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
borreplata/Test	borreplata	2024-06-26T03:20:51Z	0	null	[ "license:unlicense", "region:us" ]	null	2024-06-26T03:19:27Z	--- license: unlicense --- # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B") model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
HoangHa/selfies-roberta-large-silu	HoangHa	2024-06-26T03:22:15Z	0	null	[ "region:us" ]	null	2024-06-26T03:22:15Z	Entry not found
chopchopchuck/mts10	chopchopchuck	2024-06-26T03:22:50Z	0	null	[ "region:us" ]	null	2024-06-26T03:22:35Z	Entry not found
Katyc/llama-3-8b-Instruct-bnb-4bit-LoRA	Katyc	2024-06-26T03:23:20Z	0	transformers	[ "transformers", "safetensors", "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	2024-06-26T03:23:10Z	--- 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: Katyc - 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)
charlieoneill/jsalt-data	charlieoneill	2024-06-26T03:27:57Z	0	null	[ "region:us" ]	null	2024-06-26T03:24:54Z	Entry not found
TheRealheavy/BigSmoke	TheRealheavy	2024-06-26T03:28:01Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T03:26:38Z	--- license: openrail ---
qualcomm/Posenet-Mobilenet-Quantized	qualcomm	2024-06-26T03:30:20Z	0	pytorch	[ "pytorch", "tflite", "quantized", "android", "image-classification", "dataset:coco", "arxiv:1803.08225", "license:apache-2.0", "region:us" ]	image-classification	2024-06-26T03:30:14Z	--- datasets: - coco library_name: pytorch license: apache-2.0 pipeline_tag: image-classification tags: - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/posenet_mobilenet_quantized/web-assets/model_demo.png) # Posenet-Mobilenet-Quantized: Optimized for Mobile Deployment ## Quantized human pose estimator Posenet performs pose estimation on human images. This model is an implementation of Posenet-Mobilenet-Quantized found [here](https://github.com/rwightman/posenet-pytorch). This repository provides scripts to run Posenet-Mobilenet-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/posenet_mobilenet_quantized). ### Model Details - Model Type: Pose estimation - Model Stats: - Model checkpoint: mobilenet_v1_101 - Input resolution: 513x257 - Number of parameters: 3.31M - Model size: 3.47 MB \| Device \| Chipset \| Target Runtime \| Inference Time (ms) \| Peak Memory Range (MB) \| Precision \| Primary Compute Unit \| Target Model \| ---\|---\|---\|---\|---\|---\|---\|---\| \| Samsung Galaxy S23 Ultra (Android 13) \| Snapdragon® 8 Gen 2 \| TFLite \| 0.591 ms \| 0 - 2 MB \| INT8 \| NPU \| [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) \| Samsung Galaxy S23 Ultra (Android 13) \| Snapdragon® 8 Gen 2 \| QNN Model Library \| 0.622 ms \| 0 - 9 MB \| INT8 \| NPU \| [Posenet-Mobilenet-Quantized.so](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.so) ## Installation This model can be installed as a Python package via pip. ```bash pip install qai-hub-models ``` ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. With this API token, you can configure your client to run models on the cloud hosted devices. ```bash qai-hub configure --api_token API_TOKEN ``` Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. ## Demo off target The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input. ```bash python -m qai_hub_models.models.posenet_mobilenet_quantized.demo ``` The above demo runs a reference implementation of pre-processing, model inference, and post processing. NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.posenet_mobilenet_quantized.demo ``` ### Run model on a cloud-hosted device In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following: * Performance check on-device on a cloud-hosted device * Downloads compiled assets that can be deployed on-device for Android. * Accuracy check between PyTorch and on-device outputs. ```bash python -m qai_hub_models.models.posenet_mobilenet_quantized.export ``` ``` Profile Job summary of Posenet-Mobilenet-Quantized -------------------------------------------------- Device: Snapdragon X Elite CRD (11) Estimated Inference Time: 0.69 ms Estimated Peak Memory Range: 0.38-0.38 MB Compute Units: NPU (42) \| Total (42) ``` ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.posenet_mobilenet_quantized.demo --on-device ``` NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.posenet_mobilenet_quantized.demo -- --on-device ``` ## Deploying compiled model to Android The models can be deployed using multiple runtimes: - TensorFlow Lite (`.tflite` export): [This tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a guide to deploy the .tflite model in an Android application. - QNN (`.so` export ): This [sample app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) provides instructions on how to use the `.so` shared library in an Android application. ## View on Qualcomm® AI Hub Get more details on Posenet-Mobilenet-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/posenet_mobilenet_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License - The license for the original implementation of Posenet-Mobilenet-Quantized can be found [here](https://github.com/rwightman/posenet-pytorch/blob/master/LICENSE.txt). - The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf) ## References * [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://arxiv.org/abs/1803.08225) * [Source Model Implementation](https://github.com/rwightman/posenet-pytorch) ## Community * Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI. * For questions or feedback please [reach out to us](mailto:[email protected]).
richardkelly/Qwen-Qwen1.5-1.8B-1719372662	richardkelly	2024-06-26T03:31:02Z	0	null	[ "region:us" ]	null	2024-06-26T03:31:02Z	Entry not found
habulaj/129556109667	habulaj	2024-06-26T03:31:21Z	0	null	[ "region:us" ]	null	2024-06-26T03:31:12Z	Entry not found
qualcomm/Midas-V2-Quantized	qualcomm	2024-06-26T03:31:34Z	0	pytorch	[ "pytorch", "tflite", "quantized", "android", "depth-estimation", "arxiv:1907.01341", "license:mit", "region:us" ]	depth-estimation	2024-06-26T03:31:25Z	--- library_name: pytorch license: mit pipeline_tag: depth-estimation tags: - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/midas_quantized/web-assets/model_demo.png) # Midas-V2-Quantized: Optimized for Mobile Deployment ## Quantized Deep Convolutional Neural Network model for depth estimation Midas is designed for estimating depth at each point in an image. This model is an implementation of Midas-V2-Quantized found [here](https://github.com/isl-org/MiDaS). This repository provides scripts to run Midas-V2-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/midas_quantized). ### Model Details - Model Type: Depth estimation - Model Stats: - Model checkpoint: MiDaS_small - Input resolution: 256x256 - Number of parameters: 16.6M - Model size: 16.6 MB \| Device \| Chipset \| Target Runtime \| Inference Time (ms) \| Peak Memory Range (MB) \| Precision \| Primary Compute Unit \| Target Model \| ---\|---\|---\|---\|---\|---\|---\|---\| \| Samsung Galaxy S23 Ultra (Android 13) \| Snapdragon® 8 Gen 2 \| TFLite \| 1.154 ms \| 0 - 2 MB \| INT8 \| NPU \| [Midas-V2-Quantized.tflite](https://huggingface.co/qualcomm/Midas-V2-Quantized/blob/main/Midas-V2-Quantized.tflite) \| Samsung Galaxy S23 Ultra (Android 13) \| Snapdragon® 8 Gen 2 \| QNN Model Library \| 1.482 ms \| 0 - 275 MB \| INT8 \| NPU \| [Midas-V2-Quantized.so](https://huggingface.co/qualcomm/Midas-V2-Quantized/blob/main/Midas-V2-Quantized.so) ## Installation This model can be installed as a Python package via pip. ```bash pip install "qai-hub-models[midas_quantized]" ``` ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. With this API token, you can configure your client to run models on the cloud hosted devices. ```bash qai-hub configure --api_token API_TOKEN ``` Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. ## Demo off target The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input. ```bash python -m qai_hub_models.models.midas_quantized.demo ``` The above demo runs a reference implementation of pre-processing, model inference, and post processing. NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.midas_quantized.demo ``` ### Run model on a cloud-hosted device In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following: * Performance check on-device on a cloud-hosted device * Downloads compiled assets that can be deployed on-device for Android. * Accuracy check between PyTorch and on-device outputs. ```bash python -m qai_hub_models.models.midas_quantized.export ``` ``` Profile Job summary of Midas-V2-Quantized -------------------------------------------------- Device: Snapdragon X Elite CRD (11) Estimated Inference Time: 1.52 ms Estimated Peak Memory Range: 0.46-0.46 MB Compute Units: NPU (148) \| Total (148) ``` ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.midas_quantized.demo --on-device ``` NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.midas_quantized.demo -- --on-device ``` ## Deploying compiled model to Android The models can be deployed using multiple runtimes: - TensorFlow Lite (`.tflite` export): [This tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a guide to deploy the .tflite model in an Android application. - QNN (`.so` export ): This [sample app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) provides instructions on how to use the `.so` shared library in an Android application. ## View on Qualcomm® AI Hub Get more details on Midas-V2-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/midas_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License - The license for the original implementation of Midas-V2-Quantized can be found [here](https://github.com/isl-org/MiDaS/blob/master/LICENSE). - The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf) ## References * [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://arxiv.org/abs/1907.01341v3) * [Source Model Implementation](https://github.com/isl-org/MiDaS) ## Community * Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI. * For questions or feedback please [reach out to us](mailto:[email protected]).
Coolwowsocoolwow/Eric_Cartman	Coolwowsocoolwow	2024-06-26T03:44:39Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T03:32:02Z	--- license: openrail ---
habulaj/174829150187	habulaj	2024-06-26T03:33:03Z	0	null	[ "region:us" ]	null	2024-06-26T03:32:52Z	Entry not found
johnpaulbin/llama8b-tokipona-epoch1-chat	johnpaulbin	2024-06-26T03:50:04Z	0	transformers	[ "transformers", "safetensors", "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	2024-06-26T03:33:05Z	--- 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: johnpaulbin - 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)
Tam1032/whisper-largev3-hi	Tam1032	2024-06-26T03:33:33Z	0	null	[ "region:us" ]	null	2024-06-26T03:33:33Z	Entry not found
abinavGanesh/emty	abinavGanesh	2024-06-26T03:38:58Z	0	null	[ "region:us" ]	null	2024-06-26T03:38:58Z	Entry not found
OpilotAI/medicine-Llama3-8B-q4f16_1-Opilot	OpilotAI	2024-06-26T03:46:09Z	0	null	[ "region:us" ]	null	2024-06-26T03:41:17Z	Entry not found
