librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	library_name stringclasses 245 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
brrr836/tcp2023	brrr836	2024-06-28T02:49:02Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:49:02Z	--- license: apache-2.0 ---
Lynn83125/tcp2023	Lynn83125	2024-06-28T02:49:37Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:49:37Z	--- license: apache-2.0 ---
Sailyumina/UUU_TCP2023	Sailyumina	2024-06-28T02:49:38Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:49:38Z	--- license: apache-2.0 ---
ElvisChiu/tcp2023	ElvisChiu	2024-06-28T02:49:56Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:49:56Z	--- license: apache-2.0 ---
Lynn83125/llama2_uuu_news_qlora	Lynn83125	2024-06-28T02:50:12Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:50:12Z	--- license: apache-2.0 ---
SheyneHuang/tcp2023	SheyneHuang	2024-06-28T02:50:13Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:50:13Z	--- license: apache-2.0 ---
ElvisChiu/uuu_fine_tune_gpt2	ElvisChiu	2024-06-28T03:29:39Z	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-28T02:50:23Z	--- license: apache-2.0 ---
karta1215913/tcp2023	karta1215913	2024-06-28T02:50:26Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:50:26Z	--- license: apache-2.0 ---
Sailyumina/LLAMA2_UUU_NEWS_QLORA	Sailyumina	2024-06-28T07:35:56Z	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2024-06-28T02:50:48Z	--- base_model: NousResearch/Llama-2-7b-chat-hf library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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] ### Framework versions - PEFT 0.11.1
jia35/tcp2023	jia35	2024-06-28T02:52:02Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:52:02Z	--- license: apache-2.0 ---
LisaSS/tcp2023	LisaSS	2024-06-28T02:53:26Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:53:26Z	--- license: apache-2.0 ---
JOHN2106/T3	JOHN2106	2024-06-28T02:53:37Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:53:37Z	--- license: apache-2.0 ---
LisaSS/uuu_fine_tune_gpt2	LisaSS	2024-06-28T03:33:46Z	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-28T02:55:39Z	--- license: apache-2.0 ---
LisaSS/llama2_uuu_news_qlora	LisaSS	2024-06-28T02:55:59Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:55:59Z	--- license: apache-2.0 ---
jan-hq/llama-3-sound-cp	jan-hq	2024-06-28T07:27:03Z	null	[ "safetensors", "region:us" ]	null	2024-06-28T02:56:24Z	Entry not found
Sulochini/empDetails	Sulochini	2024-06-28T02:56:49Z	null	[ "region:us" ]	null	2024-06-28T02:56:49Z	Entry not found
l4urentz/T3	l4urentz	2024-06-28T03:03:39Z	null	[ "region:us" ]	null	2024-06-28T02:57:12Z	Entry not found
MaryCC/tcp2023	MaryCC	2024-06-28T02:58:01Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:58:01Z	--- license: apache-2.0 ---
passionful7/Linen-Like-grey	passionful7	2024-06-28T02:58:30Z	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-06-28T02:58:29Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: Linen-Like Extra Wide Pants - grey 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 - passionful7/Linen-Like-grey <Gallery /> ## Model description These are passionful7/Linen-Like-grey 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 Linen-Like Extra Wide Pants - grey to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](passionful7/Linen-Like-grey/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]
habulaj/269422239758	habulaj	2024-06-28T02:59:22Z	null	[ "region:us" ]	null	2024-06-28T02:59:19Z	Entry not found
Oriones/Eric	Oriones	2024-06-28T02:59:21Z	null	[ "region:us" ]	null	2024-06-28T02:59:21Z	Entry not found
JOJOLA/tcp2023	JOJOLA	2024-06-28T02:59:41Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T02:59:41Z	--- license: apache-2.0 ---
frankdu/llama3_chat_tune_lora	frankdu	2024-06-28T03:25:41Z	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	2024-06-28T03:00:49Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: frankdu - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit
chris208/realvisxlV40_v40LightningBakedvae.safetensors	chris208	2024-06-28T03:01:34Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T03:01:34Z	--- license: apache-2.0 ---
moritzef/model_out_1	moritzef	2024-06-28T03:02:17Z	null	[ "region:us" ]	null	2024-06-28T03:02:17Z	Entry not found
fukufuk/vsft-llava-1.5-7b-hf	fukufuk	2024-06-28T03:04:10Z	null	[ "region:us" ]	null	2024-06-28T03:04:10Z	Entry not found
mttgermano/poca-SoccerTwos	mttgermano	2024-06-28T03:06:59Z	ml-agents	[ "ml-agents", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2024-06-28T03:06:58Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: mttgermano/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
phamvi856/3d-icon-sdxl-dora	phamvi856	2024-06-28T03:07:27Z	null	[ "region:us" ]	null	2024-06-28T03:07:27Z	Entry not found
fabriziofalcon26/pregunta5	fabriziofalcon26	2024-06-28T03:09:28Z	null	[ "region:us" ]	null	2024-06-28T03:09:01Z	Entry not found
yujinqiu/k2-jp	yujinqiu	2024-06-28T03:10:34Z	null	[ "license:mit", "region:us" ]	null	2024-06-28T03:10:34Z	--- license: mit ---
elysiantech/gemma-2b-gptq-4bit-lora	elysiantech	2024-06-28T19:59:18Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T03:11:46Z	--- 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]
ShotaSato0916/multilingual-e5-large-alan-apple-overfitting	ShotaSato0916	2024-06-28T03:27:42Z	null	[ "region:us" ]	null	2024-06-28T03:12:28Z	Entry not found
henriquefr/interop4	henriquefr	2024-06-28T03:13:02Z	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	2024-06-28T03:12:41Z	--- 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: henriquefr - 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)
habulaj/144860121264	habulaj	2024-06-28T03:16:01Z	null	[ "region:us" ]	null	2024-06-28T03:15:16Z	Entry not found
gdvstd/dessin-sd3-lora	gdvstd	2024-06-28T03:18:41Z	null	[ "region:us" ]	null	2024-06-28T03:17:41Z	Entry not found
Keilahwjiwbssh/Oi	Keilahwjiwbssh	2024-06-28T03:19:32Z	null	[ "region:us" ]	null	2024-06-28T03:19:32Z	Entry not found
Kibalama/Pixelcopter-PLE-v0	Kibalama	2024-06-28T03:20:52Z	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-06-28T03:20:46Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 43.30 +/- 42.59 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . 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
gdvstd/rough-sd3-lora	gdvstd	2024-06-28T03:22:24Z	null	[ "region:us" ]	null	2024-06-28T03:22:17Z	Entry not found
Monke64/music2image	Monke64	2024-06-28T03:27:43Z	null	[ "region:us" ]	null	2024-06-28T03:27:43Z	Entry not found
