economy. American Psychological Association, www.apa.org/publication/climate-change-and-economy/2020/08/council-member-wright-jeff-kincaid-reviews-opinions-on-policies-to-reform-climate-change.html\nMegan, M. (2020). The future of healthcare: What we know and don't know. Healthline, www.healthline.com/healthline/2020/08/what-we-know-and-don't-know.html\nO'Reilly, R. (2020). Donald Trump's presidency. Fox News, www.foxnews.com/politics/presidential-race.mp3\nMaddow, R. (2020). The media is biased against the right wing. The New York Times, www.nytimes.com/2020/08/29/us/politics/the-media-is-biased-against-the-right-wing.html\nO'Reilly, R. (2020). The 2020 U.S. presidential election. CNN, www.cnn.com/2020/08/29/us/politics/the-2020-presidential-election.html\nMaddow, M. (2020). The COVID-19 pandemic is a wake-up call for the world. The Wall Street Journal, www.bloomberg.com/news/2020/08/causes-and-benefits-of-the-coVID-19-vaccine.html\nO'Reilly, R. (2020). It's time to get" # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__megatron-GPT-2-345m-EvolInstruct) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 26.35 \| \| ARC (25-shot) \| 24.06 \| \| HellaSwag (10-shot) \| 35.12 \| \| MMLU (5-shot) \| 24.48 \| \| TruthfulQA (0-shot) \| 41.25 \| \| Winogrande (5-shot) \| 54.78 \| \| GSM8K (5-shot) \| 0.38 \| \| DROP (3-shot) \| 4.39 \|
yzhuang/Qwen1.5-7B-Chat-v0.1_fictional_Korean_v1	yzhuang	2024-06-06T04:52:31Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:Qwen/Qwen1.5-7B-Chat", "base_model:finetune:Qwen/Qwen1.5-7B-Chat", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T21:20:37Z	--- license: other base_model: Qwen/Qwen1.5-7B-Chat tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: Qwen1.5-7B-Chat-v0.1_fictional_Korean_v1 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. --> # Qwen1.5-7B-Chat-v0.1_fictional_Korean_v1 This model is a fine-tuned version of [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Tan1001/My-Voice	Tan1001	2024-06-06T04:49:50Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-06T04:49:50Z	--- license: apache-2.0 ---
Sorour/llama3-base-ft-fomc	Sorour	2024-06-06T04:47:21Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T04:41:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_lora2	tyzhu	2024-06-06T04:40:48Z	4	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-04T14:01:20Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa metrics: - accuracy model-index: - name: lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa type: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa metrics: - name: Accuracy type: accuracy value: 0.7780232896652111 library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa dataset. It achieves the following results on the evaluation set: - Loss: 0.4804 - Accuracy: 0.7780 ## 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: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-------:\|:-----:\|:--------:\|:---------------:\| \| 1.5635 \| 0.9998 \| 1089 \| 0.6796 \| 1.4615 \| \| 1.4521 \| 1.9995 \| 2178 \| 0.6874 \| 1.3626 \| \| 1.2848 \| 2.9993 \| 3267 \| 0.6958 \| 1.2575 \| \| 1.1197 \| 4.0 \| 4357 \| 0.7054 \| 1.1527 \| \| 0.9756 \| 4.9998 \| 5446 \| 0.7143 \| 1.0532 \| \| 0.8393 \| 5.9995 \| 6535 \| 0.7241 \| 0.9538 \| \| 0.7125 \| 6.9993 \| 7624 \| 0.7324 \| 0.8674 \| \| 0.6144 \| 8.0 \| 8714 \| 0.7404 \| 0.7907 \| \| 0.5355 \| 8.9998 \| 9803 \| 0.7469 \| 0.7288 \| \| 0.4584 \| 9.9977 \| 10890 \| 0.7531 \| 0.6794 \| \| 0.413 \| 10.9998 \| 11979 \| 0.7577 \| 0.6292 \| \| 0.3731 \| 11.9995 \| 13068 \| 0.7616 \| 0.5926 \| \| 0.3423 \| 12.9993 \| 14157 \| 0.7656 \| 0.5620 \| \| 0.3185 \| 14.0 \| 15247 \| 0.7682 \| 0.5426 \| \| 0.2924 \| 14.9998 \| 16336 \| 0.7708 \| 0.5232 \| \| 0.2824 \| 15.9995 \| 17425 \| 0.7727 \| 0.5129 \| \| 0.2669 \| 16.9993 \| 18514 \| 0.7748 \| 0.4988 \| \| 0.2517 \| 18.0 \| 19604 \| 0.7762 \| 0.4892 \| \| 0.2376 \| 18.9998 \| 20693 \| 0.7773 \| 0.4808 \| \| 0.2316 \| 19.9977 \| 21780 \| 0.7780 \| 0.4804 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer8_16_24-def_layer8-wikitext-30	PhillipGuo	2024-06-06T04:40:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:40:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
FuturisticVibes/dolphin-2.9.2-mixtral-8x22b-8.0bpw-h8-exl2	FuturisticVibes	2024-06-06T04:40:32Z	10	2	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "generated_from_trainer", "axolotl", "conversational", "en", "dataset:cognitivecomputations/Dolphin-2.9.2", "dataset:cognitivecomputations/SystemChat-2.0", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:microsoft/orca-math-word-problems-200k", "dataset:abacusai/SystemChat-1.1", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:mistral-community/Mixtral-8x22B-v0.1", "base_model:quantized:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-06-06T03:44:26Z	--- license: apache-2.0 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - generated_from_trainer - axolotl model-index: - name: out results: [] datasets: - cognitivecomputations/Dolphin-2.9.2 - cognitivecomputations/SystemChat-2.0 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - HuggingFaceH4/ultrachat_200k - microsoft/orca-math-word-problems-200k - abacusai/SystemChat-1.1 - Locutusque/function-calling-chatml - internlm/Agent-FLAN language: - en --- I have no idea what I’m doing… if this causes the apocalypse someone please let me know. For the bold, the brave, the reckless, the rich, and the criminally insane, I present… dolphin-2.9.2-mixtral-8x22b 8.0bpw h8 EXL2 Includes [measurement.json](https://huggingface.co/FuturisticVibes/dolphin-2.9.2-mixtral-8x22b-8.0bpw-h8-exl2/tree/measurement) file for further quantization This cost so much money… Original Model: https://huggingface.co/cognitivecomputations/dolphin-2.9.2-mixtral-8x22b # Original Model Card # Dolphin 2.9.2 Mixtral 8x22b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations [![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](https://discord.gg/cognitivecomputations) Discord: https://discord.gg/cognitivecomputations <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> New in 2.9.2 is SystemChat 2.0 - a dataset designed to teach Dolphin to obey the system prompt, even over a long conversation. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/z1u6U91tL-H__7JCDbWys.png) My appreciation for the sponsors of Dolphin 2.9.2: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node - [OnDemand](https://on-demand.io/) - provided inference sponsorship, enabling creation of SystemChat This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed. The base model has 64k context, and fine-tuning was with 16k sequence length. It took 1 week on 8xH100 provided by Crusoe Cloud This model was trained FFT on 50% parameters (targeted with [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py) by Fernando Fernandes, David Golchinfar, Lucas Atkins, and Eric Hartford), using ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed Apache 2.0. I grant permission for any use, including commercial, that falls within accordance with Apache-2.0 license. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/SDWV3SvJ8xR1gjl1z0LyO.png) ## Training
PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer8_16_24-def_layer8-wikitext-30	PhillipGuo	2024-06-06T04:40:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:40:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer8_16_24-def_layer8-wikitext-29	PhillipGuo	2024-06-06T04:40:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:39:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aks1s/18Aks-16	aks1s	2024-06-06T04:39:36Z	130	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T04:38:32Z	--- license: apache-2.0 ---
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer8_16_24-def_layer8-wikitext-29	PhillipGuo	2024-06-06T04:39:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:39:09Z	--- 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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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]
aks1s/17Aks-16	aks1s	2024-06-06T04:36:16Z	130	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T04:35:09Z	--- license: apache-2.0 ---
aks1s/16Aks-16	aks1s	2024-06-06T04:32:47Z	130	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T04:31:45Z	--- license: apache-2.0 ---
Ponrudee/yolov10-finetuned-X-Ray-Baggage-3	Ponrudee	2024-06-06T04:30:42Z	0	0	ultralytics	[ "ultralytics", "safetensors", "object-detection", "yolov10", "pytorch_model_hub_mixin", "model_hub_mixin", "region:us" ]	object-detection	2024-06-06T04:30:38Z	--- library_name: ultralytics tags: - object-detection - yolov10 - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/THU-MIG/yolov10 - Docs: [More Information Needed]
RichardErkhov/crumb_-_gpt2023-gguf	RichardErkhov	2024-06-06T04:30:15Z	30	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:16:51Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gpt2023 - GGUF - Model creator: https://huggingface.co/crumb/ - Original model: https://huggingface.co/crumb/gpt2023/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [gpt2023.Q2_K.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q2_K.gguf) \| Q2_K \| 0.08GB \| \| [gpt2023.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.IQ3_XS.gguf) \| IQ3_XS \| 0.08GB \| \| [gpt2023.IQ3_S.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.IQ3_S.gguf) \| IQ3_S \| 0.08GB \| \| [gpt2023.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q3_K_S.gguf) \| Q3_K_S \| 0.08GB \| \| [gpt2023.IQ3_M.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.IQ3_M.gguf) \| IQ3_M \| 0.09GB \| \| [gpt2023.Q3_K.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q3_K.gguf) \| Q3_K \| 0.09GB \| \| [gpt2023.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q3_K_M.gguf) \| Q3_K_M \| 0.09GB \| \| [gpt2023.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q3_K_L.gguf) \| Q3_K_L \| 0.1GB \| \| [gpt2023.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.IQ4_XS.gguf) \| IQ4_XS \| 0.1GB \| \| [gpt2023.Q4_0.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q4_0.gguf) \| Q4_0 \| 0.1GB \| \| [gpt2023.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.IQ4_NL.gguf) \| IQ4_NL \| 0.1GB \| \| [gpt2023.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q4_K_S.gguf) \| Q4_K_S \| 0.1GB \| \| [gpt2023.Q4_K.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q4_K.gguf) \| Q4_K \| 0.11GB \| \| [gpt2023.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q4_K_M.gguf) \| Q4_K_M \| 0.11GB \| \| [gpt2023.Q4_1.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q4_1.gguf) \| Q4_1 \| 0.11GB \| \| [gpt2023.Q5_0.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q5_0.gguf) \| Q5_0 \| 0.11GB \| \| [gpt2023.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q5_K_S.gguf) \| Q5_K_S \| 0.11GB \| \| [gpt2023.Q5_K.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q5_K.gguf) \| Q5_K \| 0.12GB \| \| [gpt2023.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q5_K_M.gguf) \| Q5_K_M \| 0.12GB \| \| [gpt2023.Q5_1.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q5_1.gguf) \| Q5_1 \| 0.12GB \| \| [gpt2023.Q6_K.