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librarian-bots
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Venki-ds/outputs
Venki-ds
2023-09-19T13:12:11Z
3
0
peft
[ "peft", "region:us" ]
null
2023-09-19T13:11:45Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0
lapki/Llama-2-7b-panorama-QLoRA
lapki
2023-09-19T13:01:53Z
7
1
peft
[ "peft", "llama", "llama-2", "news", "text-generation", "ru", "dataset:its5Q/panorama", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]
text-generation
2023-07-28T13:24:15Z
--- language: - ru library_name: peft tags: - llama - llama-2 - news datasets: - its5Q/panorama pipeline_tag: text-generation base_model: meta-llama/Llama-2-7b-hf --- # Llama 2 7B, fine-tuned on Panorama media This repo contains the QLoRA adapter. Prompt: ``` Write a hypothetical news story based on the given headline ### Title: {prompt} Text: ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0 ### Additional information Thanks [its5Q](https://huggingface.co/its5Q) for dataset and help
ctu-aic/lora-xlm-roberta-large-squad2-csfever_v2-f1
ctu-aic
2023-09-19T13:00:53Z
12
1
peft
[ "peft", "text-classification", "cs", "dataset:ctu-aic/csfever_v2", "base_model:deepset/xlm-roberta-large-squad2", "base_model:adapter:deepset/xlm-roberta-large-squad2", "license:cc-by-sa-4.0", "region:us" ]
text-classification
2023-07-25T14:03:38Z
--- language: - cs license: cc-by-sa-4.0 library_name: peft datasets: - ctu-aic/csfever_v2 metrics: - accuracy - f1 - recall - precision pipeline_tag: text-classification base_model: deepset/xlm-roberta-large-squad2 --- # Model card for lora-xlm-roberta-large-squad2-csfever_v2-f1 ## Model details Model for natural language inference. ## Training procedure ### Framework versions - PEFT 0.4.0 ## Uses ### PEFT (Transformers) ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForSequenceClassification, Pipeline, AutoTokenizer config = PeftConfig.from_pretrained("ctu-aic/lora-xlm-roberta-large-squad2-csfever_v2-f1") model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path) model = PeftModel.from_pretrained(model, config) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) #pipeline for NLI class NliPipeline(Pipeline): def _sanitize_parameters(self, **kwargs): preprocess_kwargs = {} if "evidence" in kwargs: preprocess_kwargs["evidence"] = kwargs["evidence"] return preprocess_kwargs, {}, {} def preprocess(self, claim, evidence=""): model_input = self.tokenizer(claim, evidence, return_tensors=self.framework, truncation=True) return model_input def _forward(self, model_inputs): outputs = self.model(**model_inputs) return outputs def postprocess(self, model_outputs): logits = model_outputs.logits predictions = torch.argmax(logits, dim=-1) return {"logits": logits, "label": int(predictions[0])} nli_pipeline = NliPipeline(model=model, tokenizer=tokenizer) nli_pipeline("claim", "evidence") ```
Gustrd/open-llama-13b-lora-cabra-adapter
Gustrd
2023-09-19T12:59:53Z
6
2
peft
[ "peft", "pt", "dataset:Gustrd/dolly-15k-libretranslate-pt", "base_model:VMware/open-llama-13b-open-instruct", "base_model:adapter:VMware/open-llama-13b-open-instruct", "license:cc-by-sa-3.0", "region:us" ]
null
2023-07-18T19:57:27Z
--- language: - pt license: cc-by-sa-3.0 library_name: peft datasets: - Gustrd/dolly-15k-libretranslate-pt base_model: VMware/open-llama-13b-open-instruct --- # Cabra: A portuguese finetuned instruction Open-LLaMA LoRA adapter created with the procedures detailed at the GitHub repository: https://github.com/gustrd/cabra . This training was done at 2 epochs using one A4000 at Paperspace. The GGML version was created with llama.cpp "convert-lora-to-ggml.py". This LoRA adapter was created following the procedure --- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
Kendong/ad_dog
Kendong
2023-09-19T12:59:53Z
4
0
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-19T12:48:16Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Kendong/ad_dog These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.
fetiska/be_healthy
fetiska
2023-09-19T12:56:24Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T12:15:32Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 19.00 +/- 0.00 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r fetiska/be_healthy ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=be_healthy ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=be_healthy --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
pengold/distilbert-base-vietnamese-case
pengold
2023-09-19T12:55:26Z
117
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-cased", "base_model:finetune:distilbert/distilbert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-15T13:15:53Z
--- license: apache-2.0 base_model: distilbert-base-cased tags: - generated_from_trainer model-index: - name: distilbert-base-vietnamese-case results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-vietnamese-case This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.6995 | 1.0 | 313 | 5.7838 | | 5.7246 | 2.0 | 626 | 5.5341 | | 5.4565 | 3.0 | 939 | 5.3280 | | 5.271 | 4.0 | 1252 | 5.1409 | | 5.0514 | 5.0 | 1565 | 4.9143 | | 4.874 | 6.0 | 1878 | 4.7130 | | 4.7083 | 7.0 | 2191 | 4.5682 | | 4.5677 | 8.0 | 2504 | 4.3724 | | 4.4244 | 9.0 | 2817 | 4.3262 | | 4.3013 | 10.0 | 3130 | 4.1231 | | 4.2077 | 11.0 | 3443 | 4.1388 | | 4.1103 | 12.0 | 3756 | 3.8696 | | 4.0141 | 13.0 | 4069 | 3.8849 | | 3.9435 | 14.0 | 4382 | 3.7311 | | 3.8604 | 15.0 | 4695 | 3.7155 | | 3.804 | 16.0 | 5008 | 3.6445 | | 3.7076 | 17.0 | 5321 | 3.5784 | | 3.6807 | 18.0 | 5634 | 3.5516 | | 3.6239 | 19.0 | 5947 | 3.4008 | | 3.5729 | 20.0 | 6260 | 3.4827 | | 3.5308 | 21.0 | 6573 | 3.3921 | | 3.4707 | 22.0 | 6886 | 3.3729 | | 3.4341 | 23.0 | 7199 | 3.3543 | | 3.3989 | 24.0 | 7512 | 3.2836 | | 3.3505 | 25.0 | 7825 | 3.3003 | | 3.3256 | 26.0 | 8138 | 3.1750 | | 3.2892 | 27.0 | 8451 | 3.1930 | | 3.2614 | 28.0 | 8764 | 3.2089 | | 3.2387 | 29.0 | 9077 | 3.1978 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
Sn4kehead/TransNetV2
Sn4kehead
2023-09-19T12:55:21Z
0
3
null
[ "license:apache-2.0", "region:us" ]
null
2023-09-19T12:52:34Z
--- license: apache-2.0 --- # TransNet V2: Shot Boundary Detection Neural Network pytorch weights for TransNetV2 https://github.com/soCzech/TransNetV2
checkiejan/prefix-paraphase-50-20-auto
checkiejan
2023-09-19T12:49:54Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-19T12:49:52Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
xianglingjing/llama-2-7b-int4-text-to-sql-LoRA
xianglingjing
2023-09-19T12:41:20Z
24
1
peft
[ "peft", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]
null
2023-08-24T18:32:18Z
--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- ## Training procedure ### Framework versions - PEFT 0.5.0
yhk/speecht5_tts_voxpopuli_nl
yhk
2023-09-19T12:34:19Z
75
0
transformers
[ "transformers", "pytorch", "speecht5", "text-to-audio", "test", "generated_from_trainer", "nl", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]
text-to-audio
2023-09-18T11:00:52Z
--- language: - nl license: mit base_model: microsoft/speecht5_tts tags: - test - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4590 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5205 | 4.3 | 1000 | 0.4807 | | 0.5018 | 8.6 | 2000 | 0.4657 | | 0.4992 | 12.9 | 3000 | 0.4607 | | 0.4939 | 17.2 | 4000 | 0.4590 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
jiwon65/whisper-small_korean-zeroth
jiwon65
2023-09-19T12:25:02Z
44
2
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-19T10:42:19Z
--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-korr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-korr This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3466 - Wer: 19.9610 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3119 | 0.69 | 100 | 0.3334 | 20.6884 | | 0.1223 | 1.39 | 200 | 0.3179 | 21.4336 | | 0.0757 | 2.08 | 300 | 0.3234 | 20.3158 | | 0.0349 | 2.77 | 400 | 0.3329 | 20.8481 | | 0.0172 | 3.47 | 500 | 0.3354 | 20.1916 | | 0.0059 | 4.16 | 600 | 0.3357 | 19.7480 | | 0.0057 | 4.85 | 700 | 0.3396 | 19.9965 | | 0.0046 | 5.55 | 800 | 0.3417 | 19.7658 | | 0.0025 | 6.24 | 900 | 0.3461 | 20.0497 | | 0.0029 | 6.93 | 1000 | 0.3466 | 19.9610 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
jasonvan/llama-2-13b-chat-text2sql
jasonvan
2023-09-19T12:21:50Z
4
1
peft
[ "peft", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:adapter:meta-llama/Llama-2-13b-chat-hf", "region:us" ]
null
2023-07-28T14:06:11Z
--- library_name: peft base_model: meta-llama/Llama-2-13b-chat-hf --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
ckmfong/ppo-Huggy
ckmfong
2023-09-19T12:16:55Z
5
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-09-19T12:16:49Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** 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: ckmfong/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
lyimo/potato
lyimo
2023-09-19T12:11:41Z
0
0
fastai
[ "fastai", "region:us" ]
null
2023-09-19T12:11:34Z
--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
EnDevSols/llama-2-7b-qlora-medical
EnDevSols
2023-09-19T12:09:15Z
2
0
peft
[ "peft", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]
null
2023-07-25T13:59:22Z
--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
urbija/ner-bio-annotated-7-1
urbija
2023-09-19T12:08:37Z
106
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-09-19T09:43:23Z
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ner-bio-annotated-7-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ner-bio-annotated-7-1 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2128 - Precision: 0.8028 - Recall: 0.8397 - F1: 0.8209 - Accuracy: 0.9370 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 67 | 0.2779 | 0.7497 | 0.7533 | 0.7515 | 0.9107 | | No log | 2.0 | 134 | 0.2448 | 0.7718 | 0.7888 | 0.7802 | 0.9224 | | No log | 3.0 | 201 | 0.2289 | 0.7716 | 0.8319 | 0.8006 | 0.9287 | | No log | 4.0 | 268 | 0.2158 | 0.7995 | 0.8393 | 0.8189 | 0.9362 | | No log | 5.0 | 335 | 0.2128 | 0.8028 | 0.8397 | 0.8209 | 0.9370 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0+cpu - Datasets 2.1.0 - Tokenizers 0.13.3
Gustrd/open-llama-13b-cabra-gtpq-lora-adapter
Gustrd
2023-09-19T12:08:31Z
3
0
peft
[ "peft", "base_model:Gustrd/open-llama-13b-4bit-128g-GPTQ", "base_model:adapter:Gustrd/open-llama-13b-4bit-128g-GPTQ", "region:us" ]
null
2023-07-17T21:03:28Z
--- library_name: peft base_model: Gustrd/open-llama-13b-4bit-128g-GPTQ --- ## Training procedure ### Framework versions - PEFT 0.4.0
NursNurs/T5ForReverseDictionary
NursNurs
2023-09-19T12:08:31Z
1
0
peft
[ "peft", "region:us" ]
null
2023-08-13T15:39:22Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
Gustrd/mpt-7b-lora-cabra-adapter
Gustrd
2023-09-19T12:07:57Z
8
0
peft