head-empty-ai/Benchmark-Results	head-empty-ai	2024-07-02T21:15:47Z	null	[ "region:us" ]	null	2024-06-28T03:30:34Z	\|MODEL\|HellaSwag\|EQ_Bench\|\|% Parsed (EQ)\| \|:---\|--:\|---:\|---\|:---\| \|[argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)\|83.03\|66.52\|\|100\| \|[athirdpath/NSFW_DPO_vmgb-7b](https://huggingface.co/athirdpath/NSFW_DPO_vmgb-7b)\|85.36\|74.83\|\|100\| \|[berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)\|82.17\|69.78\|\|100\| \|[cgato/Thespis-Mistral-7b-v0.6](https://huggingface.co/cgato/Thespis-Mistral-7b-v0.6)\|81.83\|29.13\|\|79.53\| \|[chargoddard/loyal-piano-m7](https://huggingface.co/chargoddard/loyal-piano-m7)\|83.28\|72.15\|\|100\| \|[cognitivecomputations/dolphin-2.2.1-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.2.1-mistral-7b)\|81.47\|67.33\|\|100\| \|[cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)\|83.61\|71.57\|\|99.42\| \|[Crimvael/Raphael-7B](https://huggingface.co/Crimvael/Raphael-7B)\|83.47\|68.73\|\|100\| \|[Delcos/Mistral-Pygmalion-7b](https://huggingface.co/Delcos/Mistral-Pygmalion-7b)\|76.61\|17.34\|\|100\| \|[FallenMerick/Iced-Lemon-Cookie-7B](https://huggingface.co/FallenMerick/Iced-Lemon-Cookie-7B)\|85.54\|71.54\|\|100\| \|[FallenMerick/Smart-Lemon-Cookie-7B](https://huggingface.co/FallenMerick/Smart-Lemon-Cookie-7B)\|85.41\|68.12\|\|100\| \|[HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\|82.02\|58.14\|\|100\| \|[Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)\|79.76\|62.26\|\|100\| \|[jondurbin/airoboros-m-7b-3.1.2](https://huggingface.co/jondurbin/airoboros-m-7b-3.1.2)\|81.34\|38.52\|\|100\| \|[jondurbin/cinematika-7b-v0.1](https://huggingface.co/jondurbin/cinematika-7b-v0.1)\|80.31\|44.85\|\|100\| \|[KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3)\|82.65\|63.23\|\|100\| \|[KoboldAI/Mistral-7B-Erebus-v3](https://huggingface.co/KoboldAI/Mistral-7B-Erebus-v3)\|76.65\|18.19\|\|97.66\| \|[KoboldAI/Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)\|79.19\|2.10\|\|98.25\| \|[maywell/Synatra-7B-v0.3-RP](https://huggingface.co/maywell/Synatra-7B-v0.3-RP)\|80.46\|54.93\|\|100\| \|[migtissera/Synthia-7B-v3.0](https://huggingface.co/migtissera/Synthia-7B-v3.0)\|81.74\|15.03\|\|94.74\| \|[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)\|74.67\|46.82\|\|100\| \|[mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)\|83.66\|65.50\|\|99.42\| \|[mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)\|86.46\|74.21\|\|99.42\| \|[Norquinal/Mistral-7B-claude-chat](https://huggingface.co/Norquinal/Mistral-7B-claude-chat)\|83.06\|16.34\|\|99.42\| \|[NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)\|80.56\|65.93\|\|100\| \|[NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9)\|78.71\|19.25\|\|100\| \|[Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)\|81.67\|63.98\|\|99.42\| \|[rwitz/go-bruins](https://huggingface.co/rwitz/go-bruins)\|84.92\|73.62\|\|100\| \|[SanjiWatsuki/Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)\|85.25\|72.36\|\|100\| \|[SanjiWatsuki/Loyal-Macaroni-Maid-7B](https://huggingface.co/SanjiWatsuki/Loyal-Macaroni-Maid-7B)\|84.53\|73.67\|\|100\| \|[SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B)\|84.55\|68.74\|\|100\| \|[senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)\|87.42\|77.87\|\|100\| \|[TeeZee/DarkSapling-7B-v2.0](https://huggingface.co/TeeZee/DarkSapling-7B-v2.0)\|82.56\|62.19\|\|100\| \|[teknium/Hermes-Trismegistus-Mistral-7B](https://huggingface.co/teknium/Hermes-Trismegistus-Mistral-7B)\|79.21\|64.86\|\|98.83\| \|[teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)\|81.68\|65.75\|\|100\| \|[Undi95/Toppy-M-7B](https://huggingface.co/Undi95/Toppy-M-7B)\|83.52\|66.57\|\|100\| </br> \|MODEL\|HellaSwag\|EQ_Bench\|\|% Parsed (EQ)\| \|:---\|--:\|---:\|---\|:---\| \|[ABX-AI/Silver-Sun-v2-11B](https://huggingface.co/ABX-AI/Silver-Sun-v2-11B)\|86.40\|69.92\|\|100\| \|[backyardai/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B](https://huggingface.co/backyardai/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B)\|86.00\|69.25\|\|100\| \|[BlueNipples/SnowLotus-v2-10.7B](https://huggingface.co/BlueNipples/SnowLotus-v2-10.7B)\|83.42\|60.54\|\|99.42\| \|[FallenMerick/Chewy-Lemon-Cookie-11B](https://huggingface.co/FallenMerick/Chewy-Lemon-Cookie-11B)\|84.39\|76.24\|\|100\| \|[FallenMerick/Chunky-Lemon-Cookie-11B](https://huggingface.co/FallenMerick/Chunky-Lemon-Cookie-11B)\|84.36\|76.29\|\|100\| \|[froggeric/WestLake-10.7B-v2](https://huggingface.co/froggeric/WestLake-10.7B-v2)\|86.74\|73.35\|\|95.32\| \|[head-empty-ai/Mytho-Lemon-11B](https://huggingface.co/head-empty-ai/Mytho-Lemon-11B)\|79.77\|60.03\|\|100\| \|[Himitsui/KuroMitsu-11B](https://huggingface.co/Himitsui/KuroMitsu-11B)\|86.33\|70.50\|\|98.83\| \|[kyujinpy/SOLAR-Platypus-10.7B-v2](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v2)\|82.05\|25.11\|\|45.61\| \|[migtissera/Tess-10.7B-v1.5b](https://huggingface.co/migtissera/Tess-10.7B-v1.5b)\|83.82\|61.83\|\|99.42\| \|[NeverSleep/Mistral-11B-SynthIAirOmniMix](https://huggingface.co/NeverSleep/Mistral-11B-SynthIAirOmniMix)\|81.58\|55.19\|\|100\| \|[NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B)\|83.24\|63.52\|\|100\| \|[saishf/Fimbulvetr-Kuro-Lotus-10.7B](https://huggingface.co/saishf/Fimbulvetr-Kuro-Lotus-10.7B)\|86.25\|65.85\|\|100\| \|[Sao10K/Fimbulvetr-10.7B-v1](https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1)\|85.81\|65.42\|\|100\| \|[Sao10K/Fimbulvetr-11B-v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2)\|86.61\|70.00\|\|99.42\| \|[Sao10K/Frostwind-10.7B-v1](https://huggingface.co/Sao10K/Frostwind-10.7B-v1)\|84.15\|55.73\|\|99.42\| \|[Sao10K/Solstice-11B-v1](https://huggingface.co/Sao10K/Solstice-11B-v1)\|86.42\|68.24\|\|99.42\| \|[TheDrummer/Moistral-11B-v3](https://huggingface.co/TheDrummer/Moistral-11B-v3)\|86.65\|69.75\|\|99.42\| \|[Undi95/Borealis-10.7B](https://huggingface.co/Undi95/Borealis-10.7B)\|79.58\|8.27\|\|44.44\| \|[upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)\|86.35\|68.65\|\|98.25\| \|[upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)\|83.10\|28.66\|\|100\|
AdamKasumovic/llama3-8b-instruct-bactrian-x-xh-100-percent-med-high-perplexity	AdamKasumovic	2024-06-28T03:35:03Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-28T03:31:24Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fangjj/my_awesome_mind_model	fangjj	2024-06-28T03:33:10Z	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "endpoints_compatible", "region:us" ]	audio-classification	2024-06-28T03:31:30Z	Entry not found
BrallanPa/filmarv2	BrallanPa	2024-06-28T03:33:06Z	null	[ "region:us" ]	null	2024-06-28T03:32:31Z	Entry not found
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-en-100-percent-med-perplexity	AdamKasumovic	2024-06-28T03:36:34Z	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-28T03:33:35Z	--- base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
habulaj/1699716757	habulaj	2024-06-28T03:35:39Z	null	[ "region:us" ]	null	2024-06-28T03:35:36Z	Entry not found
chainup244/Qwen-Qwen1.5-1.8B-1719545883	chainup244	2024-06-28T03:39:48Z	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-28T03:38:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
nglguarino/phi2-firstTry	nglguarino	2024-06-28T04:13:19Z	null	[ "safetensors", "region:us" ]	null	2024-06-28T03:39:24Z	Entry not found
fhsp93/my_awesome_model	fhsp93	2024-06-28T03:40:41Z	null	[ "region:us" ]	null	2024-06-28T03:40:41Z	Entry not found
AniaAri/pregunta05	AniaAri	2024-06-28T03:43:50Z	null	[ "joblib", "region:us" ]	null	2024-06-28T03:43:14Z	Entry not found
Wawaworker/nkszbad	Wawaworker	2024-06-28T03:45:57Z	null	[ "region:us" ]	null	2024-06-28T03:45:57Z	Entry not found
habulaj/11386288619	habulaj	2024-06-28T03:46:23Z	null	[ "region:us" ]	null	2024-06-28T03:46:22Z	Entry not found
modeldodel/DERRIUSNew	modeldodel	2024-06-28T03:49:10Z	null	[ "license:openrail", "region:us" ]	null	2024-06-28T03:48:35Z	--- license: openrail ---
shengxuelim/q-FrozenLake-v1-4x4-noSlippery	shengxuelim	2024-06-28T03:51:40Z	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-28T03:51:38Z	--- 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="shengxuelim/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"]) ```