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q6_K.gguf) \| Q6_K \| 0.13GB \| \| [gpt2023.Q8_0.gguf](https://huggingface.co/RichardErkhov/crumb_-_gpt2023-gguf/blob/main/gpt2023.Q8_0.gguf) \| Q8_0 \| 0.17GB \| Original model description: --- license: mit language: - en tags: - causal-lm --- # GPT2(023) Model Card This is the smallest GPT-2 model (124m) from OpenAi finetuned on approximately 2.23B tokens (almost the 2.48B needed to 'chinchilla-optimally' pretrain it! It's also more tokens than Cerebras-GPT-111M was trained on in total) consisting of 1.3B from common crawl sites from 2023, 540M from ArXiv, and 390M from GitHub. The model was trained with a learning rate of 1e-4, with a warmup of 1024 steps, then decaying to 0. There were 4400 total steps during training at a batch size of 512 examples with a context length of 1024. The batch size and context length are the same as the pre-training of GPT2 itself. Training took a total of 1.18e+18 FLOs over the course of 79.32 hours locally with a 12gb RTX3060. Final train loss was 2.73. ### Evaluation of GPT2023 (in progress) \| model \| piqa acc \| winogrande acc \| lambada ppl \| lambada acc \| arc acc \| sciq acc \| wsc acc \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| pythia-70m \| 59.85 \| 51.22 \| 140.81 \| 21.40 \| 17.15 \| 65.00 \| 36.53 \| \| pythia-160m \| 62.68 \| 51.07 \| 30.03 \| 36.76 \| 19.62 \| 76.20 \| 36.58 \| \| pythia-410m \| 66.54 \| 52.24 \| 11.75 \| 49.93 \| 21.67 \| 80.80 \| 60.58 \| \| opt-125m \| 63.00 \| 50.27 \| 26.02 \| 37.90 \| 18.94 \| 75.1 \| 36.54 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| gpt2 (124m) \| 62.89 \| 51.61 \| 40.06 \| 32.56 \| 19.03 \| 75 \| 43.27 \| \| gpt2023 (124m) \| 62.02 \| 49.64 \| 34.55 \| 33.98 \| 18.94 \| 76.1 \| 36.54 \| The resulting model achieves a puplexity of 339.38, making it competative with Cerebras-590m with only 21% of the parameters, and much better than the original GPT-2 which scores 491.57! (metric explanation here: https://twitter.com/aicrumb/status/1650350363898265601 , tldr it's a joke) To demonstrate how GPT2(023) is aware of recent events, let’s take a look at a given example: ``` # About Covid-19 - - The Covid-19 ``` The model completes the text as: ``` # About Covid-19 - - The Covid-19 pandemic is the worldwide pandemic that has left thousands of people unable to enter and work in or continue their normal daily normal life. In this brief post, we examine three of the main factors that have accelerated the pandemic and predict the path the pandemic will take through the rest of the world. ``` As you can see, GPT2(023) can generate coherent and relevant text pertaining to the Covid-19 pandemic, showcasing its ability to understand recent events. However, it struggles with certain subjects that weren’t extremely relevant in it’s training data. As only 2.23 billion tokens were used during finetuning, the model may have missed out on many recent events. One of those events being the latest US election. Given text in a question and answer format: ``` Q: Who is the last president? A: Donald Trump Q: Who is the most recent president? A: ``` The model completes the text with: `Barack Obama` ### Model description (from GPT-2 model card) GPT-2 is a transformer model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. This is the smallest version of GPT-2, with 124M parameters. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='crumb/gpt2023') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('crumb/gpt2023') model = GPT2Model.from_pretrained('crumb/gpt2023') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_crumb__gpt2023) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 24.85 \| \| ARC (25-shot) \| 21.93 \| \| HellaSwag (10-shot) \| 31.11 \| \| MMLU (5-shot) \| 25.05 \| \| TruthfulQA (0-shot) \| 40.71 \| \| Winogrande (5-shot) \| 50.12 \| \| GSM8K (5-shot) \| 0.3 \| \| DROP (3-shot) \| 4.73 \|
aks1s/15Aks-16	aks1s	2024-06-06T04:29:23Z	130	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T04:28:20Z	--- license: apache-2.0 ---
vinhtran2611/test_finetune_llama3_alpaca	vinhtran2611	2024-06-06T04:26:06Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:25:58Z	--- library_name: transformers tags: - unsloth --- # 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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aks1s/13Aks-16	aks1s	2024-06-06T04:22:20Z	130	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T04:21:12Z	--- license: apache-2.0 ---
AidanNell24/tinyllama-codewello	AidanNell24	2024-06-06T04:20:01Z	144	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T04:17:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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abhayesian/LLama2_HarmBench_LAT_3	abhayesian	2024-06-06T04:18:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T04:18:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf	RichardErkhov	2024-06-06T04:17:24Z	13	0	null	[ "gguf", "arxiv:2403.17297", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-05T21:38:39Z	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-chat-20b-sft - GGUF - Model creator: https://huggingface.co/internlm/ - Original model: https://huggingface.co/internlm/internlm2-chat-20b-sft/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [internlm2-chat-20b-sft.Q2_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q2_K.gguf) \| Q2_K \| 7.03GB \| \| [internlm2-chat-20b-sft.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.IQ3_XS.gguf) \| IQ3_XS \| 7.79GB \| \| [internlm2-chat-20b-sft.IQ3_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.IQ3_S.gguf) \| IQ3_S \| 8.2GB \| \| [internlm2-chat-20b-sft.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q3_K_S.gguf) \| Q3_K_S \| 8.16GB \| \| [internlm2-chat-20b-sft.IQ3_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.IQ3_M.gguf) \| IQ3_M \| 8.5GB \| \| [internlm2-chat-20b-sft.Q3_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q3_K.gguf) \| Q3_K \| 9.05GB \| \| [internlm2-chat-20b-sft.