Datasets:

librarian-bots

/

model_cards_with_metadata

like 10

notyouruncle/Djsoda

notyouruncle

--- license: creativeml-openrail-m ---

danieliser/ppo-LunarLander-v2

danieliser

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 294.89 +/- 14.46 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

saribalgar/wav2vec2-mscd

saribalgar

Entry not found

TrynaBeGood/SAMART

TrynaBeGood

Entry not found

Ashish-shukla/test

Ashish-shukla

--- license: openrail language: - en ---

Jou1995/bert-base-multilingual-finetuned-squad

Jou1995

--- license: apache-2.0 ---

TrynaBeGood/TWICETZUYU

TrynaBeGood

Entry not found

TrynaBeGood/Yaemiko

TrynaBeGood

Entry not found

hiyukiya/appleButtButtThatLooks_v10

hiyukiya

--- license: other ---

hiyukiya/hugeAssAndBoobs_v1

hiyukiya

--- license: other ---

Praxdave/Ai

Praxdave

Entry not found

hiyukiya/taimanin6In1_taimaninv1

hiyukiya

--- license: other ---

phi0108/asr

phi0108

Entry not found

wdmz/rwkv_mybackup

wdmz

Entry not found

vorstcavry/beta-test

vorstcavry

--- title: Sovits Models emoji: 🎙️ colorFrom: gray colorTo: pink sdk: gradio sdk_version: 3.18.0 app_file: app.py pinned: false license: mit ---

KONG1CHAO/kc

KONG1CHAO

Entry not found

drnighthan/GhostMix

drnighthan

Entry not found

Kuhjio/ariella

Kuhjio

Entry not found

rohitp1/dgx1_whisper_small_distil_libri360_12_to_10_batch_16_epoch_20

rohitp1

Entry not found

circulus/SadTalker

circulus

--- license: gpl-3.0 ---

anikb/NONES

anikb

Entry not found

Saeidtorabi/Fishfootbal

Saeidtorabi

--- license: openrail ---

jtamph/magicmixRealistic

jtamph

--- license: creativeml-openrail-m ---

hahaxi/ckpt

hahaxi

--- license: apache-2.0 ---

hahaxi/lora

hahaxi

Entry not found

erictong/test

erictong

Entry not found

Makup/test

Makup

Entry not found

BertramRay/controlnet-fill-circle

BertramRay

diffusers

text-to-image

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - jax-diffusers-event inference: true --- # controlnet- BertramRay/controlnet-fill-circle These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images in the following. prompt: red circle with blue background  prompt: cyan circle with brown floral background 