PiAPI/Midjourney-API	PiAPI	2024-06-27T02:44:07Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T03:42:14Z	--- license: mit --- # Midjourney API Model Page: [Midjourney API](https://piapi.ai/midjourney-api) This model card illustartes the steps to use Midjourney API's endpoint. You can also check out other model cards: - [Faceswap API](https://huggingface.co/PiAPI/Faceswap-API) - [Suno API](https://huggingface.co/PiAPI/Suno-API) - [Dream Machine API](https://huggingface.co/PiAPI/Dream-Machine-API) Model Information Renowned for its exceptional text-to-image generative AI capabilities, Midjourney is a preferred tool among graphic designers, photographers, and creatives aiming to explore AI-driven artistry. Despite the absence of an official API from Midjourney, PiAPI has introduced the unofficial Midjourney API, empowering developers to incorporate this cutting-edge text-to-image model into their AI applications. ## Usage Steps Below we share the code snippets on how to use Midjourney API's upscale endpoint. - The programming language is Python - The origin task ID should be the task ID of the fetched imagine endpoint Create an upscale task ID <pre><code class="language-python"> <span class="hljs-keyword">import</span> http.client conn = http.client.HTTPSConnection(<span class="hljs-string">"api.piapi.ai"</span>) payload = <span class="hljs-string">"{\n \"origin_task_id\": \"9c6796dd*******1e7dfef5203b\",\n \"index\": \"1\",\n \"webhook_endpoint\": \"\",\n \"webhook_secret\": \"\"\n}"</span> headers = { <span class="hljs-built_in">'X-API-Key': "{{x-api-key}}"</span>, //Insert your API Key here <span class="hljs-built_in">'Content-Type': "application/json"</span>, <span class="hljs-built_in">'Accept': "application/json"</span> } conn.request("POST", "/mj/v2/upscale", payload, headers) res = conn.getresponse() data = res.read() <span class="hljs-keyword">print</span>(data.decode("utf-8")) </code></pre> Retrieve the task ID <pre><code class="language-python"> { <span class="hljs-built_in">"code"</span>: 200, <span class="hljs-built_in">"data"</span>: { <span class="hljs-built_in">"task_id"</span>: :3be7e0b0************d1a725da0b1d" //Record the taskID provided in your response terminal }, <span class="hljs-built_in">"message"</span>: "success" } </code></pre> Insert the upscale task ID into the fetch endpoint <pre><code class="language-python"> <span class="hljs-keyword">import</span> http.client conn = http.client.HTTPSConnection(<span class="hljs-string">"api.piapi.ai"</span>) payload = <span class="hljs-string">"{\n \"task_id\": \"3be7e0b0************d1a725da0b1d\"\n}"</span> /Replace the task ID with your task ID headers = { <span class="hljs-built_in">'Content-Type': "application/json"</span>, <span class="hljs-built_in">'Accept': "application/json"</span> } conn.request("POST", "/mj/v2/fetch", payload, headers) res = conn.getresponse() data = res.read() <span class="hljs-keyword">print</span>(data.decode("utf-8")) </code></pre> For fetch endpoint responses** - Refer to our [documentation](https://piapi.ai/docs/midjourney-api/upscale) for more detailed information. <br> ## Contact us Contact us at <a href="mailto:[email protected]">[email protected]</a> for any inquires. <br>
neuronpedia/gemma-2b-it__res-jb	neuronpedia	2024-06-26T03:45:03Z	0	null	[ "region:us" ]	null	2024-06-26T03:44:38Z	Entry not found
ben81828/meow_text	ben81828	2024-06-26T03:46:01Z	0	null	[ "region:us" ]	null	2024-06-26T03:46:01Z	Entry not found
habulaj/334731300380	habulaj	2024-06-26T03:47:46Z	0	null	[ "region:us" ]	null	2024-06-26T03:47:43Z	Entry not found
szcjerry/smat-vit-sup21k-large	szcjerry	2024-07-02T09:14:51Z	0	null	[ "license:cc-by-4.0", "region:us" ]	null	2024-06-26T03:51:00Z	--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-sup21-large model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!
szcjerry/smat-vit-dino-base	szcjerry	2024-07-02T09:09:48Z	0	null	[ "license:cc-by-4.0", "region:us" ]	null	2024-06-26T03:51:53Z	--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-dino-base model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!
szcjerry/smat-vit-dino-small	szcjerry	2024-07-02T09:08:15Z	0	null	[ "license:cc-by-4.0", "region:us" ]	null	2024-06-26T03:52:16Z	--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-dino-small model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!
Prisma-Multimodal/sae_tinyclip_40m_layer_6	Prisma-Multimodal	2024-06-26T03:53:59Z	0	null	[ "region:us" ]	null	2024-06-26T03:53:59Z	Entry not found
Prisma-Multimodal/sae_tinyclip_40m_layer_6_imagenet	Prisma-Multimodal	2024-06-26T03:54:43Z	0	null	[ "region:us" ]	null	2024-06-26T03:54:43Z	Entry not found
Prisma-Multimodal/sae_tinyclip_40m_imagenet_layer_6	Prisma-Multimodal	2024-06-26T03:54:58Z	0	null	[ "region:us" ]	null	2024-06-26T03:54:58Z	Entry not found
samsri01/slm-phi2-coversational-finetuned	samsri01	2024-06-26T03:55:00Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T03:55:00Z	--- license: apache-2.0 ---
FevenTad/V1_0.3_Base	FevenTad	2024-06-26T03:59:15Z	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T03:58:12Z	--- 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]
santosharron/privateGPT_ModelV1	santosharron	2024-06-26T04:03:26Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T04:03:26Z	--- license: mit ---
Sarbanidatabricks/speecht5_tts_voxpopuli_nl	Sarbanidatabricks	2024-06-26T04:05:47Z	0	null	[ "region:us" ]	null	2024-06-26T04:05:47Z	Entry not found
garnard1991/JESUSLOVE	garnard1991	2024-06-26T04:10:15Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T04:10:15Z	--- license: apache-2.0 ---
Dongchao/music	Dongchao	2024-06-26T09:34:13Z	0	null	[ "region:us" ]	null	2024-06-26T04:10:46Z	Entry not found
DomathID/Test	DomathID	2024-06-26T04:18:10Z	0	nemo	[ "nemo", "code", "en", "dataset:nodemixaholic/text-of-the-net", "license:mit", "region:us" ]	null	2024-06-26T04:15:36Z	--- license: mit datasets: - nodemixaholic/text-of-the-net language: - en metrics: - character library_name: nemo tags: - code --- https://www.yukinoshita.web.id https://www.penkata.com
LogCreative/Llama-3-8B-Instruct-pgfplots-finetune-q4f16_1-MLC	LogCreative	2024-06-26T10:37:48Z	1	null	[ "code", "text-generation", "conversational", "en", "dataset:LogCreative/latex-pgfplots-instruct", "base_model:unsloth/llama-3-8b-Instruct", "license:llama3", "region:us" ]	text-generation	2024-06-26T04:18:15Z	--- base_model: unsloth/llama-3-8b-Instruct license: llama3 datasets: - LogCreative/latex-pgfplots-instruct language: - en metrics: - code_eval pipeline_tag: text-generation tags: - code --- ## Usage This model is saved as [MLC LLM](https://llm.mlc.ai) format. View the [installation guide of MLC LLM](https://llm.mlc.ai/docs/install/mlc_llm) for how to install the library. Then use the following command to try the model: ```bash mlc_llm chat . ``` ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> The model is finetuned from Llama 3 LLM to provide more accurate results on generating LaTeX code of `pgfplots` package, which is based on the dataset [LogCreative/latex-pgfplots-instruct](https://huggingface.co/datasets/LogCreative/latex-pgfplots-instruct) extracted from the documentation of [`pgfplots`](https://github.com/pgf-tikz/pgfplots) LaTeX package. - Developed by: [LogCreative](https://github.com/LogCreative) - Model type: Text Generation - Language(s) (NLP): English - License: Llama 3 - Finetuned from model: [unsloth/llama-3-8b-Instruct](https://huggingface.co/unsloth/llama-3-8b) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [LogCreative/llama-pgfplots-finetune](https://github.com/LogCreative/llama-pgfplots-finetune) ## 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. --> This model is intended to generate the pgfplots LaTeX code according to the user's prompt. It is suitable for users who are not familiar with the API provided in the `pgfplots` package or does not want to consult the documentation for achieving the intention. ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [PGFPlotsEdt](https://github.com/LogCreative/PGFPlotsEdt): A PGFPlots Statistic Graph Interactive Editor. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> Any use outside the `pgfplots` package could only be of the performance of the base Llama 3 model. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This model could not provide sufficient information on other LaTeX packages and could not guarantee the absolute correctness of the generated result. ### 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. If you can not get the correct result from this model, you may need to consult the original `pgfplots` documentation for more information. ## 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. --> [LogCreative/latex-pgfplots-instruct](https://huggingface.co/datasets/LogCreative/latex-pgfplots-instruct): a datasets contains the instruction and corresponding output related to `pgfplots` and `pgfplotstable` LaTeX packages. ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> This model is finetuned based on the dataset based on [`unsloth`](https://github.com/unslothai/unsloth) library. #### Training Hyperparameters - Training regime: bf16 mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> The evaluation is based on the success compilation rate of the output LaTeX code in the test dataset. ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [LogCreative/latex-pgfplots-instruct](https://huggingface.co/datasets/LogCreative/latex-pgfplots-instruct): the test part of this dataset only contains instructions only related to the `pgfplots` package. #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> When testing, the prompt prefix is added to tell the model what role it is and what the requested response format is to only output the code without any explanation. #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> Success compilation rate: $$\frac{\text{\#Success compilation}}{\text{\#Total compilation}}\times 100\%$$ The uncessful compilation is rather LaTeX failure or the timeout case (compilation time > 20s). ### Results The test is based upon unquantized model which is in fp16 precision. - Llama 3: 34% - This model: 52% (+18%) #### Summary This model is expected to output the LaTeX code output related to the `pgfplots` package with less error compared to the baseline Llama 3 model. ## 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). - Hardware Type: Nvidia A100 80G - Hours used: 1h = 10min training + 50min testing - Cloud Provider: Private infrastructure - Carbon Emitted: 0.11kg CO2 eq. ### Framework versions - PEFT 0.11.1 - MLC LLM nightly_cu122-0.1.dev1404 - MLC AI nightly_cu122-0.15.dev404 - Unsloth 2024.6
haljazara/results	haljazara	2024-06-26T04:21:38Z	0	null	[ "region:us" ]	null	2024-06-26T04:21:38Z	Entry not found
xxlrd/deepnegative	xxlrd	2024-06-26T04:24:27Z	0	null	[ "region:us" ]	null	2024-06-26T04:24:05Z	https://civitai.com/models/4629/deep-negative-v1x?modelVersionId=5637
Coolwowsocoolwow/Kyle_Schwartz	Coolwowsocoolwow	2024-06-26T04:36:07Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T04:30:09Z	--- license: openrail ---
alexzarate/usain_bolt	alexzarate	2024-06-26T06:37:56Z	0	null	[ "region:us" ]	null	2024-06-26T04:39:32Z	Entry not found
migaraa/Gaudi_LoRA_Llama-2-7b-hf	migaraa	2024-06-28T18:42:15Z	1	transformers	[ "transformers", "safetensors", "ipex", "intel", "gaudi", "PEFT", "dataset:timdettmers/openassistant-guanaco", "arxiv:1910.09700", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T04:40:03Z	--- library_name: transformers tags: - ipex - intel - gaudi - PEFT license: apache-2.0 datasets: - timdettmers/openassistant-guanaco --- # Model Card for Model ID This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on [timdettmers/openassistant-guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). ## Model Details ### Model Description This is a fine-tuned version of the [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) model using Parameter Efficient Fine Tuning (PEFT) with Low Rank Adaptation (LoRA) on the Intel Gaudi 2 AI accelerator. This model can be used for various text generation tasks including chatbots, content creation, and other NLP applications. - Developed by: Migara Amarasinghe - Model type: LLM - Language(s) (NLP): English - Finetuned from model [optional]: [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) ## Uses ### Direct Use This model can be used for text generation tasks such as: - Chatbots - Automated content creation - Text completion and augmentation ### Out-of-Scope Use - Use in real-time applications where latency is critical - Use in highly sensitive domains without thorough evaluation and testing ### 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. ## Training Details ### Training Hyperparameters <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> - Training regime: Mixed precision training using bf16 - Number of epochs: 3 - Learning rate: 1e-4 - Batch size: 16 - Seq length: 512 ## 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: Intel Gaudi AI Accelerator - Hours used: < 1 hour ## Technical Specifications ### Compute Infrastructure #### Hardware - Intel Gaudi 2 AI Accelerator - Intel(R) Xeon(R) Platinum 8368 CPU #### Software - Transformers library - Optimum Habana library
ryo0611/Scaramouche	ryo0611	2024-06-26T04:45:12Z	0	null	[ "region:us" ]	null	2024-06-26T04:45:12Z	Entry not found