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q3_K_M.gguf) \| Q3_K_M \| 9.05GB \| \| [internlm2-chat-20b-sft.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q3_K_L.gguf) \| Q3_K_L \| 9.83GB \| \| [internlm2-chat-20b-sft.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.IQ4_XS.gguf) \| IQ4_XS \| 10.12GB \| \| [internlm2-chat-20b-sft.Q4_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q4_0.gguf) \| Q4_0 \| 10.55GB \| \| [internlm2-chat-20b-sft.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.IQ4_NL.gguf) \| IQ4_NL \| 10.65GB \| \| [internlm2-chat-20b-sft.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q4_K_S.gguf) \| Q4_K_S \| 10.62GB \| \| [internlm2-chat-20b-sft.Q4_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q4_K.gguf) \| Q4_K \| 11.16GB \| \| [internlm2-chat-20b-sft.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q4_K_M.gguf) \| Q4_K_M \| 11.16GB \| \| [internlm2-chat-20b-sft.Q4_1.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q4_1.gguf) \| Q4_1 \| 11.67GB \| \| [internlm2-chat-20b-sft.Q5_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q5_0.gguf) \| Q5_0 \| 12.79GB \| \| [internlm2-chat-20b-sft.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q5_K_S.gguf) \| Q5_K_S \| 12.79GB \| \| [internlm2-chat-20b-sft.Q5_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q5_K.gguf) \| Q5_K \| 13.11GB \| \| [internlm2-chat-20b-sft.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q5_K_M.gguf) \| Q5_K_M \| 13.11GB \| \| [internlm2-chat-20b-sft.Q5_1.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q5_1.gguf) \| Q5_1 \| 13.91GB \| \| [internlm2-chat-20b-sft.Q6_K.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q6_K.gguf) \| Q6_K \| 15.18GB \| \| [internlm2-chat-20b-sft.Q8_0.gguf](https://huggingface.co/RichardErkhov/internlm_-_internlm2-chat-20b-sft-gguf/blob/main/internlm2-chat-20b-sft.Q8_0.gguf) \| Q8_0 \| 19.66GB \| Original model description: --- pipeline_tag: text-generation license: other --- # InternLM <div align="center"> <img src="https://github.com/InternLM/InternLM/assets/22529082/b9788105-8892-4398-8b47-b513a292378e" width="200"/> <div> </div> <div align="center"> <b><font size="5">InternLM</font></b> <sup> <a href="https://internlm.intern-ai.org.cn/"> <i><font size="4">HOT</font></i> </a> </sup> <div> </div> </div> [![evaluation](https://github.com/InternLM/InternLM/assets/22529082/f80a2a58-5ddf-471a-8da4-32ab65c8fd3b)](https://github.com/internLM/OpenCompass/) [💻Github Repo](https://github.com/InternLM/InternLM) • [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new) • [📜Technical Report](https://arxiv.org/abs/2403.17297) </div> ## Introduction InternLM2 has open-sourced a 20 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics: - 200K Context window: Nearly perfect at finding needles in the haystack with 200K-long context, with leading performance on long-context tasks like LongBench and L-Eval. Try it with [LMDeploy](https://github.com/InternLM/lmdeploy) for 200K-context inference. - Outstanding comprehensive performance: Significantly better than the last generation in all dimensions, especially in reasoning, math, code, chat experience, instruction following, and creative writing, with leading performance among open-source models in similar sizes. In some evaluations, InternLM2-Chat-20B may match or even surpass ChatGPT (GPT-3.5). - Code interpreter & Data analysis: With code interpreter, InternLM2-Chat-20B obtains compatible performance with GPT-4 on GSM8K and MATH. InternLM2-Chat also provides data analysis capability. - Stronger tool use: Based on better tool utilization-related capabilities in instruction following, tool selection and reflection, InternLM2 can support more kinds of agents and multi-step tool calling for complex tasks. See [examples](https://github.com/InternLM/lagent). ## InternLM2-Chat-20B-SFT InternLM2-Chat-20B-SFT is the SFT version based on InternLM2-Base 20B, and InternLM2-Chat-20B is further trained from InternLM2-Chat-20B-SFT by Online RLHF. We release the SFT version so that the community can study the influence of RLHF deeply. ### Performance Evaluation We conducted a comprehensive evaluation of InternLM2 using the open-source evaluation tool [OpenCompass](https://github.com/internLM/OpenCompass/). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard](https://opencompass.org.cn/rank) for more evaluation results. \| Dataset\Models \| InternLM2-7B \| InternLM2-Chat-7B \| InternLM2-20B \| InternLM2-Chat-20B \| ChatGPT \| GPT-4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| MMLU \| 65.8 \| 63.7 \| 67.7 \| 66.5 \| 69.1 \| 83.0 \| \| AGIEval \| 49.9 \| 47.2 \| 53.0 \| 50.3 \| 39.9 \| 55.1 \| \| BBH \| 65.0 \| 61.2 \| 72.1 \| 68.3 \| 70.1 \| 86.7 \| \| GSM8K \| 70.8 \| 70.7 \| 76.1 \| 79.6 \| 78.2 \| 91.4 \| \| MATH \| 20.2 \| 23.0 \| 25.5 \| 31.9 \| 28.0 \| 45.8 \| \| HumanEval \| 43.3 \| 59.8 \| 48.8 \| 67.1 \| 73.2 \| 74.4 \| \| MBPP(Sanitized) \| 51.8 \| 51.4 \| 63.0 \| 65.8 \| 78.9 \| 79.0 \| - The evaluation results were obtained from [OpenCompass](https://github.com/internLM/OpenCompass/) (some data marked with , which means come from the original papers), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/). - The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/internLM/OpenCompass/), so please refer to the latest evaluation results of [OpenCompass](https://github.com/internLM/OpenCompass/). Limitations:* Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information. ### Import from Transformers To load the InternLM 20B Chat SFT model using