librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	likes int64 0 11k	library_name stringclasses 245 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
mwyoox/security-desk	mwyoox	2024-06-28T08:08:15Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-28T05:51:18Z	--- license: mit ---
youngdicey/flow-v1-depth1-dim768	youngdicey	2024-06-28T05:55:50Z	0	null	[ "region:us" ]	null	2024-06-28T05:54:41Z	Entry not found
shyxm/SDGClassification16SDGs	shyxm	2024-06-28T06:00:31Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-28T05:55:24Z	Entry not found
PMVRAJU/pmvraju	PMVRAJU	2024-06-28T05:56:05Z	0	null	[ "license:openrail++", "region:us" ]	null	2024-06-28T05:56:05Z	--- license: openrail++ ---
Masi1911/WHisperrr	Masi1911	2024-06-28T05:57:50Z	0	null	[ "region:us" ]	null	2024-06-28T05:57:34Z	# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3")
DevForML/GemmaTunnedV1	DevForML	2024-06-28T05:59:41Z	0	null	[ "region:us" ]	null	2024-06-28T05:59:41Z	Entry not found
Kash07/corgy_shoes_LoRA	Kash07	2024-06-28T06:11:17Z	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-06-28T06:04:13Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of TOK shoes widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Kash07/corgy_shoes_LoRA <Gallery /> ## Model description These are Kash07/corgy_shoes_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK shoes to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Kash07/corgy_shoes_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
LarryAIDraw/tPonynai3_v55	LarryAIDraw	2024-06-28T08:59:13Z	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-28T06:06:03Z	--- license: creativeml-openrail-m --- https://civitai.com/models/317902?modelVersionId=593760
XuZhang999/fitrack	XuZhang999	2024-06-28T06:06:10Z	0	null	[ "region:us" ]	null	2024-06-28T06:06:10Z	Entry not found
noanabeshima/dummy_tiny_stories_test	noanabeshima	2024-06-28T06:09:05Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-28T06:08:30Z	--- license: mit ---
kugelitsjust/Dude_Guy	kugelitsjust	2024-06-28T23:21:27Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-28T06:10:54Z	--- license: openrail ---
cherifkhalifah/Qwen1.5-1.8-chat_qlora_finetuned-data_e-commerce-v0.2	cherifkhalifah	2024-06-28T06:47:33Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T06:22:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
manamihasegawa/medtext_model	manamihasegawa	2024-07-01T13:25:19Z	0	null	[ "region:us" ]	null	2024-06-28T06:23:23Z	Entry not found
slelab/AES15	slelab	2024-06-28T06:46:37Z	0	null	[ "region:us" ]	null	2024-06-28T06:23:48Z	Entry not found
geraldabrhm/llama-3-8b-antonymdf-simplecontext-32lora-lr1_4-16batch	geraldabrhm	2024-06-28T08:54:38Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-28T06:24:57Z	Entry not found
sekretiv/melinda	sekretiv	2024-06-28T06:26:56Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-28T06:26:00Z	--- license: openrail ---
brivangl/vgg_kagn_bn11_v4_opt	brivangl	2024-07-02T07:04:34Z	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-06-28T06:28:56Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- ### How to use First, clone the repository: ``` git clone https://github.com/IvanDrokin/torch-conv-kan.git cd torch-conv-kan pip install -r requirements.txt ``` Then you can initialize the model and load weights. ```python import torch from models import VGGKAGN_BN model = VGGKAGN_BN.from_pretrained('brivangl/vgg_kagn_bn11_v4_opt', groups=1, degree=3, dropout=0.05, l1_decay=0, width_scale=3, affine=True, norm_layer=nn.BatchNorm2d, expected_feature_shape=(1, 1), vgg_type='VGG11v4') ``` Transforms, used for validation on Imagenet1k: ```python from torchvision.transforms import v2 transforms_val = v2.Compose([ v2.ToImage(), v2.Resize(256, antialias=True), v2.CenterCrop(224), v2.ToDtype(torch.float32, scale=True), v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) ```
AdamKasumovic/llama3-8b-instruct-bactrian-x-en-100-percent-low-med-perplexity	AdamKasumovic	2024-06-28T06:31:35Z	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-28T06:31:34Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ghredghre/gsdgsgsd	ghredghre	2024-06-28T06:33:21Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-28T06:33:21Z	--- license: openrail ---
Reizei/Chihiro	Reizei	2024-06-28T06:34:08Z	0	null	[ "license:unknown", "region:us" ]	null	2024-06-28T06:34:08Z	--- license: unknown ---
traderpedroso/best-tts	traderpedroso	2024-06-28T06:36:03Z	0	null	[ "region:us" ]	null	2024-06-28T06:34:20Z	Entry not found
Yash0109/diaratechHf_llama24265d55-c3ab-48cf-8954-7fc36f415b9e	Yash0109	2024-06-28T06:40:42Z	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-06-28T06:37:27Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator library_name: peft license: apache-2.0 tags: - trl - sft - generated_from_trainer model-index: - name: diaratechHf_llama24265d55-c3ab-48cf-8954-7fc36f415b9e 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. --> # diaratechHf_llama24265d55-c3ab-48cf-8954-7fc36f415b9e This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2
JasonLai/distilbert-base-uncased-finetuned-emotion	JasonLai	2024-06-28T06:41:13Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-28T06:39:47Z	Entry not found
imagepipeline/buk	imagepipeline	2024-06-28T06:43:17Z	0	null	[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-06-28T06:43:14Z	--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## buk <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/) Model details - buk [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/buk?id=3d7e3937-0d96-44a0-afd4-89cde2917fb4/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "3d7e3937-0d96-44a0-afd4-89cde2917fb4", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` \| Headers \| Type \| Description \| \|:----------------------\| :------- \|:-------------------------------------------------------------------------------------------------------------------\| \| `API-Key` \| `str` \| Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) \| \| `Content-Type` \| `str` \| application/json - content type of the request body \| \| Parameter \| Type \| Description \| \| :-------- \| :------- \| :------------------------- \| \| `model_id` \| `str` \| Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own\| \| `prompt` \| `str` \| Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips \| \| `num_inference_steps` \| `int [1-50]` \| Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) \| \| `guidance_scale` \| `float [1-20]` \| Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 \| \| `lora_models` \| `str, array` \| Pass the model_id(s) of LoRA models that can be found in models page \| \| `lora_weights` \| `str, array` \| Strength of the LoRA effect \| --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
imagepipeline/nudexl	imagepipeline	2024-06-28T06:44:49Z	0	null	[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-06-28T06:44:47Z	--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## nudexl <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/) Model details - nudexl [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/nudexl?id=83e6657a-c042-4703-88b3-6a47e39a595e/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "83e6657a-c042-4703-88b3-6a47e39a595e", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` \| Headers \| Type \| Description \| \|:----------------------\| :------- \|:-------------------------------------------------------------------------------------------------------------------\| \| `API-Key` \| `str` \| Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) \| \| `Content-Type` \| `str` \| application/json - content type of the request body \| \| Parameter \| Type \| Description \| \| :-------- \| :------- \| :------------------------- \| \| `model_id` \| `str` \| Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own\| \| `prompt` \| `str` \| Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips \| \| `num_inference_steps` \| `int [1-50]` \| Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) \| \| `guidance_scale` \| `float [1-20]` \| Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 \| \| `lora_models` \| `str, array` \| Pass the model_id(s) of LoRA models that can be found in models page \| \| `lora_weights` \| `str, array` \| Strength of the LoRA effect \| --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