PiAPI/Faceswap-API	PiAPI	2024-06-27T02:44:40Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T04:45:27Z	--- license: mit --- # Faceswap API Model Page: [Faceswap API](https://piapi.ai/faceswap-api) This model card illustartes the steps to use Faceswap API's endpoint. You can also check out other model cards: - [Midjourney API](https://huggingface.co/PiAPI/Midjourney-API) - [Suno API](https://huggingface.co/PiAPI/Suno-API) - [Dream Machine API](https://huggingface.co/PiAPI/Dream-Machine-API) Model Information The FaceSwap API, built on a custom AI model, allows developers to effortlessly integrate advanced face-swapping capabilities to their platforms, offering users the ability to rapidly personalize images of their choice. ## Usage Steps Below we share the code snippets on how to use the Faceswap API's endpoint. - The programming language is Python - Have 2 images (Each image must only contain one visible face) Create a task ID from the Faceswap endpoint <pre><code class="language-python"> <span class="hljs-keyword">import</span> http.client conn = http.client.HTTPSConnection(<span class="hljs-string">"api.piapi.ai"</span>) payload = <span class="hljs-string">"{\n \"target_image\": \"image1.png\",\n \"swap_image\": \"image2.png\",\n \"result_type\": \"url\"\n}"</span> headers = { <span class="hljs-built_in">'X-API-Key': "{{x-api-key}}"</span>, //Insert your API Key here <span class="hljs-built_in">'Content-Type': "application/json"</span>, <span class="hljs-built_in">'Accept': "application/json"</span> } conn.request("POST", "/api/face_swap/v1/async", payload, headers) res = conn.getresponse() data = res.read() <span class="hljs-keyword">print</span>(data.decode("utf-8")) </code></pre> Retrieve the task ID <pre><code class="language-python"> { <span class="hljs-built_in">"code"</span>: 200, <span class="hljs-built_in">"data"</span>: { <span class="hljs-built_in">"task_id"</span>: "7a7ba527**********1974d4316e22" //Record the taskID provided in your response terminal }, <span class="hljs-built_in">"message"</span>: "success" } </code></pre> Insert the Faceswap task ID into the fetch endpoint <pre><code class="language-python"> <span class="hljs-keyword">import</span> http.client conn = http.client.HTTPSConnection(<span class="hljs-string">"api.piapi.ai"</span>) payload = <span class="hljs-string">"{\n \"task_id\": \"7a7ba527********1974d4316e22\"\n}"</span> //Replace the task ID with your task ID headers = { <span class="hljs-built_in">'X-API-Key': "{{x-api-key}}"</span>, //Insert your API Key here <span class="hljs-built_in">'Content-Type': "application/json"</span>, <span class="hljs-built_in">'Accept': "application/json"</span> } conn.request("POST", "/api/face_swap/v1/fetch", payload, headers) res = conn.getresponse() data = res.read() <span class="hljs-keyword">print</span>(data.decode("utf-8")) </code></pre> For fetch endpoint responses** - Refer to our [documentation](https://piapi.ai/docs/faceswap-api/fetch) for more detailed information. <br> ## Contact us Contact us at <a href="mailto:[email protected]">[email protected]</a> for any inquires. <br>
Pragmir/pragmir	Pragmir	2024-06-26T04:49:50Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T04:49:50Z	--- license: apache-2.0 ---
munish0838/Phi-3-medium-4k-instruct-Matter-0.1-Slim-A-lora	munish0838	2024-06-26T04:52:53Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/Phi-3-medium-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T04:52:40Z	--- base_model: unsloth/Phi-3-medium-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: munish0838 - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-medium-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dhruvvaidh/Llama2-7b-hf-dv13911	dhruvvaidh	2024-06-26T04:55:12Z	0	null	[ "region:us" ]	null	2024-06-26T04:55:12Z	Entry not found
shuyuej/MedLLaMA3-70B-base-AWQ	shuyuej	2024-06-26T14:04:53Z	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	2024-06-26T04:57:51Z	--- license: apache-2.0 ---
imrazack/test	imrazack	2024-06-26T04:59:07Z	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T04:59:07Z	--- license: apache-2.0 ---
PiAPI/Suno-API	PiAPI	2024-06-27T02:45:02Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T05:02:32Z	--- license: mit --- # Suno API Model Page: [Suno API](https://piapi.ai/suno-api) This model card illustartes the steps to use Suno API's endpoint. You can also check out other model cards: - [Midjourney API](https://huggingface.co/PiAPI/Midjourney-API) - [Faceswap API](https://huggingface.co/PiAPI/Faceswap-API) - [Dream Machine API](https://huggingface.co/PiAPI/Dream-Machine-API) Model Information Developed by the Suno team in Cambridge, MA, Suno is a leading-edge text-to-music model. While it doesn't have an official API service, PiAPI has introduced an unofficial Suno API, allowing developers globally to integrate Suno’s music creation capabilities into their applications. ## Usage Steps Below we share the code snippets on how to use the Suno API's "Generate Full Song" endpoint. - The programming language is Python - This is only applicable for Extended Clips generated from the "Extend" function of the "Generate Music" endpoint. Create a task ID from the "Generate Full Song" endpoint <pre><code class="language-python"> <span class="hljs-keyword">import</span> http.client conn = http.client.HTTPSConnection(<span class="hljs-string">"api.piapi.ai"</span>) payload = <span class="hljs-string">"{\n \"clip_id\": \"0e764cab**************55f76ca44ed6\"\n}"</span> headers = { <span class="hljs-built_in">'X-API-Key': "{{x-api-key}}"</span>, //Insert your API Key here <span class="hljs-built_in">'Content-Type': "application/json"</span>, <span class="hljs-built_in">'Accept': "application/json"</span> } conn.request("POST", "/api/suno/v1/music/concat", payload, headers) res = conn.getresponse() data = res.read() <span class="hljs-keyword">print</span>(data.decode("utf-8")) </code></pre> Retrieve the task ID <pre><code class="language-python"> { <span class="hljs-built_in">"code"</span>: 200, <span class="hljs-built_in">"data"</span>: { <span class="hljs-built_in">"task_id"</span>: "5440b19a*************e92de94d5110" //Record the taskID provided in your response terminal }, <span class="hljs-built_in">"message"</span>: "success" } </code></pre> Insert the "Generate Full Song" task ID into the fetch endpoint <pre><code class="language-python"> <span class="hljs-keyword">import</span> http.client conn = http.client.HTTPSConnection(<span class="hljs-string">"api.piapi.ai"</span>) headers = { <span class="hljs-built_in">'Content-Type': "application/json"</span>, <span class="hljs-built_in">'Accept': "application/json"</span> } conn.request("GET", "/api/suno/v1/music/task_id", headers=headers) //Replace the "task_id" with your task ID res = conn.getresponse() data = res.read() <span class="hljs-keyword">print</span>(data.decode("utf-8")) </code></pre> For fetch endpoint responses** - Refer to our [documentation](https://piapi.ai/docs/suno-api/get-music) for more detailed information. <br> ## Contact us Contact us at <a href="mailto:[email protected]">[email protected]</a> for any inquires. <br>
njaana/phi3-mini-new-model-with-default-lora-adapters	njaana	2024-06-26T05:05:29Z	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T05:05:11Z	--- 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: njaana - 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)
Athaz01/Agile_Coach	Athaz01	2024-06-26T05:09:29Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T05:09:29Z	--- license: openrail ---
ZahidAhmad/lora2_model	ZahidAhmad	2024-06-26T05:10:29Z	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	2024-06-26T05:10:01Z	--- 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: ZahidAhmad - 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)
hyokwan/hkcode_solar_10.7b_unsloth16	hyokwan	2024-06-26T05:15:23Z	0	null	[ "safetensors", "license:mit", "region:us" ]	null	2024-06-26T05:10:10Z	--- license: mit ---
starnet/11-star21-06-26	starnet	2024-06-26T05:17:32Z	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	null	2024-06-26T05:10:26Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
SamaahKhan/Phi-after-fine-tuning-updated	SamaahKhan	2024-06-26T05:11:01Z	0	null	[ "region:us" ]	null	2024-06-26T05:11:01Z	Entry not found
mrkaesy/whisper-small-hi	mrkaesy	2024-06-26T05:14:18Z	0	null	[ "region:us" ]	null	2024-06-26T05:14:18Z	Entry not found
Topofthenod/q-FrozenLake-v1-4x4-noSlippery	Topofthenod	2024-06-26T05:14:23Z	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T05:14:21Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.41 +/- 0.49 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Topofthenod/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
LarryAIDraw/eula_v1	LarryAIDraw	2024-06-26T05:23:01Z	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-26T05:16:03Z	--- license: creativeml-openrail-m --- https://civitai.com/models/518893/genshin-eula
PiAPI/Dream-Machine-API	PiAPI	2024-06-27T02:45:22Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T05:16:05Z	--- license: mit --- # Dream Machine API Model Page: [Dream Machine API](https://piapi.ai/dream-machine-api) This model card illustartes the steps to use Dream Machine API endpoint. You can also check out other model cards: - [Midjourney API](https://huggingface.co/PiAPI/Midjourney-API) - [Faceswap API](https://huggingface.co/PiAPI/Faceswap-API) - [Suno API](https://huggingface.co/PiAPI/Suno-API) Model Information Dream Machine, created by Luma Labs, is an advanced AI model that swiftly produces high-quality, realistic videos from text and images. These videos boast physical accuracy, consistent characters, and naturally impactful shots. Although Luma Lab doesn’t currently provide a Dream Machine API within their Luma API suite, PiAPI has stepped up to develop the unofficial Dream Machine API. This enables developers globally to integrate cutting-edge text-to-video and image-to-video generation into their applications or platforms. ## Usage Steps Below we share the code snippets on how to use Dream Machine API's Video Generation endpoint. - The programming language is Python Create a task ID from the Video Generation endpoint <pre><code class="language-python"> <span class="hljs-keyword">import</span> http.client conn = http.client.HTTPSConnection(<span class="hljs-string">"api.piapi.ai"</span>) payload = <span class="hljs-string">"{\n \"prompt\": \"dog running\",\n \"expand_prompt\": true\n}"</span> headers = { <span class="hljs-built_in">'X-API-Key': "{{x-api-key}}"</span>, //Insert your API Key here <span class="hljs-built_in">'Content-Type': "application/json"</span>, <span class="hljs-built_in">'Accept': "application/json"</span> } conn.request("POST", "/api/luma/v1/video", payload, headers) res = conn.getresponse() data = res.read() <span class="hljs-keyword">print</span>(data.decode("utf-8")) </code></pre> Retrieve the task ID <pre><code class="language-python"> { <span class="hljs-built_in">"code"</span>: 200, <span class="hljs-built_in">"data"</span>: { <span class="hljs-built_in">"task_id"</span>: "6c4***************aaaa" //Record the taskID provided in your response terminal }, <span class="hljs-built_in">"message"</span>: "success" } </code></pre> Insert the Video Generation task ID into the fetch endpoint <pre><code class="language-python"> <span class="hljs-keyword">import</span> http.client conn = http.client.HTTPSConnection(<span class="hljs-string">"api.piapi.ai"</span>) headers = { <span class="hljs-built_in">{ 'Accept': "application/json" }</span>, } conn.request("GET", "/api/luma/v1/video/task_id", headers=headers) //Replace the "task_id" with your task ID res = conn.getresponse() data = res.read() <span class="hljs-keyword">print</span>(data.decode("utf-8")) </code></pre> For fetch endpoint responses** - Refer to our [documentation](https://piapi.ai/docs/dream-machine/get-video) for more detailed information. <br> ## Contact us Contact us at <a href="mailto:[email protected]">[email protected]</a> for any inquires. <br>
LarryAIDraw/RaidenShogunv3	LarryAIDraw	2024-06-26T05:24:15Z	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-26T05:16:39Z	--- license: creativeml-openrail-m --- https://civitai.com/models/289811/raiden-shogun-genshin-impact
LarryAIDraw/yoimiya_genshin	LarryAIDraw	2024-06-26T05:24:34Z	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-26T05:17:01Z	--- license: creativeml-openrail-m --- https://civitai.com/models/70263/ororgenshin-impact-yoimiya
LarryAIDraw/Yoimiya_mysticff_ff890	LarryAIDraw	2024-06-26T05:24:44Z	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-26T05:18:30Z	--- license: creativeml-openrail-m --- https://civitai.com/models/5979/yoimiya
LarryAIDraw/wrenchgixianyun	LarryAIDraw	2024-06-26T05:25:01Z	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-26T05:20:04Z	--- license: creativeml-openrail-m --- https://civitai.com/models/245288/xianyun-or-cloud-retainer-or-genshin-impact
