librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
iceman2434/xlm-roberta-base-ft-udpos213-top5langrandom	iceman2434	2024-06-30T20:33:54Z	null	[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]	token-classification	2024-06-30T20:31:31Z	--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: XLM-RoBERTa (base-sized model) - Randomized training order of languages - Training Data: - Combined Afrikaans, Norwegian, Vietnamese, Hebrew, & Bulgarian corpora (Top 5 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 79.01\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
Litzy619/MIS0630T3	Litzy619	2024-06-30T20:44:58Z	null	[ "region:us" ]	null	2024-06-30T20:31:54Z	Entry not found
DimensionSTP/Llama-3-KoEn-8B-scientificQA	DimensionSTP	2024-06-30T23:58:29Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "llama-3-ko", "conversational", "en", "ko", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T20:32:33Z	--- language: - en - ko license: cc-by-nc-sa-4.0 tags: - facebook - meta - pytorch - llama - llama-3 - llama-3-ko pipeline_tag: text-generation license_name: llama3 license_link: LICENSE --- ## Model Details This model is fine-tuned by beomi/Llama-3-KoEn-8B Fine-tuning dataset: Scientific QA dataset
lit9003code/melotts310	lit9003code	2024-06-30T20:33:10Z	null	[ "region:us" ]	null	2024-06-30T20:32:50Z	Entry not found
lit9003code/melotts311	lit9003code	2024-06-30T20:35:39Z	null	[ "region:us" ]	null	2024-06-30T20:34:25Z	Entry not found
asafi/Meta-Llama-3-medical-8B	asafi	2024-06-30T20:35:18Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-30T20:34:50Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: asafi - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lit9003code/melotts312	lit9003code	2024-06-30T20:37:27Z	null	[ "region:us" ]	null	2024-06-30T20:37:03Z	Entry not found
lit9003code/melotts313	lit9003code	2024-06-30T20:38:59Z	null	[ "region:us" ]	null	2024-06-30T20:38:39Z	Entry not found
lit9003code/melotts314	lit9003code	2024-06-30T20:41:26Z	null	[ "region:us" ]	null	2024-06-30T20:40:17Z	Entry not found
iceman2434/roberta-tagalog-base-ft-udpos213-top2langrandom	iceman2434	2024-06-30T20:47:29Z	null	[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]	token-classification	2024-06-30T20:41:11Z	--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz) - Randomized training order of languages - Training Data: - Combined English & Serbian corpora (Top 2 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 73.99\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
iceman2434/roberta-tagalog-base-ft-udpos213-top3langrandom	iceman2434	2024-06-30T20:47:42Z	null	[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]	token-classification	2024-06-30T20:43:44Z	--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz) - Randomized training order of languages - Training Data: - Combined English, Serbian, & Slovenian corpora (Top 3 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 71.91\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
psimm/llama-3-8B-semeval2014-task	psimm	2024-06-30T20:48:18Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-30T20:45:05Z	--- 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]
habulaj/67797950	habulaj	2024-06-30T20:46:14Z	null	[ "region:us" ]	null	2024-06-30T20:46:10Z	Entry not found
iceman2434/roberta-tagalog-base-ft-udpos213-top4langrandom	iceman2434	2024-06-30T20:50:11Z	null	[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]	token-classification	2024-06-30T20:48:01Z	--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz) - Randomized training order of languages - Training Data: - Combined English, Serbian, Slovenian, & Naija corpora (Top 4 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 72.97\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
Litzy619/MIS0630T4	Litzy619	2024-06-30T22:56:13Z	null	[ "region:us" ]	null	2024-06-30T20:48:06Z	Entry not found
habulaj/9917682106	habulaj	2024-06-30T20:50:19Z	null	[ "region:us" ]	null	2024-06-30T20:50:17Z	Entry not found
Loren85/Dick-Van-Dyke-2024-2023-voice	Loren85	2024-06-30T20:51:27Z	null	[ "license:openrail", "region:us" ]	null	2024-06-30T20:50:29Z	--- license: openrail ---
iceman2434/roberta-tagalog-base-ft-udpos213-top5langrandom	iceman2434	2024-06-30T20:52:43Z	null	[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]	token-classification	2024-06-30T20:50:55Z	--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz) - Randomized training order of languages - Training Data: - Combined English, Serbian, Slovenian, Naija, & Manx-Cadhan corpora (Top 5 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 72.52\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
eriho/MobileNetV4_TensorFlow.js_feature_vector_small.e2400_r224	eriho	2024-06-30T21:03:19Z	null	[ "tensorflow.js", "transfer learning", "feature-extraction", "license:cc-by-sa-4.0", "region:us" ]	feature-extraction	2024-06-30T20:51:29Z	--- license: cc-by-sa-4.0 pipeline_tag: feature-extraction tags: - tensorflow.js - transfer learning --- Original model: https://huggingface.co/timm/mobilenetv4_conv_small.e2400_r224_in1k/tree/main shape 1,224,224,3 hf gl
steja/whisper-medium-english	steja	2024-06-30T20:52:48Z	null	[ "region:us" ]	null	2024-06-30T20:52:48Z	Entry not found
habulaj/2003219747	habulaj	2024-06-30T20:52:54Z	null	[ "region:us" ]	null	2024-06-30T20:52:51Z	Entry not found
habulaj/195519346132	habulaj	2024-06-30T20:54:08Z	null	[ "region:us" ]	null	2024-06-30T20:53:55Z	Entry not found
42Antonio/Acb	42Antonio	2024-06-30T20:55:15Z	null	[ "region:us" ]	null	2024-06-30T20:55:14Z	Entry not found
odelz/hindi_fb1mms_timebalancedreg2	odelz	2024-06-30T20:55:18Z	null	[ "region:us" ]	null	2024-06-30T20:55:18Z	Entry not found
variante/llava-1.5-7b-llara-D-inBC-VIMA-80k	variante	2024-07-01T04:42:21Z	transformers	[ "transformers", "safetensors", "llava", "text-generation", "llara", "robotics", "vlm", "image-text-to-text", "dataset:VIMA/VIMA-Data", "arxiv:2406.20095", "license:apache-2.0", "autotrain_compatible", "region:us" ]	image-text-to-text	2024-06-30T20:57:42Z	--- inference: false pipeline_tag: image-text-to-text license: apache-2.0 datasets: - VIMA/VIMA-Data tags: - llara - llava - robotics - vlm --- <br> <be> # LLaRA Model Card This model is released with paper [LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://arxiv.org/abs/2406.20095) [Xiang Li](https://xxli.me)<sup>1</sup>, [Cristina Mata](https://openreview.net/profile?id=~Cristina_Mata1)<sup>1</sup>, [Jongwoo Park](https://github.com/jongwoopark7978)<sup>1</sup>, [Kumara Kahatapitiya](https://www3.cs.stonybrook.edu/~kkahatapitiy)<sup>1</sup>, [Yoo Sung Jang](https://yjang43.github.io/)<sup>1</sup>, [Jinghuan Shang](https://elicassion.github.io/)<sup>1</sup>, [Kanchana Ranasinghe](https://kahnchana.github.io/)<sup>1</sup>, [Ryan Burgert](https://ryanndagreat.github.io/)<sup>1</sup>, [Mu Cai](https://pages.cs.wisc.edu/~mucai/)<sup>2</sup>, [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)<sup>2</sup>, and [Michael S. Ryoo](http://michaelryoo.com/)<sup>1</sup> <sup>1</sup>Stony Brook University <sup>2</sup>University of Wisconsin-Madison ## Model details Model type: LLaRA is an open-source visuomotor policy trained by fine-tuning [LLaVA-7b-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) on instruction-following data `D-inBC`, converted from [VIMA-Data](https://huggingface.co/datasets/VIMA/VIMA-Data). For the conversion code, please refer to [convert_vima.ipynb](https://github.com/LostXine/LLaRA/blob/main/datasets/convert_vima.ipynb) Model date: llava-1.5-7b-llara-D-inBC-VIMA-80k was trained in June 2024. Paper or resources for more information: https://github.com/LostXine/LLaRA Where to send questions or comments about the model: https://github.com/LostXine/LLaRA/issues ## Intended use Primary intended uses: The primary use of LLaRA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
mohamedemam/Em2-Mistral-7b	mohamedemam	2024-07-02T09:36:18Z	peft	[ "peft", "safetensors", "autograding", "essay quetion", "sentence similarity", "en", "dataset:mohamedemam/Essay-quetions-auto-grading", "license:gpl", "region:us" ]	null	2024-06-30T20:57:51Z	--- language: - en license: gpl tags: - autograding - essay quetion - sentence similarity metrics: - accuracy library_name: peft datasets: - mohamedemam/Essay-quetions-auto-grading --- # Model Card for Model ID fine tuned version of Mistral on Essay-quetions-auto-grading ### Model Description <!-- Provide a longer summary of what this model is. --> We are thrilled to introduce our graduation project, the EM2 model, designed for automated essay grading in both Arabic and English. 📝✨ To develop this model, we first created a custom dataset for training. We adapted the QuAC and OpenOrca datasets to make them suitable for our automated essay grading application. Our model utilizes the following impressive models: Mistral: 96% LLaMA: 93% FLAN-T5: 93% BLOOMZ (Arabic): 86% MT0 (Arabic): 84% You can try our models for auto-grading on Hugging Face! 🌐 We then deployed these models for practical use. We are proud of our team's hard work and the potential impact of the EM2 model in the field of education. 🌟 #MachineLearning #AI #Education #EssayGrading #GraduationProject - Developed by: mohamed emam - Model type: decoder only - Language(s) (NLP): English - License: gpl - Finetuned from model : Mistral <!-- Provide the basic links for the model. --> - Repository: https://github.com/mohamed-em2m/Automatic-Grading-AI - ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> auto grading for essay quetions ### Explain how it work - model take three inputs first context or perfect answer + quetion on context + student answer then model output the result ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6456f2eca9b8e1fd4cbe5ebe/_O75HT2zb2TYZOEkX4YXO.png) ### Training Data - mohamedemam/Essay-quetions-auto-grading-arabic ### Training Procedure using Trl ### Pipline ```python from transformers import Pipeline import torch.nn.functional as F class MyPipeline: def __init__(self,model,tokenizer): self.model=model self.tokenizer=tokenizer def chat_Format(self,context, quetion, answer): return "Instruction:/n check answer is true or false of next quetion using context below:\n" + "#context: " + context + f".\n#quetion: " + quetion + f".\n#student answer: " + answer + ".\n#response:" def __call__(self, context, quetion, answer,generate=1,max_new_tokens=4, num_beams=2, do_sample=False,num_return_sequences=1): inp=self.chat_Format(context, quetion, answer) w = self.tokenizer(inp, add_special_tokens=True, pad_to_max_length=True, return_attention_mask=True, return_tensors='pt') response="" if(generate): outputs = self.tokenizer.batch_decode(self.model.generate(input_ids=w['input_ids'].cuda(), attention_mask=w['attention_mask'].cuda(), max_new_tokens=max_new_tokens, num_beams=num_beams, do_sample=do_sample, num_return_sequences=num_return_sequences), skip_special_tokens=True) response = outputs s =self.model(input_ids=w['input_ids'].cuda(), attention_mask=w['attention_mask'].cuda())['logits'][0][-1] s = F.softmax(s, dim=-1) yes_token_id = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize("True")[0]) no_token_id = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize("False")[0]) for i in ["Yes", "yes", "True", "true","صحيح"]: for word in self.tokenizer.tokenize(i): s[yes_token_id] += s[self.tokenizer.convert_tokens_to_ids(word)] for i in ["No", "no", "False", "false","خطأ"]: for word in self.tokenizer.tokenize(i): s[no_token_id] += s[self.tokenizer.convert_tokens_to_ids(word)] true = (s[yes_token_id] / (s[no_token_id] + s[yes_token_id])).item() return {"response": response, "true": true} context="""Large language models, such as GPT-4, are trained on vast amounts of text data to understand and generate human-like text. The deployment of these models involves several steps: Model Selection: Choosing a pre-trained model that fits the application's needs. Infrastructure Setup: Setting up the necessary hardware and software infrastructure to run the model efficiently, including cloud services, GPUs, and necessary libraries. Integration: Integrating the model into an application, which can involve setting up APIs or embedding the model directly into the software. Optimization: Fine-tuning the model for specific tasks or domains and optimizing it for performance and cost-efficiency. Monitoring and Maintenance: Ensuring the model performs well over time, monitoring for biases, and updating the model as needed.""" quetion="What are the key considerations when choosing a cloud service provider for deploying a large language model like GPT-4?" answer="""When choosing a cloud service provider for deploying a large language model like GPT-4, the key considerations include: Compute Power: Ensure the provider offers high-performance GPUs or TPUs capable of handling the computational requirements of the model. Scalability: The ability to scale resources up or down based on the application's demand to handle varying workloads efficiently. Cost: Analyze the pricing models to understand the costs associated with compute time, storage, data transfer, and any other services. Integration and Support: Availability of tools and libraries that support easy integration of the model into your applications, along with robust technical support and documentation. Security and Compliance: Ensure the provider adheres to industry standards for security and compliance, protecting sensitive data and maintaining privacy. Latency and Availability: Consider the geographical distribution of data centers to ensure low latency and high availability for your end-users. By evaluating these factors, you can select a cloud service provider that aligns with your deployment needs, ensuring efficient and cost-effective operation of your large language model.""" from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM,AutoTokenizer config = PeftConfig.from_pretrained("mohamedemam/Em2-llama-7b") base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "mohamedemam/Em2-llama-7b") tokenizer = AutoTokenizer.from_pretrained("mohamedemam/Em2-llama-7b", trust_remote_code=True) pipe=MyPipeline(model,tokenizer) print(pipe(context,quetion,answer,generate=True,max_new_tokens=4, num_beams=2, do_sample=False,num_return_sequences=1)) ``` - output:{'response': ["Instruction:/n check answer is true or false of next quetion using context below:\n#context: Large language models, such as GPT-4, are trained on vast amounts of text data to understand and generate human-like text. The deployment of these models involves several steps:\n\n Model Selection: Choosing a pre-trained model that fits the application's needs.