sekretiv/melinda2	sekretiv	2024-06-28T06:46:16Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-28T06:45:20Z	--- license: openrail ---
imagepipeline/nude	imagepipeline	2024-06-28T06:45:54Z	0	null	[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-06-28T06:45:52Z	--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## nude <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/) Model details - nude [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/nude?id=ac792c78-baf5-4d87-a80e-77fc99c84067/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "ac792c78-baf5-4d87-a80e-77fc99c84067", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` \| Headers \| Type \| Description \| \|:----------------------\| :------- \|:-------------------------------------------------------------------------------------------------------------------\| \| `API-Key` \| `str` \| Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) \| \| `Content-Type` \| `str` \| application/json - content type of the request body \| \| Parameter \| Type \| Description \| \| :-------- \| :------- \| :------------------------- \| \| `model_id` \| `str` \| Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own\| \| `prompt` \| `str` \| Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips \| \| `num_inference_steps` \| `int [1-50]` \| Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) \| \| `guidance_scale` \| `float [1-20]` \| Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 \| \| `lora_models` \| `str, array` \| Pass the model_id(s) of LoRA models that can be found in models page \| \| `lora_weights` \| `str, array` \| Strength of the LoRA effect \| --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
theDisco/fluffy-koala	theDisco	2024-07-02T12:10:36Z	0	transformers	[ "transformers", "safetensors", "endpoints_compatible", "region:us" ]	null	2024-06-28T06:46:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID In order to run this notebook you need to first create an .env file in the root directory with the following content. ```env HF_TOKEN=READ_WRITE_HF_TOKEN TOKENIZERS_PARALLELISM=false WANDB_API_KEY=WANDB_TOKEN_FOR_LOGGING ``` `HF_TOKEN` is needed for pushing the model to HF. `WANDB_API_KEY` is needed for evaluating the accuracy of fine tuning. Before running the fine tuning you need to generate fake data. To do that install the `requirements.txt` dependencies. Since the file was created on MacOS, the installation might fail on Linux system. Make sure to install faker (`pip install faker`) for the script to run properly. ``` python dataset_generator.py ``` Once these steps have been completed, you can run the fine tuning notebook. Notebook was tested with Python 3.10. ## Running inference ```python from transformers import PaliGemmaForConditionalGeneration, AutoProcessor from PIL import Image image = Image.open('path/to/image') model = PaliGemmaForConditionalGeneration.from_pretrained("theDisco/fluffy-koala") processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-448") inputs = processor(text="extract JSON.", images=image, return_tensors="pt") generated_ids = model.generate(**inputs, max_new_tokens=512) image_token_index = model.config.image_token_index num_image_tokens = len(generated_ids[generated_ids == image_token_index]) num_text_tokens = len(processor.tokenizer.encode("extract JSON.")) num_prompt_tokens = num_image_tokens + num_text_tokens + 2 generated_text = processor.batch_decode(generated_ids[:, num_prompt_tokens:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(generated_text) ```
lyleokoth/tinyllama-function-calling-v1	lyleokoth	2024-06-28T06:48:42Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-28T06:48:32Z	--- base_model: unsloth/tinyllama-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: lyleokoth - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-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)
StarCycle/GENIE_2.6M	StarCycle	2024-06-28T07:27:07Z	1	null	[ "region:us" ]	null	2024-06-28T06:49:49Z	Entry not found
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-en-100-percent-low-med-ids	AdamKasumovic	2024-06-28T06:54:42Z	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-28T06:51:45Z	--- base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ICT2214Team7/RoBERTa_conll_epoch_5	ICT2214Team7	2024-06-28T07:15:11Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:distilroberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-28T06:52:50Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: RoBERTa_conll_epoch_5 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.937014382542569 - name: Recall type: recall value: 0.9538875799394143 - name: F1 type: f1 value: 0.945375698440497 - name: Accuracy type: accuracy value: 0.9872971065631616 --- <!-- 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. --> # RoBERTa_conll_epoch_5 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0716 - Precision: 0.9370 - Recall: 0.9539 - F1: 0.9454 - Accuracy: 0.9873 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0787 \| 1.0 \| 1756 \| 0.0734 \| 0.9024 \| 0.9317 \| 0.9168 \| 0.9819 \| \| 0.0389 \| 2.0 \| 3512 \| 0.0706 \| 0.9359 \| 0.9440 \| 0.9399 \| 0.9854 \| \| 0.023 \| 3.0 \| 5268 \| 0.0632 \| 0.9340 \| 0.9483 \| 0.9411 \| 0.9864 \| \| 0.0137 \| 4.0 \| 7024 \| 0.0762 \| 0.9368 \| 0.9534 \| 0.9450 \| 0.9875 \| \| 0.0054 \| 5.0 \| 8780 \| 0.0716 \| 0.9370 \| 0.9539 \| 0.9454 \| 0.9873 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
TonyCh0pper/dqn-SpaceInvadersNoFrameskip-v4-2	TonyCh0pper	2024-06-28T06:53:44Z	0	null	[ "region:us" ]	null	2024-06-28T06:53:44Z	Entry not found
sikander0202/sikander	sikander0202	2024-06-28T06:55:05Z	0	null	[ "region:us" ]	null	2024-06-28T06:55:05Z	Entry not found
habibkhan22cp/metallama3_8b_tutorAi	habibkhan22cp	2024-07-02T08:14:45Z	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-28T06:58:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
saurabhg2083/mistral_lora_v1	saurabhg2083	2024-06-30T13:06:08Z	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T06:58:25Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
arsen7076/MMed-Llama-3-8B	arsen7076	2024-06-28T06:59:25Z	0	null	[ "region:us" ]	null	2024-06-28T06:59:25Z	Entry not found