shinben0327/q-FrozenLake-v1-4x4-noSlippery	shinben0327	2024-06-26T05:21:05Z	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T05:21:03Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="shinben0327/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Kibalama/Cartpole-v1	Kibalama	2024-06-26T05:23:51Z	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T05:23:08Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
starnet/12-star21-06-26-full	starnet	2024-06-26T05:30:03Z	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	null	2024-06-26T05:24:33Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Sahil77/my-new-shiny-tokenizer	Sahil77	2024-06-26T05:27:22Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T05:27:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
fokyoum9/Qwen-7B-Test	fokyoum9	2024-06-26T05:33:18Z	0	null	[ "region:us" ]	null	2024-06-26T05:33:18Z	Entry not found
Juliansh/chatbot	Juliansh	2024-06-26T05:33:32Z	0	null	[ "region:us" ]	null	2024-06-26T05:33:32Z	Entry not found
Litzy619/MIS0626T2F	Litzy619	2024-06-26T09:35:45Z	0	null	[ "region:us" ]	null	2024-06-26T05:36:01Z	Entry not found
Litzy619/MIS0626T1F	Litzy619	2024-06-26T10:55:28Z	0	null	[ "region:us" ]	null	2024-06-26T05:36:24Z	Entry not found
vivekdhir77/docRetrieve	vivekdhir77	2024-06-26T06:02:45Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T05:39:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
thuychang404/ptit-job-recommendation	thuychang404	2024-06-26T06:44:42Z	0	sklearn	[ "sklearn", "recommend", "recommendation system", "feature-extraction", "en", "dataset:thuychang404/job-recommendation-system", "license:wtfpl", "region:us" ]	feature-extraction	2024-06-26T05:40:23Z	--- license: wtfpl language: - en metrics: - accuracy library_name: sklearn pipeline_tag: feature-extraction tags: - recommend - recommendation system datasets: - thuychang404/job-recommendation-system ---
lionking927/s9-0626-01	lionking927	2024-06-26T05:43:16Z	0	null	[ "region:us" ]	null	2024-06-26T05:43:16Z	Entry not found
metta-ai/baseline.v0.5.5	metta-ai	2024-06-26T05:45:44Z	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "region:us" ]	reinforcement-learning	2024-06-26T05:44:42Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory --- A(n) APPO model trained on the GDY-MettaGrid environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r metta-ai/baseline.v0.5.5 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.5.5 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.5.5 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
shinben0327/Taxi-v3	shinben0327	2024-06-26T05:51:14Z	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T05:51:11Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.73 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="shinben0327/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"]) ```
wl-tookitaki/test	wl-tookitaki	2024-06-26T05:52:24Z	0	null	[ "region:us" ]	null	2024-06-26T05:52:24Z	Entry not found
loooooong/StableGarment_tryon	loooooong	2024-06-28T08:39:48Z	1	diffusers	[ "diffusers", "safetensors", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-06-26T05:53:49Z	--- license: cc-by-nc-sa-4.0 --- This is the controlnet and garment encoder for tryon task, refer to [StableGarment](https://github.com/logn-2024/StableGarment) for detail.
Topofthenod/q-Taxi-v3-unedited	Topofthenod	2024-06-26T05:55:02Z	0	null	[ "FrozenLake-v1", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T05:55:00Z	--- tags: - FrozenLake-v1 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-unedited results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1 type: FrozenLake-v1 metrics: - type: mean_reward value: 8.18 +/- 2.50 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Topofthenod/q-Taxi-v3-unedited", 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"]) ```
TenzinGayche/bo-en_tokenizer_v1_32k	TenzinGayche	2024-06-26T05:58:56Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T05:58:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Coolwowsocoolwow/Jimmy_Valmer	Coolwowsocoolwow	2024-06-26T06:03:56Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T05:59:14Z	--- license: openrail ---
YeBhoneLin10/Mandalay_lora	YeBhoneLin10	2024-06-26T05:59:47Z	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	2024-06-26T05:59:46Z	--- 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 Mandalay 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 - YeBhoneLin10/Mandalay_lora <Gallery /> ## Model description These are YeBhoneLin10/Mandalay_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 Mandalay to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](YeBhoneLin10/Mandalay_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]
Topofthenod/q-Taxi-v3-new	Topofthenod	2024-06-26T06:01:26Z	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T06:01:23Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-new results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.75 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="Topofthenod/q-Taxi-v3-new", 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"]) ```
leeloolee/gwen	leeloolee	2024-06-26T06:02:51Z	0	null	[ "region:us" ]	null	2024-06-26T06:02:46Z	Entry not found
ShaikAbdul/docreader	ShaikAbdul	2024-06-26T06:08:34Z	0	null	[ "region:us" ]	null	2024-06-26T06:08:34Z	Entry not found
jayoohwang/qlora_test	jayoohwang	2024-06-26T08:03:40Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T06:11:32Z	Entry not found
Alirezashafiei/Lisen3	Alirezashafiei	2024-06-26T06:50:57Z	0	null	[ "license:openrail", "region:us" ]	null	2024-06-26T06:11:45Z	--- license: openrail ---
v0dkapapi/FTM-Data-For-LLM	v0dkapapi	2024-06-26T06:53:51Z	0	null	[ "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2024-06-26T06:11:46Z	--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: FTM-Data-For-LLM 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. --> # FTM-Data-For-LLM This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.3.0+cu121 - Datasets 2.13.1 - Tokenizers 0.13.3
Bajiyo/trying-lm-with-bert	Bajiyo	2024-06-27T04:23:50Z	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T06:11:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Snapkriz/finetuned_deepseek_evolIinstruct_snaplogicdocs	Snapkriz	2024-06-26T06:12:03Z	0	null	[ "region:us" ]	null	2024-06-26T06:12:03Z	Entry not found
Topofthenod/q-Taxi-v3.1	Topofthenod	2024-06-26T06:14:16Z	0	null	[ "region:us" ]	null	2024-06-26T06:14:16Z	Entry not found
Sunbread/isekai-rolename-vae	Sunbread	2024-07-01T06:28:15Z	0	null	[ "license:mit", "region:us" ]	null	2024-06-26T06:17:07Z	--- license: mit ---
Topofthenod/q-Taxi-v3.2	Topofthenod	2024-06-26T06:17:38Z	0	null	[ "region:us" ]	null	2024-06-26T06:17:38Z	Entry not found
oljike/llama3-8b-aqlm-codingft	oljike	2024-06-26T06:23:51Z	0	null	[ "region:us" ]	null	2024-06-26T06:23:51Z	Entry not found
PRATIKDE/llama-3-8b-chat-doctor	PRATIKDE	2024-06-26T06:26:05Z	0	null	[ "region:us" ]	null	2024-06-26T06:26:05Z	Entry not found
julientfai/InstructLM-500M-q4f16_1-Opilot	julientfai	2024-06-26T06:26:47Z	0	null	[ "region:us" ]	null	2024-06-26T06:26:12Z	Entry not found
PRATIKDE/AIMO-NEO-X1-G7BIT	PRATIKDE	2024-06-26T06:26:26Z	0	null	[ "region:us" ]	null	2024-06-26T06:26:26Z	Entry not found
iamnguyen/Qwen2-1.5B-ORPO	iamnguyen	2024-06-26T08:59:31Z	0	null	[ "safetensors", "region:us" ]	null	2024-06-26T06:36:04Z	Entry not found
Winmodel/lora_gemma2b-it	Winmodel	2024-06-26T06:36:45Z	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T06:36:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Malaiarasu/qa_pair	Malaiarasu	2024-06-26T06:37:22Z	0	null	[ "region:us" ]	null	2024-06-26T06:37:22Z	Entry not found
ILKT/2024-06-24_22-31-28_epoch_75	ILKT	2024-06-28T14:26:56Z	0	sentence-transformers	[ "sentence-transformers", "sentence-similarity", "mteb", "feature-extraction", "en", "pl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-06-26T06:43:41Z	--- language: - en - pl model-index: - name: PLACEHOLDER results: [] pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---
VKapseln475/SlimGummies586	VKapseln475	2024-06-26T06:50:11Z	0	null	[ "region:us" ]	null	2024-06-26T06:47:42Z	# Slim Gummies France Expériences - Slim Gummies Coustumer Commentaires Avantages Prix, acheter Slim Gummies France Expériences Ces gummies naturels et cliniquement prouvés sont conçus pour aider les gens à perdre du poids et à devenir minces. Pour ceux qui souhaitent prendre des suppléments, des gélules molles contenant les ingrédients naturels de la formule sont disponibles. Il s’agit d’une capsule orale brûle-graisses qui empêche également votre corps de stocker les graisses. ## [Cliquez ici pour acheter maintenant sur le site officiel de Slim Gummies](https://justbuydm.online/slim-gummies-fr) ## Transformation des caoutchoucs amincissants « Slimming Gummies » est une formule de démarrage bien documentée qui utilise des ingrédients naturels pour induire la cétose. Il offre de puissants résultats de combustion des graisses avec un mélange surnaturel. Le meilleur jeu de perte de poids utilisé par les professionnels a le potentiel de vous éloigner de nombreuses maladies. Il ne s'agit pas seulement d'un programme de remise en forme, mais d'une option de bien-être qui vous offre le pouvoir des bêta-hydroxybutyrate cétones pour des résultats plus minces. La formule enrichie en fraise et pomme contient de la stévia naturelle pour plus de douceur. Sans sucre ajouté, juste des extraits de plantes pour une combustion rapide des graisses et des résultats puissants. La formule autonome vous permet d'améliorer la forme de votre corps tout en développant plus de masse musculaire. L'effet cicatrisant a un très bon effet sur la santé du foie. Il soutient un métabolisme sain afin que vous puissiez réellement brûler les graisses et éviter de trop manger. Ne laissez pas votre corps accumuler des calories, profitez de cette option spéciale. Les ingrédients contenus dans Sliming Gummies sont totalement efficaces et bien étiquetés pour obtenir des résultats. Il contient des concentrés et des extraits naturels, ce qui signifie que l'utilisateur peut le prendre sans aucun risque ni souci. L'approvisionnement mensuel en ours gommeux se compose d'un paquet de 30 gélules. Vous devez les prendre régulièrement une fois le matin et une fois le soir pour maintenir l'hydratation. Combinez des exercices de routine pour de meilleurs résultats et une combinaison saine. ## Quels sont les avantages précis de choisir des gummies minceur ? Les avantages de choisir des gummies minceur sont nombreux. La thérapie donne des résultats fiables, sûrs et très visibles. La formule brûle-graisse met le corps dans un état actif. Cela peut vous aider à atteindre vos objectifs de perte de poids avec plus d’énergie et de paix mentale. Voici quelques avantages de choisir la meilleure formule de perte de poids ### Pratique à consommer Consommer des gummies minceur est extrêmement simple car il n’y a pas de règles compliquées à suivre. Ajoutez simplement une gomme à la fois pour obtenir les bons nutriments. Appliquez-le deux fois par jour. Cela favorise un entraînement de musculation sans effort et une combustion plus rapide des graisses. ### Sûr et sans risque Sliming Gummies est totalement sans risque car il est accompagné d’une garantie de remboursement à 100 %. Tout utilisateur insatisfait du choix de la formule peut demander le remboursement sur le site du fabricant. ### Meilleure clarté mentale Lorsque vous vous débarrassez de l’excès de graisse toxique et des éléments indésirables, une meilleure fonction mentale se produit naturellement. Bénéficiez de niveaux d'énergie optimaux et d'une meilleure concentration grâce à la formule de perte de poids de haute qualité. C’est véritablement nourrissant pour tout le corps de haut en bas. ### Santé améliorée Les gummies minceur assurent une meilleure santé avec des taux de triglycérides qui maintiennent une bonne fonction cardiovasculaire. Les gummies de haute qualité favorisent le processus de transition et garantissent que les utilisateurs se sentent à l'aise tout en perdant du poids. ## Précautions et limites des gummies minceur Les gummies minceur sont extrêmement efficaces pour perdre du poids. Vous devez noter les éléments suivants : Parfait pour tout le monde, en particulier pour ceux qui souffrent de maladies graves et ne parviennent pas à perdre du poids. Déconseillé aux femmes enceintes et allaitantes pour quelque raison que ce soit Il est très important que vous maîtrisiez votre routine lorsque vous en consommez. N'apportez aucune lacune et ne consommez pas d'options alternatives ## [Cliquez ici pour acheter maintenant sur le site officiel de Slim Gummies](https://justbuydm.online/slim-gummies-fr)