shengxuelim/taxi-rl	shengxuelim	2024-06-28T03:53:10Z	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-28T03:53:08Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-rl results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.72 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="shengxuelim/taxi-rl", 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"]) ```
habulaj/6276047107	habulaj	2024-06-28T03:53:43Z	null	[ "region:us" ]	null	2024-06-28T03:53:37Z	Entry not found
habulaj/5143040115	habulaj	2024-06-28T03:54:09Z	null	[ "region:us" ]	null	2024-06-28T03:54:07Z	Entry not found
triplee/supernatural_dataset_3epo_model	triplee	2024-06-28T03:55:56Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T03:55:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Razer112/Goth	Razer112	2024-06-28T23:08:05Z	null	[ "license:openrail", "region:us" ]	null	2024-06-28T03:56:57Z	--- license: openrail ---
Hoangcondilonton/test	Hoangcondilonton	2024-06-28T03:57:25Z	null	[ "license:llama2", "region:us" ]	null	2024-06-28T03:57:25Z	--- license: llama2 ---
l4urentz/Pregunta5	l4urentz	2024-06-28T04:00:07Z	null	[ "region:us" ]	null	2024-06-28T03:58:44Z	Entry not found
Arodrigo/temp008	Arodrigo	2024-06-28T04:02:46Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T04:02:43Z	--- 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]
faissalb/cih_lora_llama_0.1	faissalb	2024-06-28T04:14:00Z	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	2024-06-28T04:13:29Z	--- 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: faissalb - 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)
kugelitsjust/Dr_Jib	kugelitsjust	2024-06-29T10:30:46Z	null	[ "license:openrail", "region:us" ]	null	2024-06-28T04:13:58Z	--- license: openrail ---
octadion/llama3-8B-chat-v2-gguf	octadion	2024-06-28T04:14:40Z	null	[ "region:us" ]	null	2024-06-28T04:14:40Z	Entry not found
kugelitsjust/Azzathog	kugelitsjust	2024-06-28T04:16:36Z	null	[ "license:openrail", "region:us" ]	null	2024-06-28T04:15:37Z	--- license: openrail ---
Ammartatox/llamared7	Ammartatox	2024-06-28T04:24:31Z	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-28T04:16:32Z	--- 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: Ammartatox - 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)
NAYEONCEot9cover/LISA	NAYEONCEot9cover	2024-06-28T04:25:10Z	null	[ "license:openrail", "region:us" ]	null	2024-06-28T04:18:42Z	--- license: openrail ---
Arodrigo/temp009	Arodrigo	2024-06-28T04:19:41Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T04:19:39Z	--- 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]
a1-b2-c3-d4-archana/t5-small_classification	a1-b2-c3-d4-archana	2024-06-28T04:42:16Z	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-06-28T04:22:03Z	Entry not found
abdiharyadi/indoamrbart-mbart-triple-ft-parser-no-nst-256-eps	abdiharyadi	2024-06-28T04:24:46Z	null	[ "safetensors", "region:us" ]	null	2024-06-28T04:22:46Z	Entry not found
nglguarino/my-nlp-phi2-model	nglguarino	2024-06-28T05:10:07Z	null	[ "safetensors", "region:us" ]	null	2024-06-28T04:27:49Z	Entry not found
habulaj/244864494343	habulaj	2024-06-28T04:31:49Z	null	[ "region:us" ]	null	2024-06-28T04:31:39Z	Entry not found
NoNameFactory/llama-3-8b-it-4bit-ContdPT_1_10	NoNameFactory	2024-06-28T04:36:58Z	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	2024-06-28T04:33:19Z	--- 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: NoNameFactory - 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)
Arodrigo/temp0010	Arodrigo	2024-06-28T04:33:37Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T04:33:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
jykim310/phiva-3.9b-q4f16_1-MLC	jykim310	2024-06-28T04:37:53Z	null	[ "region:us" ]	null	2024-06-28T04:33:35Z	Entry not found
habulaj/5417556370	habulaj	2024-06-28T04:39:09Z	null	[ "region:us" ]	null	2024-06-28T04:39:08Z	Entry not found
Arodrigo/temp0011	Arodrigo	2024-06-28T04:42:09Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-28T04:42:02Z	--- 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]
Djtrice/Kids	Djtrice	2024-06-28T04:46:20Z	null	[ "region:us" ]	null	2024-06-28T04:46:19Z	Entry not found
habulaj/1880918602	habulaj	2024-06-28T04:50:47Z	null	[ "region:us" ]	null	2024-06-28T04:50:42Z	Entry not found
sanskarceo1/Chyno	sanskarceo1	2024-06-28T04:51:56Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T04:51:55Z	--- license: apache-2.0 ---
DizVen/coursework	DizVen	2024-06-28T04:52:28Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T04:52:28Z	--- license: apache-2.0 ---
DrChamyoung/TextARC2.6	DrChamyoung	2024-06-28T04:54:00Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T04:54:00Z	--- license: apache-2.0 ---
pacoreyes/TopicBoundaryBERT	pacoreyes	2024-06-29T06:52:47Z	null	[ "doi:10.57967/hf/2644", "license:mit", "region:us" ]	null	2024-06-28T04:55:02Z	--- license: mit ---
saiki23/NyntchLORA	saiki23	2024-06-28T07:05:48Z	null	[ "region:us" ]	null	2024-06-28T04:58:08Z	Entry not found
ShapeKapseln33/Nexalyn678	ShapeKapseln33	2024-06-28T05:03:09Z	null	[ "region:us" ]	null	2024-06-28T04:59:50Z	[køb] Nexalyn Anmeldelser At forbrænde fedt i besværlige områder er en udfordring for mange mennesker på deres vægttabsrejse. Dette stædige kropsfedt kan være frustrerende og svært at målrette mod med kost og motion alene. Nexaslim-tillægget kan dog give den løsning, du har ledt efter. [Klik her for at købe nu fra Nexalyns officielle hjemmeside](https://capsules24x7.com/nexalyn-danmark) Nexalyn Pills er en forbedring til at øge folks opmærksomhed under seksuelle sociale lejligheder. Det er opdateringen, der kan bruges af personer med seksuelle mangler. Denne forbedring virker ved at forbedre testosteronniveauet hos mænd. Testosteroner er kemikalier, der kan bringe tilfredsstillende seksuel funktion og god seksuel ydeevne i fare. Dette redesign hjælper med at arbejde på præsentationen ved seksuelle sociale begivenheder ved at øge størrelsen af erektionen i henhold til udvidelsen af pizazzen. Et klart svar for enkeltpersoner bør også understøtte deres seksuelle grænser. ##Hvad er Nexalyn? Nexalyn virker ved at øge testosteronniveauet i den menneskelige krop. Nexalyn-tilskuddet indeholder forskellige ingredienser, der øger mængden af testosteronkemikalier og derfor understøtter seksuel præstation. Udover testosteronopdateringen udvider redesignet også kroppens blodgennemstrømning. Den øgede blodgennemstrømning fører så til bedre resultater og bedre ydeevne af forskellige organer i kroppen. De forskellige organer i kroppen, inklusive penis, vil fungere perfekt, og den seksuelle præsentation vil blive opdateret, svarende til personens vitalitet. Forbedringen omfatter nitrogenoxid, som giver bedre blodcirkulation og bedre bevidsthed blandt mennesker. ##Er ingredienserne i Nexalyn Nexalyns redesign omfatter ethvert mærkefragment. Dekorationerne er ret typiske og fokuserer stort set på nogle få bemærkelsesværdige standardsmag. Nogle af de kritiske elementer i dette redesign er som følger: Tongkat Ali: Denne specifikke effekt er ansvarlig for at øge testosteronniveauet i den menneskelige krop. [Klik her for at købe nu fra Nexalyns officielle hjemmeside](https://capsules24x7.com/nexalyn-danmark) L-arginin: Denne fiksering er underlagt udviklingen af konstansen hos den person, der dræner den. På den måde fungerer kønsorganerne bedre. Maca: Dette er også en af de afgørende dele af denne udvikling. Det er underlagt spredningen af bedre blod på samme måde som den generelle velstand for forskellige