leenag/vasista_medium_Luke	leenag	2024-06-28T13:20:09Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ymr", "base_model:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-28T07:01:25Z	--- language: - ymr license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: leenag/Malasar_Luke 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. --> # leenag/Malasar_Luke This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Spoken Bible Corpus: Malasar dataset. It achieves the following results on the evaluation set: - Loss: 0.5075 - Wer: 48.2010 ## 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: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.018 \| 11.3636 \| 250 \| 0.3526 \| 50.2570 \| \| 0.0059 \| 22.7273 \| 500 \| 0.4000 \| 49.5146 \| \| 0.0002 \| 34.0909 \| 750 \| 0.4418 \| 48.3152 \| \| 0.0001 \| 45.4545 \| 1000 \| 0.4785 \| 48.0868 \| \| 0.0 \| 56.8182 \| 1250 \| 0.4923 \| 47.8013 \| \| 0.0 \| 68.1818 \| 1500 \| 0.5008 \| 47.8013 \| \| 0.0 \| 79.5455 \| 1750 \| 0.5059 \| 48.2010 \| \| 0.0 \| 90.9091 \| 2000 \| 0.5075 \| 48.2010 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.0 - Tokenizers 0.19.1
Mantis-VL/mantis-8b-idefics2-chat-video-example_8192_qlora	Mantis-VL	2024-06-28T07:03:29Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-28T07:02:50Z	Entry not found
Yash0109/diaratechHf_llama2bf5fb73-8c2e-45c2-83f1-ae8e52fda215	Yash0109	2024-06-28T07:06:03Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-28T07:04:39Z	Entry not found
Oksana2014/1	Oksana2014	2024-06-28T07:07:12Z	0	null	[ "region:us" ]	null	2024-06-28T07:07:12Z	Entry not found
cherifkhalifah/Qwen1.5-1.8-chat_qlora_finetuned-data_e-commerce-v0.3	cherifkhalifah	2024-06-28T07:08:16Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T07:07:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
bart-lu/teat	bart-lu	2024-06-28T07:10:41Z	0	null	[ "license:bsl-1.0", "region:us" ]	null	2024-06-28T07:10:41Z	--- license: bsl-1.0 ---
Hazelley/Hamzah	Hazelley	2024-06-28T07:10:54Z	0	null	[ "region:us" ]	null	2024-06-28T07:10:54Z	Entry not found
LYshan/sd-class-butterflies-64	LYshan	2024-06-28T07:14:02Z	0	null	[ "region:us" ]	null	2024-06-28T07:14:02Z	Entry not found
habulaj/147753444204	habulaj	2024-06-28T07:21:07Z	0	null	[ "region:us" ]	null	2024-06-28T07:20:55Z	Entry not found
yabichiu/gemma2_Ollama	yabichiu	2024-06-28T07:41:25Z	0	null	[ "license:gemma", "region:us" ]	null	2024-06-28T07:22:29Z	--- license: gemma ---
jinanshii/jinz	jinanshii	2024-06-28T07:24:50Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T07:24:50Z	--- license: apache-2.0 ---
swetapatra/BERT_Enhancement	swetapatra	2024-06-28T07:35:44Z	0	null	[ "region:us" ]	null	2024-06-28T07:27:05Z	Entry not found
samidh/cope-g2b-2c-hs.s1.5.9-sx.s1.5.9o-hr.s5-sh.s5.l1e4-e10-d25-r8	samidh	2024-06-28T07:30:22Z	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-06-28T07:30:18Z	--- base_model: google/gemma-2b library_name: peft license: gemma metrics: - f1 - precision - recall tags: - trl - sft - generated_from_trainer model-index: - name: cope-g2b-2c-hs.s1.5.9-sx.s1.5.9o-hr.s5-sh.s5.l1e4-e10-d25-r8 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. --> # cope-g2b-2c-hs.s1.5.9-sx.s1.5.9o-hr.s5-sh.s5.l1e4-e10-d25-r8 This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8818 - F1: 0.8403 - Precision: 0.7653 - Recall: 0.9317 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:---------:\|:------:\| \| 0.1794 \| 1.0 \| 848 \| 0.1999 \| 0.8249 \| 0.7898 \| 0.8634 \| \| 0.1913 \| 2.0 \| 1697 \| 0.2122 \| 0.8317 \| 0.8873 \| 0.7826 \| \| 0.1182 \| 3.0 \| 2546 \| 0.2569 \| 0.8677 \| 0.8598 \| 0.8758 \| \| 0.0235 \| 4.0 \| 3395 \| 0.4534 \| 0.8278 \| 0.7487 \| 0.9255 \| \| 0.0001 \| 5.0 \| 4243 \| 0.4959 \| 0.8520 \| 0.8294 \| 0.8758 \| \| 0.0286 \| 6.0 \| 5092 \| 0.4956 \| 0.8412 \| 0.7989 \| 0.8882 \| \| 0.0004 \| 7.0 \| 5941 \| 0.8649 \| 0.8412 \| 0.7626 \| 0.9379 \| \| 0.0 \| 8.0 \| 6790 \| 0.8971 \| 0.8403 \| 0.7653 \| 0.9317 \| \| 0.0 \| 9.0 \| 7638 \| 0.8493 \| 0.8403 \| 0.7653 \| 0.9317 \| \| 0.0 \| 9.99 \| 8480 \| 0.8818 \| 0.8403 \| 0.7653 \| 0.9317 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0 - Pytorch 2.3.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Wakade/Wakade-lavanya-opt-6.1b-lora	Wakade	2024-06-28T07:32:42Z	0	null	[ "region:us" ]	null	2024-06-28T07:32:42Z	Entry not found
yuvraj108c/yolo-nas-pose-onnx	yuvraj108c	2024-06-28T07:37:48Z	0	null	[ "onnx", "region:us" ]	null	2024-06-28T07:33:02Z	Entry not found
longxia/Qwen-Qwen1.5-1.8B-1719560224	longxia	2024-06-28T07:37:15Z	0	null	[ "region:us" ]	null	2024-06-28T07:37:15Z	Entry not found
Abdrehman6224/llama-3-8b-chat-doctor	Abdrehman6224	2024-06-28T07:40:58Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T07:40:50Z	--- 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. 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]
catyung/llama3-sft-4bits-sap2english-lora_model	catyung	2024-06-28T07:41:17Z	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	2024-06-28T07:40:57Z	--- 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: catyung - 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)
vivi072/llava-1.5-7b-hf-ft-mix-vsft	vivi072	2024-06-28T07:41:18Z	0	null	[ "region:us" ]	null	2024-06-28T07:41:18Z	Entry not found
rajparmar/finetuned-tpicap-data-model	rajparmar	2024-06-28T07:43:31Z	0	null	[ "region:us" ]	null	2024-06-28T07:43:31Z	Entry not found
agrajpaudel/sdxl-dreambooth	agrajpaudel	2024-06-28T07:50:16Z	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-06-28T07:47:03Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of TOK_sks_rajesh_hamal widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - agrajpaudel/sdxl-dreambooth <Gallery /> ## Model description These are agrajpaudel/sdxl-dreambooth LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK_sks_rajesh_hamal to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](agrajpaudel/sdxl-dreambooth/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
hasininawoda/person111	hasininawoda	2024-06-28T07:48:00Z	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-06-28T07:47:59Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of TOK person widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - hasininawoda/person111 <Gallery /> ## Model description These are hasininawoda/person111 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK person to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](hasininawoda/person111/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
yuriachermann/Not-so-bright-AGI-3	yuriachermann	2024-06-28T15:21:41Z	0	peft	[ "peft", "trl", "sft", "generated_from_trainer", "Gaudi", "ipex", "ultrachat_200k", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "license:llama3", "region:us" ]	null	2024-06-28T07:48:52Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer - Gaudi - ipex - ultrachat_200k base_model: meta-llama/Meta-Llama-3-70B-Instruct model-index: - name: Not-so-bright-AGI-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Not-so-bright-AGI-3 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-5 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - bf16: True - num_train_epochs: 5 - per_device_train_batch_size: 8 - evaluation_strategy: "steps" - save_strategy: "steps" - learning_rate: 1e-5 - warmup_ratio: 0.03 - lr_scheduler_type: "linear" - max_grad_norm: 0.01 - logging_steps: 100 - do_train - do_eval - use_habana - use_lazy_mode - throughput_warmup_steps: 5 - lora_rank: 8 - lora_alpha: 32 - lora_dropout: 0.1 - lora_target_modules: "q_proj" "v_proj" - dataset_concatenation - report_to: none - max_seq_length: 512 - low_cpu_mem_usage: True - validation_split_percentage: 15 - adam_epsilon: 1e-08 ### Framework versions - PEFT 0.6.2 - Transformers 4.38.2 - Pytorch @ file:///tmp/tmp.5raBIUJfCK/torch-2.2.0a0%2Bgit8964477-cp310-cp310-linux_x86_64.whl#sha256=fe3b24f994c5e69f45942fb7def7f7e1b3617c20471f3fcadd16e4c7c85fb697 - Datasets 2.20.0 - Tokenizers 0.15.2
HoangCong/Viet-Mistral-Finetune-Full	HoangCong	2024-06-28T07:54:42Z	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-06-28T07:50:01Z	--- license: apache-2.0 ---