[ "peft", "pt", "dataset:Gustrd/dolly-15k-hippo-translated-pt-12k", "base_model:HachiML/mpt-7b-instruct-for-peft", "base_model:adapter:HachiML/mpt-7b-instruct-for-peft", "license:cc-by-3.0", "region:us" ]
null
2023-08-17T18:41:06Z
--- language: - pt license: cc-by-3.0 library_name: peft datasets: - Gustrd/dolly-15k-hippo-translated-pt-12k base_model: HachiML/mpt-7b-instruct-for-peft --- ### Cabra: A portuguese finetuned instruction Open-LLaMA LoRA adapter created with the procedures detailed at the GitHub repository: https://github.com/gustrd/cabra . This training was done at 2 epochs using two T4 at Kaggle. This LoRA adapter was created following the procedure: ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
CyberHarem/imai_kana_idolmastercinderellagirls
CyberHarem
2023-09-19T12:05:55Z
0
1
null
[ "art", "text-to-image", "dataset:CyberHarem/imai_kana_idolmastercinderellagirls", "license:mit", "region:us" ]
text-to-image
2023-09-19T11:51:10Z
--- license: mit datasets: - CyberHarem/imai_kana_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of imai_kana_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2720, you need to download `2720/imai_kana_idolmastercinderellagirls.pt` as the embedding and `2720/imai_kana_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 2720**, with the score of 0.947. The trigger words are: 1. `imai_kana_idolmastercinderellagirls` 2. `twintails, brown_hair, brown_eyes, blush, open_mouth, ribbon, smile, hair_ribbon, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.898 | [Download](5100/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.881 | [Download](4760/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.905 | [Download](4420/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.896 | [Download](4080/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.897 | [Download](3740/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.905 | [Download](3400/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.922 | [Download](3060/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | **2720** | **0.947** | [**Download**](2720/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.880 | [Download](2380/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.924 | [Download](2040/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.892 | [Download](1700/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.901 | [Download](1360/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.884 | [Download](1020/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.927 | [Download](680/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.843 | [Download](340/imai_kana_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |
Xilabs/llama-2-7B-Guanaco-QLoRA
Xilabs
2023-09-19T12:00:55Z
4
0
peft
[ "peft", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]
null
2023-07-23T17:46:00Z
--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- ## Training procedure ### Framework versions - PEFT 0.4.0
lyimo/irishpotato
lyimo
2023-09-19T11:58:49Z
0
0
fastai
[ "fastai", "region:us" ]
null
2023-09-19T11:58:41Z
--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
Undi95/Storytelling-v1-13B-lora
Undi95
2023-09-19T11:57:46Z
15
6
peft
[ "peft", "base_model:TheBloke/Llama-2-13B-fp16", "base_model:adapter:TheBloke/Llama-2-13B-fp16", "license:other", "region:us" ]
null
2023-09-07T23:39:30Z
--- license: other library_name: peft base_model: TheBloke/Llama-2-13B-fp16 --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0 I'm NOT the author of this work. I cite anon : ```shell Well, here it is. Storytelling Qlora. Trained on base llama2 13B but works flawlessly on other 13Bs. Idk about other sizes. 25MB of nsfw books, 60MB of sfwish ones. No special formatting other than *** between chapters and ⁂ between books. Takes some text to get going but once you have some context filled, it feels way better for prose than raw llama or instruct models, imho. Do whatever you want with it, I can't be bothered to maintain a HF page. WTFPL. It's just shit from nai's archive ``` Credit to "anon49"
matttvpl/model_v1
matttvpl
2023-09-19T11:56:58Z
119
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:poquad", "endpoints_compatible", "region:us" ]
question-answering
2023-04-25T15:23:44Z
--- tags: - generated_from_trainer datasets: - poquad model-index: - name: model_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_v1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 334 | 1.4651 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Ioana23/distilbert-base-uncased-finetuned-imdb
Ioana23
2023-09-19T11:47:28Z
61
0
transformers
[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-19T11:33:04Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results ### Framework versions - Transformers 4.32.1 - TensorFlow 2.10.0 - Datasets 2.14.4 - Tokenizers 0.13.3
benji1a/openllama-3b-pelt-squad_v2
benji1a
2023-09-19T11:43:19Z
1
0
peft
[ "peft", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "region:us" ]
null
2023-08-24T17:37:45Z
--- library_name: peft base_model: openlm-research/open_llama_3b_v2 --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0
derguene/saytutension-xlmroberta-v1
derguene
2023-09-19T11:32:38Z
5
0
sentence-transformers
[ "sentence-transformers", "pytorch", "xlm-roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-09-19T11:31:47Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # derguene/saytutension-xlmroberta-v1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("derguene/saytutension-xlmroberta-v1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
bertin-project/bertin-alpaca-lora-7b
bertin-project
2023-09-19T11:32:13Z
9
4
peft
[ "peft", "text-generation", "es", "dataset:bertin-project/alpaca-spanish", "license:openrail", "region:us" ]
text-generation
2023-03-27T13:58:50Z
--- language: - es license: openrail library_name: peft datasets: - bertin-project/alpaca-spanish pipeline_tag: text-generation base_model: decapoda-research/llama-7b-hf --- # BERTIN-Alpaca-LoRA 7B This is a Spanish adapter generated by fine-tuning LLaMA-7B on a [Spanish Alpaca](https://huggingface.co/datasets/bertin-project/alpaca-spanish) dataset. ## Usage ```python from peft import PeftModel from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig base_model = "decapoda-research/llama-7b-hf" tokenizer = LLaMATokenizer.from_pretrained(base_model) model = LLaMAForCausalLM.from_pretrained( base_model, load_in_8bit=True, device_map="auto", ) model = PeftModel.from_pretrained(model, "bertin-project/bertin-alpaca-lora-7b") ``` Until `PEFT` is fully supported in Hugginface's pipelines, for generation we can either consolidate the LoRA weights into the LLaMA model weights, or use the adapter's `generate()` method. Remember that the prompt still needs the English template: ```python # Generate responses def generate(instruction, input=None): if input: prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. # noqa: E501 ### Instruction: {instruction} ### Input: {input} ### Response: """ else: prompt = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. # noqa: E501 ### Instruction: {instruction} ### Response: """ 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) print(output.split("### Response:")[1].strip()) generate("Escribe un correo electrónico dando la bienvenida a un nuevo empleado llamado Manolo.") # Estimado Manolo, # # ¡Bienvenido a nuestro equipo! Estamos muy contentos de que hayas decidido unirse a nosotros y estamos ansiosos por comenzar a trabajar juntos. # # Nos gustaría darte las gracias por tu interés en nuestro equipo y esperamos que tengas un gran tiempo aquí. # # Si tienes alguna pregunta o duda, no dudes en contactarnos. # # Atentamente, # Equipo de [Nombre del Departamento] ``` ## Data The dataset is a translation to Spanish of [alpaca_data_cleaned.json](https://github.com/tloen/alpaca-lora/blob/main/alpaca_data_cleaned.json) (a clean version of the [Alpaca dataset made at Stanford](https://huggingface.co/datasets/tatsu-lab/alpaca)) using OpenAI's `gpt-3.5-turbo` model. We translated using a full-sample prompt instead of per strings, which resulted in more coherent tuples of `(instruction, input, output)` and costed around $60.0. **This dataset cannot be used to create models that compete in any way with OpenAI.** ## Finetuning To fine-tune the LLaMA model we used the code available on [Alpaca Lora](https://github.com/tloen/alpaca-lora), which provides code to finetune the LLaMA model using PEFT from Hugging Face. We run finetuning for 3 epochs using sequence length of 512 with no gradient accumulation on a single A6000 with 48GB VRAM for 12 hours on top of LLaMA-7B. ## Example outputs Stanford Alpaca: ``` Instruction: Inventa una excusa creativa para decir que no tengo que ir a la fiesta. Response: I'm sorry, I can't make it to the party. I have to stay home and take care of my sick pet. ``` BERTIN Alpaca LoRA: ``` Instruction: Inventa una excusa creativa para decir que no tengo que ir a la fiesta. Response: No puedo ir a la fiesta porque tengo que terminar un proyecto urgente. ``` Stanford Alpaca: ``` Instruction: ¿Cómo puedo comenzar una carrera como científico de datos? Responde con una lista de pasos a seguir. Response: To begin a career as a data scientist, there are a few steps to take. First, you should have a bachelor's degree in a STEM-related field, such as computer science, mathematics, statistics, or engineering. You should also have experience with programming languages such as Python, R, or Java. Additionally, it is helpful to have knowledge of machine learning, data visualization, and data analytics. Finally, it is important to stay up to date with the latest trends and technologies in the field. ``` BERTIN Alpaca LoRA: ``` Instruction: ¿Cómo puedo comenzar una carrera como científico de datos? Responde con una lista de pasos a seguir. Response: Para comenzar una carrera como científico de datos, hay varios pasos a seguir. Primero, obtener una licenciatura en matemáticas, informática o ciencias de la computación. Luego, obtener una maestría o doctorado en ciencias de la computación, informática o alguna otra área relacionada. Finalmente, obtener experiencia en el campo trabajando en proyectos de investigación o desarrollando aplicaciones. ``` You can test it using the eval notebook [here](https://colab.research.google.com/github/22-hours/cabrita/blob/main/notebooks/cabrita-lora.ipynb). ## References - [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) - [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) - [BERTIN Alpaca](https://huggingface.co/datasets/bertin-project/alpaca-spanish) - [Alpaca LoRA](https://github.com/tloen/alpaca-lora) - [ChatGPT](https://openai.com/blog/chatgpt) - [Hugging Face](https://huggingface.co/) ## Hardware Requirements For training we have used an A6000 48GB VRAM Nvidia GPU. For eval, you can use a T4.