organer i kroppen. Ginseng: Denne fiksering er forpligtet til at give folk ekstra centralitet. Dette sikrer folks sikkerhed under seksuelle møder. Det skal være klart, at alle disse er gennemtænkte koncentrater af normale smagsvarianter. Derfor er brugen af denne opgradering beskyttet. ##Hvordan skal du tage Nexalyn? Nexalyn fremstilles som en beholder, der indeholder tabletterne fra plasteret. Æsken indeholder ca. 60 tabletter, hvoraf 2 bør tages pålideligt. Den ene pille skal tages den næste dag efter morgenmaden og den anden pille efter aftensmaden. Nexalyn mandlige forstærkningspiller skal tages med varmt vand. En høj andel vand bør bruges, når du bruger dette tilbehør. Dette er bedre for at få en hurtigere effekt af forbedring. ##Hvor kan jeg nemt og hurtigt få fat i Nexalyn? Så vi har set, at Nexalyn er førende blandt de andre seksuelle præstationsfremmende kosttilskud på markedet. Hvis du lider af seksuelle mangler, kan du også prøve denne forbedring og tage de betydelige konsekvenser af denne makeover i øjnene. For at købe disse piller kan du klikke på et hvilket som helst link på denne side og få det bedste tilbud. [Klik her for at købe nu fra Nexalyns officielle hjemmeside](https://capsules24x7.com/nexalyn-danmark)
VKapseln475/Shape7545	VKapseln475	2024-06-28T05:21:11Z	null	[ "region:us" ]	null	2024-06-28T05:03:25Z	# [NEU] Shape kapseln Deutschland Bewertungen Test und Einnahme - Shape kapseln Erfahrungen Shape Kapseln Deutschland ist ein kürzlich auf den Markt gebrachtes, den Stoffwechsel ankurbelndes Nahrungsergänzungsmittel, das in den letzten Wochen auf dem Markt begeisterte Kritiken erhalten hat. In dieser Rezension zu Shape Kapseln werde ich jeden Aspekt dieser Ergänzung im Detail untersuchen, um herauszufinden, ob sie ihr Geld wert ist. ## [Klicken Sie hier, um jetzt auf der offiziellen Website von Shape Kapseln zu kaufen](https://callednews.com/shape-kapseln-de) ## Formkapseln – Ergebnisse und Langlebigkeit Um effektive Ergebnisse zu erzielen, empfiehlt der Hersteller von Shape Kapseln, das Nahrungsergänzungsmittel unbedingt über einen Zeitraum von 3 bis 5 Monaten einzunehmen. Dieser Zeitraum für die Ergebnisse hängt von verschiedenen Faktoren wie Alter, genetischer Zusammensetzung, Lebensstil, Stoffwechselrate und allgemeinem Gesundheitszustand ab. Allerdings wird behauptet, dass es kein Zurück mehr gibt, sobald Shape Kapseln Ergebnisse liefert. Damit die Ergebnisse dauerhaft anhalten, müssen Sie das Nahrungsergänzungsmittel Shape Kapseln regelmäßig einnehmen, gefolgt von einer gesunden und ausgewogenen Ernährung und einfachen Übungen. Ein gesunder Lebensstil trägt dazu bei, die Wirksamkeit dieses Stoffwechsel-Boosters zu steigern und auch die allgemeine Gesundheit und das Wohlbefinden zu verbessern. Achten Sie darauf, die empfohlene Portion Shape Kapseln nicht auszulassen, da die Ergebnisse schnell nachlassen. ## Kundenrezensionen und Beschwerden zu Shape Kapseln: Shape Kapseln Benutzererfahrung Die Kundenrezensionen zu Shape Kapseln sind bisher durchweg positiv. Ehrliche Rezensionen zu diesem Stoffwechsel-Booster sind in vertrauenswürdigen Gesundheitsforen und anderen Quellen wie Reddit, Quora und Facebook verfügbar. Auf diesen Plattformen haben zufriedene Benutzer kommentiert, dass das Nahrungsergänzungsmittel dazu beigetragen hat, die Muskelmasse zu verbessern und den Stoffwechsel anzukurbeln. Einige andere berichteten ebenfalls über einen Anstieg des Energieniveaus. Diese Kundenrezensionen von Shape Kapseln zeigen, dass diese Formel sicher und wirksam ist. ## Shape Kapseln-Pakete und Preise Ab sofort sind Shape Kapseln im Vergleich zu anderen ähnlichen Stoffwechsel-Boostern auf dem Markt zu einem viel günstigeren Preis erhältlich. Der Hersteller hat den Preis dieser stoffwechselankurbelnden Formel für einen begrenzten Zeitraum gesenkt, damit alle Menschen davon profitieren können. ### Hier die detaillierten Preispläne: Anfänger – 1 Packung für 49,95 € pro Packung + 4,95 € Versand Top-Seller – 2 Packungen für 36,65 € pro Packung + kostenloser Versand Bestseller – 3 Packungen für 39,97 € pro Packung + kostenloser Versand Dies sind die drei verschiedenen Shape Kapseln-Pakete, die derzeit zum Kauf erhältlich sind. Sie können ein beliebiges Paket wählen und Ihre Reise zur Stoffwechselankurbelung und Gewichtsabnahme ankurbeln. ## Verfügbarkeit von Formkapseln Derzeit können Sie die Shape-Kapseln-Formel nur über die offizielle Website erhalten. Der Hersteller hat diese Formel nicht auf Plattformen Dritter oder im Einzelhandel verfügbar gemacht. Allerdings könnten Duplikate der Shape-Kapseln-Formel von nicht autorisierten Verkäufern über Amazon und andere Drittplattformen verkauft werden, um uninformierte Benutzer in die Falle zu locken. Diese Replikate haben ähnliche Namen, Website-Designs usw., liefern jedoch keine ähnlichen Ergebnisse. Sie könnten auch schädliche Inhaltsstoffe enthalten. ## [Klicken Sie hier, um jetzt auf der offiziellen Website von Shape Kapseln zu kaufen](https://callednews.com/shape-kapseln-de)
wisenut-nlp-team/wisenut-llama-3-8B-LoRA	wisenut-nlp-team	2024-06-28T05:13:33Z	null	[ "safetensors", "region:us" ]	null	2024-06-28T05:08:39Z	Entry not found
jddllwqa/Qwen-Qwen1.5-7B-1719551837	jddllwqa	2024-06-28T05:17:18Z	null	[ "region:us" ]	null	2024-06-28T05:17:18Z	Entry not found
midori1255/controlnet_marmoset	midori1255	2024-06-28T05:17:24Z	null	[ "license:unknown", "region:us" ]	null	2024-06-28T05:17:24Z	--- license: unknown ---
yraziel/pnina_rozenblum	yraziel	2024-06-28T05:21:07Z	null	[ "region:us" ]	null	2024-06-28T05:20:14Z	Entry not found
salmanhermana/finetuned-food	salmanhermana	2024-06-28T05:20:50Z	null	[ "region:us" ]	null	2024-06-28T05:20:50Z	Entry not found
ningrumdaud/test_lora_llama3model	ningrumdaud	2024-06-28T05:22:38Z	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	2024-06-28T05:22:15Z	--- 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: ningrumdaud - 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)
AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-en-100-percent-low-med-perplexity	AdamKasumovic	2024-06-28T05:32:58Z	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-28T05:30:00Z	--- base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Pranavv/fork-moirai	Pranavv	2024-06-28T05:32:54Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-28T05:32:54Z	--- license: apache-2.0 ---
jddllwqa/Qwen-Qwen1.5-0.5B-1719552870	jddllwqa	2024-06-28T05:34:36Z	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "region:us" ]	null	2024-06-28T05:34:30Z	--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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] ### Framework versions - PEFT 0.11.1
ynyn011/content	ynyn011	2024-06-28T05:35:42Z	null	[ "region:us" ]	null	2024-06-28T05:35:42Z	Entry not found
sryab2001/llama3-8b-cosmic-fusion-dynamics-lora	sryab2001	2024-06-28T05:44:20Z	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	2024-06-28T05:43:49Z	--- 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: sryab2001 - 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)
riddhiparmar/test-model	riddhiparmar	2024-06-28T05:49:04Z	null	[ "region:us" ]	null	2024-06-28T05:44:54Z	Entry not found
slelab/AES14	slelab	2024-06-28T06:11:39Z	null	[ "region:us" ]	null	2024-06-28T05:47:37Z	Entry not found
riddhiparmar/test-model2	riddhiparmar	2024-06-28T05:47:42Z	null	[ "region:us" ]	null	2024-06-28T05:47:42Z	Entry not found