ICT2214Team7/RoBERTa_conll_epoch_7	ICT2214Team7	2024-06-28T08:22:05Z	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:distilroberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-28T07:51:28Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: RoBERTa_conll_epoch_7 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.944675195215152 - name: Recall type: recall value: 0.9569168630090878 - name: F1 type: f1 value: 0.9507566257001923 - name: Accuracy type: accuracy value: 0.9885704642385935 --- <!-- 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. --> # RoBERTa_conll_epoch_7 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0772 - Precision: 0.9447 - Recall: 0.9569 - F1: 0.9508 - Accuracy: 0.9886 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.078 \| 1.0 \| 1756 \| 0.0745 \| 0.9048 \| 0.9310 \| 0.9177 \| 0.9831 \| \| 0.0424 \| 2.0 \| 3512 \| 0.0702 \| 0.9317 \| 0.9451 \| 0.9383 \| 0.9851 \| \| 0.0254 \| 3.0 \| 5268 \| 0.0722 \| 0.9312 \| 0.9498 \| 0.9404 \| 0.9857 \| \| 0.0173 \| 4.0 \| 7024 \| 0.0678 \| 0.9348 \| 0.9505 \| 0.9426 \| 0.9867 \| \| 0.0086 \| 5.0 \| 8780 \| 0.0798 \| 0.9306 \| 0.9498 \| 0.9401 \| 0.9859 \| \| 0.0058 \| 6.0 \| 10536 \| 0.0786 \| 0.9406 \| 0.9562 \| 0.9483 \| 0.9881 \| \| 0.0033 \| 7.0 \| 12292 \| 0.0772 \| 0.9447 \| 0.9569 \| 0.9508 \| 0.9886 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Marie-ClairePRO/LatentColorDiffusion	Marie-ClairePRO	2024-06-28T09:08:56Z	0	null	[ "region:us" ]	null	2024-06-28T07:52:18Z	Entry not found
Vish16/q-Taxi-v3	Vish16	2024-06-28T07:54:13Z	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-28T07:52:44Z	--- 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="Vish16/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"]) ```
mhodur/llama-3-8B-Instruct-function-calling-v0.2-MLX	mhodur	2024-06-28T07:53:23Z	0	null	[ "region:us" ]	null	2024-06-28T07:53:23Z	Entry not found
Mantis-VL/mantis-8b-idefics2-classification-example_4096_regression	Mantis-VL	2024-06-30T21:35:41Z	0	transformers	[ "transformers", "safetensors", "idefics2", "text-classification", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-28T07:55:38Z	--- license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-classification-example_4096_regression 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. --> # mantis-8b-idefics2-classification-example_4096_regression This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
lyleokoth/tinyllama-timestamps-extraction-v1	lyleokoth	2024-06-28T07:57:02Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-28T07:56:38Z	--- base_model: unsloth/tinyllama-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: lyleokoth - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-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)
tiendoan/finetune_phobart	tiendoan	2024-06-28T07:57:46Z	0	null	[ "region:us" ]	null	2024-06-28T07:57:46Z	Entry not found
huggingfacepremium/Phi-3-mini-4k-instruct-onnx-GGUF	huggingfacepremium	2024-06-28T07:58:16Z	0	null	[ "region:us" ]	null	2024-06-28T07:58:16Z	Entry not found
ILKT/2024-06-23_09-09-07_epoch_14	ILKT	2024-06-28T07:58:36Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T07:58:35Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_15	ILKT	2024-06-28T07:58:53Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T07:58:53Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_16	ILKT	2024-06-28T07:59:11Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T07:59:10Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_17	ILKT	2024-06-28T07:59:29Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T07:59:28Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_18	ILKT	2024-06-28T07:59:46Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T07:59:45Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_19	ILKT	2024-06-28T08:00:03Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:00:03Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_20	ILKT	2024-06-28T08:00:22Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:00:21Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_21	ILKT	2024-06-28T08:00:45Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:00:44Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_22	ILKT	2024-06-28T08:01:04Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:01:04Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_23	ILKT	2024-06-28T08:01:26Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:01:25Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_24	ILKT	2024-06-28T08:01:49Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:01:48Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_25	ILKT	2024-06-28T08:02:10Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:02:09Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
iamalexcaspian/Myrtle-NoTimeToSpy-TLH-AlexCazares	iamalexcaspian	2024-06-28T08:03:45Z	0	null	[ "region:us" ]	null	2024-06-28T08:02:27Z	Entry not found
ILKT/2024-06-23_09-09-07_epoch_26	ILKT	2024-06-28T08:02:35Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:02:34Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_27	ILKT	2024-06-28T08:02:58Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:02:58Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_28	ILKT	2024-06-28T08:03:18Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:03:17Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_29	ILKT	2024-06-28T08:03:46Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:03:45Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_30	ILKT	2024-06-28T08:04:04Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:04:03Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_31	ILKT	2024-06-28T08:04:22Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:04:21Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_32	ILKT	2024-06-28T08:04:40Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:04:39Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_33	ILKT	2024-06-28T08:04:58Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:04:57Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_34	ILKT	2024-06-28T08:05:15Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:05:14Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
huggingfacepremium/autotrain-t5jjv-fxr8g-GGUF	huggingfacepremium	2024-06-28T08:05:26Z	0	null	[ "region:us" ]	null	2024-06-28T08:05:26Z	Entry not found
ILKT/2024-06-23_09-09-07_epoch_35	ILKT	2024-06-28T08:05:33Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:05:32Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_36	ILKT	2024-06-28T08:05:50Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:05:49Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_37	ILKT	2024-06-28T08:06:09Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:06:08Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_38	ILKT	2024-06-28T08:06:27Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:06:26Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_39	ILKT	2024-06-28T08:06:44Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:06:43Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
Sayalik45/gemma-glaive-function-calling	Sayalik45	2024-06-28T09:13:29Z	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T08:06:52Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ILKT/2024-06-23_09-09-07_epoch_40	ILKT	2024-06-28T08:07:02Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:07:01Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
ILKT/2024-06-23_09-09-07_epoch_41	ILKT	2024-06-28T08:07:19Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-28T08:07:18Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