VinsmokeMir/smallBanglaBERT_rafsan

VinsmokeMir

Entry not found

NbAiLab/nb-gpt-j-6B-norpaca

NbAiLab

transformers

text-generation

--- license: openrail datasets: - MasterThesisCBS/NorPaca library_name: transformers language: - 'no' - nb pipeline_tag: conversational tags: - alpaca widget: - text: >- Nedenfor er en instruksjon som beskriver en oppgave. Skriv et svar som fullfører forespørselen på riktig måte. ### Instruksjon: Skriv en e-post der du ønsker velkommen til en ny medarbeider ved navn Svein. ### Respons: example_title: E-mail - text: >- Nedenfor er en instruksjon som beskriver en oppgave. Skriv et svar som fullfører forespørselen på riktig måte. ### Instruksjon: Fortell meg noe om alpakkaer. ### Respons: example_title: Alpacas - text: >- Nedenfor er en instruksjon som beskriver en oppgave. Skriv et svar som fullfører forespørselen på riktig måte. ### Instruksjon: Kom med en kreativ unnskyldning for å si at jeg ikke trenger å gå på festen. ### Respons: example_title: Excuse # extra_gated_heading: "Acknowledge license to accept the repository" extra_gated_prompt: "You agree to not use the model to conduct experiments that cause harm to human subjects." extra_gated_fields: Company: text Country: text Intended Use: text # I agree to use this model for non-commercial use ONLY: checkbox # extra_gated_button_content: "Acknowledge license" --- # NB GPT-J-6B NorPaca This is a [NB GPT-J-6B](https://huggingface.co/NbAiLab/nb-gpt-j-6B) Norwegian Bokmål model fine-tuned on the [NorPaca](https://huggingface.co/datasets/MasterThesisCBS/NorPaca) dataset. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline base_model = "NbAiLab/nb-gpt-j-6B-norpaca" tokenizer = AutoTokenizer.from_pretrained(base_model) model = AutoModelForCausalLM.from_pretrained(base_model).cuda() ``` For generation, we can either use `pipeline()` or the model's `.generate()` method. Remember that the prompt needs a **Norwegian** template: ```python # Generate responses def generate(instruction, input=None): if input: prompt = f"""Nedenfor er en instruksjon som beskriver en oppgave, sammen med et input som gir ytterligere kontekst. Skriv et svar som fullfører forespørselen på riktig måte. ### Instruksjon: {instruction} ### Input: {input} ### Respons:""" else: prompt = f""""Nedenfor er en instruksjon som beskriver en oppgave. Skriv et svar som fullfører forespørselen på riktig måte. ### Instruksjon: {instruction} ### Respons:""" inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, generation_config=GenerationConfig(temperature=0.2, top_p=0.75, num_beams=4), return_dict_in_generate=True, output_scores=True, max_new_tokens=256 ) for seq in generation_output.sequences: output = tokenizer.decode(seq, skip_special_tokens=True) print(output.split("### Respons:")[-1].strip()) generate("Skriv en e-post der du ønsker velkommen til en ny medarbeider ved navn Svein.") ``` ## Data The dataset is a translation to Norwegian Bokmål of [alpaca_gpt4_data.json](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM), a clean version of the [Alpaca dataset made at Stanford](https://huggingface.co/datasets/tatsu-lab/alpaca), but generated with GPT4. **This dataset cannot be used to create models that compete in any way with OpenAI.** ## Finetuning To fine-tune the NB GPT-J-6B model we used the code available on [NB's fork of `mesh-transformer-jax`](https://github.com/NbAiLab/mesh-transformer-jax/blob/master/prepare_dataset_alpaca.py), which provides code adapt an Alpaca dataset to finetune any GPT-J-6B model. We run finetuning for 3 epochs using sequence length of 2048 on a single TPUv3-8 for 3 hours on top of NB GPT-J-6B. ## References - [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) - [Norwegian Alpaca](https://huggingface.co/datasets/NbAiLab/norwegian-alpaca) - [ChatGPT](https://openai.com/blog/chatgpt) - [Hugging Face](https://huggingface.co/) ## Hardware Requirements For training we have used a Google Cloud TPUv3-8 VM. For eval, you can use a T4.

lusils/t5-small-finetuned-xsum

lusils

Entry not found

kangaep/febtwt

kangaep

--- license: creativeml-openrail-m ---

umang-samyak/mcq-generation

umang-samyak

--- license: apache-2.0 ---

miki030/CartPole_unit4-videotest

miki030

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole_unit4-videotest results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 349.50 +/- 221.47 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

coffee0412/ChilloutMix

coffee0412

Entry not found

zoltantensorfow/a2c-PandaReachDense-v2-d

zoltantensorfow

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.23 +/- 0.09 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

kitgary/mt5-small-finetuned-amazon-en-es

kitgary

Entry not found

leehan666/bginga

leehan666

--- license: cc-by-4.0 ---

dzrex/dummy-model

dzrex

456

min004/ssafs

min004

--- license: other ---

Enoch/SoTana-30B-lora-50000

Enoch

Entry not found

swillathrilla/Swila1

swillathrilla

Entry not found

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

NazgulAshura

reinforcement-learning

--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.17 +/- 0.38 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="NazgulAshura/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"]) ```