borreplata/Test

borreplata

2024-06-26T03:20:51Z

0

null

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

null

2024-06-26T03:19:27Z

--- license: unlicense --- # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B") model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")

HoangHa/selfies-roberta-large-silu

HoangHa

2024-06-26T03:22:15Z

0

null

[ "region:us" ]

null

2024-06-26T03:22:15Z

Entry not found

chopchopchuck/mts10

chopchopchuck

2024-06-26T03:22:50Z

0

null

[ "region:us" ]

null

2024-06-26T03:22:35Z

Entry not found

Katyc/llama-3-8b-Instruct-bnb-4bit-LoRA

Katyc

2024-06-26T03:23:20Z

0

transformers

[ "transformers", "safetensors", "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

2024-06-26T03:23:10Z

--- 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:** Katyc - **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)

charlieoneill/jsalt-data

charlieoneill

2024-06-26T03:27:57Z

0

null

[ "region:us" ]

null

2024-06-26T03:24:54Z

Entry not found

TheRealheavy/BigSmoke

TheRealheavy

2024-06-26T03:28:01Z

0

null

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

null

2024-06-26T03:26:38Z

--- license: openrail ---

qualcomm/Posenet-Mobilenet-Quantized

qualcomm

2024-06-26T03:30:20Z

0

pytorch

[ "pytorch", "tflite", "quantized", "android", "image-classification", "dataset:coco", "arxiv:1803.08225", "license:apache-2.0", "region:us" ]

image-classification

2024-06-26T03:30:14Z

--- datasets: - coco library_name: pytorch license: apache-2.0 pipeline_tag: image-classification tags: - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/posenet_mobilenet_quantized/web-assets/model_demo.png) # Posenet-Mobilenet-Quantized: Optimized for Mobile Deployment ## Quantized human pose estimator Posenet performs pose estimation on human images. This model is an implementation of Posenet-Mobilenet-Quantized found [here](https://github.com/rwightman/posenet-pytorch). This repository provides scripts to run Posenet-Mobilenet-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/posenet_mobilenet_quantized). ### Model Details - **Model Type:** Pose estimation - **Model Stats:** - Model checkpoint: mobilenet_v1_101 - Input resolution: 513x257 - Number of parameters: 3.31M - Model size: 3.47 MB | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model | ---|---|---|---|---|---|---|---| | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.591 ms | 0 - 2 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.tflite](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.tflite) | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.622 ms | 0 - 9 MB | INT8 | NPU | [Posenet-Mobilenet-Quantized.so](https://huggingface.co/qualcomm/Posenet-Mobilenet-Quantized/blob/main/Posenet-Mobilenet-Quantized.so) ## Installation This model can be installed as a Python package via pip. ```bash pip install qai-hub-models ``` ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. With this API token, you can configure your client to run models on the cloud hosted devices. ```bash qai-hub configure --api_token API_TOKEN ``` Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. ## Demo off target The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input. ```bash python -m qai_hub_models.models.posenet_mobilenet_quantized.demo ``` The above demo runs a reference implementation of pre-processing, model inference, and post processing. **NOTE**: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.posenet_mobilenet_quantized.demo ``` ### Run model on a cloud-hosted device In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following: * Performance check on-device on a cloud-hosted device * Downloads compiled assets that can be deployed on-device for Android. * Accuracy check between PyTorch and on-device outputs. ```bash python -m qai_hub_models.models.posenet_mobilenet_quantized.export ``` ``` Profile Job summary of Posenet-Mobilenet-Quantized -------------------------------------------------- Device: Snapdragon X Elite CRD (11) Estimated Inference Time: 0.69 ms Estimated Peak Memory Range: 0.38-0.38 MB Compute Units: NPU (42) | Total (42) ``` ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.posenet_mobilenet_quantized.demo --on-device ``` **NOTE**: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.posenet_mobilenet_quantized.demo -- --on-device ``` ## Deploying compiled model to Android The models can be deployed using multiple runtimes: - TensorFlow Lite (`.tflite` export): [This tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a guide to deploy the .tflite model in an Android application. - QNN (`.so` export ): This [sample app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) provides instructions on how to use the `.so` shared library in an Android application. ## View on Qualcomm® AI Hub Get more details on Posenet-Mobilenet-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/posenet_mobilenet_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License - The license for the original implementation of Posenet-Mobilenet-Quantized can be found [here](https://github.com/rwightman/posenet-pytorch/blob/master/LICENSE.txt). - The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf) ## References * [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://arxiv.org/abs/1803.08225) * [Source Model Implementation](https://github.com/rwightman/posenet-pytorch) ## Community * Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI. * For questions or feedback please [reach out to us](mailto:[email protected]).

richardkelly/Qwen-Qwen1.5-1.8B-1719372662

richardkelly

2024-06-26T03:31:02Z

0

null

[ "region:us" ]

null

2024-06-26T03:31:02Z

Entry not found

habulaj/129556109667

habulaj

2024-06-26T03:31:21Z

0

null

[ "region:us" ]

null

2024-06-26T03:31:12Z

Entry not found

qualcomm/Midas-V2-Quantized

qualcomm

2024-06-26T03:31:34Z

0

pytorch

[ "pytorch", "tflite", "quantized", "android", "depth-estimation", "arxiv:1907.01341", "license:mit", "region:us" ]

depth-estimation

2024-06-26T03:31:25Z

--- library_name: pytorch license: mit pipeline_tag: depth-estimation tags: - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/midas_quantized/web-assets/model_demo.png) # Midas-V2-Quantized: Optimized for Mobile Deployment ## Quantized Deep Convolutional Neural Network model for depth estimation Midas is designed for estimating depth at each point in an image. This model is an implementation of Midas-V2-Quantized found [here](https://github.com/isl-org/MiDaS). This repository provides scripts to run Midas-V2-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/midas_quantized). ### Model Details - **Model Type:** Depth estimation - **Model Stats:** - Model checkpoint: MiDaS_small - Input resolution: 256x256 - Number of parameters: 16.6M - Model size: 16.6 MB | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model | ---|---|---|---|---|---|---|---| | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.154 ms | 0 - 2 MB | INT8 | NPU | [Midas-V2-Quantized.tflite](https://huggingface.co/qualcomm/Midas-V2-Quantized/blob/main/Midas-V2-Quantized.tflite) | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.482 ms | 0 - 275 MB | INT8 | NPU | [Midas-V2-Quantized.so](https://huggingface.co/qualcomm/Midas-V2-Quantized/blob/main/Midas-V2-Quantized.so) ## Installation This model can be installed as a Python package via pip. ```bash pip install "qai-hub-models[midas_quantized]" ``` ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. With this API token, you can configure your client to run models on the cloud hosted devices. ```bash qai-hub configure --api_token API_TOKEN ``` Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. ## Demo off target The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input. ```bash python -m qai_hub_models.models.midas_quantized.demo ``` The above demo runs a reference implementation of pre-processing, model inference, and post processing. **NOTE**: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.midas_quantized.demo ``` ### Run model on a cloud-hosted device In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following: * Performance check on-device on a cloud-hosted device * Downloads compiled assets that can be deployed on-device for Android. * Accuracy check between PyTorch and on-device outputs. ```bash python -m qai_hub_models.models.midas_quantized.export ``` ``` Profile Job summary of Midas-V2-Quantized -------------------------------------------------- Device: Snapdragon X Elite CRD (11) Estimated Inference Time: 1.52 ms Estimated Peak Memory Range: 0.46-0.46 MB Compute Units: NPU (148) | Total (148) ``` ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.midas_quantized.demo --on-device ``` **NOTE**: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.midas_quantized.demo -- --on-device ``` ## Deploying compiled model to Android The models can be deployed using multiple runtimes: - TensorFlow Lite (`.tflite` export): [This tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a guide to deploy the .tflite model in an Android application. - QNN (`.so` export ): This [sample app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) provides instructions on how to use the `.so` shared library in an Android application. ## View on Qualcomm® AI Hub Get more details on Midas-V2-Quantized's performance across various devices [here](https://aihub.qualcomm.com/models/midas_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License - The license for the original implementation of Midas-V2-Quantized can be found [here](https://github.com/isl-org/MiDaS/blob/master/LICENSE). - The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf) ## References * [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://arxiv.org/abs/1907.01341v3) * [Source Model Implementation](https://github.com/isl-org/MiDaS) ## Community * Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI. * For questions or feedback please [reach out to us](mailto:[email protected]).