mwyoox/security-desk

mwyoox

2024-06-28T08:08:15Z

0

null

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

null

2024-06-28T05:51:18Z

--- license: mit ---

youngdicey/flow-v1-depth1-dim768

youngdicey

2024-06-28T05:55:50Z

0

null

[ "region:us" ]

null

2024-06-28T05:54:41Z

Entry not found

shyxm/SDGClassification16SDGs

shyxm

2024-06-28T06:00:31Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T05:55:24Z

Entry not found

PMVRAJU/pmvraju

PMVRAJU

2024-06-28T05:56:05Z

0

null

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

null

2024-06-28T05:56:05Z

--- license: openrail++ ---

Masi1911/WHisperrr

Masi1911

2024-06-28T05:57:50Z

0

null

[ "region:us" ]

null

2024-06-28T05:57:34Z

# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3")

DevForML/GemmaTunnedV1

DevForML

2024-06-28T05:59:41Z

0

null

[ "region:us" ]

null

2024-06-28T05:59:41Z

Entry not found

Kash07/corgy_shoes_LoRA

Kash07

2024-06-28T06:11:17Z

0

diffusers

[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-06-28T06:04:13Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of TOK shoes widget: [] ---  # SDXL LoRA DreamBooth - Kash07/corgy_shoes_LoRA <Gallery /> ## Model description These are Kash07/corgy_shoes_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK shoes to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Kash07/corgy_shoes_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

LarryAIDraw/tPonynai3_v55

LarryAIDraw

2024-06-28T08:59:13Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2024-06-28T06:06:03Z

--- license: creativeml-openrail-m --- https://civitai.com/models/317902?modelVersionId=593760

XuZhang999/fitrack

XuZhang999

2024-06-28T06:06:10Z

0

null

[ "region:us" ]

null

2024-06-28T06:06:10Z

Entry not found

noanabeshima/dummy_tiny_stories_test

noanabeshima

2024-06-28T06:09:05Z

0

null

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

null

2024-06-28T06:08:30Z

--- license: mit ---

kugelitsjust/Dude_Guy

kugelitsjust

2024-06-28T23:21:27Z

0

null

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

null

2024-06-28T06:10:54Z

--- license: openrail ---

cherifkhalifah/Qwen1.5-1.8-chat_qlora_finetuned-data_e-commerce-v0.2

cherifkhalifah

2024-06-28T06:47:33Z

0

transformers

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

null

2024-06-28T06:22:26Z

--- 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]

manamihasegawa/medtext_model

manamihasegawa

2024-07-01T13:25:19Z

0

null

[ "region:us" ]

null

2024-06-28T06:23:23Z

Entry not found

slelab/AES15

slelab

2024-06-28T06:46:37Z

0

null

[ "region:us" ]

null

2024-06-28T06:23:48Z

Entry not found

geraldabrhm/llama-3-8b-antonymdf-simplecontext-32lora-lr1_4-16batch

geraldabrhm

2024-06-28T08:54:38Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T06:24:57Z

Entry not found

sekretiv/melinda

sekretiv

2024-06-28T06:26:56Z

0

null

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

null

2024-06-28T06:26:00Z

--- license: openrail ---

brivangl/vgg_kagn_bn11_v4_opt

brivangl

2024-07-02T07:04:34Z

0

transformers

[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]

null

2024-06-28T06:28:56Z

--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- ### How to use First, clone the repository: ``` git clone https://github.com/IvanDrokin/torch-conv-kan.git cd torch-conv-kan pip install -r requirements.txt ``` Then you can initialize the model and load weights. ```python import torch from models import VGGKAGN_BN model = VGGKAGN_BN.from_pretrained('brivangl/vgg_kagn_bn11_v4_opt', groups=1, degree=3, dropout=0.05, l1_decay=0, width_scale=3, affine=True, norm_layer=nn.BatchNorm2d, expected_feature_shape=(1, 1), vgg_type='VGG11v4') ``` Transforms, used for validation on Imagenet1k: ```python from torchvision.transforms import v2 transforms_val = v2.Compose([ v2.ToImage(), v2.Resize(256, antialias=True), v2.CenterCrop(224), v2.ToDtype(torch.float32, scale=True), v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) ```

AdamKasumovic/llama3-8b-instruct-bactrian-x-en-100-percent-low-med-perplexity

AdamKasumovic

2024-06-28T06:31:35Z

0

transformers

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

null

2024-06-28T06:31:34Z

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

ghredghre/gsdgsgsd

ghredghre

2024-06-28T06:33:21Z

0

null

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

null

2024-06-28T06:33:21Z

--- license: openrail ---

Reizei/Chihiro

Reizei

2024-06-28T06:34:08Z

0

null

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

null

2024-06-28T06:34:08Z

--- license: unknown ---

traderpedroso/best-tts

traderpedroso

2024-06-28T06:36:03Z

0

null

[ "region:us" ]

null

2024-06-28T06:34:20Z

Entry not found

Yash0109/diaratechHf_llama24265d55-c3ab-48cf-8954-7fc36f415b9e

Yash0109

2024-06-28T06:40:42Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

null

2024-06-28T06:37:27Z

--- base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator library_name: peft license: apache-2.0 tags: - trl - sft - generated_from_trainer model-index: - name: diaratechHf_llama24265d55-c3ab-48cf-8954-7fc36f415b9e results: [] ---  # diaratechHf_llama24265d55-c3ab-48cf-8954-7fc36f415b9e This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.20.0 - Tokenizers 0.15.2

JasonLai/distilbert-base-uncased-finetuned-emotion

JasonLai

2024-06-28T06:41:13Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-28T06:39:47Z

Entry not found

imagepipeline/buk

imagepipeline

2024-06-28T06:43:17Z

0

null

[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-06-28T06:43:14Z

--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## buk <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> **This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)** Model details - buk [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/buk?id=3d7e3937-0d96-44a0-afd4-89cde2917fb4/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "3d7e3937-0d96-44a0-afd4-89cde2917fb4", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` | Headers | Type | Description | |:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------| | `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) | | `Content-Type` | `str` | application/json - content type of the request body | | Parameter | Type | Description | | :-------- | :------- | :------------------------- | | `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own| | `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips | | `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) | | `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 | | `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page | | `lora_weights` | `str, array` | Strength of the LoRA effect | --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits

imagepipeline/nudexl

imagepipeline

2024-06-28T06:44:49Z

0

null

[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-06-28T06:44:47Z

--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## nudexl <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> **This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)** Model details - nudexl [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/nudexl?id=83e6657a-c042-4703-88b3-6a47e39a595e/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "83e6657a-c042-4703-88b3-6a47e39a595e", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` | Headers | Type | Description | |:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------| | `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) | | `Content-Type` | `str` | application/json - content type of the request body | | Parameter | Type | Description | | :-------- | :------- | :------------------------- | | `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own| | `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips | | `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) | | `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 | | `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page | | `lora_weights` | `str, array` | Strength of the LoRA effect | --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits

sekretiv/melinda2

sekretiv

2024-06-28T06:46:16Z

0

null

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

null

2024-06-28T06:45:20Z

--- license: openrail ---

imagepipeline/nude

imagepipeline

2024-06-28T06:45:54Z

0

null

[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-06-28T06:45:52Z

--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## nude <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> **This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)** Model details - nude [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/nude?id=ac792c78-baf5-4d87-a80e-77fc99c84067/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "ac792c78-baf5-4d87-a80e-77fc99c84067", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` | Headers | Type | Description | |:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------| | `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) | | `Content-Type` | `str` | application/json - content type of the request body | | Parameter | Type | Description | | :-------- | :------- | :------------------------- | | `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own| | `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips | | `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) | | `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 | | `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page | | `lora_weights` | `str, array` | Strength of the LoRA effect | --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits

theDisco/fluffy-koala

theDisco

2024-07-02T12:10:36Z

0

transformers

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

null

2024-06-28T06:46:59Z

--- library_name: transformers tags: [] --- # Model Card for Model ID In order to run this notebook you need to first create an .env file in the root directory with the following content. ```env HF_TOKEN=READ_WRITE_HF_TOKEN TOKENIZERS_PARALLELISM=false WANDB_API_KEY=WANDB_TOKEN_FOR_LOGGING ``` `HF_TOKEN` is needed for pushing the model to HF. `WANDB_API_KEY` is needed for evaluating the accuracy of fine tuning. Before running the fine tuning you need to generate fake data. To do that install the `requirements.txt` dependencies. Since the file was created on MacOS, the installation might fail on Linux system. Make sure to install faker (`pip install faker`) for the script to run properly. ``` python dataset_generator.py ``` Once these steps have been completed, you can run the fine tuning notebook. Notebook was tested with Python 3.10. ## Running inference ```python from transformers import PaliGemmaForConditionalGeneration, AutoProcessor from PIL import Image image = Image.open('path/to/image') model = PaliGemmaForConditionalGeneration.from_pretrained("theDisco/fluffy-koala") processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-448") inputs = processor(text="extract JSON.", images=image, return_tensors="pt") generated_ids = model.generate(**inputs, max_new_tokens=512) image_token_index = model.config.image_token_index num_image_tokens = len(generated_ids[generated_ids == image_token_index]) num_text_tokens = len(processor.tokenizer.encode("extract JSON.")) num_prompt_tokens = num_image_tokens + num_text_tokens + 2 generated_text = processor.batch_decode(generated_ids[:, num_prompt_tokens:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(generated_text) ```

lyleokoth/tinyllama-function-calling-v1

lyleokoth

2024-06-28T06:48:42Z

0

transformers

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

null

2024-06-28T06:48:32Z

--- base_model: unsloth/tinyllama-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** lyleokoth - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-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)

StarCycle/GENIE_2.6M

StarCycle

2024-06-28T07:27:07Z

0

1

null

[ "region:us" ]

null

2024-06-28T06:49:49Z

Entry not found

AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-en-100-percent-low-med-ids

AdamKasumovic

2024-06-28T06:54:42Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-06-28T06:51:45Z

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

ICT2214Team7/RoBERTa_conll_epoch_5

ICT2214Team7

2024-06-28T07:15:11Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:distilroberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-06-28T06:52:50Z

--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: RoBERTa_conll_epoch_5 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.937014382542569 - name: Recall type: recall value: 0.9538875799394143 - name: F1 type: f1 value: 0.945375698440497 - name: Accuracy type: accuracy value: 0.9872971065631616 ---  # RoBERTa_conll_epoch_5 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0716 - Precision: 0.9370 - Recall: 0.9539 - F1: 0.9454 - Accuracy: 0.9873 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0787 | 1.0 | 1756 | 0.0734 | 0.9024 | 0.9317 | 0.9168 | 0.9819 | | 0.0389 | 2.0 | 3512 | 0.0706 | 0.9359 | 0.9440 | 0.9399 | 0.9854 | | 0.023 | 3.0 | 5268 | 0.0632 | 0.9340 | 0.9483 | 0.9411 | 0.9864 | | 0.0137 | 4.0 | 7024 | 0.0762 | 0.9368 | 0.9534 | 0.9450 | 0.9875 | | 0.0054 | 5.0 | 8780 | 0.0716 | 0.9370 | 0.9539 | 0.9454 | 0.9873 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

TonyCh0pper/dqn-SpaceInvadersNoFrameskip-v4-2

TonyCh0pper

2024-06-28T06:53:44Z

0

null

[ "region:us" ]

null

2024-06-28T06:53:44Z

Entry not found

sikander0202/sikander

sikander0202

2024-06-28T06:55:05Z

0

null

[ "region:us" ]

null

2024-06-28T06:55:05Z

Entry not found

habibkhan22cp/metallama3_8b_tutorAi

habibkhan22cp

2024-07-02T08:14:45Z

0

transformers

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

text-classification

2024-06-28T06:58:10Z

--- 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]

saurabhg2083/mistral_lora_v1

saurabhg2083

2024-06-30T13:06:08Z

0

transformers

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

null

2024-06-28T06:58:25Z

--- library_name: transformers tags: - unsloth --- # 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]

arsen7076/MMed-Llama-3-8B

arsen7076

2024-06-28T06:59:25Z

0

null

[ "region:us" ]

null

2024-06-28T06:59:25Z

Entry not found

leenag/vasista_medium_Luke

leenag

2024-06-28T13:20:09Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ymr", "base_model:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-06-28T07:01:25Z

--- language: - ymr license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: leenag/Malasar_Luke results: [] ---  # leenag/Malasar_Luke This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Spoken Bible Corpus: Malasar dataset. It achieves the following results on the evaluation set: - Loss: 0.5075 - Wer: 48.2010 ## 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: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:-------:| | 0.018 | 11.3636 | 250 | 0.3526 | 50.2570 | | 0.0059 | 22.7273 | 500 | 0.4000 | 49.5146 | | 0.0002 | 34.0909 | 750 | 0.4418 | 48.3152 | | 0.0001 | 45.4545 | 1000 | 0.4785 | 48.0868 | | 0.0 | 56.8182 | 1250 | 0.4923 | 47.8013 | | 0.0 | 68.1818 | 1500 | 0.5008 | 47.8013 | | 0.0 | 79.5455 | 1750 | 0.5059 | 48.2010 | | 0.0 | 90.9091 | 2000 | 0.5075 | 48.2010 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.0 - Tokenizers 0.19.1

Mantis-VL/mantis-8b-idefics2-chat-video-example_8192_qlora

Mantis-VL

2024-06-28T07:03:29Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T07:02:50Z

Entry not found

Yash0109/diaratechHf_llama2bf5fb73-8c2e-45c2-83f1-ae8e52fda215

Yash0109

2024-06-28T07:06:03Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T07:04:39Z