\n Infrastructure Setup: Setting up the necessary hardware and software infrastructure to run the model efficiently, including cloud services, GPUs, and necessary libraries.\n Integration: Integrating the model into an application, which can involve setting up APIs or embedding the model directly into the software.\n Optimization: Fine-tuning the model for specific tasks or domains and optimizing it for performance and cost-efficiency.\n Monitoring and Maintenance: Ensuring the model performs well over time, monitoring for biases, and updating the model as needed..\n#quetion: What are the key considerations when choosing a cloud service provider for deploying a large language model like GPT-4?.\n#student answer: When choosing a cloud service provider for deploying a large language model like GPT-4, the key considerations include:\n Compute Power: Ensure the provider offers high-performance GPUs or TPUs capable of handling the computational requirements of the model.\n Scalability: The ability to scale resources up or down based on the application's demand to handle varying workloads efficiently.\n Cost: Analyze the pricing models to understand the costs associated with compute time, storage, data transfer, and any other services.\n Integration and Support: Availability of tools and libraries that support easy integration of the model into your applications, along with robust technical support and documentation.\n Security and Compliance: Ensure the provider adheres to industry standards for security and compliance, protecting sensitive data and maintaining privacy.\n Latency and Availability: Consider the geographical distribution of data centers to ensure low latency and high availability for your end-users.\n\nBy evaluating these factors, you can select a cloud service provider that aligns with your deployment needs, ensuring efficient and cost-effective operation of your large language model..\n#response: true the answer is"], 'true': 0.943033754825592} ### Chat Format Function This function formats the input context, question, and answer into a specific structure for the model to process. ```python def chat_Format(self, context, question, answer): return "Instruction:/n check answer is true or false of next question using context below:\n" + "#context: " + context + f".\n#question: " + question + f".\n#student answer: " + answer + ".\n#response:" ``` ## Configuration ### Dropout Probability for LoRA Layers - lora_dropout: 0.05 ### Quantization Settings - use_4bit: True - bnb_4bit_compute_dtype: "float16" - bnb_4bit_quant_type: "nf4" - use_nested_quant: False ### Output Directory - output_dir: "./results" ### Training Parameters - num_train_epochs: 1 - fp16: False - bf16: False - per_device_train_batch_size: 1 - per_device_eval_batch_size: 4 - gradient_accumulation_steps: 8 - gradient_checkpointing: True - max_grad_norm: 0.3 - learning_rate: 5e-5 - weight_decay: 0.001 - optim: "paged_adamw_8bit" - lr_scheduler_type: "constant" - max_steps: -1 - warmup_ratio: 0.03 - group_by_length: True ### Logging and Saving - save_steps: 100 - logging_steps: 25 - max_seq_length: False
variante/llava-1.5-7b-llara-D-inBC-Aux-B-VIMA-80k	variante	2024-07-01T04:42:48Z	transformers	[ "transformers", "safetensors", "llava", "text-generation", "llara", "robotics", "vlm", "image-text-to-text", "dataset:VIMA/VIMA-Data", "arxiv:2406.20095", "license:apache-2.0", "autotrain_compatible", "region:us" ]	image-text-to-text	2024-06-30T20:58:23Z	--- inference: false pipeline_tag: image-text-to-text license: apache-2.0 datasets: - VIMA/VIMA-Data tags: - llara - llava - robotics - vlm --- <br> <be> # LLaRA Model Card This model is released with paper [LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://arxiv.org/abs/2406.20095) [Xiang Li](https://xxli.me)<sup>1</sup>, [Cristina Mata](https://openreview.net/profile?id=~Cristina_Mata1)<sup>1</sup>, [Jongwoo Park](https://github.com/jongwoopark7978)<sup>1</sup>, [Kumara Kahatapitiya](https://www3.cs.stonybrook.edu/~kkahatapitiy)<sup>1</sup>, [Yoo Sung Jang](https://yjang43.github.io/)<sup>1</sup>, [Jinghuan Shang](https://elicassion.github.io/)<sup>1</sup>, [Kanchana Ranasinghe](https://kahnchana.github.io/)<sup>1</sup>, [Ryan Burgert](https://ryanndagreat.github.io/)<sup>1</sup>, [Mu Cai](https://pages.cs.wisc.edu/~mucai/)<sup>2</sup>, [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)<sup>2</sup>, and [Michael S. Ryoo](http://michaelryoo.com/)<sup>1</sup> <sup>1</sup>Stony Brook University <sup>2</sup>University of Wisconsin-Madison ## Model details Model type: LLaRA is an open-source visuomotor policy trained by fine-tuning [LLaVA-7b-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) on instruction-following data `D-inBC` and 4 auxiliary datasets, converted from [VIMA-Data](https://huggingface.co/datasets/VIMA/VIMA-Data). For the conversion code, please refer to [convert_vima.ipynb](https://github.com/LostXine/LLaRA/blob/main/datasets/convert_vima.ipynb) Model date: llava-1.5-7b-llara-D-inBC-Aux-B-VIMA-80k was trained in June 2024. Paper or resources for more information: https://github.com/LostXine/LLaRA Where to send questions or comments about the model: https://github.com/LostXine/LLaRA/issues ## Intended use Primary intended uses: The primary use of LLaRA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
variante/llava-1.5-7b-llara-D-inBC-Aux-D-VIMA-80k	variante	2024-07-01T04:43:03Z	transformers	[ "transformers", "safetensors", "llava", "text-generation", "llara", "robotics", "vlm", "image-text-to-text", "dataset:VIMA/VIMA-Data", "arxiv:2406.20095", "license:apache-2.0", "autotrain_compatible", "region:us" ]	image-text-to-text	2024-06-30T20:58:37Z	--- inference: false pipeline_tag: image-text-to-text license: apache-2.0 datasets: - VIMA/VIMA-Data tags: - llara - llava - robotics - vlm --- <br> <be> # LLaRA Model Card This model is released with paper [LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://arxiv.org/abs/2406.20095) [Xiang Li](https://xxli.me)<sup>1</sup>, [Cristina Mata](https://openreview.net/profile?id=~Cristina_Mata1)<sup>1</sup>, [Jongwoo Park](https://github.com/jongwoopark7978)<sup>1</sup>, [Kumara Kahatapitiya](https://www3.cs.stonybrook.edu/~kkahatapitiy)<sup>1</sup>, [Yoo Sung Jang](https://yjang43.github.io/)<sup>1</sup>, [Jinghuan Shang](https://elicassion.github.io/)<sup>1</sup>, [Kanchana Ranasinghe](https://kahnchana.github.io/)<sup>1</sup>, [Ryan Burgert](https://ryanndagreat.github.io/)<sup>1</sup>, [Mu Cai](https://pages.cs.wisc.edu/~mucai/)<sup>2</sup>, [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)<sup>2</sup>, and [Michael S. Ryoo](http://michaelryoo.com/)<sup>1</sup> <sup>1</sup>Stony Brook University <sup>2</sup>University of Wisconsin-Madison ## Model details Model type: LLaRA is an open-source visuomotor policy trained by fine-tuning [LLaVA-7b-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) on instruction-following data `D-inBC` and 6 auxiliary datasets, converted from [VIMA-Data](https://huggingface.co/datasets/VIMA/VIMA-Data). For the conversion code, please refer to [convert_vima.ipynb](https://github.com/LostXine/LLaRA/blob/main/datasets/convert_vima.ipynb) Model date: llava-1.5-7b-llara-D-inBC-Aux-D-VIMA-80k was trained in June 2024. Paper or resources for more information: https://github.com/LostXine/LLaRA Where to send questions or comments about the model: https://github.com/LostXine/LLaRA/issues ## Intended use Primary intended uses: The primary use of LLaRA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
Renee0v0/NeuralPipe-7B-slerp	Renee0v0	2024-06-30T21:05:24Z	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T21:00:59Z	--- base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- # NeuralPipe-7B-slerp NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Renee0v0/NeuralPipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
habulaj/297619439478	habulaj	2024-06-30T21:01:22Z	null	[ "region:us" ]	null	2024-06-30T21:01:12Z	Entry not found
habulaj/269179427694	habulaj	2024-06-30T21:03:11Z	null	[ "region:us" ]	null	2024-06-30T21:02:54Z	Entry not found
osouza/bert-large-ambiguidade-v3	osouza	2024-06-30T21:03:47Z	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-30T21:03:30Z	--- 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]
habulaj/347252492909	habulaj	2024-06-30T21:06:22Z	null	[ "region:us" ]	null	2024-06-30T21:06:13Z	Entry not found
Megnis/qdora2	Megnis	2024-06-30T21:06:45Z	null	[ "region:us" ]	null	2024-06-30T21:06:45Z	Entry not found
Sirok/sirok2	Sirok	2024-06-30T21:07:07Z	null	[ "region:us" ]	null	2024-06-30T21:07:07Z	Entry not found
habulaj/6877062242	habulaj	2024-06-30T21:08:49Z	null	[ "region:us" ]	null	2024-06-30T21:08:43Z	Entry not found
habulaj/11003984903	habulaj	2024-06-30T21:10:08Z	null	[ "region:us" ]	null	2024-06-30T21:10:03Z	Entry not found
odelz/eng_fb1mms_unbalanced	odelz	2024-06-30T21:12:17Z	null	[ "region:us" ]	null	2024-06-30T21:12:17Z	Entry not found
habulaj/98715236960	habulaj	2024-06-30T21:15:07Z	null	[ "region:us" ]	null	2024-06-30T21:15:01Z	Entry not found
selvaa/segformer-b1-finetuned-cityscapes-1024-1024-with-after-demo-ds	selvaa	2024-06-30T21:36:15Z	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/segformer-b1-finetuned-cityscapes-1024-1024", "license:other", "endpoints_compatible", "region:us" ]	null	2024-06-30T21:16:50Z	--- license: other base_model: nvidia/segformer-b1-finetuned-cityscapes-1024-1024 tags: - generated_from_trainer model-index: - name: segformer-b1-finetuned-cityscapes-1024-1024-with-after-demo-ds 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. --> # segformer-b1-finetuned-cityscapes-1024-1024-with-after-demo-ds This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0153 - Mean Iou: 0.9689 - Mean Accuracy: 0.9858 - Overall Accuracy: 0.9947 - Accuracy Default: 1e-06 - Accuracy Pipe: 0.9729 - Accuracy Floor: 0.9861 - Accuracy Background: 0.9985 - Iou Default: 1e-06 - Iou Pipe: 0.9305 - Iou Floor: 0.9802 - Iou Background: 0.9958 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Default \| Accuracy Pipe \| Accuracy Floor \| Accuracy Background \| Iou Default \| Iou Pipe \| Iou Floor \| Iou Background \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:----------------:\|:-------------:\|:--------------:\|:-------------------:\|:-----------:\|:--------:\|:---------:\|:--------------:\| \| 0.333 \| 1.0 \| 55 \| 0.1193 \| 0.8358 \| 0.8688 \| 0.9725 \| 1e-06 \| 0.6617 \| 0.9467 \| 0.9981 \| 1e-06 \| 0.5954 \| 0.9420 \| 0.9700 \| \| 0.0978 \| 2.0 \| 110 \| 0.0734 \| 0.8938 \| 0.9399 \| 0.9817 \| 1e-06 \| 0.8567 \| 0.9709 \| 0.9921 \| 1e-06 \| 0.7472 \| 0.9523 \| 0.9818 \| \| 0.0647 \| 3.0 \| 165 \| 0.0529 \| 0.9169 \| 0.9580 \| 0.9860 \| 1e-06 \| 0.9093 \| 0.9696 \| 0.9951 \| 1e-06 \| 0.8023 \| 0.9617 \| 0.9866 \| \| 0.0519 \| 4.0 \| 220 \| 0.0455 \| 0.9175 \| 0.9445 \| 0.9861 \| 1e-06 \| 0.8663 \| 0.9692 \| 0.9979 \| 1e-06 \| 0.8031 \| 0.9638 \| 0.9855 \| \| 0.0457 \| 5.0 \| 275 \| 0.0413 \| 0.9198 \| 0.9687 \| 0.9866 \| 1e-06 \| 0.9356 \| 0.9786 \| 0.9919 \| 1e-06 \| 0.8098 \| 0.9614 \| 0.9881 \| \| 0.0407 \| 6.0 \| 330 \| 0.0360 \| 0.9283 \| 0.9584 \| 0.9882 \| 1e-06 \| 0.9010 \| 0.9780 \| 0.9962 \| 1e-06 \| 0.8320 \| 0.9632 \| 0.9897 \| \| 0.0363 \| 7.0 \| 385 \| 0.0318 \| 0.9399 \| 0.9698 \| 0.9897 \| 1e-06 \| 0.9385 \| 0.9737 \| 0.9973 \| 1e-06 \| 0.8614 \| 0.9680 \| 0.9904 \| \| 0.0335 \| 8.0 \| 440 \| 0.0295 \| 0.9423 \| 0.9727 \| 0.9904 \| 1e-06 \| 0.9443 \| 0.9770 \| 0.9969 \| 1e-06 \| 0.8652 \| 0.9702 \| 0.9915 \| \| 0.0318 \| 9.0 \| 495 \| 0.0288 \| 0.9425 \| 0.9746 \| 0.9905 \| 1e-06 \| 0.9492 \| 0.9784 \| 0.9963 \| 1e-06 \| 0.8664 \| 0.9694 \| 0.9918 \| \| 0.0292 \| 10.0 \| 550 \| 0.0262 \| 0.9478 \| 0.9752 \| 0.9912 \| 1e-06 \| 0.9510 \| 0.9769 \| 0.9976 \| 1e-06 \| 0.8803 \| 0.9710 \| 0.9922 \| \| 0.0291 \| 11.0 \| 605 \| 0.0270 \| 0.9466 \| 0.9720 \| 0.9909 \| 1e-06 \| 0.9415 \| 0.9765 \| 0.9979 \| 1e-06 \| 0.8774 \| 0.9708 \| 0.9916 \| \| 0.0275 \| 12.0 \| 660 \| 0.0249 \| 0.9496 \| 0.9793 \| 0.9916 \| 1e-06 \| 0.9625 \| 0.9784 \| 0.9971 \| 1e-06 \| 0.8835 \| 0.9723 \| 0.9929 \| \| 0.0264 \| 13.0 \| 715 \| 0.0246 \| 0.9514 \| 0.9716 \| 0.9915 \| 1e-06 \| 0.9383 \| 0.9782 \| 0.9984 \| 1e-06 \| 0.8901 \| 0.9720 \| 0.9920 \| \| 0.0255 \| 14.0 \| 770 \| 0.0242 \| 0.9500 \| 0.9812 \| 0.9917 \| 1e-06 \| 0.9677 \| 0.9792 \| 0.9967 \| 1e-06 \| 0.8846 \| 0.9723 \| 0.9932 \| \| 0.0248 \| 15.0 \| 825 \| 0.0230 \| 0.9534 \| 0.9785 \| 0.9921 \| 1e-06 \| 0.9598 \| 0.9777 \| 0.9980 \| 1e-06 \| 0.8940 \| 0.9732 \| 0.9931 \| \| 0.0241 \| 16.0 \| 880 \| 0.0233 \| 0.9523 \| 0.9806 \| 0.9920 \| 1e-06 \| 0.9666 \| 0.9778 \| 0.9975 \| 1e-06 \| 0.8906 \| 0.9731 \| 0.9932 \| \| 0.023 \| 17.0 \| 935 \| 0.0215 \| 0.9562 \| 0.9778 \| 0.9925 \| 1e-06 \| 0.9553 \| 0.9801 \| 0.9982 \| 1e-06 \| 0.9015 \| 0.9738 \| 0.9934 \| \| 0.0223 \| 18.0 \| 990 \| 0.0212 \| 0.9562 \| 0.9780 \| 0.9925 \| 1e-06 \| 0.9546 \| 0.9816 \| 0.9979 \| 1e-06 \| 0.9011 \| 0.9737 \| 0.9937 \| \| 0.022 \| 19.0 \| 1045 \| 0.0205 \| 0.9558 \| 0.9810 \| 0.9927 \| 1e-06 \| 0.9640 \| 0.9813 \| 0.9975 \| 1e-06 \| 0.8995 \| 0.9737 \| 0.9941 \| \| 0.0213 \| 20.0 \| 1100 \| 0.0207 \| 0.9582 \| 0.9764 \| 0.9926 \| 1e-06 \| 0.9504 \| 0.9801 \| 0.9986 \| 1e-06 \| 0.9069 \| 0.9745 \| 0.9932 \| \| 0.0213 \| 21.0 \| 1155 \| 0.0211 \| 0.9566 \| 0.9801 \| 0.9927 \| 1e-06 \| 0.9624 \| 0.9796 \| 0.9981 \| 1e-06 \| 0.9014 \| 0.9746 \| 0.9937 \| \| 0.0206 \| 22.0 \| 1210 \| 0.0202 \| 0.9589 \| 0.9799 \| 0.9929 \| 1e-06 \| 0.9608 \| 0.9804 \| 0.9983 \| 1e-06 \| 0.9078 \| 0.9752 \| 0.9938 \| \| 0.0199 \| 23.0 \| 1265 \| 0.0194 \| 0.9596 \| 0.9813 \| 0.9931 \| 1e-06 \| 0.9644 \| 0.9812 \| 0.9981 \| 1e-06 \| 0.9096 \| 0.9750 \| 0.9942 \| \| 0.0192 \| 24.0 \| 1320 \| 0.0194 \| 0.9590 \| 0.9831 \| 0.9932 \| 1e-06 \| 0.9710 \| 0.9803 \| 0.9981 \| 1e-06 \| 0.9070 \| 0.9754 \| 0.9945 \| \| 0.019 \| 25.0 \| 1375 \| 0.0189 \| 0.9608 \| 0.9834 \| 0.9933 \| 1e-06 \| 0.9703 \| 0.9820 \| 0.9978 \| 1e-06 \| 0.9124 \| 0.9754 \| 0.9945 \| \| 0.0189 \| 26.0 \| 1430 \| 0.0195 \| 0.9602 \| 0.9822 \| 0.9932 \| 1e-06 \| 0.9675 \| 0.9808 \| 0.9983 \| 1e-06 \| 0.9103 \| 0.9758 \| 0.9943 \| \| 0.0185 \| 27.0 \| 1485 \| 0.0204 \| 0.9577 \| 0.9804 \| 0.9930 \| 1e-06 \| 0.9617 \| 0.9815 \| 0.9981 \| 1e-06 \| 0.9035 \| 0.9754 \| 0.9942 \| \| 0.0185 \| 28.0 \| 1540 \| 0.0188 \| 0.9625 \| 0.9808 \| 0.9935 \| 1e-06 \| 0.9616 \| 0.9822 \| 0.9986 \| 1e-06 \| 0.9167 \| 0.9766 \| 0.9944 \| \| 0.0178 \| 29.0 \| 1595 \| 0.0186 \| 0.9626 \| 0.9801 \| 0.9935 \| 1e-06 \| 0.9588 \| 0.9829 \| 0.9985 \| 1e-06 \| 0.9166 \| 0.9768 \| 0.9943 \| \| 0.0176 \| 30.0 \| 1650 \| 0.0192 \| 0.9622 \| 0.9802 \| 0.9935 \| 1e-06 \| 0.9594 \| 0.9826 \| 0.9986 \| 1e-06 \| 0.9156 \| 0.9766 \| 0.9945 \| \| 0.0175 \| 31.0 \| 1705 \| 0.0175 \| 0.9631 \| 0.9839 \| 0.9937 \| 1e-06 \| 0.9710 \| 0.9827 \| 0.9981 \| 1e-06 \| 0.9176 \| 0.9769 \| 0.9948 \| \| 0.017 \| 32.0 \| 1760 \| 0.0183 \| 0.9615 \| 0.9852 \| 0.9936 \| 1e-06 \| 0.9761 \| 0.9814 \| 0.9981 \| 1e-06 \| 0.9130 \| 0.9765 \| 0.9949 \| \| 0.0172 \| 33.0 \| 1815 \| 0.0173 \| 0.9646 \| 0.9834 \| 0.9938 \| 1e-06 \| 0.9690 \| 0.9830 \| 0.9984 \| 1e-06 \| 0.9218 \| 0.9772 \| 0.9948 \| \| 0.0167 \| 34.0 \| 1870 \| 0.0175 \| 0.9625 \| 0.9857 \| 0.9938 \| 1e-06 \| 0.9768 \| 0.9822 \| 0.9981 \| 1e-06 \| 0.9156 \| 0.9769 \| 0.9951 \| \| 0.0164 \| 35.0 \| 1925 \| 0.0170 \| 0.9643 \| 0.9854 \| 0.9940 \| 1e-06 \| 0.9749 \| 0.9832 \| 0.9981 \| 1e-06 \| 0.9200 \| 0.9776 \| 0.9952 \| \| 0.016 \| 36.0 \| 1980 \| 0.0166 \| 0.9657 \| 0.9844 \| 0.9941 \| 1e-06 \| 0.9710 \| 0.9837 \| 0.9984 \| 1e-06 \| 0.9237 \| 0.9782 \| 0.9952 \| \| 0.0161 \| 37.0 \| 2035 \| 0.0169 \| 0.9661 \| 0.9830 \| 0.9941 \| 1e-06 \| 0.9668 \| 0.9834 \| 0.9987 \| 1e-06 \| 0.9254 \| 0.9780 \| 0.9949 \| \| 0.0156 \| 38.0 \| 2090 \| 0.0172 \| 0.9648 \| 0.9840 \| 0.9939 \| 1e-06 \| 0.9706 \| 0.9829 \| 0.9984 \| 1e-06 \| 0.9220 \| 0.9774 \| 0.9949 \| \| 0.0156 \| 39.0 \| 2145 \| 0.0170 \| 0.9640 \| 0.9857 \| 0.9940 \| 1e-06 \| 0.9769 \| 0.9817 \| 0.9985 \| 1e-06 \| 0.9192 \| 0.9774 \| 0.9953 \| \| 0.0152 \| 40.0 \| 2200 \| 0.0164 \| 0.9667 \| 0.9845 \| 0.9942 \| 1e-06 \| 0.9710 \| 0.9839 \| 0.9985 \| 1e-06 \| 0.9267 \| 0.9783 \| 0.9952 \| \| 0.0153 \| 41.0 \| 2255 \| 0.0164 \| 0.9663 \| 0.9854 \| 0.9942 \| 1e-06 \| 0.9748 \| 0.9830 \| 0.9985 \| 1e-06 \| 0.9256 \| 0.9780 \| 0.9953 \| \| 0.016 \| 42.0 \| 2310 \| 0.0162 \| 0.9662 \| 0.9854 \| 0.9942 \| 1e-06 \| 0.9744 \| 0.9833 \| 0.9985 \| 1e-06 \| 0.9254 \| 0.9778 \| 0.9954 \| \| 0.0157 \| 43.0 \| 2365 \| 0.0162 \| 0.9670 \| 0.9849 \| 0.9943 \| 1e-06 \| 0.9724 \| 0.9837 \| 0.9986 \| 1e-06 \| 0.9269 \| 0.9786 \| 0.9953 \| \| 0.0148 \| 44.0 \| 2420 \| 0.0167 \| 0.9671 \| 0.9850 \| 0.9943 \| 1e-06 \| 0.9719 \| 0.9849 \| 0.9983 \| 1e-06 \| 0.9273 \| 0.9786 \| 0.9953 \| \| 0.0149 \| 45.0 \| 2475 \| 0.0165 \| 0.9660 \| 0.9853 \| 0.9943 \| 1e-06 \| 0.9730 \| 0.9846 \| 0.9983 \| 1e-06 \| 0.9235 \| 0.9789 \| 0.9955 \| \| 0.0144 \| 46.0 \| 2530 \| 0.0154 \| 0.9670 \| 0.9870 \| 0.9945 \| 1e-06 \| 0.9784 \| 0.9844 \| 0.9983 \| 1e-06 \| 0.9260 \| 0.9791 \| 0.9958 \| \| 0.0142 \| 47.0 \| 2585 \| 0.0150 \| 0.9685 \| 0.9865 \| 0.9946 \| 1e-06 \| 0.9762 \| 0.9847 \| 0.9985 \| 1e-06 \| 0.9302 \| 0.9794 \| 0.9957 \| \| 0.0142 \| 48.0 \| 2640 \| 0.0154 \| 0.9672 \| 0.9870 \| 0.9945 \| 1e-06 \| 0.9784 \| 0.9841 \| 0.9984 \| 1e-06 \| 0.9268 \| 0.9792 \| 0.9957 \| \| 0.0144 \| 49.0 \| 2695 \| 0.0152 \| 0.9677 \| 0.9862 \| 0.9945 \| 1e-06 \| 0.9754 \| 0.9847 \| 0.9985 \| 1e-06 \| 0.9284 \| 0.9791 \| 0.9957 \| \| 0.0141 \| 50.0 \| 2750 \| 0.0154 \| 0.9681 \| 0.9857 \| 0.9946 \| 1e-06 \| 0.9729 \| 0.9857 \| 0.9984 \| 1e-06 \| 0.9289 \| 0.9796 \| 0.9957 \| \| 0.0136 \| 51.0 \| 2805 \| 0.0153 \| 0.9690 \| 0.9855 \| 0.9947 \| 1e-06 \| 0.9728 \| 0.9850 \| 0.9987 \| 1e-06 \| 0.9317 \| 0.9797 \| 0.9957 \| \| 0.0138 \| 52.0 \| 2860 \| 0.0150 \| 0.9691 \| 0.9866 \| 0.9947 \| 1e-06 \| 0.9767 \| 0.9846 \| 0.9986 \| 1e-06 \| 0.9320 \| 0.9796 \| 0.9957 \| \| 0.014 \| 53.0 \| 2915 \| 0.0158 \| 0.9673 \| 0.9853 \| 0.9945 \| 1e-06 \| 0.9720 \| 0.9855 \| 0.9984 \| 1e-06 \| 0.9266 \| 0.9798 \| 0.9956 \| \| 0.0136 \| 54.0 \| 2970 \| 0.0154 \| 0.9693 \| 0.9857 \| 0.9948 \| 1e-06 \| 0.9725 \| 0.9863 \| 0.9985 \| 1e-06 \| 0.9319 \| 0.9802 \| 0.9958 \| \| 0.0138 \| 55.0 \| 3025 \| 0.0154 \| 0.9692 \| 0.9853 \| 0.9947 \| 1e-06 \| 0.9717 \| 0.9855 \| 0.9986 \| 1e-06 \| 0.9323 \| 0.9798 \| 0.9956 \| \| 0.0134 \| 56.0 \| 3080 \| 0.0153 \| 0.9689 \| 0.9857 \| 0.9947 \| 1e-06 \| 0.9728 \| 0.9860 \| 0.9984 \| 1e-06 \| 0.9312 \| 0.9797 \| 0.9957 \| \| 0.0135 \| 57.0 \| 3135 \| 0.0154 \| 0.9695 \| 0.9863 \| 0.9948 \| 1e-06 \| 0.9747 \| 0.9855 \| 0.9986 \| 1e-06 \| 0.9325 \| 0.9800 \| 0.9958 \| \| 0.0133 \| 58.0 \| 3190 \| 0.0154 \| 0.9689 \| 0.9859 \| 0.9947 \| 1e-06 \| 0.9739 \| 0.9854 \| 0.9985 \| 1e-06 \| 0.9313 \| 0.9798 \| 0.9957 \| \| 0.0134 \| 59.0 \| 3245 \| 0.0152 \| 0.9696 \| 0.9862 \| 0.9948 \| 1e-06 \| 0.9745 \| 0.9856 \| 0.9986 \| 1e-06 \| 0.9328 \| 0.9801 \| 0.9958 \| \| 0.0138 \| 60.0 \| 3300 \| 0.0153 \| 0.9689 \| 0.9858 \| 0.9947 \| 1e-06 \| 0.9729 \| 0.9861 \| 0.9985 \| 1e-06 \| 0.9305 \| 0.9802 \| 0.9958 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1 - Datasets 2.15.0 - Tokenizers 0.15.0
habulaj/112492325026	habulaj	2024-06-30T21:18:44Z	null	[ "region:us" ]	null	2024-06-30T21:18:43Z	Entry not found
DraughtMonkeKZ/TibetanMacaque	DraughtMonkeKZ	2024-06-30T21:19:00Z	null	[ "license:gpl-3.0", "region:us" ]	null	2024-06-30T21:19:00Z	--- license: gpl-3.0 ---
ramy21/yolounder	ramy21	2024-06-30T21:32:14Z	null	[ "region:us" ]	null	2024-06-30T21:19:47Z	# My YOLO Model This model is trained using PyTorch Lightning.
habulaj/2388242475	habulaj	2024-06-30T21:21:20Z	null	[ "region:us" ]	null	2024-06-30T21:21:13Z	Entry not found
mgh6/TCS_MLM_SaProt	mgh6	2024-07-01T20:54:35Z	transformers	[ "transformers", "tensorboard", "safetensors", "esm", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-06-30T21:22:34Z	Entry not found
DewEfresh/Neo_7b-merge10	DewEfresh	2024-06-30T21:23:35Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "DewEfresh/neo_7b", "conversational", "base_model:DewEfresh/neo_7b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T21:22:53Z	--- base_model: - DewEfresh/neo_7b - DewEfresh/neo_7b tags: - merge - mergekit - lazymergekit - DewEfresh/neo_7b --- # Neo_7b-merge10 Neo_7b-merge10 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) ## 🧩 Configuration ```yaml slices: - sources: - model: DewEfresh/neo_7b layer_range: [0, 0] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [1, 1] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [2, 2] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [4, 4] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [5, 5] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [6, 6] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [8, 8] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [9, 9] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [10, 10] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [12, 12] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [13, 13] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [14, 14] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [16, 16] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [17, 17] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [18, 18] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [20, 20] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [21, 21] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [22, 22] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [24, 24] - model: DewEfresh/neo_7b layer_range: [27, 27] - sources: - model: DewEfresh/neo_7b layer_range: [25, 25] - model: DewEfresh/neo_7b layer_range: [27, 27] - sources: - model: DewEfresh/neo_7b layer_range: [26, 26] - model: DewEfresh/neo_7b layer_range: [27, 27] merge_method: slerp base_model: DewEfresh/neo_7b parameters: t: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DewEfresh/Neo_7b-merge10" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Marcelojtc/bart-cnn-samsum-peft	Marcelojtc	2024-06-30T21:38:35Z	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:ingeniumacademy/bart-cnn-samsum-finetuned", "license:mit", "region:us" ]	null	2024-06-30T21:24:22Z	--- base_model: ingeniumacademy/bart-cnn-samsum-finetuned datasets: - samsum library_name: peft license: mit tags: - generated_from_trainer model-index: - name: bart-cnn-samsum-peft 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. --> # bart-cnn-samsum-peft This model is a fine-tuned version of [ingeniumacademy/bart-cnn-samsum-finetuned](https://huggingface.co/ingeniumacademy/bart-cnn-samsum-finetuned) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.1345 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.078 \| 1.0 \| 19 \| 0.1345 \| \| 0.0865 \| 2.0 \| 38 \| 0.1345 \| \| 0.0768 \| 3.0 \| 57 \| 0.1345 \| \| 0.079 \| 4.0 \| 76 \| 0.1345 \| \| 0.0916 \| 5.0 \| 95 \| 0.1345 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
habulaj/54258170221	habulaj	2024-06-30T21:26:44Z	null	[ "region:us" ]	null	2024-06-30T21:26:38Z	Entry not found
silveroxides/AnimagineXL31_X_AutismMixPony_mergeproject	silveroxides	2024-06-30T22:17:54Z	null	[ "region:us" ]	null	2024-06-30T21:30:21Z	Entry not found
DewEfresh/Neo_7b-merge11	DewEfresh	2024-06-30T21:32:06Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "DewEfresh/neo_7b", "conversational", "base_model:DewEfresh/neo_7b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T21:31:20Z	--- base_model: - DewEfresh/neo_7b - DewEfresh/neo_7b tags: - merge - mergekit - lazymergekit - DewEfresh/neo_7b --- # Neo_7b-merge11 Neo_7b-merge11 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) ## 🧩 Configuration ```yaml slices: - sources: - model: DewEfresh/neo_7b layer_range: [0, 0] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [1, 1] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [2, 2] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [4, 4] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [5, 5] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [6, 6] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [8, 8] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [9, 9] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [10, 10] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [12, 12] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [13, 13] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [14, 14] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [16, 16] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [17, 17] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [18, 18] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [20, 20] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [21, 21] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [22, 22] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [24, 24] - model: DewEfresh/neo_7b layer_range: [27, 27] - sources: - model: DewEfresh/neo_7b layer_range: [25, 25] - model: DewEfresh/neo_7b layer_range: [27, 27] - sources: - model: DewEfresh/neo_7b layer_range: [26, 26] - model: DewEfresh/neo_7b layer_range: [27, 27] merge_method: slerp base_model: DewEfresh/neo_7b parameters: t: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DewEfresh/Neo_7b-merge11" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