Coolwowsocoolwow/Eric_Cartman

Coolwowsocoolwow

2024-06-26T03:44:39Z

0

null

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

null

2024-06-26T03:32:02Z

--- license: openrail ---

habulaj/174829150187

habulaj

2024-06-26T03:33:03Z

0

null

[ "region:us" ]

null

2024-06-26T03:32:52Z

Entry not found

johnpaulbin/llama8b-tokipona-epoch1-chat

johnpaulbin

2024-06-26T03:50:04Z

0

transformers

[ "transformers", "safetensors", "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

2024-06-26T03:33:05Z

--- 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:** johnpaulbin - **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)

Tam1032/whisper-largev3-hi

Tam1032

2024-06-26T03:33:33Z

0

null

[ "region:us" ]

null

2024-06-26T03:33:33Z

Entry not found

abinavGanesh/emty

abinavGanesh

2024-06-26T03:38:58Z

0

null

[ "region:us" ]

null

2024-06-26T03:38:58Z

Entry not found

OpilotAI/medicine-Llama3-8B-q4f16_1-Opilot

OpilotAI

2024-06-26T03:46:09Z

0

null

[ "region:us" ]

null

2024-06-26T03:41:17Z

Entry not found

PiAPI/Midjourney-API

PiAPI

2024-06-27T02:44:07Z

0

null

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

null

2024-06-26T03:42:14Z

--- license: mit --- # Midjourney API **Model Page:** [Midjourney API](https://piapi.ai/midjourney-api) This model card illustartes the steps to use Midjourney API's endpoint. You can also check out other model cards: - [Faceswap API](https://huggingface.co/PiAPI/Faceswap-API) - [Suno API](https://huggingface.co/PiAPI/Suno-API) - [Dream Machine API](https://huggingface.co/PiAPI/Dream-Machine-API) **Model Information** Renowned for its exceptional text-to-image generative AI capabilities, Midjourney is a preferred tool among graphic designers, photographers, and creatives aiming to explore AI-driven artistry. Despite the absence of an official API from Midjourney, PiAPI has introduced the unofficial Midjourney API, empowering developers to incorporate this cutting-edge text-to-image model into their AI applications. ## Usage Steps Below we share the code snippets on how to use Midjourney API's upscale endpoint. - The programming language is Python - The origin task ID should be the task ID of the fetched imagine endpoint **Create an upscale task ID** <pre><code class="language-python"> import http.client conn = http.client.HTTPSConnection("api.piapi.ai") payload = "{\n \"origin_task_id\": \"9c6796dd*********1e7dfef5203b\",\n \"index\": \"1\",\n \"webhook_endpoint\": \"\",\n \"webhook_secret\": \"\"\n}" headers = { 'X-API-Key': "{{x-api-key}}", //Insert your API Key here 'Content-Type': "application/json", 'Accept': "application/json" } conn.request("POST", "/mj/v2/upscale", payload, headers) res = conn.getresponse() data = res.read() print(data.decode("utf-8")) </code></pre> **Retrieve the task ID** <pre><code class="language-python"> { "code": 200, "data": { "task_id": :3be7e0b0****************d1a725da0b1d" //Record the taskID provided in your response terminal }, "message": "success" } </code></pre> **Insert the upscale task ID into the fetch endpoint** <pre><code class="language-python"> import http.client conn = http.client.HTTPSConnection("api.piapi.ai") payload = "{\n \"task_id\": \"3be7e0b0****************d1a725da0b1d\"\n}" /Replace the task ID with your task ID headers = { 'Content-Type': "application/json", 'Accept': "application/json" } conn.request("POST", "/mj/v2/fetch", payload, headers) res = conn.getresponse() data = res.read() print(data.decode("utf-8")) </code></pre> **For fetch endpoint responses** - Refer to our [documentation](https://piapi.ai/docs/midjourney-api/upscale) for more detailed information. ## Contact us Contact us at <a href="mailto:[email protected]">[email protected]</a> for any inquires.

neuronpedia/gemma-2b-it__res-jb

neuronpedia

2024-06-26T03:45:03Z

0

null

[ "region:us" ]

null

2024-06-26T03:44:38Z

Entry not found

ben81828/meow_text

ben81828

2024-06-26T03:46:01Z

0

null

[ "region:us" ]

null

2024-06-26T03:46:01Z

Entry not found

habulaj/334731300380

habulaj

2024-06-26T03:47:46Z

0

null

[ "region:us" ]

null

2024-06-26T03:47:43Z

Entry not found

szcjerry/smat-vit-sup21k-large

szcjerry

2024-07-02T09:14:51Z

0

null

[ "license:cc-by-4.0", "region:us" ]

null

2024-06-26T03:51:00Z

--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-sup21-large model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!

szcjerry/smat-vit-dino-base

szcjerry

2024-07-02T09:09:48Z

0

null

[ "license:cc-by-4.0", "region:us" ]

null

2024-06-26T03:51:53Z

--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-dino-base model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!

szcjerry/smat-vit-dino-small

szcjerry

2024-07-02T09:08:15Z

0

null

[ "license:cc-by-4.0", "region:us" ]

null

2024-06-26T03:52:16Z

--- license: cc-by-4.0 --- This repo contains the SMAT meta-tuned vit-dino-small model checkpoint for PyTorch. ### How to use With our implementation here on [github](https://github.com/szc12153/sparse_meta_tuning), you can load the pre-trained weights by calling ``` model.load_state_dict(torch.load(/path/to/checkpoint.pt)) ``` For inference with ProtoNet on a few-shot learning task: ``` # outputs is a dictionary outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model=None # None for direct inference with a ProtoNet classifier ) y_q_pred = outputs['y_q_pred'] ``` For inference with task-specific full fine-tuning then inference: ``` # outputs is a dictionary model.args.meta_learner.inner_lr.lr = lr # set the learning rate for fine-tuning model.args.meta_learner.num_finetune_steps = num_finetune_steps # set the number of fine-tuning steps outputs = model(x_s=x_s, # support inputs y_s=y_s, # support labels x_q=x_q, # query inputs y_q=None, # predict for query labels finetune_model="full" # {'full','lora'} ) y_q_pred = outputs['y_q_pred'] ``` You can visit our [github](https://github.com/szc12153/sparse_meta_tuning) repo for more details on training and inference!

Prisma-Multimodal/sae_tinyclip_40m_layer_6

Prisma-Multimodal

2024-06-26T03:53:59Z

0

null

[ "region:us" ]

null

2024-06-26T03:53:59Z

Entry not found

Prisma-Multimodal/sae_tinyclip_40m_layer_6_imagenet

Prisma-Multimodal

2024-06-26T03:54:43Z

0

null

[ "region:us" ]

null

2024-06-26T03:54:43Z

Entry not found

Prisma-Multimodal/sae_tinyclip_40m_imagenet_layer_6

Prisma-Multimodal

2024-06-26T03:54:58Z

0

null

[ "region:us" ]

null

2024-06-26T03:54:58Z

Entry not found

samsri01/slm-phi2-coversational-finetuned

samsri01

2024-06-26T03:55:00Z

0

null

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

null

2024-06-26T03:55:00Z

--- license: apache-2.0 ---

FevenTad/V1_0.3_Base

FevenTad

2024-06-26T03:59:15Z

0

transformers

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

null

2024-06-26T03:58:12Z

--- 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]

santosharron/privateGPT_ModelV1

santosharron

2024-06-26T04:03:26Z

0

null

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

null

2024-06-26T04:03:26Z

--- license: mit ---

Sarbanidatabricks/speecht5_tts_voxpopuli_nl

Sarbanidatabricks

2024-06-26T04:05:47Z

0

null

[ "region:us" ]

null

2024-06-26T04:05:47Z

Entry not found

garnard1991/JESUSLOVE

garnard1991

2024-06-26T04:10:15Z

0

null

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

null

2024-06-26T04:10:15Z

--- license: apache-2.0 ---

Dongchao/music

Dongchao

2024-06-26T09:34:13Z

0

null

[ "region:us" ]

null

2024-06-26T04:10:46Z

Entry not found

DomathID/Test

DomathID

2024-06-26T04:18:10Z

0

nemo

[ "nemo", "code", "en", "dataset:nodemixaholic/text-of-the-net", "license:mit", "region:us" ]

null

2024-06-26T04:15:36Z

--- license: mit datasets: - nodemixaholic/text-of-the-net language: - en metrics: - character library_name: nemo tags: - code --- https://www.yukinoshita.web.id https://www.penkata.com

LogCreative/Llama-3-8B-Instruct-pgfplots-finetune-q4f16_1-MLC

LogCreative

2024-06-26T10:37:48Z

0

1

null

[ "code", "text-generation", "conversational", "en", "dataset:LogCreative/latex-pgfplots-instruct", "base_model:unsloth/llama-3-8b-Instruct", "license:llama3", "region:us" ]

text-generation

2024-06-26T04:18:15Z

--- base_model: unsloth/llama-3-8b-Instruct license: llama3 datasets: - LogCreative/latex-pgfplots-instruct language: - en metrics: - code_eval pipeline_tag: text-generation tags: - code --- ## Usage This model is saved as [MLC LLM](https://llm.mlc.ai) format. View the [installation guide of MLC LLM](https://llm.mlc.ai/docs/install/mlc_llm) for how to install the library. Then use the following command to try the model: ```bash mlc_llm chat . ``` ## Model Details ### Model Description  The model is finetuned from Llama 3 LLM to provide more accurate results on generating LaTeX code of `pgfplots` package, which is based on the dataset [LogCreative/latex-pgfplots-instruct](https://huggingface.co/datasets/LogCreative/latex-pgfplots-instruct) extracted from the documentation of [`pgfplots`](https://github.com/pgf-tikz/pgfplots) LaTeX package. - **Developed by:** [LogCreative](https://github.com/LogCreative) - **Model type:** Text Generation - **Language(s) (NLP):** English - **License:** Llama 3 - **Finetuned from model:** [unsloth/llama-3-8b-Instruct](https://huggingface.co/unsloth/llama-3-8b) ### Model Sources  - **Repository:** [LogCreative/llama-pgfplots-finetune](https://github.com/LogCreative/llama-pgfplots-finetune) ## Uses  This model is intended to generate the pgfplots LaTeX code according to the user's prompt. It is suitable for users who are not familiar with the API provided in the `pgfplots` package or does not want to consult the documentation for achieving the intention. ### Direct Use  [PGFPlotsEdt](https://github.com/LogCreative/PGFPlotsEdt): A PGFPlots Statistic Graph Interactive Editor. ### Out-of-Scope Use  Any use outside the `pgfplots` package could only be of the performance of the base Llama 3 model. ## Bias, Risks, and Limitations  This model could not provide sufficient information on other LaTeX packages and could not guarantee the absolute correctness of the generated result. ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. If you can not get the correct result from this model, you may need to consult the original `pgfplots` documentation for more information. ## Training Details ### Training Data  [LogCreative/latex-pgfplots-instruct](https://huggingface.co/datasets/LogCreative/latex-pgfplots-instruct): a datasets contains the instruction and corresponding output related to `pgfplots` and `pgfplotstable` LaTeX packages. ### Training Procedure  This model is finetuned based on the dataset based on [`unsloth`](https://github.com/unslothai/unsloth) library. #### Training Hyperparameters - **Training regime:** bf16 mixed precision  ## Evaluation  The evaluation is based on the success compilation rate of the output LaTeX code in the test dataset. ### Testing Data, Factors & Metrics #### Testing Data  [LogCreative/latex-pgfplots-instruct](https://huggingface.co/datasets/LogCreative/latex-pgfplots-instruct): the test part of this dataset only contains instructions only related to the `pgfplots` package. #### Factors  When testing, the prompt prefix is added to tell the model what role it is and what the requested response format is to only output the code without any explanation. #### Metrics  Success compilation rate: $$\frac{\text{\#Success compilation}}{\text{\#Total compilation}}\times 100\%$$ The uncessful compilation is rather LaTeX failure or the timeout case (compilation time > 20s). ### Results The test is based upon unquantized model which is in fp16 precision. - Llama 3: 34% - **This model: 52% (+18%)** #### Summary This model is expected to output the LaTeX code output related to the `pgfplots` package with less error compared to the baseline Llama 3 model. ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). - **Hardware Type:** Nvidia A100 80G - **Hours used:** 1h = 10min training + 50min testing - **Cloud Provider:** Private infrastructure - **Carbon Emitted:** 0.11kg CO2 eq. ### Framework versions - PEFT 0.11.1 - MLC LLM nightly_cu122-0.1.dev1404 - MLC AI nightly_cu122-0.15.dev404 - Unsloth 2024.6

haljazara/results

haljazara

2024-06-26T04:21:38Z

0

null

[ "region:us" ]

null

2024-06-26T04:21:38Z

Entry not found

xxlrd/deepnegative

xxlrd

2024-06-26T04:24:27Z

0

null

[ "region:us" ]

null

2024-06-26T04:24:05Z

https://civitai.com/models/4629/deep-negative-v1x?modelVersionId=5637

Coolwowsocoolwow/Kyle_Schwartz

Coolwowsocoolwow

2024-06-26T04:36:07Z

0

null

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

null

2024-06-26T04:30:09Z

--- license: openrail ---

alexzarate/usain_bolt

alexzarate

2024-06-26T06:37:56Z

0

null

[ "region:us" ]

null

2024-06-26T04:39:32Z

Entry not found

migaraa/Gaudi_LoRA_Llama-2-7b-hf

migaraa

2024-06-28T18:42:15Z

0

1

transformers

[ "transformers", "safetensors", "ipex", "intel", "gaudi", "PEFT", "dataset:timdettmers/openassistant-guanaco", "arxiv:1910.09700", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-26T04:40:03Z