NbAiLab/nb-alpaca-lora-7b
NbAiLab
2023-09-19T11:32:00Z
10
5
peft
[ "peft", "safetensors", "text-generation", "no", "nb", "dataset:NbAiLab/norwegian-alpaca", "license:openrail", "region:us" ]
text-generation
2023-03-27T11:28:50Z
--- language: - 'no' - nb license: openrail library_name: peft datasets: - NbAiLab/norwegian-alpaca pipeline_tag: text-generation base_model: decapoda-research/llama-7b-hf --- # NB-Alpaca-LoRA 7B This is an Norwegian adapter generated by fine-tuning LLaMA-7B on a [Norwegian Alpaca](https://huggingface.co/datasets/NbAiLab/norwegian-alpaca) dataset. ## Usage ```python from peft import PeftModel from transformers import LlamaTokenizer, LlamaForCausalLM base_model = "decapoda-research/llama-7b-hf" tokenizer = LlamaTokenizer.from_pretrained(base_model) model = LlamaForCausalLM.from_pretrained( base_model, load_in_8bit=True, device_map="auto", ) model = PeftModel.from_pretrained(model, "NbAiLab/nb-alpaca-lora-7b") ``` For generation, the promtp still needs the English template: ```python from transformers import pipeline pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) instruction = "Skriv en e-post der du ønsker velkommen til en ny medarbeider ved navn Svein" pipe(f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: """) # Kjære Svein, # # Velkommen til vårt team! Vi er så glade for å ha deg med oss. Vi ser frem til å hjelpe deg med å nå dine mål og oppnå dine drømmer. # # Vi er alltid tilgjengelige hvis du har noen spørsmål eller ønsker å diskutere noen av våre prosjekter. # # Vi ser frem til å jobbe sammen med deg! # # Med vennlig ``` ## Data The dataset is a translation to Norwegian Bokmål of [alpaca_data_cleaned.json](https://github.com/tloen/alpaca-lora/blob/main/alpaca_data_cleaned.json) (a clean version of the [Alpaca dataset made at Stanford](https://huggingface.co/datasets/tatsu-lab/alpaca)) using OpenAI's `gpt-3.5-turbo` model. We translated using a full-sample prompt instead of per strings, which resulted in more coherent tuples of `(instruction, input, output)` and costed around $60.0. **This dataset cannot be used to create models that compete in any way with OpenAI.** ## Finetuning To fine-tune the LLaMA model we used the code available on [Alpaca Lora](https://github.com/tloen/alpaca-lora), which provides code to finetune the LLaMA model using PEFT from Hugging Face. We run finetuning for 3 epochs using sequence length of 512 with no gradient accumulation on a single A6000 with 48GB VRAM for 12 hours on top of LLaMA-7B. ## Example outputs Stanford Alpaca: ``` Instruction: Kom med en kreativ unnskyldning for å si at jeg ikke trenger å gå på festen. Response: I'm sorry, but I don't need to go to the party. ``` NB-Alpaca-LoRA-7B: ``` Instruction: Kom med en kreativ unnskyldning for å si at jeg ikke trenger å gå på festen. Response: Jeg beklager at jeg ikke kan gå på festen, men jeg tror at det er bedre at jeg ikke gjør det. ``` [NB-GTP-J-6B-Alpaca](https://huggingface.co/NbAiLab/nb-gpt-j-6B-alpaca): ``` Instruction: Kom med en kreativ unnskyldning for å si at jeg ikke trenger å gå på festen. Response: Jeg beklager, men jeg har en avtale jeg ikke kan avlyse. ``` Stanford Alpaca: ``` Instruction: Hvordan kan jeg starte en karriere som dataforsker? Skriv i listeform. Response: Starting a career as a data scientist requires a combination of education, experience, and skills. 1. Obtain a Bachelor's or Master's degree in computer science, statistics, mathematics, or a related field. 2. Gain practical experience through internships or part-time jobs. 3. Learn programming languages such as Python, R, and SQL. 4. Develop strong analytical and problem-solving skills. 5. Build a portfolio of projects to showcase your work. 6. Network with other data scientists to learn ``` NB-Alpaca-LoRA-7B: ``` Instruction: Hvordan kan jeg starte en karriere som dataforsker? Skriv i listeform. Response: 1. Søk etter en universitetsgrad i datavitenskap, matematikk, informatikk eller en relatert fag. 2. Søk etter en jobb som dataforsker eller dataanalytiker. 3. Utvikle dine ferdigheter i datavitenskap, matematikk, informatikk eller en relatert fag. 4. Utvikle dine ferdigheter innen dataanalyse. ``` [NB-GTP-J-6B-Alpaca](https://huggingface.co/NbAiLab/nb-gpt-j-6B-alpaca): ``` Instruction: Hvordan kan jeg starte en karriere som dataforsker? Skriv i listeform. Response: 1. Lær programmeringsspråk som Python og Java. 2. Skaff deg en grad i informatikk, datavitenskap eller relaterte felt. 3. Bli sertifisert innen datamaskinsyn, maskinlæring eller andre relaterte felt. 4. Bygg nettverk med andre dataforskere. 5. Delta på konferanser og kurs for å holde deg oppdatert på de siste utviklingene innen feltet. ``` You can test it using the eval notebook [here](https://colab.research.google.com/github/22-hours/cabrita/blob/main/notebooks/cabrita-lora.ipynb). ## References - [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) - [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) - [Norwegian Alpaca](https://huggingface.co/datasets/NbAiLab/norwegian-alpaca) - [Alpaca LoRA](https://github.com/tloen/alpaca-lora) - [ChatGPT](https://openai.com/blog/chatgpt) - [Hugging Face](https://huggingface.co/) ## Hardware Requirements For training we have used an A6000 48GB VRAM Nvidia GPU. For eval, you can use a T4.
radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram
radiogroup-crits
2023-09-19T11:21:43Z
89
1
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "hf-asr-leaderboard", "it", "mozilla-foundation/common_voice_8_0", "speech", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-29T08:31:46Z
--- language: - it license: apache-2.0 datasets: - mozilla-foundation/common_voice_8_0 metrics: - wer - cer tags: - audio - automatic-speech-recognition - hf-asr-leaderboard - it - mozilla-foundation/common_voice_8_0 - speech - wav2vec2 model-index: - name: XLS-R Wav2Vec2 Italian by radiogroup crits results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8.0 italian type: mozilla-foundation/common_voice_8_0 args: it metrics: - name: Test WER type: wer value: 9.04 - name: Test CER type: cer value: 2.2 - name: Test WER (+LM) type: wer value: 6.24 - name: Test CER (+LM) type: cer value: 1.67 --- # XLS-R-1B-ITALIAN-DOC4LM-5GRAM ## Fine-tuned XLS-R 1B model for speech recognition in Italian Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Italian using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual TEDx](http://www.openslr.org/100), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli). When using this model, make sure that your speech input is sampled at 16kHz. ## Language model information Our language model was generated using a dataset of Italian wikipedia articles and manual transcriptions of radio newspapers and television programs. ## Download CommonVoice8.0 dataset for italian language ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_8_0", "it", use_auth_token=True) ``` ## Evaluation Commands To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`: ```bash python eval.py --model_id radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram --dataset mozilla-foundation/common_voice_8_0 --config it --split test --log_outputs --greedy mv log_mozilla-foundation_common_voice_8_0_it_test_predictions.txt log_mozilla-foundation_common_voice_8_0_it_test_predictions_greedy.txt mv log_mozilla-foundation_common_voice_8_0_it_test_targets.txt log_mozilla-foundation_common_voice_8_0_it_test_targets_greedy.txt mv mozilla-foundation_common_voice_8_0_it_test_eval_results.txt mozilla-foundation_common_voice_8_0_it_test_eval_results_greedy.txt python eval.py --model_id radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram --dataset mozilla-foundation/common_voice_8_0 --config it --split test --log_outputs mv log_mozilla-foundation_common_voice_8_0_it_test_predictions.txt log_mozilla-foundation_common_voice_8_0_it_test_predictions_lm.txt mv log_mozilla-foundation_common_voice_8_0_it_test_targets.txt log_mozilla-foundation_common_voice_8_0_it_test_targets_lm.txt mv mozilla-foundation_common_voice_8_0_it_test_eval_results.txt mozilla-foundation_common_voice_8_0_it_test_eval_results_lm.txt ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{crits2022wav2vec2-xls-r-1b-italian-doc4lm-5gram, title={XLS-R Wav2Vec2 Italian by radiogroup crits}, author={Teraoni Prioletti Raffaele, Casagranda Paolo and Russo Francesco}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram}}, year={2022} } ```
omarelsayeed/retriever
omarelsayeed
2023-09-19T11:19:00Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-09-18T19:12:49Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 24000 with parameters: ``` {'batch_size': 50, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 1e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
0sunfire0/Llama_7B_Test08
0sunfire0
2023-09-19T11:17:26Z
2
0
peft
[ "peft", "region:us" ]
null
2023-07-04T10:28:18Z
--- library_name: peft base_model: decapoda-research/llama-7b-hf --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0
SHENMU007/neunit_BASE_V9.5.13
SHENMU007
2023-09-19T11:14:38Z
75
0
transformers
[ "transformers", "pytorch", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]
text-to-audio
2023-09-19T09:49:48Z
--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
EladAssia/ppo-LunarLander-v2
EladAssia
2023-09-19T11:14:04Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T11:13:42Z
--- 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: 286.92 +/- 22.27 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 ... ```
AhmedBou/Falcon_7B_Science_Exam_QLoRA
AhmedBou
2023-09-19T11:12:13Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-19T11:12:10Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
dbecker1/test_lora_mdl3
dbecker1
2023-09-19T11:08:35Z
1
1
diffusers
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-19T10:30:03Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - dbecker1/test_lora_mdl3 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None dataset. You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Phoenix10062002/llama2-faq-chatbot
Phoenix10062002
2023-09-19T11:06:33Z
5
0
peft
[ "peft", "base_model:TinyPixel/Llama-2-7B-bf16-sharded", "base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded", "region:us" ]
null
2023-08-04T14:36:15Z
--- library_name: peft base_model: TinyPixel/Llama-2-7B-bf16-sharded --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
Sarmila/pubmed-bert-mlm-squad-covidqa
Sarmila
2023-09-19T11:05:04Z
122
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "base_model:Sarmila/pubmed-bert-mlm", "base_model:finetune:Sarmila/pubmed-bert-mlm", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2023-09-19T07:18:33Z
--- license: mit base_model: Sarmila/pubmed-bert-mlm tags: - generated_from_trainer datasets: - covid_qa_deepset model-index: - name: pubmed-bert-mlm-squad-covidqa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pubmed-bert-mlm-squad-covidqa This model is a fine-tuned version of [Sarmila/pubmed-bert-mlm](https://huggingface.co/Sarmila/pubmed-bert-mlm) on the covid_qa_deepset dataset. It achieves the following results on the evaluation set: - Loss: 0.5812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 51 | 0.5056 | | No log | 2.0 | 102 | 0.5423 | | No log | 3.0 | 153 | 0.5812 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
royokong/prompteol-opt-1.3b
royokong
2023-09-19T11:02:36Z
4
1
peft
[ "peft", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "region:us" ]
null
2023-07-27T14:47:42Z
--- library_name: peft base_model: facebook/opt-1.3b --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
legacy107/flan-t5-large-bottleneck-adapter-cpgQA
legacy107
2023-09-19T10:53:57Z
10
0