brrr836/tcp2023

brrr836

2024-06-28T02:49:02Z

0

null

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

null

2024-06-28T02:49:02Z

--- license: apache-2.0 ---

Lynn83125/tcp2023

Lynn83125

2024-06-28T02:49:37Z

0

null

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

null

2024-06-28T02:49:37Z

--- license: apache-2.0 ---

Sailyumina/UUU_TCP2023

Sailyumina

2024-06-28T02:49:38Z

0

null

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

null

2024-06-28T02:49:38Z

--- license: apache-2.0 ---

ElvisChiu/tcp2023

ElvisChiu

2024-06-28T02:49:56Z

0

null

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

null

2024-06-28T02:49:56Z

--- license: apache-2.0 ---

Lynn83125/llama2_uuu_news_qlora

Lynn83125

2024-06-28T02:50:12Z

0

null

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

null

2024-06-28T02:50:12Z

--- license: apache-2.0 ---

SheyneHuang/tcp2023

SheyneHuang

2024-06-28T02:50:13Z

0

null

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

null

2024-06-28T02:50:13Z

--- license: apache-2.0 ---

ElvisChiu/uuu_fine_tune_gpt2

ElvisChiu

2024-06-28T03:29:39Z

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-28T02:50:23Z

--- license: apache-2.0 ---

karta1215913/tcp2023

karta1215913

2024-06-28T02:50:26Z

0

null

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

null

2024-06-28T02:50:26Z

--- license: apache-2.0 ---

Sailyumina/LLAMA2_UUU_NEWS_QLORA

Sailyumina

2024-06-28T07:35:56Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "region:us" ]

null

2024-06-28T02:50:48Z

--- base_model: NousResearch/Llama-2-7b-chat-hf library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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] ### Framework versions - PEFT 0.11.1

jia35/tcp2023

jia35

2024-06-28T02:52:02Z

0

null

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

null

2024-06-28T02:52:02Z

--- license: apache-2.0 ---

LisaSS/tcp2023

LisaSS

2024-06-28T02:53:26Z

0

null

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

null

2024-06-28T02:53:26Z

--- license: apache-2.0 ---

JOHN2106/T3

JOHN2106

2024-06-28T02:53:37Z

0

null

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

null

2024-06-28T02:53:37Z

--- license: apache-2.0 ---

LisaSS/uuu_fine_tune_gpt2

LisaSS

2024-06-28T03:33:46Z

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-28T02:55:39Z

--- license: apache-2.0 ---

LisaSS/llama2_uuu_news_qlora

LisaSS

2024-06-28T02:55:59Z

0

null

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

null

2024-06-28T02:55:59Z

--- license: apache-2.0 ---

jan-hq/llama-3-sound-cp

jan-hq

2024-06-28T07:27:03Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T02:56:24Z

Entry not found

Sulochini/empDetails

Sulochini

2024-06-28T02:56:49Z

0

null

[ "region:us" ]

null

2024-06-28T02:56:49Z

Entry not found

l4urentz/T3

l4urentz

2024-06-28T03:03:39Z

0

null

[ "region:us" ]

null

2024-06-28T02:57:12Z

Entry not found

MaryCC/tcp2023

MaryCC

2024-06-28T02:58:01Z

0

null

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

null

2024-06-28T02:58:01Z

--- license: apache-2.0 ---

passionful7/Linen-Like-grey

passionful7

2024-06-28T02:58:30Z

0

diffusers

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

text-to-image

2024-06-28T02:58:29Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers instance_prompt: Linen-Like Extra Wide Pants - grey widget: [] ---  # SDXL LoRA DreamBooth - passionful7/Linen-Like-grey <Gallery /> ## Model description These are passionful7/Linen-Like-grey 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 Linen-Like Extra Wide Pants - grey to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](passionful7/Linen-Like-grey/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]

habulaj/269422239758

habulaj

2024-06-28T02:59:22Z

0

null

[ "region:us" ]

null

2024-06-28T02:59:19Z

Entry not found

Oriones/Eric

Oriones

2024-06-28T02:59:21Z

0

null

[ "region:us" ]

null

2024-06-28T02:59:21Z

Entry not found

JOJOLA/tcp2023

JOJOLA

2024-06-28T02:59:41Z

0

null

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

null

2024-06-28T02:59:41Z

--- license: apache-2.0 ---

frankdu/llama3_chat_tune_lora

frankdu

2024-06-28T03:25:41Z

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

2024-06-28T03:00:49Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** frankdu - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit

chris208/realvisxlV40_v40LightningBakedvae.safetensors

chris208

2024-06-28T03:01:34Z

0

null

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

null

2024-06-28T03:01:34Z

--- license: apache-2.0 ---

moritzef/model_out_1

moritzef

2024-06-28T03:02:17Z

0

null

[ "region:us" ]

null

2024-06-28T03:02:17Z

Entry not found

fukufuk/vsft-llava-1.5-7b-hf

fukufuk

2024-06-28T03:04:10Z

0

null

[ "region:us" ]

null

2024-06-28T03:04:10Z

Entry not found

mttgermano/poca-SoccerTwos

mttgermano

2024-06-28T03:06:59Z

0

ml-agents

[ "ml-agents", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2024-06-28T03:06:58Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: mttgermano/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

phamvi856/3d-icon-sdxl-dora

phamvi856

2024-06-28T03:07:27Z

0

null

[ "region:us" ]

null

2024-06-28T03:07:27Z

Entry not found

fabriziofalcon26/pregunta5

fabriziofalcon26

2024-06-28T03:09:28Z

0

null

[ "region:us" ]

null

2024-06-28T03:09:01Z

Entry not found

yujinqiu/k2-jp

yujinqiu

2024-06-28T03:10:34Z

0

null

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

null

2024-06-28T03:10:34Z

--- license: mit ---

elysiantech/gemma-2b-gptq-4bit-lora

elysiantech

2024-06-28T19:59:18Z

0

transformers

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

null

2024-06-28T03:11:46Z

--- 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]

ShotaSato0916/multilingual-e5-large-alan-apple-overfitting

ShotaSato0916

2024-06-28T03:27:42Z

0

null

[ "region:us" ]

null

2024-06-28T03:12:28Z

Entry not found

henriquefr/interop4

henriquefr

2024-06-28T03:13:02Z

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

2024-06-28T03:12:41Z

--- 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:** henriquefr - **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)

habulaj/144860121264

habulaj

2024-06-28T03:16:01Z

0

null

[ "region:us" ]

null

2024-06-28T03:15:16Z

Entry not found

gdvstd/dessin-sd3-lora

gdvstd

2024-06-28T03:18:41Z

0

null

[ "region:us" ]

null

2024-06-28T03:17:41Z

Entry not found

Keilahwjiwbssh/Oi

Keilahwjiwbssh

2024-06-28T03:19:32Z

0

null

[ "region:us" ]

null

2024-06-28T03:19:32Z

Entry not found

Kibalama/Pixelcopter-PLE-v0

Kibalama

2024-06-28T03:20:52Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2024-06-28T03:20:46Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 43.30 +/- 42.59 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . 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

gdvstd/rough-sd3-lora

gdvstd

2024-06-28T03:22:24Z

0

null

[ "region:us" ]

null

2024-06-28T03:22:17Z

Entry not found

Monke64/music2image

Monke64

2024-06-28T03:27:43Z

0

null

[ "region:us" ]

null

2024-06-28T03:27:43Z

Entry not found

head-empty-ai/Benchmark-Results

head-empty-ai

2024-07-02T21:15:47Z

0

null

[ "region:us" ]