Transformers, use the following code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-20b-sft", trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error. model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-20b-sft", torch_dtype=torch.float16, trust_remote_code=True).cuda() model = model.eval() response, history = model.chat(tokenizer, "hello", history=[]) print(response) # Hello! How can I help you today? response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history) print(response) ``` The responses can be streamed using `stream_chat`: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "internlm/internlm2-chat-20b-sft" model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = model.eval() length = 0 for response, history in model.stream_chat(tokenizer, "Hello", history=[]): print(response[length:], flush=True, end="") length = len(response) ``` ## Deployment ### LMDeploy LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. ```bash pip install lmdeploy ``` You can run batch inference locally with the following python code: ```python import lmdeploy pipe = lmdeploy.pipeline("internlm/internlm2-chat-20b-sft") response = pipe(["Hi, pls intro yourself", "Shanghai is"]) print(response) ``` Or you can launch an OpenAI compatible server with the following command: ```bash lmdeploy serve api_server internlm/internlm2-chat-20b-sft --model-name internlm2-chat-20b-sft --server-port 23333 ``` Then you can send a chat request to the server: ```bash curl http://localhost:23333/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-20b-sft", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Introduce deep learning to me."} ] }' ``` Find more details in the [LMDeploy documentation](https://lmdeploy.readthedocs.io/en/latest/) ### vLLM Launch OpenAI compatible server with `vLLM>=0.3.2`: ```bash pip install vllm ``` ```bash python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-20b-sft --served-model-name internlm2-chat-20b-sft --trust-remote-code ``` Then you can send a chat request to the server: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-20b-sft", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Introduce deep learning to me."} ] }' ``` Find more details in the [vLLM documentation](https://docs.vllm.ai/en/latest/index.html) ## Open Source License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the [application form (English)](https://wj.qq.com/s2/12727483/5dba/)/[申请表（中文）](https://wj.qq.com/s2/12725412/f7c1/). For other questions or collaborations, please contact <[email protected]>. ## Citation ``` @misc{cai2024internlm2, title={InternLM2 Technical Report}, author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin}, year={2024}, eprint={2403.17297}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## 简介 InternLM2 ，即书生·浦语大模型第二代，开源了面向实用场景的200亿参数基础模型与对话模型（InternLM2-Chat-20B）。模型具有以下特点： - 有效支持20万字超长上下文：模型在20万字长输入中几乎完美地实现长文“大海捞针”，而且在 LongBench 和 L-Eval 等长文任务中的表现也达到开源模型中的领先水平。可以通过 [LMDeploy](https://github.com/InternLM/lmdeploy) 尝试20万字超长上下文推理。 - 综合性能全面提升：各能力维度相比上一代模型全面进步，在推理、数学、代码、对话体验、指令遵循和创意写作等方面的能力提升尤为显著，综合性能达到同量级开源模型的领先水平，在重点能力评测上 InternLM2-Chat-20B 能比肩甚至超越 ChatGPT （GPT-3.5）。 - 代码解释器与数据分析：在配合代码解释器（code-interpreter）的条件下，InternLM2-Chat-20B 在 GSM8K 和 MATH 上可以达到和 GPT-4 相仿的水平。基于在数理和工具方面强大的基础能力，InternLM2-Chat 提供了实用的数据分析能力。 - 工具调用能力整体升级：基于更强和更具有泛化性的指令理解、工具筛选与结果反思等能力，新版模型可以更可靠地支持复杂智能体的搭建，支持对工具进行有效的多轮调用，完成较复杂的任务。可以查看更多[样例](https://github.com/InternLM/lagent)。 ## InternLM2-Chat-20B-SFT InternLM2-Chat-20B-SFT 基于 InternLM2-Base-20B 经过有监督微调（SFT）训练而来，InternLM2-Chat-20B 在 InternLM2-Chat-20B-SFT 的基础上进一步经历了 Online RLHF。我们开源 SFT 模型以便利社区对 RLHF 的研究。 ### 性能评测我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://opencompass.org.cn/rank)获取更多的评测结果。 \| 评测集\模型 \| InternLM2-7B \| InternLM2-Chat-7B \| InternLM2-20B \| InternLM2-Chat-20B \| ChatGPT \| GPT-4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| MMLU \| 65.8 \| 63.7 \| 67.7 \| 66.5 \| 69.1 \| 83.0 \| \| AGIEval \| 49.9 \| 47.2 \| 53.0 \| 50.3 \| 39.9 \| 55.1 \| \| BBH \| 65.0 \| 61.2 \| 72.1 \| 68.3 \| 70.1 \| 86.7 \| \| GSM8K \| 70.8 \| 70.7 \| 76.1 \| 79.6 \| 78.2 \| 91.4 \| \| MATH \| 20.2 \| 23.0 \| 25.5 \| 31.9 \| 28.0 \| 45.8 \| \| HumanEval \| 43.3 \| 59.8 \| 48.8 \| 67.1 \| 73.2 \| 74.4 \| \| MBPP(Sanitized) \| 51.8 \| 51.4 \| 63.0 \| 65.8 \| 78.9 \| 79.0 \| - 以上评测结果基于 [OpenCompass](https://github.com/internLM/OpenCompass/) 获得（部分数据标注``代表数据来自原始论文），具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。 - 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。局限性：* 尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。 ### 通过 Transformers 加载通过以下的代码加载 InternLM 20B Chat SFT 模型 ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-20b-sft", trust_remote_code=True) # `torch_dtype=torch.float16` 可以令模型以 float16 精度加载，否则 transformers 会将模型加载为 float32，导致显存不足 model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-20b-sft", torch_dtype=torch.float16, trust_remote_code=True).cuda() model = model.eval() response, history = model.chat(tokenizer, "你好", history=[]) print(response) # 你好！有什么我可以帮助你的吗？ response, history = model.chat(tokenizer, "请提供三个管理时间的建议。", history=history) print(response) ``` 如果想进行流式生成，则可以使用 `stream_chat` 接口： ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "internlm/internlm2-chat-20b-sft" model = AutoModelForCausalLM.from_pretrained(model_path, torch_dype=torch.float16, trust_remote_code=True).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = model.eval() length = 0 for response, history in model.stream_chat(tokenizer, "你好", history=[]): print(response[length:], flush=True, end="") length = len(response) ``` ## 部署 ### LMDeploy LMDeploy 由 MMDeploy 和 MMRazor 团队联合开发，是涵盖了 LLM 任务的全套轻量化、部署和服务解决方案。 ```bash pip install lmdeploy ``` 你可以使用以下 python 代码进行本地批量推理: ```python import lmdeploy pipe = lmdeploy.pipeline("internlm/internlm2-chat-20b-sft") response = pipe(["Hi, pls intro yourself", "Shanghai is"]) print(response) ``` 或者你可以使用以下命令启动兼容 OpenAI API 的服务: ```bash lmdeploy serve api_server internlm/internlm2-chat-20b-sft --server-port 23333 ``` 然后你可以向服务端发起一个聊天请求: ```bash curl http://localhost:23333/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-20b-sft", "messages": [ {"role": "system", "content": "你是个友善的AI助手。"}, {"role": "user", "content": "介绍一下深度学习。"} ] }' ``` 更多信息请查看 [LMDeploy 文档](https://lmdeploy.readthedocs.io/en/latest/) ### vLLM 使用`vLLM>=0.3.2`启动兼容 OpenAI API 的服务: ```bash pip install vllm ``` ```bash python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-20b-sft --trust-remote-code ``` 然后你可以向服务端发起一个聊天请求: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-20b-sft", "messages": [ {"role": "system", "content": "你是个友善的AI助手。"}, {"role": "user", "content": "介绍一下深度学习。"} ] }' ``` 更多信息请查看 [vLLM 文档](https://docs.vllm.ai/en/latest/index.html) ## 开源许可证本仓库的代码依照 Apache-2.0 协议开源。模型权重对学术研究完全开放，也可申请免费的商业使用授权（[申请表](https://wj.qq.com/s2/12725412/f7c1/)）。其他问题与合作请联系 <[email protected]>。 ## 引用 ``` @misc{cai2024internlm2, title={InternLM2 Technical Report}, author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin}, year={2024}, eprint={2403.17297}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
magnifi/parser_user_v1-0605-epoch20-0.002_user_only	magnifi	2024-06-06T04:14:02Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T04:12:10Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ansilmbabl/vit-base-patch16-224-in21k-cards-base-classifier-defects-finder	ansilmbabl	2024-06-06T04:12:47Z	220	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-05T07:04:42Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-cards-base-classifier-defects-finder 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. --> # vit-base-patch16-224-in21k-cards-base-classifier-defects-finder This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0683 - Accuracy: 0.999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 1.4892 \| 0.9929 \| 70 \| 1.3366 \| 0.859 \| \| 0.4362 \| 2.0 \| 141 \| 0.4142 \| 0.971 \| \| 0.231 \| 2.9929 \| 211 \| 0.2250 \| 0.988 \| \| 0.1654 \| 4.0 \| 282 \| 0.1687 \| 0.982 \| \| 0.1289 \| 4.9929 \| 352 \| 0.1322 \| 0.991 \| \| 0.0999 \| 6.0 \| 423 \| 0.1184 \| 0.988 \| \| 0.0824 \| 6.9929 \| 493 \| 0.0852 \| 0.996 \| \| 0.0789 \| 8.0 \| 564 \| 0.0809 \| 0.998 \| \| 0.07 \| 8.9929 \| 634 \| 0.0723 \| 0.997 \| \| 0.067 \| 9.9291 \| 700 \| 0.0683 \| 0.999 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
wheeee/wheeee	wheeee	2024-06-06T04:09:25Z	195	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-06T04:09:00Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: wheeee results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8059701323509216 --- # wheeee Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)
embunna/resnet-18-finetuned	embunna	2024-06-06T04:06:08Z	236	0	transformers	[ "transformers", "tensorboard", "safetensors", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-18", "base_model:finetune:microsoft/resnet-18", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-06T04:05:40Z	--- license: apache-2.0 base_model: microsoft/resnet-18 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: resnet-18-finetuned results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.3645833333333333 --- <!-- 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. --> # resnet-18-finetuned This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 48006217018842836977297404198912.0000 - Accuracy: 0.3646 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------------------------------:\|:------:\|:----:\|:-------------------------------------:\|:--------:\| \| No log \| 0.8889 \| 6 \| 48006217018842836977297404198912.0000 \| 0.3646 \| \| 50170425382569737119999364956160.0000 \| 1.9259 \| 13 \| 48006217018842836977297404198912.0000 \| 0.3646 \| \| 50170425382569737119999364956160.0000 \| 2.6667 \| 18 \| 48006217018842836977297404198912.0000 \| 0.3646 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
han439/distilbert-base-uncased-distilled-clinc	han439	2024-06-06T04:02:31Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-06T01:24:16Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc 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. --> # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.0065 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 318 \| 1.8065 \| 0.7381 \| \| 2.1458 \| 2.0 \| 636 \| 0.9402 \| 0.8710 \| \| 2.1458 \| 3.0 \| 954 \| 0.5425 \| 0.9155 \| \| 0.8421 \| 4.0 \| 1272 \| 0.3809 \| 0.9329 \| \| 0.3795 \| 5.0 \| 1590 \| 0.3137 \| 0.9390 \| \| 0.3795 \| 6.0 \| 1908 \| 0.2874 \| 0.9432 \| \| 0.2481 \| 7.0 \| 2226 \| 0.2729 \| 0.9435 \| \| 0.2037 \| 8.0 \| 2544 \| 0.2650 \| 0.9445 \| \| 0.2037 \| 9.0 \| 2862 \| nan \| 0.0065 \| \| 0.1903 \| 10.0 \| 3180 \| nan \| 0.0065 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu118 - Datasets 2.19.2 - Tokenizers 0.19.1
hdve/Qwen-Qwen1.5-7B-1717646194	hdve	2024-06-06T03:59:49Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T03:57:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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johnpaulbin/e2wire-phi3-mini-4	johnpaulbin	2024-06-06T03:58:29Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-06T03:56:52Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: johnpaulbin - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