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-08-31T13:29:44Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: flan-t5-large-bottleneck-adapter-cpgQA results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-large-bottleneck-adapter-cpgQA This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1175 - Squad: {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} - Bleu: {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Squad | Bleu | |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.4902 | 0.23 | 100 | 0.1695 | {'exact_match': 59.61538461538461, 'f1': 88.39664262292322} | {'bleu': 0.8611708764560243, 'precisions': [0.8791469194312796, 0.8657487091222031, 0.8552631578947368, 0.8448979591836735], 'brevity_penalty': 1.0, 'length_ratio': 1.0284321689683185, 'translation_length': 1266, 'reference_length': 1231} | | 0.3577 | 0.45 | 200 | 0.3243 | {'exact_match': 47.11538461538461, 'f1': 75.97696037540817} | {'bleu': 0.44597697779640594, 'precisions': [0.9202211690363349, 0.9087779690189329, 0.8994360902255639, 0.8948979591836734], 'brevity_penalty': 0.49236704919459706, 'length_ratio': 0.5852981969486823, 'translation_length': 1266, 'reference_length': 2163} | | 0.2751 | 0.68 | 300 | 0.1577 | {'exact_match': 69.23076923076923, 'f1': 89.48763228957931} | {'bleu': 0.8601252797928449, 'precisions': [0.8925750394944708, 0.878657487091222, 0.8656015037593985, 0.8561224489795919], 'brevity_penalty': 0.985104158338853, 'length_ratio': 0.9852140077821012, 'translation_length': 1266, 'reference_length': 1285} | | 0.5794 | 0.9 | 400 | 0.4970 | {'exact_match': 32.69230769230769, 'f1': 67.89210636760458} | {'bleu': 0.5849757239612657, 'precisions': [0.7282780410742496, 0.693631669535284, 0.6635338345864662, 0.6387755102040816], 'brevity_penalty': 0.8599604506941122, 'length_ratio': 0.8689087165408373, 'translation_length': 1266, 'reference_length': 1457} | | 0.2114 | 1.13 | 500 | 0.1245 | {'exact_match': 67.3076923076923, 'f1': 89.96309177836906} | {'bleu': 0.8997821698527838, 'precisions': [0.9360189573459715, 0.9285714285714286, 0.9238721804511278, 0.9204081632653062], 'brevity_penalty': 0.9704302027764995, 'length_ratio': 0.9708588957055214, 'translation_length': 1266, 'reference_length': 1304} | | 0.1765 | 1.36 | 600 | 0.1214 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1822 | 1.58 | 700 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.14 | 1.81 | 800 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1456 | 2.04 | 900 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1172 | 2.26 | 1000 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1376 | 2.49 | 1100 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1683 | 2.71 | 1200 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.0717 | 2.94 | 1300 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1038 | 3.17 | 1400 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.0812 | 3.39 | 1500 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1887 | 3.62 | 1600 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.0824 | 3.85 | 1700 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1046 | 4.07 | 1800 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.0952 | 4.3 | 1900 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1054 | 4.52 | 2000 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1603 | 4.75 | 2100 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1643 | 4.98 | 2200 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1326 | 5.2 | 2300 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1922 | 5.43 | 2400 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.1154 | 5.66 | 2500 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | | 0.07 | 5.88 | 2600 | 0.1175 | {'exact_match': 74.03846153846153, 'f1': 92.73025873728763} | {'bleu': 0.9331748310720637, 'precisions': [0.9447077409162717, 0.9380378657487092, 0.9332706766917294, 0.9285714285714286], 'brevity_penalty': 0.9968454284876576, 'length_ratio': 0.9968503937007874, 'translation_length': 1266, 'reference_length': 1270} | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
Luciano/lora-4bit-Llama-2-13b-hf-lener_br
Luciano
2023-09-19T10:53:54Z
6
0
peft
[ "peft", "base_model:meta-llama/Llama-2-13b-hf", "base_model:adapter:meta-llama/Llama-2-13b-hf", "region:us" ]
null
2023-08-20T11:16:06Z
--- library_name: peft base_model: meta-llama/Llama-2-13b-hf --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
Luciano/Llama-2-7b-chat-hf-dolly-mini
Luciano
2023-09-19T10:52:32Z
3
0
peft
[ "peft", "pytorch", "llama", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]
null
2023-08-29T11:10:04Z
--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
Luciano/lora-4bit-Llama-2-7b-hf-lener_br
Luciano
2023-09-19T10:52:13Z
55
0
peft
[ "peft", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]
null
2023-08-06T11:45:27Z
--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
mhenrichsen/context-aware-splitter-1b
mhenrichsen
2023-09-19T10:45:24Z
182
5
transformers
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "da", "dataset:mhenrichsen/context-aware-splits", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-18T08:26:42Z
--- license: apache-2.0 datasets: - mhenrichsen/context-aware-splits language: - da --- # Context Aware Splitter 7b model available [here](https://huggingface.co/mhenrichsen/context-aware-splitter-7b). CAS is a text splitter for Retrieval Augmented Generation. It's trained on 12.3k danish texts with a token count of 13.4m. ## What does it do? CAS takes a text (str), reads and understands the contexts and then provides the best splits based on a defined word count. It returns a dict with the keys: - splits: list[str] - topic: str ## Code example ```python from transformers import AutoTokenizer, TextStreamer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mhenrichsen/context-aware-splitter-1b") tokenizer = AutoTokenizer.from_pretrained("mhenrichsen/context-aware-splitter-1b") streamer = TextStreamer(tokenizer, skip_special_tokens=True) WORD_SPLIT_COUNT = 50 prompt_template = """### Instruction: Din opgave er at segmentere en given tekst i separate dele, så hver del giver mening og kan læses uafhængigt af de andre. Hvis det giver mening, må der kan være et overlap mellem delene. Hver del skal ideelt indeholde {word_count} ord. ### Input: {text} ### Response: """ artikel = """Kina er stærkt utilfreds med, at Tysklands udenrigsminister, Annalena Baerbock, har omtalt den kinesiske præsident Xi Jinping som en diktator. - Bemærkningerne fra Tyskland er ekstremt absurde, krænker Kinas politiske værdighed alvorligt og er en åben politisk provokation, udtalte talsperson fra det kinesiske udenrigsministerium Mao Ning i går ifølge CNN. Bemærkningen fra udenrigsminister Annalena Baerbock faldt i et interview om krigen i Ukraine med Fox News i sidste uge. - Hvis Putin skulle vinde denne krig, hvilket signal ville det så sende til andre diktatorer i verden, som Xi, som den kinesiske præsident?, sagde hun. Tysklands ambassadør i Kina, Patricia Flor, har som konsekvens af udtalelsen været til en kammeratlig samtale, oplyser det tyske udenrigsministerium til CNN.""" tokens = tokenizer( prompt_template.format(text=artikel, word_count=WORD_SPLIT_COUNT), return_tensors='pt' )['input_ids'] # Generate output generation_output = model.generate( tokens, streamer=streamer, max_length = 8194, eos_token_id = 29913 ) ``` Example: ``` ### Instruction: Din opgave er at segmentere en given tekst i separate dele, så hver del giver mening og kan læses uafhængigt af de andre. Hvis det giver mening, må der kan være et overlap mellem delene. Hver del skal ideelt indeholde 50 ord. ### Input: Munkebjerg er et overvejende middelklassekvarter beliggende i det centrale Odense Munkebjerg grænser op til Hunderup i vest, hvor det afgrænses af Hjallesevej, og byens centrum i nord. Kvarteret har status som et familievenligt boligkvarter med både lejligheder (i området omkring H.C Andersensgade) og parcelhuse som på og omkring Munkebjergvej og Munkebjergskolen. Socialdemokratiet står traditionelt set stærkt i området, som det også ses på resultaterne af stemmer afgivet ved valgstedet Munkebjergskolen fra folketingsvalget i 2011, hvor partiet fik 24,8% af stemmerne. Dog vinder partiet Venstre samt Det Radikale Venstre også bred opbakning i kvarteret med henholdsvis 20,7 og 12,6% af stemmerne ligeledes fra valget i 2011. De fleste af kvarterets børn går på den lokale Munkebjergskolen, mens enkelte går på Odense Friskole og/eller Giersings Realskole. Munkebjergkvarteret er desuden hjemsted for fodboldklubben OKS. Munkebjergkvarteret kaldes i dagligtale for "Munken". ### Response: ``` This returns the following dictionary: ``` {'splits': ['Munkebjerg er et overvejende middelklassekvarter beliggende i det centrale Odense. Munkebjerg grænser op til Hunderup i vest, hvor det afgrænses af Hjallesevej, og byens centrum i nord. Kvarteret har status som et familievenligt boligkvarter med både lejligheder (i området omkring H.C Andersensgade) og parcelhuse som på og omkring Munkebjergvej og Munkebjergskolen.', 'Socialdemokratiet står traditionelt set stærkt i området, som det også ses på resultaterne af stemmer afgivet ved valgstedet Munkebjergskolen fra folketingsvalget i 2011, hvor partiet fik 24,8% af stemmerne. Dog vinder partiet Venstre samt Det Radikale Venstre også bred opbakning i kvarteret med henholdsvis 20,7 og 12,6% af stemmerne ligeledes fra valget i 2011.', "De fleste af kvarterets børn går på den lokale Munkebjergskolen, mens enkelte går på Odense Friskole og/eller Giersings Realskole. Munkebjergkvarteret er desuden hjemsted for fodboldklubben OKS. Munkebjergkvarteret kaldes i dagligtale for 'Munken'."], 'topic': 'Beskrivelse af Munkebjergkvarteret i Odense.'} ``` ## Prompt format The model follows alpaca format. ``` ### Instruction: Din opgave er at segmentere en given tekst i separate dele, så hver del giver mening og kan læses uafhængigt af de andre. Hvis det giver mening, må der kan være et overlap mellem delene. Hver del skal ideelt indeholde {WORD_COUNT} ord. ### Input: {TEXT} ### Response: ```
monsterapi/Gptj-6b_alpaca-gpt4
monsterapi
2023-09-19T10:45:10Z
14
0
peft
[ "peft", "gptj-6b", "instruct", "instruct-alpaca", "alpaca", "gpt4", "dataset:vicgalle/alpaca-gpt4", "base_model:EleutherAI/gpt-j-6b", "base_model:adapter:EleutherAI/gpt-j-6b", "region:us" ]
null
2023-06-28T06:44:13Z
--- library_name: peft tags: - gptj-6b - instruct - instruct-alpaca - alpaca - gpt4 datasets: - vicgalle/alpaca-gpt4 base_model: EleutherAI/gpt-j-6b --- We finetuned gptj-6b on Code-Alpaca-Instruct Dataset (vicgalle/alpaca-gpt4) for 10 epochs or ~ 50,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset is vicgalle/alpaca-gpt4 unfiltered, The finetuning session got completed in 7 hours and costed us only `$25` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: vicgalle/alpaca-gpt4 - Dataset: vicgalle/alpaca-gpt4 - Learning rate: 0.0003 - Number of epochs: 5 - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 --- license: apache-2.0 ---
monsterapi/llama2-7b-tiny-codes-code-generation
monsterapi
2023-09-19T10:43:52Z
4
1
peft
[ "peft", "llama2", "llama2-7b", "code generation", "code-generation", "code", "instruct", "instruct-code", "code-alpaca", "alpaca-instruct", "alpaca", "llama7b", "gpt2", "dataset:nampdn-ai/tiny-codes", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:apache-2.0", "region:us" ]
null
2023-08-16T14:46:07Z
--- license: apache-2.0 library_name: peft tags: - llama2 - llama2-7b - code generation - code-generation - code - instruct - instruct-code - code-alpaca - alpaca-instruct - alpaca - llama7b - gpt2 datasets: - nampdn-ai/tiny-codes base_model: meta-llama/Llama-2-7b-hf --- ## Training procedure We finetuned [Llama 2 7B model](https://huggingface.co/meta-llama/Llama-2-7b-hf) from Meta on [nampdn-ai/tiny-codes](https://huggingface.co/datasets/nampdn-ai/tiny-codes) for ~ 10,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). This dataset contains **1.63 million rows** and is a collection of short and clear code snippets that can help LLM models learn how to reason with both natural and programming languages. The dataset covers a wide range of programming languages, such as Python, TypeScript, JavaScript, Ruby, Julia, Rust, C++, Bash, Java, C#, and Go. It also includes two database languages: Cypher (for graph databases) and SQL (for relational databases) in order to study the relationship of entities. The finetuning session got completed in 193 minutes and costed us only ~ `$7.5` for the entire finetuning run! #### Hyperparameters & Run details: - Model Path: meta-llama/Llama-2-7b-hf - Dataset: nampdn-ai/tiny-codes - Learning rate: 0.0002 - Number of epochs: 1 (10k steps) - Data split: Training: 90% / Validation: 10% - Gradient accumulation steps: 1 ### Framework versions - PEFT 0.4.0 ### Loss metrics: ![training loss](train-loss.png "Training loss")
Carve/tracer_b7
Carve
2023-09-19T10:31:03Z
0
12
null
[ "license:apache-2.0", "region:us" ]
null
2022-09-01T15:43:56Z
--- license: apache-2.0 --- `tracer-b7.pth` - Pretrained TRACER with EfficientNet v1 b7 encoder. `tracer-b7-carveset-finetuned.pth` - The model of tracer b7, which has been finetuned on the CarveSet dataset. This model achieves an average F-Beta score of 96.2% on the test set.