ramy21/newyolos	ramy21	2024-06-30T21:35:18Z	null	[ "region:us" ]	null	2024-06-30T21:35:18Z	Entry not found
variante/llara-maskrcnn	variante	2024-07-01T04:43:20Z	null	[ "llara", "robotics", "vlm", "object-detection", "dataset:VIMA/VIMA-Data", "arxiv:2406.20095", "license:apache-2.0", "region:us" ]	object-detection	2024-06-30T21:36:03Z	--- inference: false license: apache-2.0 datasets: - VIMA/VIMA-Data tags: - llara - robotics - vlm pipeline_tag: object-detection --- <br> <be> # Model Card This model is released with paper [LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://arxiv.org/abs/2406.20095) [Xiang Li](https://xxli.me)<sup>1</sup>, [Cristina Mata](https://openreview.net/profile?id=~Cristina_Mata1)<sup>1</sup>, [Jongwoo Park](https://github.com/jongwoopark7978)<sup>1</sup>, [Kumara Kahatapitiya](https://www3.cs.stonybrook.edu/~kkahatapitiy)<sup>1</sup>, [Yoo Sung Jang](https://yjang43.github.io/)<sup>1</sup>, [Jinghuan Shang](https://elicassion.github.io/)<sup>1</sup>, [Kanchana Ranasinghe](https://kahnchana.github.io/)<sup>1</sup>, [Ryan Burgert](https://ryanndagreat.github.io/)<sup>1</sup>, [Mu Cai](https://pages.cs.wisc.edu/~mucai/)<sup>2</sup>, [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)<sup>2</sup>, and [Michael S. Ryoo](http://michaelryoo.com/)<sup>1</sup> <sup>1</sup>Stony Brook University <sup>2</sup>University of Wisconsin-Madison ## Model details Model type: This repository contains three models trained on three subsets respectively, converted from [VIMA-Data](https://huggingface.co/datasets/VIMA/VIMA-Data). For the conversion code, please refer to [convert_vima.ipynb](https://github.com/LostXine/LLaRA/blob/main/datasets/convert_vima.ipynb) Paper or resources for more information: https://github.com/LostXine/LLaRA Where to send questions or comments about the model: https://github.com/LostXine/LLaRA/issues
Frixi/ROA_PR	Frixi	2024-06-30T21:36:20Z	null	[ "license:openrail", "region:us" ]	null	2024-06-30T21:36:05Z	--- license: openrail ---
tom1-ll/Lilyallroundv2	tom1-ll	2024-06-30T21:39:00Z	null	[ "license:openrail", "region:us" ]	null	2024-06-30T21:37:24Z	--- license: openrail ---
imelike/new-turkishReviews-tokenizer	imelike	2024-06-30T21:37:29Z	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-30T21:37:27Z	--- 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]
habulaj/3855139265	habulaj	2024-06-30T21:39:57Z	null	[ "region:us" ]	null	2024-06-30T21:39:49Z	Entry not found
gkMSDA/FinChat_Mistral7B_DPO_V2	gkMSDA	2024-06-30T21:52:38Z	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T21:46:50Z	--- 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. 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]
habulaj/368315333833	habulaj	2024-06-30T21:49:04Z	null	[ "region:us" ]	null	2024-06-30T21:49:01Z	Entry not found
Adam3/Michael-Kranz	Adam3	2024-06-30T21:50:27Z	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-3-medium", "region:us" ]	text-to-image	2024-06-30T21:49:02Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/1001125444.jpg base_model: stabilityai/stable-diffusion-3-medium instance_prompt: null --- # Michael kranz <Gallery /> ## Download model [Download](/Adam3/Michael-Kranz/tree/main) them in the Files & versions tab.
Mome757mome/moka	Mome757mome	2024-06-30T21:49:51Z	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2024-06-30T21:49:51Z	--- license: bigscience-bloom-rail-1.0 ---
habulaj/135763111248	habulaj	2024-06-30T21:50:18Z	null	[ "region:us" ]	null	2024-06-30T21:50:16Z	Entry not found
MHRDYN7/dnv2-base	MHRDYN7	2024-06-30T21:55:36Z	transformers	[ "transformers", "safetensors", "dnv2", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-30T21:52:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
habulaj/5320041046	habulaj	2024-06-30T21:53:56Z	null	[ "region:us" ]	null	2024-06-30T21:53:49Z	Entry not found
habulaj/137258123550	habulaj	2024-06-30T21:57:21Z	null	[ "region:us" ]	null	2024-06-30T21:57:16Z	Entry not found
nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr1e4_dec1e3_bs16	nsugianto	2024-07-02T14:34:11Z	transformers	[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "generated_from_trainer", "base_model:nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr5e5_dec1e4_bs12", "license:mit", "endpoints_compatible", "region:us" ]	object-detection	2024-06-30T21:58:38Z	--- license: mit base_model: nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr5e5_dec1e4_bs12 tags: - generated_from_trainer model-index: - name: tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr1e4_dec1e3_bs16 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. --> # tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr1e4_dec1e3_bs16 This model is a fine-tuned version of [nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr5e5_dec1e4_bs12](https://huggingface.co/nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr5e5_dec1e4_bs12) 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1
nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr1e4_dec1e3_bs16	nsugianto	2024-07-03T01:30:50Z	transformers	[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "generated_from_trainer", "base_model:nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr5e5_dec1e4_bs12", "license:mit", "endpoints_compatible", "region:us" ]	object-detection	2024-06-30T21:59:38Z	--- license: mit base_model: nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr5e5_dec1e4_bs12 tags: - generated_from_trainer model-index: - name: tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr1e4_dec1e3_bs16 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. --> # tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr1e4_dec1e3_bs16 This model is a fine-tuned version of [nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr5e5_dec1e4_bs12](https://huggingface.co/nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr5e5_dec1e4_bs12) 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1
khanhnn55/naschainv8	khanhnn55	2024-07-02T23:19:43Z	null	[ "region:us" ]	null	2024-06-30T22:03:31Z	Entry not found
NiluferUcar/CNN_Convolutional_Neural_Network_Models	NiluferUcar	2024-06-30T22:08:12Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-30T22:03:50Z	--- license: apache-2.0 ---
nicolebar/3d	nicolebar	2024-06-30T22:04:38Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-30T22:04:38Z	--- license: apache-2.0 ---
JEFFERSONMUSIC/JKGOLDENERAV3	JEFFERSONMUSIC	2024-06-30T22:08:25Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-30T22:07:09Z	--- license: apache-2.0 ---
chaanks/UTMOS	chaanks	2024-06-30T22:14:47Z	null	[ "region:us" ]	null	2024-06-30T22:13:36Z	Entry not found
Coolwowsocoolwow/Random_Assertive_EGirl	Coolwowsocoolwow	2024-06-30T22:20:01Z	null	[ "license:openrail", "region:us" ]	null	2024-06-30T22:16:56Z	--- license: openrail ---
habulaj/7282053427	habulaj	2024-06-30T22:18:22Z	null	[ "region:us" ]	null	2024-06-30T22:18:20Z	Entry not found
Intaa/Lucas	Intaa	2024-07-02T22:51:53Z	null	[ "region:us" ]	null	2024-06-30T22:18:35Z	Entry not found
TheDima/resnet50-dog-breed-identification	TheDima	2024-06-30T22:20:03Z	fastai	[ "fastai", "region:us" ]	null	2024-06-30T22:19:27Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
chopchopchuck/mts100	chopchopchuck	2024-06-30T22:21:12Z	null	[ "region:us" ]	null	2024-06-30T22:20:47Z	Entry not found
habulaj/2436026491	habulaj	2024-06-30T22:21:04Z	null	[ "region:us" ]	null	2024-06-30T22:20:52Z	Entry not found
chopchopchuck/mts101	chopchopchuck	2024-06-30T22:22:46Z	null	[ "region:us" ]	null	2024-06-30T22:22:27Z	Entry not found
chopchopchuck/mts102	chopchopchuck	2024-06-30T22:25:13Z	null	[ "region:us" ]	null	2024-06-30T22:24:03Z	Entry not found
habulaj/7712156082	habulaj	2024-06-30T22:24:54Z	null	[ "region:us" ]	null	2024-06-30T22:24:48Z	Entry not found
rambaldi47/q-FrozenLake-v1-4x4-noSlippery	rambaldi47	2024-06-30T22:24:58Z	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-30T22:24:55Z	--- 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="rambaldi47/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"]) ```
slattybenzo/checkpoint	slattybenzo	2024-06-30T22:26:09Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-30T22:26:09Z	--- license: apache-2.0 ---
chopchopchuck/mts105	chopchopchuck	2024-06-30T22:27:43Z	null	[ "region:us" ]	null	2024-06-30T22:27:21Z	Entry not found
habulaj/367577333123	habulaj	2024-06-30T22:28:44Z	null	[ "region:us" ]	null	2024-06-30T22:28:24Z	Entry not found
rambaldi47/Taxi-v3	rambaldi47	2024-06-30T23:32:06Z	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-30T22:28:41Z	--- 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: 7.56 +/- 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="rambaldi47/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"]) ```
chopchopchuck/mts106	chopchopchuck	2024-06-30T22:29:32Z	null	[ "region:us" ]	null	2024-06-30T22:29:10Z	Entry not found
chopchopchuck/mts107	chopchopchuck	2024-06-30T22:31:18Z	null	[ "region:us" ]	null	2024-06-30T22:30:58Z	Entry not found
habulaj/200255390394	habulaj	2024-06-30T22:32:24Z	null	[ "region:us" ]	null	2024-06-30T22:31:50Z	Entry not found
chopchopchuck/mts108	chopchopchuck	2024-06-30T22:32:57Z	null	[ "region:us" ]	null	2024-06-30T22:32:34Z	Entry not found
martimfasantos/tinyllama-1.1b-sum-sft-full_LR4e-5	martimfasantos	2024-07-01T00:26:23Z	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:martimfasantos/openai-summarize-tldr", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T22:33:27Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - martimfasantos/openai-summarize-tldr model-index: - name: tinyllama-1.1b-sum-sft-full_LR4e-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinyllama-1.1b-sum-sft-full_LR4e-5 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the martimfasantos/openai-summarize-tldr dataset. It achieves the following results on the evaluation set: - Loss: 2.1087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.1044 \| 0.9997 \| 1476 \| 2.1087 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1
chopchopchuck/mts109	chopchopchuck	2024-06-30T22:34:38Z	null	[ "region:us" ]	null	2024-06-30T22:34:15Z	Entry not found
chopchopchuck/mts110	chopchopchuck	2024-06-30T22:36:13Z	null	[ "region:us" ]	null	2024-06-30T22:35:52Z	Entry not found
habulaj/1109616775	habulaj	2024-06-30T22:36:17Z	null	[ "region:us" ]	null	2024-06-30T22:36:11Z	Entry not found
AIGym/fast-mini-webtext-65536	AIGym	2024-06-30T22:36:47Z	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-30T22:36:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mikedata/q-FrozenLake-v1-4x4-noSlippery	mikedata	2024-06-30T22:40:14Z	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-30T22:39:46Z	--- 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="mikedata/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"]) ```
Homiebear/R2-D2	Homiebear	2024-06-30T22:44:51Z	null	[ "license:openrail", "region:us" ]	null	2024-06-30T22:44:30Z	--- license: openrail ---
Spbou4-hilma/HILMA-FIN-7B	Spbou4-hilma	2024-06-30T23:03:25Z	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-06-30T22:45:31Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: meta-llama/Meta-Llama-3-8B-Instruct widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
habulaj/244479215760	habulaj	2024-06-30T22:46:26Z	null	[ "region:us" ]	null	2024-06-30T22:46:18Z	Entry not found
EmoHugAI/xlm-roberta-base-finetuned-panx-de	EmoHugAI	2024-06-30T23:34:41Z	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-30T22:46:34Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de 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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3159 - F1: 0.8543 ## 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: 24 - eval_batch_size: 24 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.3578 \| 1.0 \| 525 \| 0.2204 \| 0.7870 \| \| 0.1946 \| 2.0 \| 1050 \| 0.2063 \| 0.8072 \| \| 0.1314 \| 3.0 \| 1575 \| 0.2037 \| 0.8318 \| \| 0.0924 \| 4.0 \| 2100 \| 0.2161 \| 0.8363 \| \| 0.0641 \| 5.0 \| 2625 \| 0.2472 \| 0.8418 \| \| 0.046 \| 6.0 \| 3150 \| 0.2754 \| 0.8409 \| \| 0.0306 \| 7.0 \| 3675 \| 0.2718 \| 0.8509 \| \| 0.0205 \| 8.0 \| 4200 \| 0.3045 \| 0.8563 \| \| 0.0128 \| 9.0 \| 4725 \| 0.3148 \| 0.8568 \| \| 0.0091 \| 10.0 \| 5250 \| 0.3159 \| 0.8543 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
mikedata/q-Taxi-V3	mikedata	2024-06-30T22:48:55Z	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-30T22:47:40Z	--- 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="mikedata/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"]) ```