--- library_name: transformers tags: - ipex - intel - gaudi - PEFT license: apache-2.0 datasets: - timdettmers/openassistant-guanaco --- # Model Card for Model ID This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on [timdettmers/openassistant-guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). ## Model Details ### Model Description This is a fine-tuned version of the [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) model using Parameter Efficient Fine Tuning (PEFT) with Low Rank Adaptation (LoRA) on the Intel Gaudi 2 AI accelerator. This model can be used for various text generation tasks including chatbots, content creation, and other NLP applications. - **Developed by:** Migara Amarasinghe - **Model type:** LLM - **Language(s) (NLP):** English - **Finetuned from model [optional]:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) ## Uses ### Direct Use This model can be used for text generation tasks such as: - Chatbots - Automated content creation - Text completion and augmentation ### Out-of-Scope Use - Use in real-time applications where latency is critical - Use in highly sensitive domains without thorough evaluation and testing ### 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. ## Training Details ### Training Hyperparameters  - Training regime: Mixed precision training using bf16 - Number of epochs: 3 - Learning rate: 1e-4 - Batch size: 16 - Seq length: 512 ## 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:** Intel Gaudi AI Accelerator - **Hours used:** < 1 hour ## Technical Specifications ### Compute Infrastructure #### Hardware - Intel Gaudi 2 AI Accelerator - Intel(R) Xeon(R) Platinum 8368 CPU #### Software - Transformers library - Optimum Habana library

ryo0611/Scaramouche

ryo0611

2024-06-26T04:45:12Z

0

null

[ "region:us" ]

null

2024-06-26T04:45:12Z

Entry not found

PiAPI/Faceswap-API

PiAPI

2024-06-27T02:44:40Z

0

null

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

null

2024-06-26T04:45:27Z

--- license: mit --- # Faceswap API **Model Page:** [Faceswap API](https://piapi.ai/faceswap-api) This model card illustartes the steps to use Faceswap API's endpoint. You can also check out other model cards: - [Midjourney API](https://huggingface.co/PiAPI/Midjourney-API) - [Suno API](https://huggingface.co/PiAPI/Suno-API) - [Dream Machine API](https://huggingface.co/PiAPI/Dream-Machine-API) **Model Information** The FaceSwap API, built on a custom AI model, allows developers to effortlessly integrate advanced face-swapping capabilities to their platforms, offering users the ability to rapidly personalize images of their choice. ## Usage Steps Below we share the code snippets on how to use the Faceswap API's endpoint. - The programming language is Python - Have 2 images (Each image must only contain one visible face) **Create a task ID from the Faceswap endpoint** <pre><code class="language-python"> import http.client conn = http.client.HTTPSConnection("api.piapi.ai") payload = "{\n \"target_image\": \"image1.png\",\n \"swap_image\": \"image2.png\",\n \"result_type\": \"url\"\n}" headers = { 'X-API-Key': "{{x-api-key}}", //Insert your API Key here 'Content-Type': "application/json", 'Accept': "application/json" } conn.request("POST", "/api/face_swap/v1/async", payload, headers) res = conn.getresponse() data = res.read() print(data.decode("utf-8")) </code></pre> **Retrieve the task ID** <pre><code class="language-python"> { "code": 200, "data": { "task_id": "7a7ba527************1974d4316e22" //Record the taskID provided in your response terminal }, "message": "success" } </code></pre> **Insert the Faceswap task ID into the fetch endpoint** <pre><code class="language-python"> import http.client conn = http.client.HTTPSConnection("api.piapi.ai") payload = "{\n \"task_id\": \"7a7ba527************1974d4316e22\"\n}" //Replace the task ID with your task ID headers = { 'X-API-Key': "{{x-api-key}}", //Insert your API Key here 'Content-Type': "application/json", 'Accept': "application/json" } conn.request("POST", "/api/face_swap/v1/fetch", payload, headers) res = conn.getresponse() data = res.read() print(data.decode("utf-8")) </code></pre> **For fetch endpoint responses** - Refer to our [documentation](https://piapi.ai/docs/faceswap-api/fetch) for more detailed information. ## Contact us Contact us at <a href="mailto:[email protected]">[email protected]</a> for any inquires.

Pragmir/pragmir

Pragmir

2024-06-26T04:49:50Z

0

null

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

null

2024-06-26T04:49:50Z

--- license: apache-2.0 ---

munish0838/Phi-3-medium-4k-instruct-Matter-0.1-Slim-A-lora

munish0838

2024-06-26T04:52:53Z

0

transformers

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

null

2024-06-26T04:52:40Z

--- base_model: unsloth/Phi-3-medium-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** munish0838 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-medium-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

dhruvvaidh/Llama2-7b-hf-dv13911

dhruvvaidh

2024-06-26T04:55:12Z

0

null

[ "region:us" ]

null

2024-06-26T04:55:12Z

Entry not found

shuyuej/MedLLaMA3-70B-base-AWQ

shuyuej

2024-06-26T14:04:53Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]

text-generation

2024-06-26T04:57:51Z

--- license: apache-2.0 ---

imrazack/test

imrazack

2024-06-26T04:59:07Z

0

null

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

null

2024-06-26T04:59:07Z

--- license: apache-2.0 ---

PiAPI/Suno-API

PiAPI

2024-06-27T02:45:02Z

0

null

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

null

2024-06-26T05:02:32Z

--- license: mit --- # Suno API **Model Page:** [Suno API](https://piapi.ai/suno-api) This model card illustartes the steps to use Suno API's endpoint. You can also check out other model cards: - [Midjourney API](https://huggingface.co/PiAPI/Midjourney-API) - [Faceswap API](https://huggingface.co/PiAPI/Faceswap-API) - [Dream Machine API](https://huggingface.co/PiAPI/Dream-Machine-API) **Model Information** Developed by the Suno team in Cambridge, MA, Suno is a leading-edge text-to-music model. While it doesn't have an official API service, PiAPI has introduced an unofficial Suno API, allowing developers globally to integrate Suno’s music creation capabilities into their applications. ## Usage Steps Below we share the code snippets on how to use the Suno API's "Generate Full Song" endpoint. - The programming language is Python - This is only applicable for Extended Clips generated from the "Extend" function of the "Generate Music" endpoint. **Create a task ID from the "Generate Full Song" endpoint** <pre><code class="language-python"> import http.client conn = http.client.HTTPSConnection("api.piapi.ai") payload = "{\n \"clip_id\": \"0e764cab****************55f76ca44ed6\"\n}" headers = { 'X-API-Key': "{{x-api-key}}", //Insert your API Key here 'Content-Type': "application/json", 'Accept': "application/json" } conn.request("POST", "/api/suno/v1/music/concat", payload, headers) res = conn.getresponse() data = res.read() print(data.decode("utf-8")) </code></pre> **Retrieve the task ID** <pre><code class="language-python"> { "code": 200, "data": { "task_id": "5440b19a*****************e92de94d5110" //Record the taskID provided in your response terminal }, "message": "success" } </code></pre> **Insert the "Generate Full Song" task ID into the fetch endpoint** <pre><code class="language-python"> import http.client conn = http.client.HTTPSConnection("api.piapi.ai") headers = { 'Content-Type': "application/json", 'Accept': "application/json" } conn.request("GET", "/api/suno/v1/music/task_id", headers=headers) //Replace the "task_id" with your task ID res = conn.getresponse() data = res.read() print(data.decode("utf-8")) </code></pre> **For fetch endpoint responses** - Refer to our [documentation](https://piapi.ai/docs/suno-api/get-music) for more detailed information. ## Contact us Contact us at <a href="mailto:[email protected]">[email protected]</a> for any inquires.

njaana/phi3-mini-new-model-with-default-lora-adapters

njaana

2024-06-26T05:05:29Z

0

transformers

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

null

2024-06-26T05:05:11Z

--- 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:** njaana - **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)

Athaz01/Agile_Coach

Athaz01

2024-06-26T05:09:29Z

0

null

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

null

2024-06-26T05:09:29Z

--- license: openrail ---

ZahidAhmad/lora2_model

ZahidAhmad

2024-06-26T05:10:29Z

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

2024-06-26T05:10:01Z

--- 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:** ZahidAhmad - **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)

hyokwan/hkcode_solar_10.7b_unsloth16

hyokwan

2024-06-26T05:15:23Z

0

null

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

null

2024-06-26T05:10:10Z

--- license: mit ---

starnet/11-star21-06-26

starnet

2024-06-26T05:17:32Z

0

null

[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

null

2024-06-26T05:10:26Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

SamaahKhan/Phi-after-fine-tuning-updated

SamaahKhan

2024-06-26T05:11:01Z

0

null

[ "region:us" ]

null

2024-06-26T05:11:01Z

Entry not found

mrkaesy/whisper-small-hi

mrkaesy

2024-06-26T05:14:18Z

0

null

[ "region:us" ]

null

2024-06-26T05:14:18Z

Entry not found

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

Topofthenod

2024-06-26T05:14:23Z

0

null

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

reinforcement-learning

2024-06-26T05:14:21Z

--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.41 +/- 0.49 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Topofthenod/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

LarryAIDraw/eula_v1

LarryAIDraw

2024-06-26T05:23:01Z

0

null

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

null

2024-06-26T05:16:03Z

--- license: creativeml-openrail-m --- https://civitai.com/models/518893/genshin-eula