CyberHarem/ooishi_izumi_idolmastercinderellagirls
CyberHarem
2023-09-19T10:25:51Z
0
1
null
[ "art", "text-to-image", "dataset:CyberHarem/ooishi_izumi_idolmastercinderellagirls", "license:mit", "region:us" ]
text-to-image
2023-09-19T10:08:09Z
--- license: mit datasets: - CyberHarem/ooishi_izumi_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of ooishi_izumi_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5040, you need to download `5040/ooishi_izumi_idolmastercinderellagirls.pt` as the embedding and `5040/ooishi_izumi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 5040**, with the score of 0.949. The trigger words are: 1. `ooishi_izumi_idolmastercinderellagirls` 2. `long_hair, brown_eyes, blush, black_hair, breasts, smile, bangs, medium_breasts, hair_between_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:----------------------------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-----------------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5400 | 0.929 | [Download](5400/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-5400](5400/previews/pattern_1.png) | [<NSFW, click to see>](5400/previews/pattern_2.png) | ![pattern_3-5400](5400/previews/pattern_3.png) | ![pattern_4-5400](5400/previews/pattern_4.png) | [<NSFW, click to see>](5400/previews/pattern_5.png) | [<NSFW, click to see>](5400/previews/pattern_6.png) | [<NSFW, click to see>](5400/previews/pattern_7.png) | ![pattern_8-5400](5400/previews/pattern_8.png) | ![pattern_9-5400](5400/previews/pattern_9.png) | ![pattern_10-5400](5400/previews/pattern_10.png) | [<NSFW, click to see>](5400/previews/pattern_11.png) | [<NSFW, click to see>](5400/previews/bikini.png) | [<NSFW, click to see>](5400/previews/bondage.png) | [<NSFW, click to see>](5400/previews/free.png) | ![maid-5400](5400/previews/maid.png) | ![miko-5400](5400/previews/miko.png) | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) | ![suit-5400](5400/previews/suit.png) | ![yukata-5400](5400/previews/yukata.png) | | **5040** | **0.949** | [**Download**](5040/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-5040](5040/previews/pattern_1.png) | [<NSFW, click to see>](5040/previews/pattern_2.png) | ![pattern_3-5040](5040/previews/pattern_3.png) | ![pattern_4-5040](5040/previews/pattern_4.png) | [<NSFW, click to see>](5040/previews/pattern_5.png) | [<NSFW, click to see>](5040/previews/pattern_6.png) | [<NSFW, click to see>](5040/previews/pattern_7.png) | ![pattern_8-5040](5040/previews/pattern_8.png) | ![pattern_9-5040](5040/previews/pattern_9.png) | ![pattern_10-5040](5040/previews/pattern_10.png) | [<NSFW, click to see>](5040/previews/pattern_11.png) | [<NSFW, click to see>](5040/previews/bikini.png) | [<NSFW, click to see>](5040/previews/bondage.png) | [<NSFW, click to see>](5040/previews/free.png) | ![maid-5040](5040/previews/maid.png) | ![miko-5040](5040/previews/miko.png) | [<NSFW, click to see>](5040/previews/nude.png) | [<NSFW, click to see>](5040/previews/nude2.png) | ![suit-5040](5040/previews/suit.png) | ![yukata-5040](5040/previews/yukata.png) | | 4680 | 0.870 | [Download](4680/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-4680](4680/previews/pattern_1.png) | [<NSFW, click to see>](4680/previews/pattern_2.png) | ![pattern_3-4680](4680/previews/pattern_3.png) | ![pattern_4-4680](4680/previews/pattern_4.png) | [<NSFW, click to see>](4680/previews/pattern_5.png) | [<NSFW, click to see>](4680/previews/pattern_6.png) | [<NSFW, click to see>](4680/previews/pattern_7.png) | ![pattern_8-4680](4680/previews/pattern_8.png) | ![pattern_9-4680](4680/previews/pattern_9.png) | ![pattern_10-4680](4680/previews/pattern_10.png) | [<NSFW, click to see>](4680/previews/pattern_11.png) | [<NSFW, click to see>](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) | [<NSFW, click to see>](4680/previews/free.png) | ![maid-4680](4680/previews/maid.png) | ![miko-4680](4680/previews/miko.png) | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) | ![suit-4680](4680/previews/suit.png) | ![yukata-4680](4680/previews/yukata.png) | | 4320 | 0.866 | [Download](4320/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | [<NSFW, click to see>](4320/previews/pattern_2.png) | ![pattern_3-4320](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | [<NSFW, click to see>](4320/previews/pattern_5.png) | [<NSFW, click to see>](4320/previews/pattern_6.png) | [<NSFW, click to see>](4320/previews/pattern_7.png) | ![pattern_8-4320](4320/previews/pattern_8.png) | ![pattern_9-4320](4320/previews/pattern_9.png) | ![pattern_10-4320](4320/previews/pattern_10.png) | [<NSFW, click to see>](4320/previews/pattern_11.png) | [<NSFW, click to see>](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | [<NSFW, click to see>](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | 3960 | 0.865 | [Download](3960/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-3960](3960/previews/pattern_1.png) | [<NSFW, click to see>](3960/previews/pattern_2.png) | ![pattern_3-3960](3960/previews/pattern_3.png) | ![pattern_4-3960](3960/previews/pattern_4.png) | [<NSFW, click to see>](3960/previews/pattern_5.png) | [<NSFW, click to see>](3960/previews/pattern_6.png) | [<NSFW, click to see>](3960/previews/pattern_7.png) | ![pattern_8-3960](3960/previews/pattern_8.png) | ![pattern_9-3960](3960/previews/pattern_9.png) | ![pattern_10-3960](3960/previews/pattern_10.png) | [<NSFW, click to see>](3960/previews/pattern_11.png) | [<NSFW, click to see>](3960/previews/bikini.png) | [<NSFW, click to see>](3960/previews/bondage.png) | [<NSFW, click to see>](3960/previews/free.png) | ![maid-3960](3960/previews/maid.png) | ![miko-3960](3960/previews/miko.png) | [<NSFW, click to see>](3960/previews/nude.png) | [<NSFW, click to see>](3960/previews/nude2.png) | ![suit-3960](3960/previews/suit.png) | ![yukata-3960](3960/previews/yukata.png) | | 3600 | 0.904 | [Download](3600/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-3600](3600/previews/pattern_1.png) | [<NSFW, click to see>](3600/previews/pattern_2.png) | ![pattern_3-3600](3600/previews/pattern_3.png) | ![pattern_4-3600](3600/previews/pattern_4.png) | [<NSFW, click to see>](3600/previews/pattern_5.png) | [<NSFW, click to see>](3600/previews/pattern_6.png) | [<NSFW, click to see>](3600/previews/pattern_7.png) | ![pattern_8-3600](3600/previews/pattern_8.png) | ![pattern_9-3600](3600/previews/pattern_9.png) | ![pattern_10-3600](3600/previews/pattern_10.png) | [<NSFW, click to see>](3600/previews/pattern_11.png) | [<NSFW, click to see>](3600/previews/bikini.png) | [<NSFW, click to see>](3600/previews/bondage.png) | [<NSFW, click to see>](3600/previews/free.png) | ![maid-3600](3600/previews/maid.png) | ![miko-3600](3600/previews/miko.png) | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) | ![suit-3600](3600/previews/suit.png) | ![yukata-3600](3600/previews/yukata.png) | | 3240 | 0.940 | [Download](3240/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-3240](3240/previews/pattern_1.png) | [<NSFW, click to see>](3240/previews/pattern_2.png) | ![pattern_3-3240](3240/previews/pattern_3.png) | ![pattern_4-3240](3240/previews/pattern_4.png) | [<NSFW, click to see>](3240/previews/pattern_5.png) | [<NSFW, click to see>](3240/previews/pattern_6.png) | [<NSFW, click to see>](3240/previews/pattern_7.png) | ![pattern_8-3240](3240/previews/pattern_8.png) | ![pattern_9-3240](3240/previews/pattern_9.png) | ![pattern_10-3240](3240/previews/pattern_10.png) | [<NSFW, click to see>](3240/previews/pattern_11.png) | [<NSFW, click to see>](3240/previews/bikini.png) | [<NSFW, click to see>](3240/previews/bondage.png) | [<NSFW, click to see>](3240/previews/free.png) | ![maid-3240](3240/previews/maid.png) | ![miko-3240](3240/previews/miko.png) | [<NSFW, click to see>](3240/previews/nude.png) | [<NSFW, click to see>](3240/previews/nude2.png) | ![suit-3240](3240/previews/suit.png) | ![yukata-3240](3240/previews/yukata.png) | | 2880 | 0.903 | [Download](2880/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-2880](2880/previews/pattern_1.png) | [<NSFW, click to see>](2880/previews/pattern_2.png) | ![pattern_3-2880](2880/previews/pattern_3.png) | ![pattern_4-2880](2880/previews/pattern_4.png) | [<NSFW, click to see>](2880/previews/pattern_5.png) | [<NSFW, click to see>](2880/previews/pattern_6.png) | [<NSFW, click to see>](2880/previews/pattern_7.png) | ![pattern_8-2880](2880/previews/pattern_8.png) | ![pattern_9-2880](2880/previews/pattern_9.png) | ![pattern_10-2880](2880/previews/pattern_10.png) | [<NSFW, click to see>](2880/previews/pattern_11.png) | [<NSFW, click to see>](2880/previews/bikini.png) | [<NSFW, click to see>](2880/previews/bondage.png) | [<NSFW, click to see>](2880/previews/free.png) | ![maid-2880](2880/previews/maid.png) | ![miko-2880](2880/previews/miko.png) | [<NSFW, click to see>](2880/previews/nude.png) | [<NSFW, click to see>](2880/previews/nude2.png) | ![suit-2880](2880/previews/suit.png) | ![yukata-2880](2880/previews/yukata.png) | | 2520 | 0.922 | [Download](2520/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-2520](2520/previews/pattern_1.png) | [<NSFW, click to see>](2520/previews/pattern_2.png) | ![pattern_3-2520](2520/previews/pattern_3.png) | ![pattern_4-2520](2520/previews/pattern_4.png) | [<NSFW, click to see>](2520/previews/pattern_5.png) | [<NSFW, click to see>](2520/previews/pattern_6.png) | [<NSFW, click to see>](2520/previews/pattern_7.png) | ![pattern_8-2520](2520/previews/pattern_8.png) | ![pattern_9-2520](2520/previews/pattern_9.png) | ![pattern_10-2520](2520/previews/pattern_10.png) | [<NSFW, click to see>](2520/previews/pattern_11.png) | [<NSFW, click to see>](2520/previews/bikini.png) | [<NSFW, click to see>](2520/previews/bondage.png) | [<NSFW, click to see>](2520/previews/free.png) | ![maid-2520](2520/previews/maid.png) | ![miko-2520](2520/previews/miko.png) | [<NSFW, click to see>](2520/previews/nude.png) | [<NSFW, click to see>](2520/previews/nude2.png) | ![suit-2520](2520/previews/suit.png) | ![yukata-2520](2520/previews/yukata.png) | | 2160 | 0.854 | [Download](2160/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-2160](2160/previews/pattern_1.png) | [<NSFW, click to see>](2160/previews/pattern_2.png) | ![pattern_3-2160](2160/previews/pattern_3.png) | ![pattern_4-2160](2160/previews/pattern_4.png) | [<NSFW, click to see>](2160/previews/pattern_5.png) | [<NSFW, click to see>](2160/previews/pattern_6.png) | [<NSFW, click to see>](2160/previews/pattern_7.png) | ![pattern_8-2160](2160/previews/pattern_8.png) | ![pattern_9-2160](2160/previews/pattern_9.png) | ![pattern_10-2160](2160/previews/pattern_10.png) | [<NSFW, click to see>](2160/previews/pattern_11.png) | [<NSFW, click to see>](2160/previews/bikini.png) | [<NSFW, click to see>](2160/previews/bondage.png) | [<NSFW, click to see>](2160/previews/free.png) | ![maid-2160](2160/previews/maid.png) | ![miko-2160](2160/previews/miko.png) | [<NSFW, click to see>](2160/previews/nude.png) | [<NSFW, click to see>](2160/previews/nude2.png) | ![suit-2160](2160/previews/suit.png) | ![yukata-2160](2160/previews/yukata.png) | | 1800 | 0.801 | [Download](1800/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-1800](1800/previews/pattern_1.png) | [<NSFW, click to see>](1800/previews/pattern_2.png) | ![pattern_3-1800](1800/previews/pattern_3.png) | ![pattern_4-1800](1800/previews/pattern_4.png) | [<NSFW, click to see>](1800/previews/pattern_5.png) | [<NSFW, click to see>](1800/previews/pattern_6.png) | [<NSFW, click to see>](1800/previews/pattern_7.png) | ![pattern_8-1800](1800/previews/pattern_8.png) | ![pattern_9-1800](1800/previews/pattern_9.png) | ![pattern_10-1800](1800/previews/pattern_10.png) | [<NSFW, click to see>](1800/previews/pattern_11.png) | [<NSFW, click to see>](1800/previews/bikini.png) | [<NSFW, click to see>](1800/previews/bondage.png) | [<NSFW, click to see>](1800/previews/free.png) | ![maid-1800](1800/previews/maid.png) | ![miko-1800](1800/previews/miko.png) | [<NSFW, click to see>](1800/previews/nude.png) | [<NSFW, click to