iceman2434/xlm-roberta-base-ft-udpos213-top5langrandom

iceman2434

2024-06-30T20:33:54Z

0

null

[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]

token-classification

2024-06-30T20:31:31Z

--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: XLM-RoBERTa (base-sized model) - Randomized training order of languages - Training Data: - Combined Afrikaans, Norwegian, Vietnamese, Hebrew, & Bulgarian corpora (Top 5 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 79.01\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB

Litzy619/MIS0630T3

Litzy619

2024-06-30T20:44:58Z

0

null

[ "region:us" ]

null

2024-06-30T20:31:54Z

Entry not found

DimensionSTP/Llama-3-KoEn-8B-scientificQA

DimensionSTP

2024-06-30T23:58:29Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "llama-3-ko", "conversational", "en", "ko", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T20:32:33Z

--- language: - en - ko license: cc-by-nc-sa-4.0 tags: - facebook - meta - pytorch - llama - llama-3 - llama-3-ko pipeline_tag: text-generation license_name: llama3 license_link: LICENSE --- ## Model Details **This model is fine-tuned by beomi/Llama-3-KoEn-8B** **Fine-tuning dataset: Scientific QA dataset**

lit9003code/melotts310

lit9003code

2024-06-30T20:33:10Z

0

null

[ "region:us" ]

null

2024-06-30T20:32:50Z

Entry not found

lit9003code/melotts311

lit9003code

2024-06-30T20:35:39Z

0

null

[ "region:us" ]

null

2024-06-30T20:34:25Z

Entry not found

asafi/Meta-Llama-3-medical-8B

asafi

2024-06-30T20:35:18Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-30T20:34:50Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** asafi - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

lit9003code/melotts312

lit9003code

2024-06-30T20:37:27Z

0

null

[ "region:us" ]

null

2024-06-30T20:37:03Z

Entry not found

lit9003code/melotts313

lit9003code

2024-06-30T20:38:59Z

0

null

[ "region:us" ]

null

2024-06-30T20:38:39Z

Entry not found

lit9003code/melotts314

lit9003code

2024-06-30T20:41:26Z

0

null

[ "region:us" ]

null

2024-06-30T20:40:17Z

Entry not found

iceman2434/roberta-tagalog-base-ft-udpos213-top2langrandom

iceman2434

2024-06-30T20:47:29Z

0

null

[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]

token-classification

2024-06-30T20:41:11Z

--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz) - Randomized training order of languages - Training Data: - Combined English & Serbian corpora (Top 2 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 73.99\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB

iceman2434/roberta-tagalog-base-ft-udpos213-top3langrandom

iceman2434

2024-06-30T20:47:42Z

0

null

[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]

token-classification

2024-06-30T20:43:44Z

--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz) - Randomized training order of languages - Training Data: - Combined English, Serbian, & Slovenian corpora (Top 3 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 71.91\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB

psimm/llama-3-8B-semeval2014-task

psimm

2024-06-30T20:48:18Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-30T20:45:05Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

habulaj/67797950

habulaj

2024-06-30T20:46:14Z

0

null

[ "region:us" ]

null

2024-06-30T20:46:10Z

Entry not found

iceman2434/roberta-tagalog-base-ft-udpos213-top4langrandom

iceman2434

2024-06-30T20:50:11Z

0

null

[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]

token-classification

2024-06-30T20:48:01Z

--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz) - Randomized training order of languages - Training Data: - Combined English, Serbian, Slovenian, & Naija corpora (Top 4 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 72.97\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB

Litzy619/MIS0630T4

Litzy619

2024-06-30T22:56:13Z

0

null

[ "region:us" ]

null

2024-06-30T20:48:06Z

Entry not found

habulaj/9917682106

habulaj

2024-06-30T20:50:19Z

0

null

[ "region:us" ]

null

2024-06-30T20:50:17Z

Entry not found

Loren85/Dick-Van-Dyke-2024-2023-voice

Loren85

2024-06-30T20:51:27Z

0

null

[ "license:openrail", "region:us" ]

null

2024-06-30T20:50:29Z

--- license: openrail ---

iceman2434/roberta-tagalog-base-ft-udpos213-top5langrandom

iceman2434

2024-06-30T20:52:43Z

0

null

[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]

token-classification

2024-06-30T20:50:55Z

--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: RoBERTa Tagalog Base (Jan Christian Blaise Cruz) - Randomized training order of languages - Training Data: - Combined English, Serbian, Slovenian, Naija, & Manx-Cadhan corpora (Top 5 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 72.52\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB

eriho/MobileNetV4_TensorFlow.js_feature_vector_small.e2400_r224

eriho

2024-06-30T21:03:19Z

0

null

[ "tensorflow.js", "transfer learning", "feature-extraction", "license:cc-by-sa-4.0", "region:us" ]

feature-extraction

2024-06-30T20:51:29Z

--- license: cc-by-sa-4.0 pipeline_tag: feature-extraction tags: - tensorflow.js - transfer learning --- Original model: https://huggingface.co/timm/mobilenetv4_conv_small.e2400_r224_in1k/tree/main shape 1,224,224,3 hf gl

steja/whisper-medium-english

steja

2024-06-30T20:52:48Z

0

null

[ "region:us" ]

null

2024-06-30T20:52:48Z

Entry not found

habulaj/2003219747

habulaj

2024-06-30T20:52:54Z

0

null

[ "region:us" ]

null

2024-06-30T20:52:51Z

Entry not found

habulaj/195519346132

habulaj

2024-06-30T20:54:08Z

0

null

[ "region:us" ]

null

2024-06-30T20:53:55Z

Entry not found

42Antonio/Acb

42Antonio

2024-06-30T20:55:15Z

0

null

[ "region:us" ]

null

2024-06-30T20:55:14Z

Entry not found

odelz/hindi_fb1mms_timebalancedreg2

odelz

2024-06-30T20:55:18Z

0

null

[ "region:us" ]

null

2024-06-30T20:55:18Z

Entry not found

variante/llava-1.5-7b-llara-D-inBC-VIMA-80k

variante

2024-07-01T04:42:21Z

0

transformers

[ "transformers", "safetensors", "llava", "text-generation", "llara", "robotics", "vlm", "image-text-to-text", "dataset:VIMA/VIMA-Data", "arxiv:2406.20095", "license:apache-2.0", "autotrain_compatible", "region:us" ]

image-text-to-text

2024-06-30T20:57:42Z

--- inference: false pipeline_tag: image-text-to-text license: apache-2.0 datasets: - VIMA/VIMA-Data tags: - llara - llava - robotics - vlm --- <be> # LLaRA Model Card This model is released with paper **[LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://arxiv.org/abs/2406.20095)** [Xiang Li](https://xxli.me)1, [Cristina Mata](https://openreview.net/profile?id=~Cristina_Mata1)1, [Jongwoo Park](https://github.com/jongwoopark7978)1, [Kumara Kahatapitiya](https://www3.cs.stonybrook.edu/~kkahatapitiy)1, [Yoo Sung Jang](https://yjang43.github.io/)1, [Jinghuan Shang](https://elicassion.github.io/)1, [Kanchana Ranasinghe](https://kahnchana.github.io/)1, [Ryan Burgert](https://ryanndagreat.github.io/)1, [Mu Cai](https://pages.cs.wisc.edu/~mucai/)2, [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)2, and [Michael S. Ryoo](http://michaelryoo.com/)1 1Stony Brook University 2University of Wisconsin-Madison ## Model details **Model type:** LLaRA is an open-source visuomotor policy trained by fine-tuning [LLaVA-7b-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) on instruction-following data `D-inBC`, converted from [VIMA-Data](https://huggingface.co/datasets/VIMA/VIMA-Data). For the conversion code, please refer to [convert_vima.ipynb](https://github.com/LostXine/LLaRA/blob/main/datasets/convert_vima.ipynb) **Model date:** llava-1.5-7b-llara-D-inBC-VIMA-80k was trained in June 2024. **Paper or resources for more information:** https://github.com/LostXine/LLaRA **Where to send questions or comments about the model:** https://github.com/LostXine/LLaRA/issues ## Intended use **Primary intended uses:** The primary use of LLaRA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

mohamedemam/Em2-Mistral-7b

mohamedemam

2024-07-02T09:36:18Z

0

peft

[ "peft", "safetensors", "autograding", "essay quetion", "sentence similarity", "en", "dataset:mohamedemam/Essay-quetions-auto-grading", "license:gpl", "region:us" ]

null

2024-06-30T20:57:51Z

--- language: - en license: gpl tags: - autograding - essay quetion - sentence similarity metrics: - accuracy library_name: peft datasets: - mohamedemam/Essay-quetions-auto-grading --- # Model Card for Model ID fine tuned version of Mistral on Essay-quetions-auto-grading ### Model Description  We are thrilled to introduce our graduation project, the EM2 model, designed for automated essay grading in both Arabic and English. 📝✨ To develop this model, we first created a custom dataset for training. We adapted the QuAC and OpenOrca datasets to make them suitable for our automated essay grading application. Our model utilizes the following impressive models: Mistral: 96% LLaMA: 93% FLAN-T5: 93% BLOOMZ (Arabic): 86% MT0 (Arabic): 84% You can try our models for auto-grading on Hugging Face! 🌐 We then deployed these models for practical use. We are proud of our team's hard work and the potential impact of the EM2 model in the field of education. 🌟 #MachineLearning #AI #Education #EssayGrading #GraduationProject - **Developed by:** mohamed emam - **Model type:** decoder only - **Language(s) (NLP):** English - **License:** gpl - **Finetuned from model :** Mistral  - **Repository:** https://github.com/mohamed-em2m/Automatic-Grading-AI - ### Direct Use  auto grading for essay quetions ### Explain how it work - model take three inputs first context or perfect answer + quetion on context + student answer then model output the result ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6456f2eca9b8e1fd4cbe5ebe/_O75HT2zb2TYZOEkX4YXO.png) ### Training Data - **mohamedemam/Essay-quetions-auto-grading-arabic** ### Training Procedure using Trl ### Pipline ```python from transformers import Pipeline import torch.nn.functional as F class MyPipeline: def __init__(self,model,tokenizer): self.model=model self.tokenizer=tokenizer def chat_Format(self,context, quetion, answer): return "Instruction:/n check answer is true or false of next quetion using context below:\n" + "#context: " + context + f".\n#quetion: " + quetion + f".\n#student answer: " + answer + ".\n#response:" def __call__(self, context, quetion, answer,generate=1,max_new_tokens=4, num_beams=2, do_sample=False,num_return_sequences=1): inp=self.chat_Format(context, quetion, answer) w = self.tokenizer(inp, add_special_tokens=True, pad_to_max_length=True, return_attention_mask=True, return_tensors='pt') response="" if(generate): outputs = self.tokenizer.batch_decode(self.model.generate(input_ids=w['input_ids'].cuda(), attention_mask=w['attention_mask'].cuda(), max_new_tokens=max_new_tokens, num_beams=num_beams, do_sample=do_sample, num_return_sequences=num_return_sequences), skip_special_tokens=True) response = outputs s =self.model(input_ids=w['input_ids'].cuda(), attention_mask=w['attention_mask'].cuda())['logits'][0][-1] s = F.softmax(s, dim=-1) yes_token_id = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize("True")[0]) no_token_id = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize("False")[0]) for i in ["Yes", "yes", "True", "true","صحيح"]: for word in self.tokenizer.tokenize(i): s[yes_token_id] += s[self.tokenizer.convert_tokens_to_ids(word)] for i in ["No", "no", "False", "false","خطأ"]: for word in self.tokenizer.tokenize(i): s[no_token_id] += s[self.tokenizer.convert_tokens_to_ids(word)] true = (s[yes_token_id] / (s[no_token_id] + s[yes_token_id])).item() return {"response": response, "true": true} context="""Large language models, such as GPT-4, are trained on vast amounts of text data to understand and generate human-like text. The deployment of these models involves several steps: Model Selection: Choosing a pre-trained model that fits the application's needs. Infrastructure Setup: Setting up the necessary hardware and software infrastructure to run the model efficiently, including cloud services, GPUs, and necessary libraries. Integration: Integrating the model into an application, which can involve setting up APIs or embedding the model directly into the software. Optimization: Fine-tuning the model for specific tasks or domains and optimizing it for performance and cost-efficiency. Monitoring and Maintenance: Ensuring the model performs well over time, monitoring for biases, and updating the model as needed.""" quetion="What are the key considerations when choosing a cloud service provider for deploying a large language model like GPT-4?" answer="""When choosing a cloud service provider for deploying a large language model like GPT-4, the key considerations include: Compute Power: Ensure the provider offers high-performance GPUs or TPUs capable of handling the computational requirements of the model. Scalability: The ability to scale resources up or down based on the application's demand to handle varying workloads efficiently. Cost: Analyze the pricing models to understand the costs associated with compute time, storage, data transfer, and any other services. Integration and Support: Availability of tools and libraries that support easy integration of the model into your applications, along with robust technical support and documentation. Security and Compliance: Ensure the provider adheres to industry standards for security and compliance, protecting sensitive data and maintaining privacy. Latency and Availability: Consider the geographical distribution of data centers to ensure low latency and high availability for your end-users. By evaluating these factors, you can select a cloud service provider that aligns with your deployment needs, ensuring efficient and cost-effective operation of your large language model.""" from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM,AutoTokenizer config = PeftConfig.from_pretrained("mohamedemam/Em2-llama-7b") base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "mohamedemam/Em2-llama-7b") tokenizer = AutoTokenizer.from_pretrained("mohamedemam/Em2-llama-7b", trust_remote_code=True) pipe=MyPipeline(model,tokenizer) print(pipe(context,quetion,answer,generate=True,max_new_tokens=4, num_beams=2, do_sample=False,num_return_sequences=1)) ``` - **output:**{'response': ["Instruction:/n check answer is true or false of next quetion using context below:\n#context: Large language models, such as GPT-4, are trained on vast amounts of text data to understand and generate human-like text. The deployment of these models involves several steps:\n\n Model Selection: Choosing a pre-trained model that fits the application's needs.\n Infrastructure Setup: Setting up the necessary hardware and software infrastructure to run the model efficiently, including cloud services, GPUs, and necessary libraries.\n Integration: Integrating the model into an application, which can involve setting up APIs or embedding the model directly into the software.\n Optimization: Fine-tuning the model for specific tasks or domains and optimizing it for performance and cost-efficiency.\n Monitoring and Maintenance: Ensuring the model performs well over time, monitoring for biases, and updating the model as needed..\n#quetion: What are the key considerations when choosing a cloud service provider for deploying a large language model like GPT-4?.\n#student answer: When choosing a cloud service provider for deploying a large language model like GPT-4, the key considerations include:\n Compute Power: Ensure the provider offers high-performance GPUs or TPUs capable of handling the computational requirements of the model.\n Scalability: The ability to scale resources up or down based on the application's demand to handle varying workloads efficiently.\n Cost: Analyze the pricing models to understand the costs associated with compute time, storage, data transfer, and any other services.\n Integration and Support: Availability of tools and libraries that support easy integration of the model into your applications, along with robust technical support and documentation.\n Security and Compliance: Ensure the provider adheres to industry standards for security and compliance, protecting sensitive data and maintaining privacy.\n Latency and Availability: Consider the geographical distribution of data centers to ensure low latency and high availability for your end-users.\n\nBy evaluating these factors, you can select a cloud service provider that aligns with your deployment needs, ensuring efficient and cost-effective operation of your large language model..\n#response: true the answer is"], 'true': 0.943033754825592} ### Chat Format Function This function formats the input context, question, and answer into a specific structure for the model to process. ```python def chat_Format(self, context, question, answer): return "Instruction:/n check answer is true or false of next question using context below:\n" + "#context: " + context + f".\n#question: " + question + f".\n#student answer: " + answer + ".\n#response:" ``` ## Configuration ### Dropout Probability for LoRA Layers - **lora_dropout:** 0.05 ### Quantization Settings - **use_4bit:** True - **bnb_4bit_compute_dtype:** "float16" - **bnb_4bit_quant_type:** "nf4" - **use_nested_quant:** False ### Output Directory - **output_dir:** "./results" ### Training Parameters - **num_train_epochs:** 1 - **fp16:** False - **bf16:** False - **per_device_train_batch_size:** 1 - **per_device_eval_batch_size:** 4 - **gradient_accumulation_steps:** 8 - **gradient_checkpointing:** True - **max_grad_norm:** 0.3 - **learning_rate:** 5e-5 - **weight_decay:** 0.001 - **optim:** "paged_adamw_8bit" - **lr_scheduler_type:** "constant" - **max_steps:** -1 - **warmup_ratio:** 0.03 - **group_by_length:** True ### Logging and Saving - **save_steps:** 100 - **logging_steps:** 25 - **max_seq_length:** False