iskhare/model_5k_out	iskhare	2024-06-06T03:54:39Z	1	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "diffusers-training", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-06-06T00:17:07Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - diffusers-training base_model: stabilityai/stable-diffusion-2-1-base inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-iskhare/model_5k_out These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. ## 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]
vietgpt/sailor-1.8B	vietgpt	2024-06-06T03:53:22Z	148	5	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T03:49:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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bobbyw/deberta-v3-large_v3_mentioned_common_entities	bobbyw	2024-06-06T03:51:35Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:bobbyw/deberta-v3-large_v3_mentioned_common_entities", "base_model:finetune:bobbyw/deberta-v3-large_v3_mentioned_common_entities", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-05T18:52:46Z	--- license: mit base_model: bobbyw/deberta-v3-large_v3_mentioned_common_entities tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: deberta-v3-large_v3_mentioned_common_entities results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deberta-v3-large_v3_mentioned_common_entities This model is a fine-tuned version of [bobbyw/deberta-v3-large_v3_mentioned_common_entities](https://huggingface.co/bobbyw/deberta-v3-large_v3_mentioned_common_entities) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1014 - Accuracy: 0.0218 - F1: 0.0427 - Precision: 0.0218 - Recall: 1.0 - Learning Rate: 0.0 ## 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: 2e-06 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Rate \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-----:\| \| 0.1523 \| 1.0 \| 689 \| 0.1039 \| 0.0218 \| 0.0427 \| 0.0218 \| 1.0 \| 0.001 \| \| 0.1404 \| 2.0 \| 1378 \| 0.1014 \| 0.0218 \| 0.0427 \| 0.0218 \| 1.0 \| 0.0 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
tsavage68/UTI_L3_1000steps_1e6rate_SFT	tsavage68	2024-06-06T03:47:58Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T15:41:53Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: UTI_L3_1000steps_1e6rate_SFT 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. --> # UTI_L3_1000steps_1e6rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9883 ## 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-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 2.5921 \| 0.3333 \| 25 \| 2.4381 \| \| 1.8551 \| 0.6667 \| 50 \| 1.5631 \| \| 1.2769 \| 1.0 \| 75 \| 1.1985 \| \| 1.1027 \| 1.3333 \| 100 \| 1.1215 \| \| 1.0509 \| 1.6667 \| 125 \| 1.1006 \| \| 0.9917 \| 2.0 \| 150 \| 1.0852 \| \| 0.9325 \| 2.3333 \| 175 \| 1.0986 \| \| 0.9627 \| 2.6667 \| 200 \| 1.0883 \| \| 0.9724 \| 3.0 \| 225 \| 1.0865 \| \| 0.7795 \| 3.3333 \| 250 \| 1.1249 \| \| 0.7455 \| 3.6667 \| 275 \| 1.1105 \| \| 0.7684 \| 4.0 \| 300 \| 1.1214 \| \| 0.6135 \| 4.3333 \| 325 \| 1.1762 \| \| 0.5911 \| 4.6667 \| 350 \| 1.2296 \| \| 0.6302 \| 5.0 \| 375 \| 1.2176 \| \| 0.4435 \| 5.3333 \| 400 \| 1.3544 \| \| 0.4558 \| 5.6667 \| 425 \| 1.3765 \| \| 0.4538 \| 6.0 \| 450 \| 1.3526 \| \| 0.2966 \| 6.3333 \| 475 \| 1.5173 \| \| 0.2836 \| 6.6667 \| 500 \| 1.5129 \| \| 0.3147 \| 7.0 \| 525 \| 1.4603 \| \| 0.2252 \| 7.3333 \| 550 \| 1.6120 \| \| 0.2143 \| 7.6667 \| 575 \| 1.6538 \| \| 0.1922 \| 8.0 \| 600 \| 1.6461 \| \| 0.1429 \| 8.3333 \| 625 \| 1.7717 \| \| 0.1491 \| 8.6667 \| 650 \| 1.8011 \| \| 0.1707 \| 9.0 \| 675 \| 1.8125 \| \| 0.1189 \| 9.3333 \| 700 \| 1.8928 \| \| 0.1274 \| 9.6667 \| 725 \| 1.9053 \| \| 0.1289 \| 10.0 \| 750 \| 1.9127 \| \| 0.111 \| 10.3333 \| 775 \| 1.9630 \| \| 0.1082 \| 10.6667 \| 800 \| 1.9689 \| \| 0.1139 \| 11.0 \| 825 \| 1.9652 \| \| 0.1062 \| 11.3333 \| 850 \| 1.9791 \| \| 0.1071 \| 11.6667 \| 875 \| 1.9866 \| \| 0.1053 \| 12.0 \| 900 \| 1.9890 \| \| 0.1087 \| 12.3333 \| 925 \| 1.9848 \| \| 0.1079 \| 12.6667 \| 950 \| 1.9866 \| \| 0.0994 \| 13.0 \| 975 \| 1.9883 \| \| 0.1007 \| 13.3333 \| 1000 \| 1.9883 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
miles0825/Qwen-Qwen1.5-1.8B-1717644578	miles0825	2024-06-06T03:37:24Z	147	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T03:29:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RavenK/TAC-ViT-base	RavenK	2024-06-06T03:36:52Z	161	0	transformers	[ "transformers", "pytorch", "safetensors", "clip_vision_model", "feature-extraction", "license:mit", "endpoints_compatible", "region:us" ]	feature-extraction	2023-11-22T02:28:52Z	--- license: mit --- # TAC depth encoder <!-- Provide a quick summary of what the model is/does. --> This model is used for encoding a depth image into a dense feature. Caution, the model does not contain the last FC layer. So, the output features are not aligned with RGB. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> The model is pre-trained with RGB-D contrastive objectives, named TAC. Different from InfoNCE-based loss fuctions, TAC leverages the similarity between videos frames and estimate a similarity matrix as soft labels. The backbone of this version is ViT-B/32. The pre-training is conducted on a new unified RGB-D database, UniRGBD. ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [TAC](https://github.com/RavenKiller/TAC) - Paper: [Learning Depth Representation from RGB-D Videos by Time-Aware Contrastive Pre-training](https://ieeexplore.ieee.org/document/10288539) ## 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 Uses ``` from transformers import CLIPImageProcessor, CLIPVisionModel, CLIPVisionConfig import numpy as np tac_depth_model = CLIPVisionModel.from_pretrained("RavenK/TAC-ViT-base") tac_depth_processor = CLIPImageProcessor.from_pretrained("RavenK/TAC-ViT-base") # Assume test.png is a depth image with a scale factor 1000 MIN_DEPTH = 0.0 MAX_DEPTH = 10.0 DEPTH_SCALE = 1000 depth_path = "test.png" depth = Image.open(depth_path) depth = np.array(depth).astype("float32") / DEPTH_SCALE # to meters depth = np.clip(depth, MIN_DEPTH, MAX_DEPTH) # clip to [MIN_DEPTH, MAX_DEPTH] depth = (depth - MIN_DEPTH) / (MAX_DEPTH - MIN_DEPTH) # normalize to [0,1] depth = np.expand_dims(depth, axis=2).repeat(3, axis=2) # extend to 3 channels depth = tac_depth_processor(depth, do_rescale=False, return_tensors="pt").pixel_values # preprocess (resize, normalize and to tensor) outputs = tac_depth_model(pixel_values=depth) outputs = outputs["last_hidden_state"][:, 0, :] # get embedding without FC. may be used for other downstream fine-tuning ``` ### Other Uses Please refer to the [demo](https://github.com/RavenKiller/TAC/blob/main/scripts/demo.ipynb) in our code repository. ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> ``` @ARTICLE{10288539, author={He, Zongtao and Wang, Liuyi and Dang, Ronghao and Li, Shu and Yan, Qingqing and Liu, Chengju and Chen, Qijun}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, title={Learning Depth Representation From RGB-D Videos by Time-Aware Contrastive Pre-Training}, year={2024}, volume={34}, number={6}, pages={4143-4158}, doi={10.1109/TCSVT.2023.3326373}} ```
McLuian/FT-Llama-3-8B-Instruct-GSM8K-100.Q4_0.gguf	McLuian	2024-06-06T03:29:44Z	7	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-05T08:08:52Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: LuianMC - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RayBernard/llama-3-8B-Intruct-ft	RayBernard	2024-06-06T03:28:47Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-06-05T01:41:39Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: llama-3-8B-Intruct-ft 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. --> # llama-3-8B-Intruct-ft This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - training_steps: 128 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.1.0.post2+cxx11.abi - Datasets 2.19.2 - Tokenizers 0.19.1
magnifi/parser_user_v1-0605-epoch5-0.001_user_only	magnifi	2024-06-06T03:23:37Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T03:21:43Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
McLuian/FT-Llama-3-8B-Instruct-GSM8K-50-16bit	McLuian	2024-06-06T03:15:44Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T03:09:22Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: LuianMC - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MrezaPRZ/CodeLlama-7B-sqlite-expert	MrezaPRZ	2024-06-06T03:13:18Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T03:10:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
v000000/L3-8B-Poppy-Sunspice-experiment-c	v000000	2024-06-06T03:12:50Z	10	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Nitral-AI/Poppy_Porpoise-0.72-L3-8B", "base_model:merge:Nitral-AI/Poppy_Porpoise-0.72-L3-8B", "base_model:Nitral-Archive/Poppy_Porpoise-Biomix", "base_model:merge:Nitral-Archive/Poppy_Porpoise-Biomix", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:merge:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "base_model:cgato/L3-TheSpice-8b-v0.8.3", "base_model:merge:cgato/L3-TheSpice-8b-v0.8.3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T03:03:01Z	--- base_model: - crestf411/L3-8B-sunfall-abliterated-v0.2 - Nitral-AI/Poppy_Porpoise-0.72-L3-8B - Nitral-Archive/Poppy_Porpoise-Biomix - NousResearch/Meta-Llama-3-8B-Instruct - Hastagaras/HALU-8B-LLAMA3-BRSLURP - Sao10K/L3-8B-Stheno-v3.2 - cgato/L3-TheSpice-8b-v0.8.3 library_name: transformers tags: - mergekit - merge --- ### untested experiment ignore ### untested experiment ignore ### untested experiment ignore ### untested experiment ignore # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [crestf411/L3-8B-sunfall-abliterated-v0.2](https://huggingface.co/crestf411/L3-8B-sunfall-abliterated-v0.2) * [Nitral-AI/Poppy_Porpoise-0.72-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-0.72-L3-8B) * [Nitral-Archive/Poppy_Porpoise-Biomix](https://huggingface.co/Nitral-Archive/Poppy_Porpoise-Biomix) * [Hastagaras/HALU-8B-LLAMA3-BRSLURP](https://huggingface.co/Hastagaras/HALU-8B-LLAMA3-BRSLURP) * [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) * [cgato/L3-TheSpice-8b-v0.8.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: crestf411/L3-8B-sunfall-abliterated-v0.2 parameters: weight: 0.1 density: 0.18 - model: Hastagaras/HALU-8B-LLAMA3-BRSLURP parameters: weight: 0.1 density: 0.3 - model: Nitral-Archive/Poppy_Porpoise-Biomix parameters: weight: 0.1 density: 0.42 - model: cgato/L3-TheSpice-8b-v0.8.3 parameters: weight: 0.2 density: 0.54 - model: Sao10K/L3-8B-Stheno-v3.2 parameters: weight: 0.2 density: 0.66 - model: Nitral-AI/Poppy_Porpoise-0.72-L3-8B parameters: weight: 0.3 density: 0.78 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B-Instruct parameters: int8_mask: true dtype: bfloat16 ```
mo27harakani/finetuning-sentiment-model-3000-samples	mo27harakani	2024-06-06T03:11:19Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-06T03:05:11Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3531 - Accuracy: 0.8567 - F1: 0.8617 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
Sekais/voiz	Sekais	2024-06-06T03:08:55Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-06T03:08:55Z	--- license: creativeml-openrail-m ---
McLuian/FT-Llama-3-8B-Instruct-GSM8K-50-LoRA	McLuian	2024-06-06T03:08:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-06T03:08:19Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: LuianMC - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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