null

2024-06-28T03:30:34Z

|MODEL|HellaSwag|EQ_Bench||% Parsed (EQ)| |:---|--:|---:|---|:---| |[argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)|83.03|66.52||100| |[athirdpath/NSFW_DPO_vmgb-7b](https://huggingface.co/athirdpath/NSFW_DPO_vmgb-7b)|85.36|74.83||100| |[berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)|82.17|69.78||100| |[cgato/Thespis-Mistral-7b-v0.6](https://huggingface.co/cgato/Thespis-Mistral-7b-v0.6)|81.83|29.13||79.53| |[chargoddard/loyal-piano-m7](https://huggingface.co/chargoddard/loyal-piano-m7)|83.28|72.15||100| |[cognitivecomputations/dolphin-2.2.1-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.2.1-mistral-7b)|81.47|67.33||100| |[cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)|83.61|71.57||99.42| |[Crimvael/Raphael-7B](https://huggingface.co/Crimvael/Raphael-7B)|83.47|68.73||100| |[Delcos/Mistral-Pygmalion-7b](https://huggingface.co/Delcos/Mistral-Pygmalion-7b)|76.61|17.34||100| |[FallenMerick/Iced-Lemon-Cookie-7B](https://huggingface.co/FallenMerick/Iced-Lemon-Cookie-7B)|85.54|71.54||100| |[FallenMerick/Smart-Lemon-Cookie-7B](https://huggingface.co/FallenMerick/Smart-Lemon-Cookie-7B)|85.41|68.12||100| |[HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)|82.02|58.14||100| |[Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)|79.76|62.26||100| |[jondurbin/airoboros-m-7b-3.1.2](https://huggingface.co/jondurbin/airoboros-m-7b-3.1.2)|81.34|38.52||100| |[jondurbin/cinematika-7b-v0.1](https://huggingface.co/jondurbin/cinematika-7b-v0.1)|80.31|44.85||100| |[KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3)|82.65|63.23||100| |[KoboldAI/Mistral-7B-Erebus-v3](https://huggingface.co/KoboldAI/Mistral-7B-Erebus-v3)|76.65|18.19||97.66| |[KoboldAI/Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)|79.19|2.10||98.25| |[maywell/Synatra-7B-v0.3-RP](https://huggingface.co/maywell/Synatra-7B-v0.3-RP)|80.46|54.93||100| |[migtissera/Synthia-7B-v3.0](https://huggingface.co/migtissera/Synthia-7B-v3.0)|81.74|15.03||94.74| |[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)|74.67|46.82||100| |[mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)|83.66|65.50||99.42| |[mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)|86.46|74.21||99.42| |[Norquinal/Mistral-7B-claude-chat](https://huggingface.co/Norquinal/Mistral-7B-claude-chat)|83.06|16.34||99.42| |[NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)|80.56|65.93||100| |[NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9)|78.71|19.25||100| |[Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)|81.67|63.98||99.42| |[rwitz/go-bruins](https://huggingface.co/rwitz/go-bruins)|84.92|73.62||100| |[SanjiWatsuki/Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)|85.25|72.36||100| |[SanjiWatsuki/Loyal-Macaroni-Maid-7B](https://huggingface.co/SanjiWatsuki/Loyal-Macaroni-Maid-7B)|84.53|73.67||100| |[SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B)|84.55|68.74||100| |[senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)|87.42|77.87||100| |[TeeZee/DarkSapling-7B-v2.0](https://huggingface.co/TeeZee/DarkSapling-7B-v2.0)|82.56|62.19||100| |[teknium/Hermes-Trismegistus-Mistral-7B](https://huggingface.co/teknium/Hermes-Trismegistus-Mistral-7B)|79.21|64.86||98.83| |[teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)|81.68|65.75||100| |[Undi95/Toppy-M-7B](https://huggingface.co/Undi95/Toppy-M-7B)|83.52|66.57||100| </br> |MODEL|HellaSwag|EQ_Bench||% Parsed (EQ)| |:---|--:|---:|---|:---| |[ABX-AI/Silver-Sun-v2-11B](https://huggingface.co/ABX-AI/Silver-Sun-v2-11B)|86.40|69.92||100| |[backyardai/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B](https://huggingface.co/backyardai/Fimbulvetr-Holodeck-Erebus-Westlake-10.7B)|86.00|69.25||100| |[BlueNipples/SnowLotus-v2-10.7B](https://huggingface.co/BlueNipples/SnowLotus-v2-10.7B)|83.42|60.54||99.42| |[FallenMerick/Chewy-Lemon-Cookie-11B](https://huggingface.co/FallenMerick/Chewy-Lemon-Cookie-11B)|84.39|76.24||100| |[FallenMerick/Chunky-Lemon-Cookie-11B](https://huggingface.co/FallenMerick/Chunky-Lemon-Cookie-11B)|84.36|76.29||100| |[froggeric/WestLake-10.7B-v2](https://huggingface.co/froggeric/WestLake-10.7B-v2)|86.74|73.35||95.32| |[head-empty-ai/Mytho-Lemon-11B](https://huggingface.co/head-empty-ai/Mytho-Lemon-11B)|79.77|60.03||100| |[Himitsui/KuroMitsu-11B](https://huggingface.co/Himitsui/KuroMitsu-11B)|86.33|70.50||98.83| |[kyujinpy/SOLAR-Platypus-10.7B-v2](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v2)|82.05|25.11||45.61| |[migtissera/Tess-10.7B-v1.5b](https://huggingface.co/migtissera/Tess-10.7B-v1.5b)|83.82|61.83||99.42| |[NeverSleep/Mistral-11B-SynthIAirOmniMix](https://huggingface.co/NeverSleep/Mistral-11B-SynthIAirOmniMix)|81.58|55.19||100| |[NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B)|83.24|63.52||100| |[saishf/Fimbulvetr-Kuro-Lotus-10.7B](https://huggingface.co/saishf/Fimbulvetr-Kuro-Lotus-10.7B)|86.25|65.85||100| |[Sao10K/Fimbulvetr-10.7B-v1](https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1)|85.81|65.42||100| |[Sao10K/Fimbulvetr-11B-v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2)|86.61|70.00||99.42| |[Sao10K/Frostwind-10.7B-v1](https://huggingface.co/Sao10K/Frostwind-10.7B-v1)|84.15|55.73||99.42| |[Sao10K/Solstice-11B-v1](https://huggingface.co/Sao10K/Solstice-11B-v1)|86.42|68.24||99.42| |[TheDrummer/Moistral-11B-v3](https://huggingface.co/TheDrummer/Moistral-11B-v3)|86.65|69.75||99.42| |[Undi95/Borealis-10.7B](https://huggingface.co/Undi95/Borealis-10.7B)|79.58|8.27||44.44| |[upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)|86.35|68.65||98.25| |[upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)|83.10|28.66||100|

AdamKasumovic/llama3-8b-instruct-bactrian-x-xh-100-percent-med-high-perplexity

AdamKasumovic

2024-06-28T03:35:03Z

0

transformers

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

text-generation

2024-06-28T03:31:24Z

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

fangjj/my_awesome_mind_model

fangjj

2024-06-28T03:33:10Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "endpoints_compatible", "region:us" ]

audio-classification

2024-06-28T03:31:30Z

Entry not found

BrallanPa/filmarv2

BrallanPa

2024-06-28T03:33:06Z

0

null

[ "region:us" ]

null

2024-06-28T03:32:31Z

Entry not found

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

AdamKasumovic

2024-06-28T03:36:34Z

0

transformers

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

text-generation

2024-06-28T03:33:35Z

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

habulaj/1699716757

habulaj

2024-06-28T03:35:39Z

0

null

[ "region:us" ]

null

2024-06-28T03:35:36Z

Entry not found

chainup244/Qwen-Qwen1.5-1.8B-1719545883

chainup244

2024-06-28T03:39:48Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-28T03:38:04Z

--- 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]

nglguarino/phi2-firstTry

nglguarino

2024-06-28T04:13:19Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T03:39:24Z