Entry not found

Oksana2014/1

Oksana2014

2024-06-28T07:07:12Z

0

null

[ "region:us" ]

null

2024-06-28T07:07:12Z

Entry not found

cherifkhalifah/Qwen1.5-1.8-chat_qlora_finetuned-data_e-commerce-v0.3

cherifkhalifah

2024-06-28T07:08:16Z

0

transformers

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

null

2024-06-28T07:07:31Z

--- 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]

bart-lu/teat

bart-lu

2024-06-28T07:10:41Z

0

null

[ "license:bsl-1.0", "region:us" ]

null

2024-06-28T07:10:41Z

--- license: bsl-1.0 ---

Hazelley/Hamzah

Hazelley

2024-06-28T07:10:54Z

0

null

[ "region:us" ]

null

2024-06-28T07:10:54Z

Entry not found

LYshan/sd-class-butterflies-64

LYshan

2024-06-28T07:14:02Z

0

null

[ "region:us" ]

null

2024-06-28T07:14:02Z

Entry not found

habulaj/147753444204

habulaj

2024-06-28T07:21:07Z

0

null

[ "region:us" ]

null

2024-06-28T07:20:55Z

Entry not found

yabichiu/gemma2_Ollama

yabichiu

2024-06-28T07:41:25Z

0

null

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

null

2024-06-28T07:22:29Z

--- license: gemma ---

jinanshii/jinz

jinanshii

2024-06-28T07:24:50Z

0

null

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

null

2024-06-28T07:24:50Z

--- license: apache-2.0 ---

swetapatra/BERT_Enhancement

swetapatra

2024-06-28T07:35:44Z

0

null

[ "region:us" ]

null

2024-06-28T07:27:05Z

Entry not found

samidh/cope-g2b-2c-hs.s1.5.9-sx.s1.5.9o-hr.s5-sh.s5.l1e4-e10-d25-r8

samidh

2024-06-28T07:30:22Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b", "license:gemma", "region:us" ]

null

2024-06-28T07:30:18Z

--- base_model: google/gemma-2b library_name: peft license: gemma metrics: - f1 - precision - recall tags: - trl - sft - generated_from_trainer model-index: - name: cope-g2b-2c-hs.s1.5.9-sx.s1.5.9o-hr.s5-sh.s5.l1e4-e10-d25-r8 results: [] ---  # cope-g2b-2c-hs.s1.5.9-sx.s1.5.9o-hr.s5-sh.s5.l1e4-e10-d25-r8 This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8818 - F1: 0.8403 - Precision: 0.7653 - Recall: 0.9317 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:| | 0.1794 | 1.0 | 848 | 0.1999 | 0.8249 | 0.7898 | 0.8634 | | 0.1913 | 2.0 | 1697 | 0.2122 | 0.8317 | 0.8873 | 0.7826 | | 0.1182 | 3.0 | 2546 | 0.2569 | 0.8677 | 0.8598 | 0.8758 | | 0.0235 | 4.0 | 3395 | 0.4534 | 0.8278 | 0.7487 | 0.9255 | | 0.0001 | 5.0 | 4243 | 0.4959 | 0.8520 | 0.8294 | 0.8758 | | 0.0286 | 6.0 | 5092 | 0.4956 | 0.8412 | 0.7989 | 0.8882 | | 0.0004 | 7.0 | 5941 | 0.8649 | 0.8412 | 0.7626 | 0.9379 | | 0.0 | 8.0 | 6790 | 0.8971 | 0.8403 | 0.7653 | 0.9317 | | 0.0 | 9.0 | 7638 | 0.8493 | 0.8403 | 0.7653 | 0.9317 | | 0.0 | 9.99 | 8480 | 0.8818 | 0.8403 | 0.7653 | 0.9317 | ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0 - Pytorch 2.3.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

Wakade/Wakade-lavanya-opt-6.1b-lora

Wakade

2024-06-28T07:32:42Z

0

null

[ "region:us" ]

null

2024-06-28T07:32:42Z

Entry not found

yuvraj108c/yolo-nas-pose-onnx

yuvraj108c

2024-06-28T07:37:48Z

0

null

[ "onnx", "region:us" ]

null

2024-06-28T07:33:02Z

Entry not found

longxia/Qwen-Qwen1.5-1.8B-1719560224

longxia

2024-06-28T07:37:15Z

0

null

[ "region:us" ]

null

2024-06-28T07:37:15Z

Entry not found

Abdrehman6224/llama-3-8b-chat-doctor

Abdrehman6224

2024-06-28T07:40:58Z

0

transformers

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

null

2024-06-28T07:40:50Z

--- 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]

catyung/llama3-sft-4bits-sap2english-lora_model

catyung

2024-06-28T07:41:17Z

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

2024-06-28T07:40:57Z

--- 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:** catyung - **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)

vivi072/llava-1.5-7b-hf-ft-mix-vsft

vivi072

2024-06-28T07:41:18Z

0

null

[ "region:us" ]

null

2024-06-28T07:41:18Z

Entry not found

rajparmar/finetuned-tpicap-data-model

rajparmar

2024-06-28T07:43:31Z

0

null

[ "region:us" ]

null

2024-06-28T07:43:31Z

Entry not found

agrajpaudel/sdxl-dreambooth

agrajpaudel

2024-06-28T07:50:16Z

0

diffusers

[ "diffusers", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-06-28T07:47:03Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of TOK_sks_rajesh_hamal widget: [] ---  # SDXL LoRA DreamBooth - agrajpaudel/sdxl-dreambooth <Gallery /> ## Model description These are agrajpaudel/sdxl-dreambooth LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK_sks_rajesh_hamal to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](agrajpaudel/sdxl-dreambooth/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

hasininawoda/person111

hasininawoda

2024-06-28T07:48:00Z

0

diffusers

[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-06-28T07:47:59Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: a photo of TOK person widget: [] ---  # SDXL LoRA DreamBooth - hasininawoda/person111 <Gallery /> ## Model description These are hasininawoda/person111 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK person to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](hasininawoda/person111/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

yuriachermann/Not-so-bright-AGI-3

yuriachermann

2024-06-28T15:21:41Z

0

peft

[ "peft", "trl", "sft", "generated_from_trainer", "Gaudi", "ipex", "ultrachat_200k", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "license:llama3", "region:us" ]

null

2024-06-28T07:48:52Z

--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer - Gaudi - ipex - ultrachat_200k base_model: meta-llama/Meta-Llama-3-70B-Instruct model-index: - name: Not-so-bright-AGI-3 results: [] ---  # Not-so-bright-AGI-3 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-5 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - bf16: True - num_train_epochs: 5 - per_device_train_batch_size: 8 - evaluation_strategy: "steps" - save_strategy: "steps" - learning_rate: 1e-5 - warmup_ratio: 0.03 - lr_scheduler_type: "linear" - max_grad_norm: 0.01 - logging_steps: 100 - do_train - do_eval - use_habana - use_lazy_mode - throughput_warmup_steps: 5 - lora_rank: 8 - lora_alpha: 32 - lora_dropout: 0.1 - lora_target_modules: "q_proj" "v_proj" - dataset_concatenation - report_to: none - max_seq_length: 512 - low_cpu_mem_usage: True - validation_split_percentage: 15 - adam_epsilon: 1e-08 ### Framework versions - PEFT 0.6.2 - Transformers 4.38.2 - Pytorch @ file:///tmp/tmp.5raBIUJfCK/torch-2.2.0a0%2Bgit8964477-cp310-cp310-linux_x86_64.whl#sha256=fe3b24f994c5e69f45942fb7def7f7e1b3617c20471f3fcadd16e4c7c85fb697 - Datasets 2.20.0 - Tokenizers 0.15.2