variante/llava-1.5-7b-llara-D-inBC-Aux-B-VIMA-80k

variante

2024-07-01T04:42:48Z

0

transformers

[ "transformers", "safetensors", "llava", "text-generation", "llara", "robotics", "vlm", "image-text-to-text", "dataset:VIMA/VIMA-Data", "arxiv:2406.20095", "license:apache-2.0", "autotrain_compatible", "region:us" ]

image-text-to-text

2024-06-30T20:58:23Z

--- inference: false pipeline_tag: image-text-to-text license: apache-2.0 datasets: - VIMA/VIMA-Data tags: - llara - llava - robotics - vlm --- <be> # LLaRA Model Card This model is released with paper **[LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://arxiv.org/abs/2406.20095)** [Xiang Li](https://xxli.me)1, [Cristina Mata](https://openreview.net/profile?id=~Cristina_Mata1)1, [Jongwoo Park](https://github.com/jongwoopark7978)1, [Kumara Kahatapitiya](https://www3.cs.stonybrook.edu/~kkahatapitiy)1, [Yoo Sung Jang](https://yjang43.github.io/)1, [Jinghuan Shang](https://elicassion.github.io/)1, [Kanchana Ranasinghe](https://kahnchana.github.io/)1, [Ryan Burgert](https://ryanndagreat.github.io/)1, [Mu Cai](https://pages.cs.wisc.edu/~mucai/)2, [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)2, and [Michael S. Ryoo](http://michaelryoo.com/)1 1Stony Brook University 2University of Wisconsin-Madison ## Model details **Model type:** LLaRA is an open-source visuomotor policy trained by fine-tuning [LLaVA-7b-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) on instruction-following data `D-inBC` and 4 auxiliary datasets, converted from [VIMA-Data](https://huggingface.co/datasets/VIMA/VIMA-Data). For the conversion code, please refer to [convert_vima.ipynb](https://github.com/LostXine/LLaRA/blob/main/datasets/convert_vima.ipynb) **Model date:** llava-1.5-7b-llara-D-inBC-Aux-B-VIMA-80k was trained in June 2024. **Paper or resources for more information:** https://github.com/LostXine/LLaRA **Where to send questions or comments about the model:** https://github.com/LostXine/LLaRA/issues ## Intended use **Primary intended uses:** The primary use of LLaRA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

variante/llava-1.5-7b-llara-D-inBC-Aux-D-VIMA-80k

variante

2024-07-01T04:43:03Z

0

transformers

[ "transformers", "safetensors", "llava", "text-generation", "llara", "robotics", "vlm", "image-text-to-text", "dataset:VIMA/VIMA-Data", "arxiv:2406.20095", "license:apache-2.0", "autotrain_compatible", "region:us" ]

image-text-to-text

2024-06-30T20:58:37Z

--- inference: false pipeline_tag: image-text-to-text license: apache-2.0 datasets: - VIMA/VIMA-Data tags: - llara - llava - robotics - vlm --- <be> # LLaRA Model Card This model is released with paper **[LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://arxiv.org/abs/2406.20095)** [Xiang Li](https://xxli.me)1, [Cristina Mata](https://openreview.net/profile?id=~Cristina_Mata1)1, [Jongwoo Park](https://github.com/jongwoopark7978)1, [Kumara Kahatapitiya](https://www3.cs.stonybrook.edu/~kkahatapitiy)1, [Yoo Sung Jang](https://yjang43.github.io/)1, [Jinghuan Shang](https://elicassion.github.io/)1, [Kanchana Ranasinghe](https://kahnchana.github.io/)1, [Ryan Burgert](https://ryanndagreat.github.io/)1, [Mu Cai](https://pages.cs.wisc.edu/~mucai/)2, [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)2, and [Michael S. Ryoo](http://michaelryoo.com/)1 1Stony Brook University 2University of Wisconsin-Madison ## Model details **Model type:** LLaRA is an open-source visuomotor policy trained by fine-tuning [LLaVA-7b-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) on instruction-following data `D-inBC` and 6 auxiliary datasets, converted from [VIMA-Data](https://huggingface.co/datasets/VIMA/VIMA-Data). For the conversion code, please refer to [convert_vima.ipynb](https://github.com/LostXine/LLaRA/blob/main/datasets/convert_vima.ipynb) **Model date:** llava-1.5-7b-llara-D-inBC-Aux-D-VIMA-80k was trained in June 2024. **Paper or resources for more information:** https://github.com/LostXine/LLaRA **Where to send questions or comments about the model:** https://github.com/LostXine/LLaRA/issues ## Intended use **Primary intended uses:** The primary use of LLaRA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Renee0v0/NeuralPipe-7B-slerp

Renee0v0

2024-06-30T21:05:24Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T21:00:59Z

--- base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- # NeuralPipe-7B-slerp NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Renee0v0/NeuralPipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

habulaj/297619439478

habulaj

2024-06-30T21:01:22Z

0

null

[ "region:us" ]

null

2024-06-30T21:01:12Z

Entry not found

habulaj/269179427694

habulaj

2024-06-30T21:03:11Z

0

null

[ "region:us" ]

null

2024-06-30T21:02:54Z

Entry not found

osouza/bert-large-ambiguidade-v3

osouza

2024-06-30T21:03:47Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-30T21:03:30Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

habulaj/347252492909

habulaj

2024-06-30T21:06:22Z

0

null

[ "region:us" ]

null

2024-06-30T21:06:13Z

Entry not found

Megnis/qdora2

Megnis

2024-06-30T21:06:45Z

0

null

[ "region:us" ]

null

2024-06-30T21:06:45Z

Entry not found

Sirok/sirok2

Sirok

2024-06-30T21:07:07Z

0

null

[ "region:us" ]

null

2024-06-30T21:07:07Z

Entry not found

habulaj/6877062242

habulaj

2024-06-30T21:08:49Z

0

null

[ "region:us" ]

null

2024-06-30T21:08:43Z

Entry not found

habulaj/11003984903

habulaj

2024-06-30T21:10:08Z

0

null

[ "region:us" ]

null

2024-06-30T21:10:03Z

Entry not found

odelz/eng_fb1mms_unbalanced

odelz

2024-06-30T21:12:17Z

0

null

[ "region:us" ]

null

2024-06-30T21:12:17Z

Entry not found

habulaj/98715236960

habulaj

2024-06-30T21:15:07Z

0

null

[ "region:us" ]

null

2024-06-30T21:15:01Z

Entry not found

selvaa/segformer-b1-finetuned-cityscapes-1024-1024-with-after-demo-ds

selvaa

2024-06-30T21:36:15Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/segformer-b1-finetuned-cityscapes-1024-1024", "license:other", "endpoints_compatible", "region:us" ]