see>](1800/previews/nude2.png) | ![suit-1800](1800/previews/suit.png) | ![yukata-1800](1800/previews/yukata.png) | | 1440 | 0.803 | [Download](1440/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-1440](1440/previews/pattern_1.png) | [<NSFW, click to see>](1440/previews/pattern_2.png) | ![pattern_3-1440](1440/previews/pattern_3.png) | ![pattern_4-1440](1440/previews/pattern_4.png) | [<NSFW, click to see>](1440/previews/pattern_5.png) | [<NSFW, click to see>](1440/previews/pattern_6.png) | [<NSFW, click to see>](1440/previews/pattern_7.png) | ![pattern_8-1440](1440/previews/pattern_8.png) | ![pattern_9-1440](1440/previews/pattern_9.png) | ![pattern_10-1440](1440/previews/pattern_10.png) | [<NSFW, click to see>](1440/previews/pattern_11.png) | [<NSFW, click to see>](1440/previews/bikini.png) | [<NSFW, click to see>](1440/previews/bondage.png) | [<NSFW, click to see>](1440/previews/free.png) | ![maid-1440](1440/previews/maid.png) | ![miko-1440](1440/previews/miko.png) | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) | ![suit-1440](1440/previews/suit.png) | ![yukata-1440](1440/previews/yukata.png) | | 1080 | 0.769 | [Download](1080/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-1080](1080/previews/pattern_1.png) | [<NSFW, click to see>](1080/previews/pattern_2.png) | ![pattern_3-1080](1080/previews/pattern_3.png) | ![pattern_4-1080](1080/previews/pattern_4.png) | [<NSFW, click to see>](1080/previews/pattern_5.png) | [<NSFW, click to see>](1080/previews/pattern_6.png) | [<NSFW, click to see>](1080/previews/pattern_7.png) | ![pattern_8-1080](1080/previews/pattern_8.png) | ![pattern_9-1080](1080/previews/pattern_9.png) | ![pattern_10-1080](1080/previews/pattern_10.png) | [<NSFW, click to see>](1080/previews/pattern_11.png) | [<NSFW, click to see>](1080/previews/bikini.png) | [<NSFW, click to see>](1080/previews/bondage.png) | [<NSFW, click to see>](1080/previews/free.png) | ![maid-1080](1080/previews/maid.png) | ![miko-1080](1080/previews/miko.png) | [<NSFW, click to see>](1080/previews/nude.png) | [<NSFW, click to see>](1080/previews/nude2.png) | ![suit-1080](1080/previews/suit.png) | ![yukata-1080](1080/previews/yukata.png) | | 720 | 0.541 | [Download](720/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-720](720/previews/pattern_1.png) | [<NSFW, click to see>](720/previews/pattern_2.png) | ![pattern_3-720](720/previews/pattern_3.png) | ![pattern_4-720](720/previews/pattern_4.png) | [<NSFW, click to see>](720/previews/pattern_5.png) | [<NSFW, click to see>](720/previews/pattern_6.png) | [<NSFW, click to see>](720/previews/pattern_7.png) | ![pattern_8-720](720/previews/pattern_8.png) | ![pattern_9-720](720/previews/pattern_9.png) | ![pattern_10-720](720/previews/pattern_10.png) | [<NSFW, click to see>](720/previews/pattern_11.png) | [<NSFW, click to see>](720/previews/bikini.png) | [<NSFW, click to see>](720/previews/bondage.png) | [<NSFW, click to see>](720/previews/free.png) | ![maid-720](720/previews/maid.png) | ![miko-720](720/previews/miko.png) | [<NSFW, click to see>](720/previews/nude.png) | [<NSFW, click to see>](720/previews/nude2.png) | ![suit-720](720/previews/suit.png) | ![yukata-720](720/previews/yukata.png) | | 360 | 0.621 | [Download](360/ooishi_izumi_idolmastercinderellagirls.zip) | ![pattern_1-360](360/previews/pattern_1.png) | [<NSFW, click to see>](360/previews/pattern_2.png) | ![pattern_3-360](360/previews/pattern_3.png) | ![pattern_4-360](360/previews/pattern_4.png) | [<NSFW, click to see>](360/previews/pattern_5.png) | [<NSFW, click to see>](360/previews/pattern_6.png) | [<NSFW, click to see>](360/previews/pattern_7.png) | ![pattern_8-360](360/previews/pattern_8.png) | ![pattern_9-360](360/previews/pattern_9.png) | ![pattern_10-360](360/previews/pattern_10.png) | [<NSFW, click to see>](360/previews/pattern_11.png) | [<NSFW, click to see>](360/previews/bikini.png) | [<NSFW, click to see>](360/previews/bondage.png) | [<NSFW, click to see>](360/previews/free.png) | ![maid-360](360/previews/maid.png) | ![miko-360](360/previews/miko.png) | [<NSFW, click to see>](360/previews/nude.png) | [<NSFW, click to see>](360/previews/nude2.png) | ![suit-360](360/previews/suit.png) | ![yukata-360](360/previews/yukata.png) |
trieudemo11/llama_7b_attrb_cate_4m_2
trieudemo11
2023-09-19T10:25:07Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-19T10:24:53Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0
royokong/prompteol-opt-2.7b
royokong
2023-09-19T10:21:14Z
390
0
peft
[ "peft", "base_model:facebook/opt-2.7b", "base_model:adapter:facebook/opt-2.7b", "region:us" ]
null
2023-07-27T15:02:56Z
--- library_name: peft base_model: facebook/opt-2.7b --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
Wariano/bsc-bio-ehr-es-vih-juicio_anam_urgen
Wariano
2023-09-19T10:13:27Z
24
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-18T06:38:21Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bsc-bio-ehr-es-vih-juicio_anam_urgen results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bsc-bio-ehr-es-vih-juicio_anam_urgen This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0364 - Positives Preds: 1040 - Negative Preds: 208738 - Positives Refs: 1961 - Negative Refs: 207817 - Tp: 826 - Fn: 1135 - Fp: 214 - Tn: 207603 - Accuracy: 0.9936 - Precision: 0.7942 - Recall: 0.4212 - F1: 0.5505 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Positives Preds | Negative Preds | Positives Refs | Negative Refs | Tp | Fn | Fp | Tn | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:------:|:---------------:|:---------------:|:--------------:|:--------------:|:-------------:|:---:|:----:|:---:|:------:|:--------:|:---------:|:------:|:------:| | 0.0372 | 1.0 | 26223 | 0.0358 | 1276 | 208502 | 1961 | 207817 | 888 | 1073 | 388 | 207429 | 0.9930 | 0.6959 | 0.4528 | 0.5487 | | 0.04 | 2.0 | 52446 | 0.0364 | 1223 | 208555 | 1961 | 207817 | 873 | 1088 | 350 | 207467 | 0.9931 | 0.7138 | 0.4452 | 0.5484 | | 0.037 | 3.0 | 78669 | 0.0362 | 1251 | 208527 | 1961 | 207817 | 870 | 1091 | 381 | 207436 | 0.9930 | 0.6954 | 0.4437 | 0.5417 | | 0.0368 | 4.0 | 104892 | 0.0361 | 1125 | 208653 | 1961 | 207817 | 848 | 1113 | 277 | 207540 | 0.9934 | 0.7538 | 0.4324 | 0.5496 | | 0.0367 | 5.0 | 131115 | 0.0364 | 1040 | 208738 | 1961 | 207817 | 826 | 1135 | 214 | 207603 | 0.9936 | 0.7942 | 0.4212 | 0.5505 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
MattStammers/appo-Humanoid
MattStammers
2023-09-19T10:11:16Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T10:11:11Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: mujoco_humanoid type: mujoco_humanoid metrics: - type: mean_reward value: 6743.15 +/- 2083.46 name: mean_reward verified: false --- A(n) **APPO** model trained on the **mujoco_humanoid** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-Humanoid ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_humanoid --train_dir=./train_dir --experiment=appo-Humanoid ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_humanoid --train_dir=./train_dir --experiment=appo-Humanoid --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
iashchak/ruGPT-3.5-13B-ggml
iashchak
2023-09-19T10:05:25Z
12
15
transformers
[ "transformers", "gpt2", "ruGPT", "GGML", "NLP", "Text Generation", "ru", "en", "license:mit", "endpoints_compatible", "region:us" ]
null
2023-09-10T18:48:07Z
--- language: - ru - en tags: - ruGPT - GGML - NLP - Text Generation license: "mit" --- # ruGPT-3.5-13B Converted to GGML Format / ruGPT-3.5-13B Конвертированная в формат GGML ## Model Description / Описание модели ### English This repository contains a GGML-formatted version of the [ruGPT-3.5-13B model](https://huggingface.co/ai-forever/ruGPT-3.5-13B) originally hosted on Hugging Face. The model has 13 billion parameters and was initially trained on a 300GB dataset from various domains. It was further fine-tuned on 100GB of code and legal documents. The model understands both Russian and English. #### Dataset Details - **Training Data**: 300GB from various domains - **Fine-tuning Data**: 100GB of code and legal documents - **Technical Specs**: Trained using Deepspeed and Megatron libraries on 300B tokens dataset for 3 epochs, around 45 days on 512 V100 GPUs. Fine-tuned for 1 epoch with a sequence length of 2048, around 20 days on 200 A100 GPUs. - **Perplexity**: Around 8.8 for Russian language #### Usage ##### For QuantizationType.Q4_0 and ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q4_0-ggjt.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q4_0 and ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q4_0.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q4_1 and ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q4_1-ggjt.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q4_1 and ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q4_1.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q5_0 and ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q5_0-ggjt.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q5_0 and ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q5_0.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q5_1 and ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q5_1-ggjt.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q5_1 and ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q5_1.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q8_0 and ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q8_0-ggjt.bin") print(model.generate("The meaning of life is ").text) ``` ##### For QuantizationType.Q8_0 and ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q8_0.bin") print(model.generate("The meaning of life is ").text) ``` ##### f16 Version ```python # f16 Version from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-f16.bin") print(model.generate("Смысл жизни в ").text) ``` #### Compatibility While this model is intended to be compatible with any GGML-compatible UI, it has not been extensively tested in such environments. Use at your own risk. ### Русский Этот репозиторий содержит версию модели [ruGPT-3.5-13B](https://huggingface.co/ai-forever/ruGPT-3.5-13B) в формате GGML. Модель имеет 13 миллиардов параметров и изначально обучалась на 300ГБ данных из различных доменов. Далее она была дообучена на 100ГБ кода и юридических документов. Модель понимает как русский, так и английский языки. #### Детали набора данных - **Тренировочные данные**: 300ГБ из различных доменов - **Данные для дообучения**: 100ГБ кода и юридических документов - **Технические характеристики**: Обучена с использованием библиотек Deepspeed и Megatron на наборе данных из 300 миллиардов токенов за 3 эпохи, примерно 45 дней на 512 GPU V100. Дообучена 1 эпоху с длиной последовательности 2048, примерно 20 дней на 200 GPU A100. - **Перплексия**: Около 8,8 для русского языка #### Использование ##### Для QuantizationType.Q4_0 и ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q4_0-ggjt.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q4_0 и ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q4_0.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q4_1 и ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q4_1-ggjt.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q4_1 и ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q4_1.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q5_0 и ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q5_0-ggjt.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q5_0 и ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q5_0.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q5_1 и ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q5_1-ggjt.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q5_1 и ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q5_1.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q8_0 и ContainerType.GGJT ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q8_0-ggjt.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Для QuantizationType.Q8_0 и ContainerType.GGML ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-q8_0.bin") print(model.generate("Смысл жизни в ").text) ``` ##### Версия f16 ```python from llm_rs import AutoModel model = AutoModel.from_pretrained("iashchak/ruGPT-3.5-13B-ggml", model_file="ruGPT-3.5-13B-f16.bin") print(model.generate("Смысл жизни в ").text) ``` #### Совместимость Хотя эта модель предназначена для совместимости с любым GGML-совместимым интерфейсом, она не была тщательно протестирована в таких средах. Используйте на свой страх и риск.