Entry not found

fhsp93/my_awesome_model

fhsp93

2024-06-28T03:40:41Z

0

null

[ "region:us" ]

null

2024-06-28T03:40:41Z

Entry not found

AniaAri/pregunta05

AniaAri

2024-06-28T03:43:50Z

0

null

[ "joblib", "region:us" ]

null

2024-06-28T03:43:14Z

Entry not found

Wawaworker/nkszbad

Wawaworker

2024-06-28T03:45:57Z

0

null

[ "region:us" ]

null

2024-06-28T03:45:57Z

Entry not found

habulaj/11386288619

habulaj

2024-06-28T03:46:23Z

0

null

[ "region:us" ]

null

2024-06-28T03:46:22Z

Entry not found

modeldodel/DERRIUSNew

modeldodel

2024-06-28T03:49:10Z

0

null

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

null

2024-06-28T03:48:35Z

--- license: openrail ---

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

shengxuelim

2024-06-28T03:51:40Z

0

null

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

reinforcement-learning

2024-06-28T03:51:38Z

--- 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="shengxuelim/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"]) ```

shengxuelim/taxi-rl

shengxuelim

2024-06-28T03:53:10Z

0

null

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

reinforcement-learning

2024-06-28T03:53:08Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-rl results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.72 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="shengxuelim/taxi-rl", 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"]) ```

habulaj/6276047107

habulaj

2024-06-28T03:53:43Z

0

null

[ "region:us" ]

null

2024-06-28T03:53:37Z

Entry not found

habulaj/5143040115

habulaj

2024-06-28T03:54:09Z

0

null

[ "region:us" ]

null

2024-06-28T03:54:07Z

Entry not found

triplee/supernatural_dataset_3epo_model

triplee

2024-06-28T03:55:56Z

0

transformers

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

null

2024-06-28T03:55:07Z

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

Razer112/Goth

Razer112

2024-06-28T23:08:05Z

0

null

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

null

2024-06-28T03:56:57Z

--- license: openrail ---

Hoangcondilonton/test

Hoangcondilonton

2024-06-28T03:57:25Z

0

null

[ "license:llama2", "region:us" ]

null

2024-06-28T03:57:25Z

--- license: llama2 ---

l4urentz/Pregunta5

l4urentz

2024-06-28T04:00:07Z

0

null

[ "region:us" ]

null

2024-06-28T03:58:44Z

Entry not found

Arodrigo/temp008

Arodrigo

2024-06-28T04:02:46Z

0

transformers

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

null

2024-06-28T04:02:43Z

--- 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]

faissalb/cih_lora_llama_0.1

faissalb

2024-06-28T04:14:00Z

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

2024-06-28T04:13:29Z

--- 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:** faissalb - **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)

kugelitsjust/Dr_Jib

kugelitsjust

2024-06-29T10:30:46Z

0

null

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

null

2024-06-28T04:13:58Z

--- license: openrail ---

octadion/llama3-8B-chat-v2-gguf

octadion

2024-06-28T04:14:40Z

0

null

[ "region:us" ]

null

2024-06-28T04:14:40Z

Entry not found

kugelitsjust/Azzathog

kugelitsjust

2024-06-28T04:16:36Z

0

null

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

null

2024-06-28T04:15:37Z

--- license: openrail ---

Ammartatox/llamared7

Ammartatox

2024-06-28T04:24:31Z

0

transformers

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

text-generation

2024-06-28T04:16:32Z

--- 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:** Ammartatox - **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)

NAYEONCEot9cover/LISA

NAYEONCEot9cover

2024-06-28T04:25:10Z

0

null

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

null

2024-06-28T04:18:42Z

--- license: openrail ---

Arodrigo/temp009

Arodrigo

2024-06-28T04:19:41Z

0

transformers

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

null

2024-06-28T04:19:39Z

--- 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]

a1-b2-c3-d4-archana/t5-small_classification

a1-b2-c3-d4-archana

2024-06-28T04:42:16Z

0

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-06-28T04:22:03Z

Entry not found

abdiharyadi/indoamrbart-mbart-triple-ft-parser-no-nst-256-eps

abdiharyadi

2024-06-28T04:24:46Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T04:22:46Z

Entry not found

nglguarino/my-nlp-phi2-model

nglguarino

2024-06-28T05:10:07Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T04:27:49Z

Entry not found

habulaj/244864494343

habulaj

2024-06-28T04:31:49Z

0

null

[ "region:us" ]

null

2024-06-28T04:31:39Z

Entry not found

NoNameFactory/llama-3-8b-it-4bit-ContdPT_1_10

NoNameFactory

2024-06-28T04:36:58Z

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

2024-06-28T04:33:19Z

--- 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:** NoNameFactory - **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)

Arodrigo/temp0010

Arodrigo

2024-06-28T04:33:37Z

0

transformers

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

null

2024-06-28T04:33:34Z

--- 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]

jykim310/phiva-3.9b-q4f16_1-MLC

jykim310

2024-06-28T04:37:53Z

0

null

[ "region:us" ]

null

2024-06-28T04:33:35Z

Entry not found

habulaj/5417556370

habulaj

2024-06-28T04:39:09Z

0

null

[ "region:us" ]

null

2024-06-28T04:39:08Z

Entry not found

Arodrigo/temp0011

Arodrigo

2024-06-28T04:42:09Z

0

transformers

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

null

2024-06-28T04:42:02Z

--- 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]

Djtrice/Kids

Djtrice

2024-06-28T04:46:20Z

0

null

[ "region:us" ]

null

2024-06-28T04:46:19Z

Entry not found

habulaj/1880918602

habulaj

2024-06-28T04:50:47Z

0

null

[ "region:us" ]

null

2024-06-28T04:50:42Z

Entry not found

sanskarceo1/Chyno

sanskarceo1

2024-06-28T04:51:56Z

0

null

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

null

2024-06-28T04:51:55Z

--- license: apache-2.0 ---

DizVen/coursework

DizVen

2024-06-28T04:52:28Z

0

null

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

null

2024-06-28T04:52:28Z

--- license: apache-2.0 ---

DrChamyoung/TextARC2.6

DrChamyoung

2024-06-28T04:54:00Z

0

null

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

null

2024-06-28T04:54:00Z

--- license: apache-2.0 ---

pacoreyes/TopicBoundaryBERT

pacoreyes

2024-06-29T06:52:47Z

0

null

[ "doi:10.57967/hf/2644", "license:mit", "region:us" ]

null

2024-06-28T04:55:02Z

--- license: mit ---

saiki23/NyntchLORA

saiki23

2024-06-28T07:05:48Z

0

null

[ "region:us" ]

null

2024-06-28T04:58:08Z

Entry not found

ShapeKapseln33/Nexalyn678

ShapeKapseln33

2024-06-28T05:03:09Z

0

null

[ "region:us" ]

null

2024-06-28T04:59:50Z

[køb] Nexalyn Anmeldelser At forbrænde fedt i besværlige områder er en udfordring for mange mennesker på deres vægttabsrejse. Dette stædige kropsfedt kan være frustrerende og svært at målrette mod med kost og motion alene. Nexaslim-tillægget kan dog give den løsning, du har ledt efter. **[Klik her for at købe nu fra Nexalyns officielle hjemmeside](https://capsules24x7.com/nexalyn-danmark)** Nexalyn Pills er en forbedring til at øge folks opmærksomhed under seksuelle sociale lejligheder. Det er opdateringen, der kan bruges af personer med seksuelle mangler. Denne forbedring virker ved at forbedre testosteronniveauet hos mænd. Testosteroner er kemikalier, der kan bringe tilfredsstillende seksuel funktion og god seksuel ydeevne i fare. Dette redesign hjælper med at arbejde på præsentationen ved seksuelle sociale begivenheder ved at øge størrelsen af erektionen i henhold til udvidelsen af pizazzen. Et klart svar for enkeltpersoner bør også understøtte deres seksuelle grænser. ##Hvad er Nexalyn? Nexalyn virker ved at øge testosteronniveauet i den menneskelige krop. Nexalyn-tilskuddet indeholder forskellige ingredienser, der øger mængden af testosteronkemikalier og derfor understøtter seksuel præstation. Udover testosteronopdateringen udvider redesignet også kroppens blodgennemstrømning. Den øgede blodgennemstrømning fører så til bedre resultater og bedre ydeevne af forskellige organer i kroppen. De forskellige organer i kroppen, inklusive penis, vil fungere perfekt, og den seksuelle præsentation vil blive opdateret, svarende til personens vitalitet. Forbedringen omfatter nitrogenoxid, som giver bedre blodcirkulation og bedre bevidsthed blandt mennesker. ##Er ingredienserne i Nexalyn Nexalyns redesign omfatter ethvert mærkefragment. Dekorationerne er ret typiske og fokuserer stort set på nogle få bemærkelsesværdige standardsmag. Nogle af de kritiske elementer i dette redesign er som følger: Tongkat Ali: Denne specifikke effekt er ansvarlig for at øge testosteronniveauet i den menneskelige krop. **[Klik her for at købe nu fra Nexalyns officielle hjemmeside](https://capsules24x7.com/nexalyn-danmark)** L-arginin: Denne fiksering er underlagt udviklingen af konstansen hos den person, der dræner den. På den måde fungerer kønsorganerne bedre. Maca: Dette er også en af de afgørende dele af denne udvikling. Det er underlagt spredningen af bedre blod på samme måde som den generelle velstand for forskellige organer i kroppen. Ginseng: Denne fiksering er forpligtet til at give folk ekstra centralitet. Dette sikrer folks sikkerhed under seksuelle møder. Det skal være klart, at alle disse er gennemtænkte koncentrater af normale smagsvarianter. Derfor er brugen af denne opgradering beskyttet. ##Hvordan skal du tage Nexalyn? Nexalyn fremstilles som en beholder, der indeholder tabletterne fra plasteret. Æsken indeholder ca. 60 tabletter, hvoraf 2 bør tages pålideligt. Den ene pille skal tages den næste dag efter morgenmaden og den anden pille efter aftensmaden. Nexalyn mandlige forstærkningspiller skal tages med varmt vand. En høj andel vand bør bruges, når du bruger dette tilbehør. Dette er bedre for at få en hurtigere effekt af forbedring. ##Hvor kan jeg nemt og hurtigt få fat i Nexalyn? Så vi har set, at Nexalyn er førende blandt de andre seksuelle præstationsfremmende kosttilskud på markedet. Hvis du lider af seksuelle mangler, kan du også prøve denne forbedring og tage de betydelige konsekvenser af denne makeover i øjnene. For at købe disse piller kan du klikke på et hvilket som helst link på denne side og få det bedste tilbud. **[Klik her for at købe nu fra Nexalyns officielle hjemmeside](https://capsules24x7.com/nexalyn-danmark)**