null

2024-06-30T21:16:50Z

--- license: other base_model: nvidia/segformer-b1-finetuned-cityscapes-1024-1024 tags: - generated_from_trainer model-index: - name: segformer-b1-finetuned-cityscapes-1024-1024-with-after-demo-ds results: [] ---  # segformer-b1-finetuned-cityscapes-1024-1024-with-after-demo-ds This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0153 - Mean Iou: 0.9689 - Mean Accuracy: 0.9858 - Overall Accuracy: 0.9947 - Accuracy Default: 1e-06 - Accuracy Pipe: 0.9729 - Accuracy Floor: 0.9861 - Accuracy Background: 0.9985 - Iou Default: 1e-06 - Iou Pipe: 0.9305 - Iou Floor: 0.9802 - Iou Background: 0.9958 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Default | Accuracy Pipe | Accuracy Floor | Accuracy Background | Iou Default | Iou Pipe | Iou Floor | Iou Background | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------:|:--------------:|:-------------------:|:-----------:|:--------:|:---------:|:--------------:| | 0.333 | 1.0 | 55 | 0.1193 | 0.8358 | 0.8688 | 0.9725 | 1e-06 | 0.6617 | 0.9467 | 0.9981 | 1e-06 | 0.5954 | 0.9420 | 0.9700 | | 0.0978 | 2.0 | 110 | 0.0734 | 0.8938 | 0.9399 | 0.9817 | 1e-06 | 0.8567 | 0.9709 | 0.9921 | 1e-06 | 0.7472 | 0.9523 | 0.9818 | | 0.0647 | 3.0 | 165 | 0.0529 | 0.9169 | 0.9580 | 0.9860 | 1e-06 | 0.9093 | 0.9696 | 0.9951 | 1e-06 | 0.8023 | 0.9617 | 0.9866 | | 0.0519 | 4.0 | 220 | 0.0455 | 0.9175 | 0.9445 | 0.9861 | 1e-06 | 0.8663 | 0.9692 | 0.9979 | 1e-06 | 0.8031 | 0.9638 | 0.9855 | | 0.0457 | 5.0 | 275 | 0.0413 | 0.9198 | 0.9687 | 0.9866 | 1e-06 | 0.9356 | 0.9786 | 0.9919 | 1e-06 | 0.8098 | 0.9614 | 0.9881 | | 0.0407 | 6.0 | 330 | 0.0360 | 0.9283 | 0.9584 | 0.9882 | 1e-06 | 0.9010 | 0.9780 | 0.9962 | 1e-06 | 0.8320 | 0.9632 | 0.9897 | | 0.0363 | 7.0 | 385 | 0.0318 | 0.9399 | 0.9698 | 0.9897 | 1e-06 | 0.9385 | 0.9737 | 0.9973 | 1e-06 | 0.8614 | 0.9680 | 0.9904 | | 0.0335 | 8.0 | 440 | 0.0295 | 0.9423 | 0.9727 | 0.9904 | 1e-06 | 0.9443 | 0.9770 | 0.9969 | 1e-06 | 0.8652 | 0.9702 | 0.9915 | | 0.0318 | 9.0 | 495 | 0.0288 | 0.9425 | 0.9746 | 0.9905 | 1e-06 | 0.9492 | 0.9784 | 0.9963 | 1e-06 | 0.8664 | 0.9694 | 0.9918 | | 0.0292 | 10.0 | 550 | 0.0262 | 0.9478 | 0.9752 | 0.9912 | 1e-06 | 0.9510 | 0.9769 | 0.9976 | 1e-06 | 0.8803 | 0.9710 | 0.9922 | | 0.0291 | 11.0 | 605 | 0.0270 | 0.9466 | 0.9720 | 0.9909 | 1e-06 | 0.9415 | 0.9765 | 0.9979 | 1e-06 | 0.8774 | 0.9708 | 0.9916 | | 0.0275 | 12.0 | 660 | 0.0249 | 0.9496 | 0.9793 | 0.9916 | 1e-06 | 0.9625 | 0.9784 | 0.9971 | 1e-06 | 0.8835 | 0.9723 | 0.9929 | | 0.0264 | 13.0 | 715 | 0.0246 | 0.9514 | 0.9716 | 0.9915 | 1e-06 | 0.9383 | 0.9782 | 0.9984 | 1e-06 | 0.8901 | 0.9720 | 0.9920 | | 0.0255 | 14.0 | 770 | 0.0242 | 0.9500 | 0.9812 | 0.9917 | 1e-06 | 0.9677 | 0.9792 | 0.9967 | 1e-06 | 0.8846 | 0.9723 | 0.9932 | | 0.0248 | 15.0 | 825 | 0.0230 | 0.9534 | 0.9785 | 0.9921 | 1e-06 | 0.9598 | 0.9777 | 0.9980 | 1e-06 | 0.8940 | 0.9732 | 0.9931 | | 0.0241 | 16.0 | 880 | 0.0233 | 0.9523 | 0.9806 | 0.9920 | 1e-06 | 0.9666 | 0.9778 | 0.9975 | 1e-06 | 0.8906 | 0.9731 | 0.9932 | | 0.023 | 17.0 | 935 | 0.0215 | 0.9562 | 0.9778 | 0.9925 | 1e-06 | 0.9553 | 0.9801 | 0.9982 | 1e-06 | 0.9015 | 0.9738 | 0.9934 | | 0.0223 | 18.0 | 990 | 0.0212 | 0.9562 | 0.9780 | 0.9925 | 1e-06 | 0.9546 | 0.9816 | 0.9979 | 1e-06 | 0.9011 | 0.9737 | 0.9937 | | 0.022 | 19.0 | 1045 | 0.0205 | 0.9558 | 0.9810 | 0.9927 | 1e-06 | 0.9640 | 0.9813 | 0.9975 | 1e-06 | 0.8995 | 0.9737 | 0.9941 | | 0.0213 | 20.0 | 1100 | 0.0207 | 0.9582 | 0.9764 | 0.9926 | 1e-06 | 0.9504 | 0.9801 | 0.9986 | 1e-06 | 0.9069 | 0.9745 | 0.9932 | | 0.0213 | 21.0 | 1155 | 0.0211 | 0.9566 | 0.9801 | 0.9927 | 1e-06 | 0.9624 | 0.9796 | 0.9981 | 1e-06 | 0.9014 | 0.9746 | 0.9937 | | 0.0206 | 22.0 | 1210 | 0.0202 | 0.9589 | 0.9799 | 0.9929 | 1e-06 | 0.9608 | 0.9804 | 0.9983 | 1e-06 | 0.9078 | 0.9752 | 0.9938 | | 0.0199 | 23.0 | 1265 | 0.0194 | 0.9596 | 0.9813 | 0.9931 | 1e-06 | 0.9644 | 0.9812 | 0.9981 | 1e-06 | 0.9096 | 0.9750 | 0.9942 | | 0.0192 | 24.0 | 1320 | 0.0194 | 0.9590 | 0.9831 | 0.9932 | 1e-06 | 0.9710 | 0.9803 | 0.9981 | 1e-06 | 0.9070 | 0.9754 | 0.9945 | | 0.019 | 25.0 | 1375 | 0.0189 | 0.9608 | 0.9834 | 0.9933 | 1e-06 | 0.9703 | 0.9820 | 0.9978 | 1e-06 | 0.9124 | 0.9754 | 0.9945 | | 0.0189 | 26.0 | 1430 | 0.0195 | 0.9602 | 0.9822 | 0.9932 | 1e-06 | 0.9675 | 0.9808 | 0.9983 | 1e-06 | 0.9103 | 0.9758 | 0.9943 | | 0.0185 | 27.0 | 1485 | 0.0204 | 0.9577 | 0.9804 | 0.9930 | 1e-06 | 0.9617 | 0.9815 | 0.9981 | 1e-06 | 0.9035 | 0.9754 | 0.9942 | | 0.0185 | 28.0 | 1540 | 0.0188 | 0.9625 | 0.9808 | 0.9935 | 1e-06 | 0.9616 | 0.9822 | 0.9986 | 1e-06 | 0.9167 | 0.9766 | 0.9944 | | 0.0178 | 29.0 | 1595 | 0.0186 | 0.9626 | 0.9801 | 0.9935 | 1e-06 | 0.9588 | 0.9829 | 0.9985 | 1e-06 | 0.9166 | 0.9768 | 0.9943 | | 0.0176 | 30.0 | 1650 | 0.0192 | 0.9622 | 0.9802 | 0.9935 | 1e-06 | 0.9594 | 0.9826 | 0.9986 | 1e-06 | 0.9156 | 0.9766 | 0.9945 | | 0.0175 | 31.0 | 1705 | 0.0175 | 0.9631 | 0.9839 | 0.9937 | 1e-06 | 0.9710 | 0.9827 | 0.9981 | 1e-06 | 0.9176 | 0.9769 | 0.9948 | | 0.017 | 32.0 | 1760 | 0.0183 | 0.9615 | 0.9852 | 0.9936 | 1e-06 | 0.9761 | 0.9814 | 0.9981 | 1e-06 | 0.9130 | 0.9765 | 0.9949 | | 0.0172 | 33.0 | 1815 | 0.0173 | 0.9646 | 0.9834 | 0.9938 | 1e-06 | 0.9690 | 0.9830 | 0.9984 | 1e-06 | 0.9218 | 0.9772 | 0.9948 | | 0.0167 | 34.0 | 1870 | 0.0175 | 0.9625 | 0.9857 | 0.9938 | 1e-06 | 0.9768 | 0.9822 | 0.9981 | 1e-06 | 0.9156 | 0.9769 | 0.9951 | | 0.0164 | 35.0 | 1925 | 0.0170 | 0.9643 | 0.9854 | 0.9940 | 1e-06 | 0.9749 | 0.9832 | 0.9981 | 1e-06 | 0.9200 | 0.9776 | 0.9952 | | 0.016 | 36.0 | 1980 | 0.0166 | 0.9657 | 0.9844 | 0.9941 | 1e-06 | 0.9710 | 0.9837 | 0.9984 | 1e-06 | 0.9237 | 0.9782 | 0.9952 | | 0.0161 | 37.0 | 2035 | 0.0169 | 0.9661 | 0.9830 | 0.9941 | 1e-06 | 0.9668 | 0.9834 | 0.9987 | 1e-06 | 0.9254 | 0.9780 | 0.9949 | | 0.0156 | 38.0 | 2090 | 0.0172 | 0.9648 | 0.9840 | 0.9939 | 1e-06 | 0.9706 | 0.9829 | 0.9984 | 1e-06 | 0.9220 | 0.9774 | 0.9949 | | 0.0156 | 39.0 | 2145 | 0.0170 | 0.9640 | 0.9857 | 0.9940 | 1e-06 | 0.9769 | 0.9817 | 0.9985 | 1e-06 | 0.9192 | 0.9774 | 0.9953 | | 0.0152 | 40.0 | 2200 | 0.0164 | 0.9667 | 0.9845 | 0.9942 | 1e-06 | 0.9710 | 0.9839 | 0.9985 | 1e-06 | 0.9267 | 0.9783 | 0.9952 | | 0.0153 | 41.0 | 2255 | 0.0164 | 0.9663 | 0.9854 | 0.9942 | 1e-06 | 0.9748 | 0.9830 | 0.9985 | 1e-06 | 0.9256 | 0.9780 | 0.9953 | | 0.016 | 42.0 | 2310 | 0.0162 | 0.9662 | 0.9854 | 0.9942 | 1e-06 | 0.9744 | 0.9833 | 0.9985 | 1e-06 | 0.9254 | 0.9778 | 0.9954 | | 0.0157 | 43.0 | 2365 | 0.0162 | 0.9670 | 0.9849 | 0.9943 | 1e-06 | 0.9724 | 0.9837 | 0.9986 | 1e-06 | 0.9269 | 0.9786 | 0.9953 | | 0.0148 | 44.0 | 2420 | 0.0167 | 0.9671 | 0.9850 | 0.9943 | 1e-06 | 0.9719 | 0.9849 | 0.9983 | 1e-06 | 0.9273 | 0.9786 | 0.9953 | | 0.0149 | 45.0 | 2475 | 0.0165 | 0.9660 | 0.9853 | 0.9943 | 1e-06 | 0.9730 | 0.9846 | 0.9983 | 1e-06 | 0.9235 | 0.9789 | 0.9955 | | 0.0144 | 46.0 | 2530 | 0.0154 | 0.9670 | 0.9870 | 0.9945 | 1e-06 | 0.9784 | 0.9844 | 0.9983 | 1e-06 | 0.9260 | 0.9791 | 0.9958 | | 0.0142 | 47.0 | 2585 | 0.0150 | 0.9685 | 0.9865 | 0.9946 | 1e-06 | 0.9762 | 0.9847 | 0.9985 | 1e-06 | 0.9302 | 0.9794 | 0.9957 | | 0.0142 | 48.0 | 2640 | 0.0154 | 0.9672 | 0.9870 | 0.9945 | 1e-06 | 0.9784 | 0.9841 | 0.9984 | 1e-06 | 0.9268 | 0.9792 | 0.9957 | | 0.0144 | 49.0 | 2695 | 0.0152 | 0.9677 | 0.9862 | 0.9945 | 1e-06 | 0.9754 | 0.9847 | 0.9985 | 1e-06 | 0.9284 | 0.9791 | 0.9957 | | 0.0141 | 50.0 | 2750 | 0.0154 | 0.9681 | 0.9857 | 0.9946 | 1e-06 | 0.9729 | 0.9857 | 0.9984 | 1e-06 | 0.9289 | 0.9796 | 0.9957 | | 0.0136 | 51.0 | 2805 | 0.0153 | 0.9690 | 0.9855 | 0.9947 | 1e-06 | 0.9728 | 0.9850 | 0.9987 | 1e-06 | 0.9317 | 0.9797 | 0.9957 | | 0.0138 | 52.0 | 2860 | 0.0150 | 0.9691 | 0.9866 | 0.9947 | 1e-06 | 0.9767 | 0.9846 | 0.9986 | 1e-06 | 0.9320 | 0.9796 | 0.9957 | | 0.014 | 53.0 | 2915 | 0.0158 | 0.9673 | 0.9853 | 0.9945 | 1e-06 | 0.9720 | 0.9855 | 0.9984 | 1e-06 | 0.9266 | 0.9798 | 0.9956 | | 0.0136 | 54.0 | 2970 | 0.0154 | 0.9693 | 0.9857 | 0.9948 | 1e-06 | 0.9725 | 0.9863 | 0.9985 | 1e-06 | 0.9319 | 0.9802 | 0.9958 | | 0.0138 | 55.0 | 3025 | 0.0154 | 0.9692 | 0.9853 | 0.9947 | 1e-06 | 0.9717 | 0.9855 | 0.9986 | 1e-06 | 0.9323 | 0.9798 | 0.9956 | | 0.0134 | 56.0 | 3080 | 0.0153 | 0.9689 | 0.9857 | 0.9947 | 1e-06 | 0.9728 | 0.9860 | 0.9984 | 1e-06 | 0.9312 | 0.9797 | 0.9957 | | 0.0135 | 57.0 | 3135 | 0.0154 | 0.9695 | 0.9863 | 0.9948 | 1e-06 | 0.9747 | 0.9855 | 0.9986 | 1e-06 | 0.9325 | 0.9800 | 0.9958 | | 0.0133 | 58.0 | 3190 | 0.0154 | 0.9689 | 0.9859 | 0.9947 | 1e-06 | 0.9739 | 0.9854 | 0.9985 | 1e-06 | 0.9313 | 0.9798 | 0.9957 | | 0.0134 | 59.0 | 3245 | 0.0152 | 0.9696 | 0.9862 | 0.9948 | 1e-06 | 0.9745 | 0.9856 | 0.9986 | 1e-06 | 0.9328 | 0.9801 | 0.9958 | | 0.0138 | 60.0 | 3300 | 0.0153 | 0.9689 | 0.9858 | 0.9947 | 1e-06 | 0.9729 | 0.9861 | 0.9985 | 1e-06 | 0.9305 | 0.9802 | 0.9958 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1 - Datasets 2.15.0 - Tokenizers 0.15.0

habulaj/112492325026

habulaj

2024-06-30T21:18:44Z

0

null

[ "region:us" ]

null

2024-06-30T21:18:43Z

Entry not found

DraughtMonkeKZ/TibetanMacaque

DraughtMonkeKZ

2024-06-30T21:19:00Z

0

null

[ "license:gpl-3.0", "region:us" ]

null

2024-06-30T21:19:00Z

--- license: gpl-3.0 ---

ramy21/yolounder

ramy21

2024-06-30T21:32:14Z

0

null

[ "region:us" ]

null

2024-06-30T21:19:47Z

# My YOLO Model This model is trained using PyTorch Lightning.

habulaj/2388242475

habulaj

2024-06-30T21:21:20Z

0

null

[ "region:us" ]

null

2024-06-30T21:21:13Z

Entry not found

mgh6/TCS_MLM_SaProt

mgh6

2024-07-01T20:54:35Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "esm", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-06-30T21:22:34Z

Entry not found

DewEfresh/Neo_7b-merge10

DewEfresh

2024-06-30T21:23:35Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "DewEfresh/neo_7b", "conversational", "base_model:DewEfresh/neo_7b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T21:22:53Z

--- base_model: - DewEfresh/neo_7b - DewEfresh/neo_7b tags: - merge - mergekit - lazymergekit - DewEfresh/neo_7b --- # Neo_7b-merge10 Neo_7b-merge10 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) ## 🧩 Configuration ```yaml slices: - sources: - model: DewEfresh/neo_7b layer_range: [0, 0] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [1, 1] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [2, 2] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [4, 4] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [5, 5] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [6, 6] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [8, 8] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [9, 9] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [10, 10] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [12, 12] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [13, 13] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [14, 14] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [16, 16] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [17, 17] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [18, 18] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [20, 20] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [21, 21] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [22, 22] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [24, 24] - model: DewEfresh/neo_7b layer_range: [27, 27] - sources: - model: DewEfresh/neo_7b layer_range: [25, 25] - model: DewEfresh/neo_7b layer_range: [27, 27] - sources: - model: DewEfresh/neo_7b layer_range: [26, 26] - model: DewEfresh/neo_7b layer_range: [27, 27] merge_method: slerp base_model: DewEfresh/neo_7b parameters: t: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DewEfresh/Neo_7b-merge10" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

Marcelojtc/bart-cnn-samsum-peft

Marcelojtc

2024-06-30T21:38:35Z

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:ingeniumacademy/bart-cnn-samsum-finetuned", "license:mit", "region:us" ]

null

2024-06-30T21:24:22Z

--- base_model: ingeniumacademy/bart-cnn-samsum-finetuned datasets: - samsum library_name: peft license: mit tags: - generated_from_trainer model-index: - name: bart-cnn-samsum-peft results: [] ---  # bart-cnn-samsum-peft This model is a fine-tuned version of [ingeniumacademy/bart-cnn-samsum-finetuned](https://huggingface.co/ingeniumacademy/bart-cnn-samsum-finetuned) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.1345 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.078 | 1.0 | 19 | 0.1345 | | 0.0865 | 2.0 | 38 | 0.1345 | | 0.0768 | 3.0 | 57 | 0.1345 | | 0.079 | 4.0 | 76 | 0.1345 | | 0.0916 | 5.0 | 95 | 0.1345 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

habulaj/54258170221

habulaj

2024-06-30T21:26:44Z

0

null

[ "region:us" ]

null

2024-06-30T21:26:38Z

Entry not found

silveroxides/AnimagineXL31_X_AutismMixPony_mergeproject

silveroxides

2024-06-30T22:17:54Z

0

null

[ "region:us" ]

null

2024-06-30T21:30:21Z

Entry not found

DewEfresh/Neo_7b-merge11

DewEfresh

2024-06-30T21:32:06Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "DewEfresh/neo_7b", "conversational", "base_model:DewEfresh/neo_7b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T21:31:20Z