davidkim205/komt-Llama-2-7b-chat-hf-ggml
davidkim205
2023-09-19T10:00:39Z
0
6
null
[ "facebook", "meta", "pytorch", "llama", "llama-2", "llama-2-chat", "text-generation", "en", "ko", "license:apache-2.0", "region:us" ]
text-generation
2023-08-16T07:21:58Z
--- language: - en - ko pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 - llama-2-chat license: apache-2.0 --- # komt : korean multi task instruction tuning model Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities. However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs). ## Model Details * **Model Developers** : davidkim(changyeon kim) * **Repository** : https://github.com/davidkim205/komt * **quant methods** : q4_0, q4_1, q5_0, q5_1, q2_k, q3_k, q3_k_m, q3_k_l, q4_k, q4_k_s, q4_k_m, q5_k, q5_k_s, q5_k_m, q8_0, q4_0
HumanCompatibleAI/sac-seals-Swimmer-v1
HumanCompatibleAI
2023-09-19T09:57:29Z
2
0
stable-baselines3
[ "stable-baselines3", "seals/Swimmer-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:56:38Z
--- library_name: stable-baselines3 tags: - seals/Swimmer-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Swimmer-v1 type: seals/Swimmer-v1 metrics: - type: mean_reward value: 28.90 +/- 1.67 name: mean_reward verified: false --- # **SAC** Agent playing **seals/Swimmer-v1** This is a trained model of a **SAC** agent playing **seals/Swimmer-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo sac --env seals/Swimmer-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Swimmer-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo sac --env seals/Swimmer-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Swimmer-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/Swimmer-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/Swimmer-v1 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 100000), ('gamma', 0.995), ('learning_rate', 0.00039981805535514633), ('learning_starts', 1000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -2.689958330139309, 'net_arch': [400, 300], 'use_sde': False}), ('tau', 0.01), ('train_freq', 256), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
HumanCompatibleAI/sac-seals-Ant-v1
HumanCompatibleAI
2023-09-19T09:54:44Z
2
0
stable-baselines3
[ "stable-baselines3", "seals/Ant-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:53:41Z
--- library_name: stable-baselines3 tags: - seals/Ant-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Ant-v1 type: seals/Ant-v1 metrics: - type: mean_reward value: 1004.15 +/- 26.60 name: mean_reward verified: false --- # **SAC** Agent playing **seals/Ant-v1** This is a trained model of a **SAC** agent playing **seals/Ant-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo sac --env seals/Ant-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Ant-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo sac --env seals/Ant-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Ant-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/Ant-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/Ant-v1 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('buffer_size', 1000000), ('gamma', 0.98), ('learning_rate', 0.0018514039303149058), ('learning_starts', 1000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -2.2692589009754176, 'net_arch': [256, 256], 'use_sde': False}), ('tau', 0.05), ('train_freq', 64), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
HumanCompatibleAI/sac-seals-Hopper-v1
HumanCompatibleAI
2023-09-19T09:52:21Z
3
0
stable-baselines3
[ "stable-baselines3", "seals/Hopper-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:51:24Z
--- library_name: stable-baselines3 tags: - seals/Hopper-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SAC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Hopper-v1 type: seals/Hopper-v1 metrics: - type: mean_reward value: 2279.30 +/- 124.09 name: mean_reward verified: false --- # **SAC** Agent playing **seals/Hopper-v1** This is a trained model of a **SAC** agent playing **seals/Hopper-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo sac --env seals/Hopper-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Hopper-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo sac --env seals/Hopper-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo sac --env seals/Hopper-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo sac --env seals/Hopper-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo sac --env seals/Hopper-v1 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 100000), ('gamma', 0.98), ('learning_rate', 0.001709807687567946), ('learning_starts', 1000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'log_std_init': -1.6829391077276037, 'net_arch': [256, 256], 'use_sde': False}), ('tau', 0.08), ('train_freq', 32), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
EladAssia/LunarLander-v2
EladAssia
2023-09-19T09:48:54Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:48:34Z
--- 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: 296.39 +/- 17.72 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 ... ```
HumanCompatibleAI/ppo-seals-Humanoid-v1
HumanCompatibleAI
2023-09-19T09:47:36Z
5
0
stable-baselines3
[ "stable-baselines3", "seals/Humanoid-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:46:15Z
--- library_name: stable-baselines3 tags: - seals/Humanoid-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Humanoid-v1 type: seals/Humanoid-v1 metrics: - type: mean_reward value: 3224.12 +/- 925.36 name: mean_reward verified: false --- # **PPO** Agent playing **seals/Humanoid-v1** This is a trained model of a **PPO** agent playing **seals/Humanoid-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env seals/Humanoid-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Humanoid-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo --env seals/Humanoid-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Humanoid-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Humanoid-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Humanoid-v1 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 0.2), ('ent_coef', 2.0745206045994986e-05), ('gae_lambda', 0.92), ('gamma', 0.999), ('learning_rate', 2.0309225666232827e-05), ('max_grad_norm', 0.5), ('n_envs', 1), ('n_epochs', 20), ('n_steps', 2048), ('n_timesteps', 10000000.0), ('normalize', {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.ReLU'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [256, 256], 'vf': [256, 256]}]}), ('vf_coef', 0.819262464558427), ('normalize_kwargs', {'norm_obs': {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
HumanCompatibleAI/ppo-seals-Hopper-v1
HumanCompatibleAI
2023-09-19T09:45:56Z
0
0
stable-baselines3
[ "stable-baselines3", "seals/Hopper-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:44:39Z
--- library_name: stable-baselines3 tags: - seals/Hopper-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Hopper-v1 type: seals/Hopper-v1 metrics: - type: mean_reward value: 203.45 +/- 1.19 name: mean_reward verified: false --- # **PPO** Agent playing **seals/Hopper-v1** This is a trained model of a **PPO** agent playing **seals/Hopper-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env seals/Hopper-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Hopper-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo --env seals/Hopper-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Hopper-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Hopper-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Hopper-v1 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('clip_range', 0.1), ('ent_coef', 0.0010159833764878474), ('gae_lambda', 0.98), ('gamma', 0.995), ('learning_rate', 0.0003904770450788824), ('max_grad_norm', 0.9), ('n_envs', 1), ('n_epochs', 20), ('n_steps', 2048), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.995, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.ReLU'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.20315938606555833), ('normalize_kwargs', {'norm_obs': {'gamma': 0.995, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
HumanCompatibleAI/ppo-seals-Swimmer-v1
HumanCompatibleAI
2023-09-19T09:44:26Z
0
0
stable-baselines3
[ "stable-baselines3", "seals/Swimmer-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:43:45Z
--- library_name: stable-baselines3 tags: - seals/Swimmer-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Swimmer-v1 type: seals/Swimmer-v1 metrics: - type: mean_reward value: 292.84 +/- 3.69 name: mean_reward verified: false --- # **PPO** Agent playing **seals/Swimmer-v1** This is a trained model of a **PPO** agent playing **seals/Swimmer-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env seals/Swimmer-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Swimmer-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo --env seals/Swimmer-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Swimmer-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Swimmer-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Swimmer-v1 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 8), ('clip_range', 0.1), ('ent_coef', 5.167107294612664e-08), ('gae_lambda', 0.95), ('gamma', 0.999), ('learning_rate', 0.0001214437022727675), ('max_grad_norm', 2), ('n_epochs', 20), ('n_steps', 2048), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.6162112311062333), ('normalize_kwargs', {'norm_obs': {'gamma': 0.999, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
EladAssia/LunarLanderV2
EladAssia
2023-09-19T09:44:16Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:43:43Z
--- 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: 274.49 +/- 17.73 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 ... ```
HumanCompatibleAI/ppo-seals-Ant-v1
HumanCompatibleAI
2023-09-19T09:43:32Z
49
0
stable-baselines3
[ "stable-baselines3", "seals/Ant-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T09:42:28Z
--- library_name: stable-baselines3 tags: - seals/Ant-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/Ant-v1 type: seals/Ant-v1 metrics: - type: mean_reward value: 2461.22 +/- 674.80 name: mean_reward verified: false --- # **PPO** Agent playing **seals/Ant-v1** This is a trained model of a **PPO** agent playing **seals/Ant-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env seals/Ant-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Ant-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo --env seals/Ant-v1 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/Ant-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/Ant-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/Ant-v1 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 16), ('clip_range', 0.3), ('ent_coef', 3.1441389214159857e-06), ('gae_lambda', 0.8), ('gamma', 0.995), ('learning_rate', 0.00017959211641976886), ('max_grad_norm', 0.9), ('n_epochs', 10), ('n_steps', 2048), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.995, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.4351450387648799), ('normalize_kwargs', {'norm_obs': {'gamma': 0.995, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
HumanCompatibleAI/ppo-seals-CartPole-v0
HumanCompatibleAI
2023-09-19T09:41:41Z
737
16
stable-baselines3
[ "stable-baselines3", "seals/CartPole-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-29T13:39:32Z