VKapseln475/Shape7545

VKapseln475

2024-06-28T05:21:11Z

0

null

[ "region:us" ]

null

2024-06-28T05:03:25Z

# [NEU] Shape kapseln Deutschland Bewertungen Test und Einnahme - Shape kapseln Erfahrungen Shape Kapseln Deutschland ist ein kürzlich auf den Markt gebrachtes, den Stoffwechsel ankurbelndes Nahrungsergänzungsmittel, das in den letzten Wochen auf dem Markt begeisterte Kritiken erhalten hat. In dieser Rezension zu Shape Kapseln werde ich jeden Aspekt dieser Ergänzung im Detail untersuchen, um herauszufinden, ob sie ihr Geld wert ist. ## **[Klicken Sie hier, um jetzt auf der offiziellen Website von Shape Kapseln zu kaufen](https://callednews.com/shape-kapseln-de)** ## Formkapseln – Ergebnisse und Langlebigkeit Um effektive Ergebnisse zu erzielen, empfiehlt der Hersteller von Shape Kapseln, das Nahrungsergänzungsmittel unbedingt über einen Zeitraum von 3 bis 5 Monaten einzunehmen. Dieser Zeitraum für die Ergebnisse hängt von verschiedenen Faktoren wie Alter, genetischer Zusammensetzung, Lebensstil, Stoffwechselrate und allgemeinem Gesundheitszustand ab. Allerdings wird behauptet, dass es kein Zurück mehr gibt, sobald Shape Kapseln Ergebnisse liefert. Damit die Ergebnisse dauerhaft anhalten, müssen Sie das Nahrungsergänzungsmittel Shape Kapseln regelmäßig einnehmen, gefolgt von einer gesunden und ausgewogenen Ernährung und einfachen Übungen. Ein gesunder Lebensstil trägt dazu bei, die Wirksamkeit dieses Stoffwechsel-Boosters zu steigern und auch die allgemeine Gesundheit und das Wohlbefinden zu verbessern. Achten Sie darauf, die empfohlene Portion Shape Kapseln nicht auszulassen, da die Ergebnisse schnell nachlassen. ## Kundenrezensionen und Beschwerden zu Shape Kapseln: Shape Kapseln Benutzererfahrung Die Kundenrezensionen zu Shape Kapseln sind bisher durchweg positiv. Ehrliche Rezensionen zu diesem Stoffwechsel-Booster sind in vertrauenswürdigen Gesundheitsforen und anderen Quellen wie Reddit, Quora und Facebook verfügbar. Auf diesen Plattformen haben zufriedene Benutzer kommentiert, dass das Nahrungsergänzungsmittel dazu beigetragen hat, die Muskelmasse zu verbessern und den Stoffwechsel anzukurbeln. Einige andere berichteten ebenfalls über einen Anstieg des Energieniveaus. Diese Kundenrezensionen von Shape Kapseln zeigen, dass diese Formel sicher und wirksam ist. ## Shape Kapseln-Pakete und Preise Ab sofort sind Shape Kapseln im Vergleich zu anderen ähnlichen Stoffwechsel-Boostern auf dem Markt zu einem viel günstigeren Preis erhältlich. Der Hersteller hat den Preis dieser stoffwechselankurbelnden Formel für einen begrenzten Zeitraum gesenkt, damit alle Menschen davon profitieren können. ### Hier die detaillierten Preispläne: Anfänger – 1 Packung für 49,95 € pro Packung + 4,95 € Versand Top-Seller – 2 Packungen für 36,65 € pro Packung + kostenloser Versand Bestseller – 3 Packungen für 39,97 € pro Packung + kostenloser Versand Dies sind die drei verschiedenen Shape Kapseln-Pakete, die derzeit zum Kauf erhältlich sind. Sie können ein beliebiges Paket wählen und Ihre Reise zur Stoffwechselankurbelung und Gewichtsabnahme ankurbeln. ## Verfügbarkeit von Formkapseln Derzeit können Sie die Shape-Kapseln-Formel nur über die offizielle Website erhalten. Der Hersteller hat diese Formel nicht auf Plattformen Dritter oder im Einzelhandel verfügbar gemacht. Allerdings könnten Duplikate der Shape-Kapseln-Formel von nicht autorisierten Verkäufern über Amazon und andere Drittplattformen verkauft werden, um uninformierte Benutzer in die Falle zu locken. Diese Replikate haben ähnliche Namen, Website-Designs usw., liefern jedoch keine ähnlichen Ergebnisse. Sie könnten auch schädliche Inhaltsstoffe enthalten. ## **[Klicken Sie hier, um jetzt auf der offiziellen Website von Shape Kapseln zu kaufen](https://callednews.com/shape-kapseln-de)**

wisenut-nlp-team/wisenut-llama-3-8B-LoRA

wisenut-nlp-team

2024-06-28T05:13:33Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-28T05:08:39Z

Entry not found

jddllwqa/Qwen-Qwen1.5-7B-1719551837

jddllwqa

2024-06-28T05:17:18Z

0

null

[ "region:us" ]

null

2024-06-28T05:17:18Z

Entry not found

midori1255/controlnet_marmoset

midori1255

2024-06-28T05:17:24Z

0

null

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

null

2024-06-28T05:17:24Z

--- license: unknown ---

yraziel/pnina_rozenblum

yraziel

2024-06-28T05:21:07Z

0

null

[ "region:us" ]

null

2024-06-28T05:20:14Z

Entry not found

salmanhermana/finetuned-food

salmanhermana

2024-06-28T05:20:50Z

0

null

[ "region:us" ]

null

2024-06-28T05:20:50Z

Entry not found

ningrumdaud/test_lora_llama3model

ningrumdaud

2024-06-28T05:22:38Z

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

2024-06-28T05:22:15Z

--- 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:** ningrumdaud - **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)

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

AdamKasumovic

2024-06-28T05:32:58Z

0

transformers

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

text-generation

2024-06-28T05:30:00Z

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

Pranavv/fork-moirai

Pranavv

2024-06-28T05:32:54Z

0

null

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

null

2024-06-28T05:32:54Z

--- license: apache-2.0 ---

jddllwqa/Qwen-Qwen1.5-0.5B-1719552870

jddllwqa

2024-06-28T05:34:36Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "region:us" ]

null

2024-06-28T05:34:30Z

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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] ### Framework versions - PEFT 0.11.1

ynyn011/content

ynyn011

2024-06-28T05:35:42Z

0

null

[ "region:us" ]

null

2024-06-28T05:35:42Z

Entry not found

sryab2001/llama3-8b-cosmic-fusion-dynamics-lora

sryab2001

2024-06-28T05:44:20Z

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

2024-06-28T05:43:49Z

--- 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:** sryab2001 - **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)

riddhiparmar/test-model

riddhiparmar

2024-06-28T05:49:04Z

0

null

[ "region:us" ]

null

2024-06-28T05:44:54Z

Entry not found

slelab/AES14

slelab

2024-06-28T06:11:39Z

0

null

[ "region:us" ]

null

2024-06-28T05:47:37Z

Entry not found

riddhiparmar/test-model2

riddhiparmar

2024-06-28T05:47:42Z

0

null

[ "region:us" ]

null

2024-06-28T05:47:42Z

Entry not found