HoangCong/Viet-Mistral-Finetune-Full

HoangCong

2024-06-28T07:54:42Z

0

null

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

null

2024-06-28T07:50:01Z

--- license: apache-2.0 ---

ICT2214Team7/RoBERTa_conll_epoch_7

ICT2214Team7

2024-06-28T08:22:05Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:distilroberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-06-28T07:51:28Z

--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: RoBERTa_conll_epoch_7 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.944675195215152 - name: Recall type: recall value: 0.9569168630090878 - name: F1 type: f1 value: 0.9507566257001923 - name: Accuracy type: accuracy value: 0.9885704642385935 ---  # RoBERTa_conll_epoch_7 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0772 - Precision: 0.9447 - Recall: 0.9569 - F1: 0.9508 - Accuracy: 0.9886 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.078 | 1.0 | 1756 | 0.0745 | 0.9048 | 0.9310 | 0.9177 | 0.9831 | | 0.0424 | 2.0 | 3512 | 0.0702 | 0.9317 | 0.9451 | 0.9383 | 0.9851 | | 0.0254 | 3.0 | 5268 | 0.0722 | 0.9312 | 0.9498 | 0.9404 | 0.9857 | | 0.0173 | 4.0 | 7024 | 0.0678 | 0.9348 | 0.9505 | 0.9426 | 0.9867 | | 0.0086 | 5.0 | 8780 | 0.0798 | 0.9306 | 0.9498 | 0.9401 | 0.9859 | | 0.0058 | 6.0 | 10536 | 0.0786 | 0.9406 | 0.9562 | 0.9483 | 0.9881 | | 0.0033 | 7.0 | 12292 | 0.0772 | 0.9447 | 0.9569 | 0.9508 | 0.9886 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

Marie-ClairePRO/LatentColorDiffusion

Marie-ClairePRO

2024-06-28T09:08:56Z

0

null

[ "region:us" ]

null

2024-06-28T07:52:18Z

Entry not found

Vish16/q-Taxi-v3

Vish16

2024-06-28T07:54:13Z

0

null

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

reinforcement-learning

2024-06-28T07:52:44Z

--- 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="Vish16/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"]) ```

mhodur/llama-3-8B-Instruct-function-calling-v0.2-MLX

mhodur

2024-06-28T07:53:23Z

0

null

[ "region:us" ]

null

2024-06-28T07:53:23Z

Entry not found

Mantis-VL/mantis-8b-idefics2-classification-example_4096_regression

Mantis-VL

2024-06-30T21:35:41Z

0

transformers

[ "transformers", "safetensors", "idefics2", "text-classification", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-28T07:55:38Z

--- license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-classification-example_4096_regression results: [] ---  # mantis-8b-idefics2-classification-example_4096_regression This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

lyleokoth/tinyllama-timestamps-extraction-v1

lyleokoth

2024-06-28T07:57:02Z

0

transformers

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

null

2024-06-28T07:56:38Z

--- base_model: unsloth/tinyllama-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** lyleokoth - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-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)

tiendoan/finetune_phobart

tiendoan

2024-06-28T07:57:46Z

0

null

[ "region:us" ]

null

2024-06-28T07:57:46Z

Entry not found

huggingfacepremium/Phi-3-mini-4k-instruct-onnx-GGUF

huggingfacepremium

2024-06-28T07:58:16Z

0

null

[ "region:us" ]

null

2024-06-28T07:58:16Z

Entry not found

ILKT/2024-06-23_09-09-07_epoch_14

ILKT

2024-06-28T07:58:36Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T07:58:35Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_15

ILKT

2024-06-28T07:58:53Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T07:58:53Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_16

ILKT

2024-06-28T07:59:11Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T07:59:10Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_17

ILKT

2024-06-28T07:59:29Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T07:59:28Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_18

ILKT

2024-06-28T07:59:46Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T07:59:45Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_19

ILKT

2024-06-28T08:00:03Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:00:03Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_20

ILKT

2024-06-28T08:00:22Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:00:21Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_21

ILKT

2024-06-28T08:00:45Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:00:44Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_22

ILKT

2024-06-28T08:01:04Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:01:04Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_23

ILKT

2024-06-28T08:01:26Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:01:25Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_24

ILKT

2024-06-28T08:01:49Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:01:48Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_25

ILKT

2024-06-28T08:02:10Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:02:09Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

iamalexcaspian/Myrtle-NoTimeToSpy-TLH-AlexCazares

iamalexcaspian

2024-06-28T08:03:45Z

0

null

[ "region:us" ]

null

2024-06-28T08:02:27Z

Entry not found

ILKT/2024-06-23_09-09-07_epoch_26

ILKT

2024-06-28T08:02:35Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:02:34Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_27

ILKT

2024-06-28T08:02:58Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:02:58Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_28

ILKT

2024-06-28T08:03:18Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:03:17Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_29

ILKT

2024-06-28T08:03:46Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:03:45Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_30

ILKT

2024-06-28T08:04:04Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:04:03Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_31

ILKT

2024-06-28T08:04:22Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:04:21Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_32

ILKT

2024-06-28T08:04:40Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:04:39Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_33

ILKT

2024-06-28T08:04:58Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:04:57Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_34

ILKT

2024-06-28T08:05:15Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:05:14Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

huggingfacepremium/autotrain-t5jjv-fxr8g-GGUF

huggingfacepremium

2024-06-28T08:05:26Z

0

null

[ "region:us" ]

null

2024-06-28T08:05:26Z

Entry not found

ILKT/2024-06-23_09-09-07_epoch_35

ILKT

2024-06-28T08:05:33Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:05:32Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_36

ILKT

2024-06-28T08:05:50Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:05:49Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_37

ILKT

2024-06-28T08:06:09Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:06:08Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_38

ILKT

2024-06-28T08:06:27Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:06:26Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_39

ILKT

2024-06-28T08:06:44Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:06:43Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

Sayalik45/gemma-glaive-function-calling

Sayalik45

2024-06-28T09:13:29Z

0

transformers

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

null

2024-06-28T08:06:52Z

--- library_name: transformers tags: - unsloth --- # 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]

ILKT/2024-06-23_09-09-07_epoch_40

ILKT

2024-06-28T08:07:02Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:07:01Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

ILKT/2024-06-23_09-09-07_epoch_41

ILKT

2024-06-28T08:07:19Z

0

sentence-transformers

[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-06-28T08:07:18Z

--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---