--- base_model: - DewEfresh/neo_7b - DewEfresh/neo_7b tags: - merge - mergekit - lazymergekit - DewEfresh/neo_7b --- # Neo_7b-merge11 Neo_7b-merge11 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) ## 🧩 Configuration ```yaml slices: - sources: - model: DewEfresh/neo_7b layer_range: [0, 0] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [1, 1] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [2, 2] - model: DewEfresh/neo_7b layer_range: [3, 3] - sources: - model: DewEfresh/neo_7b layer_range: [4, 4] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [5, 5] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [6, 6] - model: DewEfresh/neo_7b layer_range: [7, 7] - sources: - model: DewEfresh/neo_7b layer_range: [8, 8] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [9, 9] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [10, 10] - model: DewEfresh/neo_7b layer_range: [11, 11] - sources: - model: DewEfresh/neo_7b layer_range: [12, 12] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [13, 13] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [14, 14] - model: DewEfresh/neo_7b layer_range: [15, 15] - sources: - model: DewEfresh/neo_7b layer_range: [16, 16] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [17, 17] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [18, 18] - model: DewEfresh/neo_7b layer_range: [19, 19] - sources: - model: DewEfresh/neo_7b layer_range: [20, 20] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [21, 21] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [22, 22] - model: DewEfresh/neo_7b layer_range: [23, 23] - sources: - model: DewEfresh/neo_7b layer_range: [24, 24] - model: DewEfresh/neo_7b layer_range: [27, 27] - sources: - model: DewEfresh/neo_7b layer_range: [25, 25] - model: DewEfresh/neo_7b layer_range: [27, 27] - sources: - model: DewEfresh/neo_7b layer_range: [26, 26] - model: DewEfresh/neo_7b layer_range: [27, 27] merge_method: slerp base_model: DewEfresh/neo_7b parameters: t: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DewEfresh/Neo_7b-merge11" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

ramy21/newyolos

ramy21

2024-06-30T21:35:18Z

0

null

[ "region:us" ]

null

2024-06-30T21:35:18Z

Entry not found

variante/llara-maskrcnn

variante

2024-07-01T04:43:20Z

0

null

[ "llara", "robotics", "vlm", "object-detection", "dataset:VIMA/VIMA-Data", "arxiv:2406.20095", "license:apache-2.0", "region:us" ]

object-detection

2024-06-30T21:36:03Z

--- inference: false license: apache-2.0 datasets: - VIMA/VIMA-Data tags: - llara - robotics - vlm pipeline_tag: object-detection --- <be> # Model Card This model is released with paper **[LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://arxiv.org/abs/2406.20095)** [Xiang Li](https://xxli.me)1, [Cristina Mata](https://openreview.net/profile?id=~Cristina_Mata1)1, [Jongwoo Park](https://github.com/jongwoopark7978)1, [Kumara Kahatapitiya](https://www3.cs.stonybrook.edu/~kkahatapitiy)1, [Yoo Sung Jang](https://yjang43.github.io/)1, [Jinghuan Shang](https://elicassion.github.io/)1, [Kanchana Ranasinghe](https://kahnchana.github.io/)1, [Ryan Burgert](https://ryanndagreat.github.io/)1, [Mu Cai](https://pages.cs.wisc.edu/~mucai/)2, [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)2, and [Michael S. Ryoo](http://michaelryoo.com/)1 1Stony Brook University 2University of Wisconsin-Madison ## Model details **Model type:** This repository contains three models trained on three subsets respectively, converted from [VIMA-Data](https://huggingface.co/datasets/VIMA/VIMA-Data). For the conversion code, please refer to [convert_vima.ipynb](https://github.com/LostXine/LLaRA/blob/main/datasets/convert_vima.ipynb) **Paper or resources for more information:** https://github.com/LostXine/LLaRA **Where to send questions or comments about the model:** https://github.com/LostXine/LLaRA/issues

Frixi/ROA_PR

Frixi

2024-06-30T21:36:20Z

0

null

[ "license:openrail", "region:us" ]

null

2024-06-30T21:36:05Z

--- license: openrail ---

tom1-ll/Lilyallroundv2

tom1-ll

2024-06-30T21:39:00Z

0

null

[ "license:openrail", "region:us" ]

null

2024-06-30T21:37:24Z

--- license: openrail ---

imelike/new-turkishReviews-tokenizer

imelike

2024-06-30T21:37:29Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-30T21:37:27Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

habulaj/3855139265

habulaj

2024-06-30T21:39:57Z

0

null

[ "region:us" ]

null

2024-06-30T21:39:49Z

Entry not found

gkMSDA/FinChat_Mistral7B_DPO_V2

gkMSDA

2024-06-30T21:52:38Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T21:46:50Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

habulaj/368315333833

habulaj

2024-06-30T21:49:04Z

0

null

[ "region:us" ]

null

2024-06-30T21:49:01Z

Entry not found

Adam3/Michael-Kranz

Adam3

2024-06-30T21:50:27Z

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-3-medium", "region:us" ]

text-to-image

2024-06-30T21:49:02Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/1001125444.jpg base_model: stabilityai/stable-diffusion-3-medium instance_prompt: null --- # Michael kranz <Gallery /> ## Download model [Download](/Adam3/Michael-Kranz/tree/main) them in the Files & versions tab.

Mome757mome/moka

Mome757mome

2024-06-30T21:49:51Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2024-06-30T21:49:51Z

--- license: bigscience-bloom-rail-1.0 ---

habulaj/135763111248

habulaj

2024-06-30T21:50:18Z

0

null

[ "region:us" ]

null

2024-06-30T21:50:16Z

Entry not found

MHRDYN7/dnv2-base

MHRDYN7

2024-06-30T21:55:36Z

0

transformers

[ "transformers", "safetensors", "dnv2", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-30T21:52:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

habulaj/5320041046

habulaj

2024-06-30T21:53:56Z

0

null

[ "region:us" ]

null

2024-06-30T21:53:49Z

Entry not found

habulaj/137258123550

habulaj

2024-06-30T21:57:21Z

0

null

[ "region:us" ]

null

2024-06-30T21:57:16Z

Entry not found

nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr1e4_dec1e3_bs16

nsugianto

2024-07-02T14:34:11Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "generated_from_trainer", "base_model:nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr5e5_dec1e4_bs12", "license:mit", "endpoints_compatible", "region:us" ]

object-detection

2024-06-30T21:58:38Z

--- license: mit base_model: nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr5e5_dec1e4_bs12 tags: - generated_from_trainer model-index: - name: tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr1e4_dec1e3_bs16 results: [] ---  # tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr1e4_dec1e3_bs16 This model is a fine-tuned version of [nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr5e5_dec1e4_bs12](https://huggingface.co/nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s_adjpar6_lr5e5_dec1e4_bs12) 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr1e4_dec1e3_bs16

nsugianto

2024-07-03T01:30:50Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "generated_from_trainer", "base_model:nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr5e5_dec1e4_bs12", "license:mit", "endpoints_compatible", "region:us" ]

object-detection

2024-06-30T21:59:38Z

--- license: mit base_model: nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr5e5_dec1e4_bs12 tags: - generated_from_trainer model-index: - name: tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr1e4_dec1e3_bs16 results: [] ---  # tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr1e4_dec1e3_bs16 This model is a fine-tuned version of [nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr5e5_dec1e4_bs12](https://huggingface.co/nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_semicplx_v1_s1_226s_adjpar6_lr5e5_dec1e4_bs12) 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

khanhnn55/naschainv8

khanhnn55

2024-07-02T23:19:43Z

0

null

[ "region:us" ]

null

2024-06-30T22:03:31Z

Entry not found

NiluferUcar/CNN_Convolutional_Neural_Network_Models

NiluferUcar

2024-06-30T22:08:12Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-30T22:03:50Z

--- license: apache-2.0 ---

nicolebar/3d

nicolebar

2024-06-30T22:04:38Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-30T22:04:38Z

--- license: apache-2.0 ---

JEFFERSONMUSIC/JKGOLDENERAV3

JEFFERSONMUSIC

2024-06-30T22:08:25Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-30T22:07:09Z

--- license: apache-2.0 ---

chaanks/UTMOS

chaanks

2024-06-30T22:14:47Z

0

null

[ "region:us" ]

null

2024-06-30T22:13:36Z

Entry not found

Coolwowsocoolwow/Random_Assertive_EGirl

Coolwowsocoolwow

2024-06-30T22:20:01Z

0

null

[ "license:openrail", "region:us" ]

null

2024-06-30T22:16:56Z

--- license: openrail ---

habulaj/7282053427

habulaj

2024-06-30T22:18:22Z

0

null

[ "region:us" ]

null

2024-06-30T22:18:20Z

Entry not found

Intaa/Lucas

Intaa

2024-07-02T22:51:53Z

0

null

[ "region:us" ]

null

2024-06-30T22:18:35Z

Entry not found

TheDima/resnet50-dog-breed-identification

TheDima

2024-06-30T22:20:03Z

0

fastai

[ "fastai", "region:us" ]

null

2024-06-30T22:19:27Z

--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

chopchopchuck/mts100

chopchopchuck

2024-06-30T22:21:12Z

0

null

[ "region:us" ]

null

2024-06-30T22:20:47Z

Entry not found

habulaj/2436026491

habulaj

2024-06-30T22:21:04Z

0

null

[ "region:us" ]

null

2024-06-30T22:20:52Z

Entry not found

chopchopchuck/mts101

chopchopchuck

2024-06-30T22:22:46Z

0

null

[ "region:us" ]

null

2024-06-30T22:22:27Z

Entry not found

chopchopchuck/mts102

chopchopchuck

2024-06-30T22:25:13Z

0

null

[ "region:us" ]

null

2024-06-30T22:24:03Z

Entry not found

habulaj/7712156082

habulaj

2024-06-30T22:24:54Z

0

null

[ "region:us" ]

null

2024-06-30T22:24:48Z

Entry not found

rambaldi47/q-FrozenLake-v1-4x4-noSlippery

rambaldi47

2024-06-30T22:24:58Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-06-30T22:24:55Z

--- 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="rambaldi47/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"]) ```

slattybenzo/checkpoint

slattybenzo

2024-06-30T22:26:09Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-30T22:26:09Z

--- license: apache-2.0 ---

chopchopchuck/mts105

chopchopchuck

2024-06-30T22:27:43Z

0

null

[ "region:us" ]

null

2024-06-30T22:27:21Z

Entry not found

habulaj/367577333123

habulaj

2024-06-30T22:28:44Z

0

null

[ "region:us" ]

null

2024-06-30T22:28:24Z

Entry not found

rambaldi47/Taxi-v3

rambaldi47

2024-06-30T23:32:06Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-06-30T22:28:41Z

--- 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: 7.56 +/- 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="rambaldi47/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"]) ```

chopchopchuck/mts106

chopchopchuck

2024-06-30T22:29:32Z

0

null

[ "region:us" ]

null

2024-06-30T22:29:10Z

Entry not found

chopchopchuck/mts107

chopchopchuck

2024-06-30T22:31:18Z

0

null

[ "region:us" ]

null

2024-06-30T22:30:58Z

Entry not found

habulaj/200255390394

habulaj

2024-06-30T22:32:24Z

0

null

[ "region:us" ]

null

2024-06-30T22:31:50Z

Entry not found

chopchopchuck/mts108

chopchopchuck

2024-06-30T22:32:57Z

0

null

[ "region:us" ]

null

2024-06-30T22:32:34Z

Entry not found

martimfasantos/tinyllama-1.1b-sum-sft-full_LR4e-5

martimfasantos

2024-07-01T00:26:23Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:martimfasantos/openai-summarize-tldr", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T22:33:27Z

--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - martimfasantos/openai-summarize-tldr model-index: - name: tinyllama-1.1b-sum-sft-full_LR4e-5 results: [] ---  # tinyllama-1.1b-sum-sft-full_LR4e-5 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the martimfasantos/openai-summarize-tldr dataset. It achieves the following results on the evaluation set: - Loss: 2.1087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.1044 | 0.9997 | 1476 | 2.1087 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1

chopchopchuck/mts109

chopchopchuck

2024-06-30T22:34:38Z

0

null

[ "region:us" ]

null

2024-06-30T22:34:15Z

Entry not found

chopchopchuck/mts110

chopchopchuck

2024-06-30T22:36:13Z

0

null

[ "region:us" ]

null

2024-06-30T22:35:52Z

Entry not found

habulaj/1109616775

habulaj

2024-06-30T22:36:17Z

0

null

[ "region:us" ]

null

2024-06-30T22:36:11Z

Entry not found

AIGym/fast-mini-webtext-65536

AIGym

2024-06-30T22:36:47Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-30T22:36:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mikedata/q-FrozenLake-v1-4x4-noSlippery

mikedata

2024-06-30T22:40:14Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-06-30T22:39:46Z

--- 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="mikedata/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"]) ```

Homiebear/R2-D2

Homiebear

2024-06-30T22:44:51Z

0

null

[ "license:openrail", "region:us" ]

null

2024-06-30T22:44:30Z

--- license: openrail ---

Spbou4-hilma/HILMA-FIN-7B

Spbou4-hilma

2024-06-30T23:03:25Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "endpoints_compatible", "region:us" ]

text-generation

2024-06-30T22:45:31Z

--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: meta-llama/Meta-Llama-3-8B-Instruct widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

habulaj/244479215760

habulaj

2024-06-30T22:46:26Z

0

null

[ "region:us" ]

null

2024-06-30T22:46:18Z

Entry not found

EmoHugAI/xlm-roberta-base-finetuned-panx-de

EmoHugAI

2024-06-30T23:34:41Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-06-30T22:46:34Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: [] ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3159 - F1: 0.8543 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3578 | 1.0 | 525 | 0.2204 | 0.7870 | | 0.1946 | 2.0 | 1050 | 0.2063 | 0.8072 | | 0.1314 | 3.0 | 1575 | 0.2037 | 0.8318 | | 0.0924 | 4.0 | 2100 | 0.2161 | 0.8363 | | 0.0641 | 5.0 | 2625 | 0.2472 | 0.8418 | | 0.046 | 6.0 | 3150 | 0.2754 | 0.8409 | | 0.0306 | 7.0 | 3675 | 0.2718 | 0.8509 | | 0.0205 | 8.0 | 4200 | 0.3045 | 0.8563 | | 0.0128 | 9.0 | 4725 | 0.3148 | 0.8568 | | 0.0091 | 10.0 | 5250 | 0.3159 | 0.8543 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

mikedata/q-Taxi-V3

mikedata

2024-06-30T22:48:55Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-06-30T22:47:40Z

--- 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="mikedata/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"]) ```