hahha96/haha96

hahha96

jjj

lenoidasz/menruinyanko

lenoidasz

--- license: creativeml-openrail-m ---

NazgulAshura/NazgulAshura

NazgulAshura

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: NazgulAshura results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="NazgulAshura/NazgulAshura", 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"]) ```

aravind168/bert-large-cased-whole-word-masking-finetuned-squad-finetuned-squad

aravind168

Entry not found

AriffNazhan/zoala_bertopic_042023

AriffNazhan

Entry not found

aravind168/distilbert-base-uncased-finetuned-squad

aravind168

Entry not found

LEITO213/sakura

LEITO213

--- license: openrail ---

aravind168/bert-large-uncased-whole-word-masking-squad2-finetuned-squad

aravind168

Entry not found

Annotation-AI/sam-vit-h-decoder-onnx

Annotation-AI

Entry not found

valhalla/muse-cc12m-test-52k

valhalla

transformers

Entry not found

Alankar66/cricket-testv2

Alankar66

Entry not found

shrey9669/sam_ocr_donut

shrey9669

transformers

Entry not found

aravind168/deberta-v3-base-squad2-finetuned-squad

aravind168

Entry not found

manavshrivastava/testmodel

manavshrivastava

--- license: apache-2.0 ---

mohammad4lx/mohammadai

mohammad4lx

Entry not found

Jihene/cGAN

Jihene

--- license: mit ---

kangaep/dadali

kangaep

--- license: creativeml-openrail-m ---

philz1337x/deliberate

philz1337x

diffusers

text-to-image

# DELIBERATE #### All in One / Any Case Version This model provides you the ability to create anything you want.</br> The more power of prompt knowledges you have, the better results you'll get.</br> It basically means that you'll never get a perfect result with just a few words.</br> You have to fill out your prompt line extremely detailed.  #### Who find this model perfect: - NSFW masters - Meticulous anatomy artists - Creative prompters - Art designers Dive into the perfect creations world with [my prompts](https://civitai.com/models/4823/deliberate "my prompts").</br> Your research will be appreciated, so feel free to show everyone, what you can get with this model --- license: bigscience-openrail-m ---

luongduong9x/nhatro

luongduong9x

--- license: openrail ---

yif271/yanwenface2_600

yif271

diffusers

text-to-image

Entry not found

sleepotimer/Model_A

sleepotimer

--- license: creativeml-openrail-m tags: - stable-diffusion --- random mixed anime models <img src="https://huggingface.co/sleepotimer/Model_A/resolve/main/example1.png" width="768px"> <img src="https://huggingface.co/sleepotimer/Model_A/resolve/main/example2.png" width="768px"> <img src="https://huggingface.co/sleepotimer/Model_A/resolve/main/example3.png" width="768px">

Kunto/Carolina

Kunto

--- license: creativeml-openrail-m ---

Patrickaa/ggml-model-q4_0

Patrickaa

--- license: apache-2.0 ---

navneetdahiya/nav

navneetdahiya

--- license: openrail ---

tkharisov7/results

tkharisov7

Entry not found

rebels/qa

rebels

Entry not found

chaosNaix/SD_other_models_backup

chaosNaix

Entry not found

alterhouse/SDNyashers

alterhouse

--- license: openrail ---

jinooring/llama-ko-alpaca-lab-001

jinooring

Entry not found

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

Simaoqi

reinforcement-learning

--- 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="Simaoqi/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"]) ```