PiAPI/Dream-Machine-API

PiAPI

2024-06-27T02:45:22Z

0

null

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

null

2024-06-26T05:16:05Z

--- license: mit --- # Dream Machine API **Model Page:** [Dream Machine API](https://piapi.ai/dream-machine-api) This model card illustartes the steps to use Dream Machine API endpoint. You can also check out other model cards: - [Midjourney API](https://huggingface.co/PiAPI/Midjourney-API) - [Faceswap API](https://huggingface.co/PiAPI/Faceswap-API) - [Suno API](https://huggingface.co/PiAPI/Suno-API) **Model Information** Dream Machine, created by Luma Labs, is an advanced AI model that swiftly produces high-quality, realistic videos from text and images. These videos boast physical accuracy, consistent characters, and naturally impactful shots. Although Luma Lab doesn’t currently provide a Dream Machine API within their Luma API suite, PiAPI has stepped up to develop the unofficial Dream Machine API. This enables developers globally to integrate cutting-edge text-to-video and image-to-video generation into their applications or platforms. ## Usage Steps Below we share the code snippets on how to use Dream Machine API's Video Generation endpoint. - The programming language is Python **Create a task ID from the Video Generation endpoint** <pre><code class="language-python"> import http.client conn = http.client.HTTPSConnection("api.piapi.ai") payload = "{\n \"prompt\": \"dog running\",\n \"expand_prompt\": true\n}" headers = { 'X-API-Key': "{{x-api-key}}", //Insert your API Key here 'Content-Type': "application/json", 'Accept': "application/json" } conn.request("POST", "/api/luma/v1/video", payload, headers) res = conn.getresponse() data = res.read() print(data.decode("utf-8")) </code></pre> **Retrieve the task ID** <pre><code class="language-python"> { "code": 200, "data": { "task_id": "6c4*****************aaaa" //Record the taskID provided in your response terminal }, "message": "success" } </code></pre> **Insert the Video Generation task ID into the fetch endpoint** <pre><code class="language-python"> import http.client conn = http.client.HTTPSConnection("api.piapi.ai") headers = { { 'Accept': "application/json" }, } conn.request("GET", "/api/luma/v1/video/task_id", headers=headers) //Replace the "task_id" with your task ID res = conn.getresponse() data = res.read() print(data.decode("utf-8")) </code></pre> **For fetch endpoint responses** - Refer to our [documentation](https://piapi.ai/docs/dream-machine/get-video) for more detailed information. ## Contact us Contact us at <a href="mailto:[email protected]">[email protected]</a> for any inquires.

LarryAIDraw/RaidenShogunv3

LarryAIDraw

2024-06-26T05:24:15Z

0

null

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

null

2024-06-26T05:16:39Z

--- license: creativeml-openrail-m --- https://civitai.com/models/289811/raiden-shogun-genshin-impact

LarryAIDraw/yoimiya_genshin

LarryAIDraw

2024-06-26T05:24:34Z

0

null

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

null

2024-06-26T05:17:01Z

--- license: creativeml-openrail-m --- https://civitai.com/models/70263/ororgenshin-impact-yoimiya

LarryAIDraw/Yoimiya_mysticff_ff890

LarryAIDraw

2024-06-26T05:24:44Z

0

null

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

null

2024-06-26T05:18:30Z

--- license: creativeml-openrail-m --- https://civitai.com/models/5979/yoimiya

LarryAIDraw/wrenchgixianyun

LarryAIDraw

2024-06-26T05:25:01Z

0

null

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

null

2024-06-26T05:20:04Z

--- license: creativeml-openrail-m --- https://civitai.com/models/245288/xianyun-or-cloud-retainer-or-genshin-impact

shinben0327/q-FrozenLake-v1-4x4-noSlippery

shinben0327

2024-06-26T05:21:05Z

0

null

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

reinforcement-learning

2024-06-26T05:21:03Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="shinben0327/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Kibalama/Cartpole-v1

Kibalama

2024-06-26T05:23:51Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2024-06-26T05:23:08Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

starnet/12-star21-06-26-full

starnet

2024-06-26T05:30:03Z

0

null

[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

null

2024-06-26T05:24:33Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

Sahil77/my-new-shiny-tokenizer

Sahil77

2024-06-26T05:27:22Z

0

transformers

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

null

2024-06-26T05:27:21Z

--- 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]

fokyoum9/Qwen-7B-Test

fokyoum9

2024-06-26T05:33:18Z

0

null

[ "region:us" ]

null

2024-06-26T05:33:18Z

Entry not found

Juliansh/chatbot

Juliansh

2024-06-26T05:33:32Z

0

null

[ "region:us" ]

null

2024-06-26T05:33:32Z

Entry not found

Litzy619/MIS0626T2F

Litzy619

2024-06-26T09:35:45Z

0

null

[ "region:us" ]

null

2024-06-26T05:36:01Z

Entry not found

Litzy619/MIS0626T1F

Litzy619

2024-06-26T10:55:28Z

0

null

[ "region:us" ]

null

2024-06-26T05:36:24Z

Entry not found

vivekdhir77/docRetrieve

vivekdhir77

2024-06-26T06:02:45Z

0

transformers

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

null

2024-06-26T05:39:24Z

--- 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]

thuychang404/ptit-job-recommendation

thuychang404

2024-06-26T06:44:42Z

0

sklearn

[ "sklearn", "recommend", "recommendation system", "feature-extraction", "en", "dataset:thuychang404/job-recommendation-system", "license:wtfpl", "region:us" ]

feature-extraction

2024-06-26T05:40:23Z

--- license: wtfpl language: - en metrics: - accuracy library_name: sklearn pipeline_tag: feature-extraction tags: - recommend - recommendation system datasets: - thuychang404/job-recommendation-system ---

lionking927/s9-0626-01

lionking927

2024-06-26T05:43:16Z

0

null

[ "region:us" ]

null

2024-06-26T05:43:16Z

Entry not found

metta-ai/baseline.v0.5.5

metta-ai

2024-06-26T05:45:44Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "region:us" ]

reinforcement-learning

2024-06-26T05:44:42Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory --- A(n) **APPO** model trained on the **GDY-MettaGrid** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r metta-ai/baseline.v0.5.5 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.5.5 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.5.5 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

shinben0327/Taxi-v3

shinben0327

2024-06-26T05:51:14Z

0

null

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

reinforcement-learning

2024-06-26T05:51:11Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.73 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="shinben0327/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"]) ```

wl-tookitaki/test

wl-tookitaki

2024-06-26T05:52:24Z

0

null

[ "region:us" ]

null

2024-06-26T05:52:24Z

Entry not found

loooooong/StableGarment_tryon

loooooong

2024-06-28T08:39:48Z

0

1

diffusers

[ "diffusers", "safetensors", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-06-26T05:53:49Z

--- license: cc-by-nc-sa-4.0 --- This is the controlnet and garment encoder for tryon task, refer to [StableGarment](https://github.com/logn-2024/StableGarment) for detail.

Topofthenod/q-Taxi-v3-unedited

Topofthenod

2024-06-26T05:55:02Z

0

null

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

reinforcement-learning

2024-06-26T05:55:00Z

--- tags: - FrozenLake-v1 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-unedited results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1 type: FrozenLake-v1 metrics: - type: mean_reward value: 8.18 +/- 2.50 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Topofthenod/q-Taxi-v3-unedited", 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"]) ```

TenzinGayche/bo-en_tokenizer_v1_32k

TenzinGayche

2024-06-26T05:58:56Z

0

transformers

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

null

2024-06-26T05:58:55Z

--- 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]

Coolwowsocoolwow/Jimmy_Valmer

Coolwowsocoolwow

2024-06-26T06:03:56Z

0

null

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

null

2024-06-26T05:59:14Z

--- license: openrail ---

YeBhoneLin10/Mandalay_lora

YeBhoneLin10

2024-06-26T05:59:47Z

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

2024-06-26T05:59:46Z

--- 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 Mandalay widget: [] ---  # SDXL LoRA DreamBooth - YeBhoneLin10/Mandalay_lora <Gallery /> ## Model description These are YeBhoneLin10/Mandalay_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 Mandalay to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](YeBhoneLin10/Mandalay_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]

Topofthenod/q-Taxi-v3-new

Topofthenod

2024-06-26T06:01:26Z

0

null

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

reinforcement-learning

2024-06-26T06:01:23Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-new results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.75 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="Topofthenod/q-Taxi-v3-new", 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"]) ```

leeloolee/gwen

leeloolee

2024-06-26T06:02:51Z

0

null

[ "region:us" ]

null

2024-06-26T06:02:46Z

Entry not found

ShaikAbdul/docreader

ShaikAbdul

2024-06-26T06:08:34Z

0

null

[ "region:us" ]

null

2024-06-26T06:08:34Z

Entry not found

jayoohwang/qlora_test

jayoohwang

2024-06-26T08:03:40Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T06:11:32Z

Entry not found

Alirezashafiei/Lisen3

Alirezashafiei

2024-06-26T06:50:57Z

0

null

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

null

2024-06-26T06:11:45Z

--- license: openrail ---

v0dkapapi/FTM-Data-For-LLM

v0dkapapi

2024-06-26T06:53:51Z

0

null

[ "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "region:us" ]

null

2024-06-26T06:11:46Z

--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: FTM-Data-For-LLM results: [] ---  # FTM-Data-For-LLM This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.32.0 - Pytorch 2.3.0+cu121 - Datasets 2.13.1 - Tokenizers 0.13.3

Bajiyo/trying-lm-with-bert

Bajiyo

2024-06-27T04:23:50Z

0

transformers

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

null

2024-06-26T06:11:46Z

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

Snapkriz/finetuned_deepseek_evolIinstruct_snaplogicdocs

Snapkriz

2024-06-26T06:12:03Z

0

null

[ "region:us" ]

null

2024-06-26T06:12:03Z

Entry not found

Topofthenod/q-Taxi-v3.1

Topofthenod

2024-06-26T06:14:16Z

0

null

[ "region:us" ]

null

2024-06-26T06:14:16Z

Entry not found

Sunbread/isekai-rolename-vae

Sunbread

2024-07-01T06:28:15Z

0

null

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

null

2024-06-26T06:17:07Z

--- license: mit ---

Topofthenod/q-Taxi-v3.2

Topofthenod

2024-06-26T06:17:38Z

0

null

[ "region:us" ]

null

2024-06-26T06:17:38Z

Entry not found

oljike/llama3-8b-aqlm-codingft

oljike

2024-06-26T06:23:51Z

0

null

[ "region:us" ]

null

2024-06-26T06:23:51Z

Entry not found

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

PRATIKDE

2024-06-26T06:26:05Z

0

null

[ "region:us" ]

null

2024-06-26T06:26:05Z

Entry not found

julientfai/InstructLM-500M-q4f16_1-Opilot

julientfai

2024-06-26T06:26:47Z

0

null

[ "region:us" ]

null

2024-06-26T06:26:12Z

Entry not found

PRATIKDE/AIMO-NEO-X1-G7BIT

PRATIKDE

2024-06-26T06:26:26Z

0

null

[ "region:us" ]

null

2024-06-26T06:26:26Z

Entry not found

iamnguyen/Qwen2-1.5B-ORPO

iamnguyen

2024-06-26T08:59:31Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T06:36:04Z

Entry not found

Winmodel/lora_gemma2b-it

Winmodel

2024-06-26T06:36:45Z

0

transformers

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

null

2024-06-26T06:36:39Z

--- 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]

Malaiarasu/qa_pair

Malaiarasu

2024-06-26T06:37:22Z

0

null

[ "region:us" ]

null

2024-06-26T06:37:22Z

Entry not found

ILKT/2024-06-24_22-31-28_epoch_75

ILKT

2024-06-28T14:26:56Z

0

sentence-transformers

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

sentence-similarity

2024-06-26T06:43:41Z

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

VKapseln475/SlimGummies586

VKapseln475

2024-06-26T06:50:11Z

0

null

[ "region:us" ]

null

2024-06-26T06:47:42Z

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