devonho/demo-hf-CartPole-v1

devonho

Entry not found

hamzan/marah

hamzan

--- license: openrail ---

Ruhul011/Ruhul

Ruhul011

Entry not found

wuaiql/cjj

wuaiql

Entry not found

lksy/llama_13b_ru_gpt4_alpaca

lksy

text2text-generation

--- datasets: - yahma/alpaca_cleaned - lksy/ru_instruct_gpt4 language: - ru pipeline_tag: text2text-generation inference: false --- Based on [LLaMA 13B](https://huggingface.co/yahma/llama-13b-hf). Trained on 4 LoRA modules. Parameters: ``` { "base_model_name_or_path": "./llama-30b-hf", "bias": "none", "enable_lora": null, "fan_in_fan_out": false, "inference_mode": true, "lora_alpha": 16, "lora_dropout": 0.05, "merge_weights": false, "modules_to_save": null, "peft_type": "LORA", "r": 16, "target_modules": [ "q_proj", "v_proj", "k_proj", "o_proj" ], "task_type": "CAUSAL_LM" } ``` Cutoff length set to 512 ``` Prompt template: { "description": "A shorter template to experiment with.", "prompt_input": "### Задание:\n{instruction}\n\n### Вход:\n{input}\n\n### Ответ:\n", "prompt_no_input": "### Задание:\n{instruction}\n\n### Ответ:\n", "response_split": "### Ответ:" } ``` [WandB report](https://wandb.ai/lksy/huggingface/runs/oj1ezptd) Epochs: 4 Loss: 0.853

vdo/videocrafter-pruned-weights

vdo

--- license: cc-by-nc-4.0 ---

loulou08860/fats-waller

loulou08860

Entry not found

korat-dishant-2003/dkrespar

korat-dishant-2003

using this model you can extract skills from resume ! this can work on both pdf and docx formate

loulou08860/fats

loulou08860

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### fats Dreambooth model trained by loulou08860 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

openclimatefix/pvmetnet-nwp

openclimatefix

transformers

Entry not found

camenduru/improved-aesthetic-predictor

camenduru

# CLIP+MLP Aesthetic Score Predictor Train, use and visualize an aesthetic score predictor ( how much people like on average an image ) based on a simple neural net that takes CLIP embeddings as inputs. Link to the AVA training data ( already prepared) : https://drive.google.com/drive/folders/186XiniJup5Rt9FXsHiAGWhgWz-nmCK_r?usp=sharing Visualizations of all images from LAION 5B (english subset with 2.37B images) in 40 buckets with the model sac+logos+ava1-l14-linearMSE.pth: http://captions.christoph-schuhmann.de/aesthetic_viz_laion_sac+logos+ava1-l14-linearMSE-en-2.37B.html

naweedX/guyme

naweedX

Entry not found

toriki/iLoHA3216mk10

toriki

Entry not found

fabin1790/test

fabin1790

Entry not found

cocoup/chatbot

cocoup

allennlp

--- license: openrail datasets: - anon8231489123/ShareGPT_Vicuna_unfiltered language: - aa metrics: - accuracy library_name: allennlp tags: - chemistry ---

aryan29/Self-harm-detection

aryan29

Entry not found

ckeyer/vicuna-65b-delta

ckeyer

--- license: mit ---

wittich/JAA

wittich

Models for https://github.com/denniswittich/JointAppearanceAdaptation --- license: mit ---

camenduru/hagrid-classification-512p

camenduru

Entry not found

ianZzzzzz/GLM-130B-quant-int4-4gpu

ianZzzzzz

GLM-130B模型的int4量化版本，可在四张3090Ti的情况下进行推理。 An int4 quantized version of the GLM-130B model that can be inferred with 4 * 3090Ti . --- license: apache-2.0 --- iannobug@gmail.com

PogerRenrose/NeuromorphicAI

PogerRenrose

--- license: gpl-2.0 language: - en --- This is a basic Model Card Project as part of IITH AI5073, Neuromorphic AI

Zapper127/ppo-LunarLander-v2

Zapper127

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 234.32 +/- 31.06 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

wasure/cuteKorean

wasure

--- license: other ---

Bainbridge/gpt2-kl_1_03_hscnspecial-hs_cn_testing

Bainbridge

Entry not found

Dollu/Dollu_SimpleAnime

Dollu

--- license: other ---

miki030/PixelCopter-v0

miki030

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PixelCopter-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 73.30 +/- 64.55 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

wasure/UnderTable

wasure

--- license: other ---