--- library_name: stable-baselines3 tags: - seals/CartPole-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/CartPole-v0 type: seals/CartPole-v0 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **PPO** Agent playing **seals/CartPole-v0** This is a trained model of a **PPO** agent playing **seals/CartPole-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env seals/CartPole-v0 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/CartPole-v0 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo --env seals/CartPole-v0 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/CartPole-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/CartPole-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/CartPole-v0 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 0.4), ('ent_coef', 0.008508727919228772), ('gae_lambda', 0.9), ('gamma', 0.9999), ('learning_rate', 0.0012403278189645594), ('max_grad_norm', 0.8), ('n_envs', 8), ('n_epochs', 10), ('n_steps', 512), ('n_timesteps', 100000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.ReLU'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.489343896591493), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
HumanCompatibleAI/ppo-seals-MountainCar-v0
HumanCompatibleAI
2023-09-19T09:41:07Z
19
1
stable-baselines3
[ "stable-baselines3", "seals/MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-07-25T10:59:50Z
--- library_name: stable-baselines3 tags: - seals/MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: seals/MountainCar-v0 type: seals/MountainCar-v0 metrics: - type: mean_reward value: -97.00 +/- 8.26 name: mean_reward verified: false --- # **PPO** Agent playing **seals/MountainCar-v0** This is a trained model of a **PPO** agent playing **seals/MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env seals/MountainCar-v0 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-v0 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo --env seals/MountainCar-v0 -orga HumanCompatibleAI -f logs/ python -m rl_zoo3.enjoy --algo ppo --env seals/MountainCar-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env seals/MountainCar-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env seals/MountainCar-v0 -f logs/ -orga HumanCompatibleAI ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('clip_range', 0.2), ('ent_coef', 6.4940755116195606e-06), ('gae_lambda', 0.98), ('gamma', 0.99), ('learning_rate', 0.0004476103728105138), ('max_grad_norm', 1), ('n_envs', 16), ('n_epochs', 20), ('n_steps', 256), ('n_timesteps', 1000000.0), ('normalize', {'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}), ('policy', 'MlpPolicy'), ('policy_kwargs', {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>, 'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>, 'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}), ('vf_coef', 0.25988158989488963), ('normalize_kwargs', {'norm_obs': {'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}, 'norm_reward': False})]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Vivekup/whisper-small
Vivekup
2023-09-19T09:22:11Z
76
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-19T09:12:17Z
--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Vivekup results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Vivekup This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3
adhishezio/model
adhishezio
2023-09-19T09:07:01Z
29
0
diffusers
[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-09-19T08:02:47Z
--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - adhishezio/model This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
YaminiMahesh/llma2-7b-text-to-sql
YaminiMahesh
2023-09-19T09:05:51Z
21
1
peft
[ "peft", "region:us" ]
null
2023-09-19T08:13:38Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
bongo2112/sdxl-db-mwijaku-headshot
bongo2112
2023-09-19T09:05:29Z
2
1
diffusers
[ "diffusers", "tensorboard", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]
text-to-image
2023-09-19T09:01:16Z
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of mwijakudc man tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
Leekp/toonmaker5
Leekp
2023-09-19T09:02:08Z
1
2
diffusers
[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]
text-to-image
2023-09-19T09:02:00Z
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: Korean webtoon image depicting a character named baek tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
dg845/unidiffuser-diffusers
dg845
2023-09-19T08:48:10Z
13
1
diffusers
[ "diffusers", "text-to-image", "image-to-text", "image-captioning", "image-variation", "text-variation", "multi-modality", "generative model", "arxiv:2303.06555", "license:agpl-3.0", "diffusers:UniDiffuserPipeline", "region:us" ]
text-to-image
2023-04-25T05:02:36Z
--- license: agpl-3.0 tags: - text-to-image - image-to-text - image-captioning - image-variation - text-variation - multi-modality - generative model --- This model is a version of the UniDiffuser-v1 ([original code](https://github.com/thu-ml/unidiffuser), [original model](https://huggingface.co/thu-ml/unidiffuser-v1)) checkpoint which is compatible with `diffusers`. This is one of two models from the original UniDiffuser release, the other being [UniDiffuser-v0](). From the original model card: UniDiffuser is a unified diffusion framework to fit all distributions relevant to a set of multi-modal data in one transformer. UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead. Specifically, UniDiffuser employs a variation of transformer, called [U-ViT](https://github.com/baofff/U-ViT), which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from [Stable Diffusion](https://github.com/CompVis/stable-diffusion), a pretrained [image ViT-B/32 CLIP encoder](https://github.com/openai/CLIP), a pretrained [text ViT-L CLIP encoder](https://huggingface.co/openai/clip-vit-large-patch14), and a [GPT-2](https://github.com/openai/gpt-2) text decoder finetuned by ourselves. We provide two versions of UniDiffuser: - [UniDiffuser-v0](https://huggingface.co/thu-ml/unidiffuser-v0): This version is trained on [LAION-5B](https://laion.ai/), which contains noisy webdata of text-image pairs. - [UniDiffuser-v1](https://huggingface.co/thu-ml/unidiffuser-v1): This version is resumed from UniDiffuser-v0, and is further trained with a set of less noisy internal text-image pairs. It uses a flag as its input to distinguish webdata and internal data during training. ## Example ```python import requests import torch from PIL import Image from io import BytesIO from diffusers import UniDiffuserPipeline device = "cuda" model_id_or_path = "dg845/unidiffuser-diffusers" pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path) pipe.to(device) # Joint image-text generation. The generation task is automatically inferred. sample = pipe(num_inference_steps=20, guidance_scale=8.0) image = sample.images[0] text = sample.text[0] image.save("unidiffuser_sample_joint_image.png") print(text) # The mode can be set manually. The following is equivalent to the above: pipe.set_joint_mode() sample2 = pipe(num_inference_steps=20, guidance_scale=8.0) # Note that if you set the mode manually the pipeline will no longer attempt # to automatically infer the mode. You can re-enable this with reset_mode(). pipe.reset_mode() # Text-to-image generation. prompt = "an elephant under the sea" sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0) t2i_image = sample.images[0] t2i_image.save("unidiffuser_sample_text2img_image.png") # Image-to-text generation. image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg" response = requests.get(image_url) init_image = Image.open(BytesIO(response.content)).convert("RGB") init_image = init_image.resize((512, 512)) sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0) i2t_text = sample.text[0] print(text) # Image variation can be performed with a image-to-text generation followed by a text-to-image generation: sample = pipe(prompt=i2t_text, num_inference_steps=20, guidance_scale=8.0) final_image = sample.images[0] final_image.save("unidiffuser_image_variation_sample.png") # Text variation can be performed with a text-to-image generation followed by a image-to-text generation: sample = pipe(image=t2i_image, num_inference_steps=20, guidance_scale=8.0) final_prompt = sample.text[0] print(final_prompt) ``` ## Model Details - **Model type:** Diffusion-based multi-modal generation model - **Language(s):** English - **License:** agpl-3.0 - **Model Description:** This is a model that can perform image, text, text-to-image, image-to-text, and image-text pair generation. Its main component is a [U-ViT](https://github.com/baofff/U-ViT), which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from [Stable Diffusion](https://github.com/CompVis/stable-diffusion), a pretrained [image ViT-B/32 CLIP encoder](https://github.com/openai/CLIP), a pretrained [text ViT-L CLIP encoder](https://huggingface.co/openai/clip-vit-large-patch14), and a [GPT-2](https://github.com/openai/gpt-2) text decoder finetuned by ourselves. - **Resources for more information:** [GitHub Repository](https://github.com/thu-ml/unidiffuser), [Paper](https://arxiv.org/abs/2303.06555). ## Direct Use _Note: Most of this section is taken from the [Stable Diffusion model card](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), but applies in the same way to UniDiffuser_. The model should be used following the agpl-3.0 license. Possible usage includes - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
takumi12/id2pg_pattern2_en_batchsize8_epoch12
takumi12
2023-09-19T08:37:02Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-19T08:36:56Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0
Sarmila/pubmed-bert-squad-covidqa
Sarmila
2023-09-19T08:34:58Z
73
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "biology", "en", "dataset:covid_qa_deepset", "dataset:squad", "base_model:Sarmila/pubmed-bert-squad-covidqa", "base_model:finetune:Sarmila/pubmed-bert-squad-covidqa", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2023-09-17T05:49:24Z
--- license: mit base_model: Sarmila/pubmed-bert-squad-covidqa tags: - generated_from_trainer - biology datasets: - covid_qa_deepset - squad model-index: - name: pubmed-bert-squad-covidqa results: [] language: - en pipeline_tag: question-answering --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pubmed-bert-squad-covidqa This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the squad qa first, covid_qa_deepset dataset. It achieves the following results on the evaluation set for squad: {'exact_match': 59.0, 'f1': 76.32473929579194} - Loss 1.003116 It achieves the following results on the evaluation set for covidqa: - Loss: 0.4876 ## Model description This model is trained with an intention of testing pumed bert bionlp language model for question answering pipeline. While testing on our custom dataset, we reliazed that the model when used directly for QA did not perform well at all. Hence, we decided to train on covidqa to make model accustomed with answer extraction. While, covidqa data is very similar to what we intended to use, it is samll in number hence resulting not much improvement. Therefore, we firt trained the model in squad dataset which is larger in number. Then, we trained the model for covid qa. Hence, squad helped model to learn how to extract answers and covid qa helped us to train the model on domain similar to ours i.e. biomedicine further, we have first performed MLM using our dataset on pubmed bert bionlp and then performed exactly same üiüeline to see the difference which is [here] ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 51 | 0.4001 | | No log | 2.0 | 102 | 0.4524 | | No log | 3.0 | 153 | 0.4876 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
CyberHarem/kiryuu_tsukasa_idolmastercinderellagirls
CyberHarem
2023-09-19T08:28:08Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/kiryuu_tsukasa_idolmastercinderellagirls", "license:mit", "region:us" ]
text-to-image
2023-09-19T08:12:41Z
--- license: mit datasets: - CyberHarem/kiryuu_tsukasa_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of kiryuu_tsukasa_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3740, you need to download `3740/kiryuu_tsukasa_idolmastercinderellagirls.pt` as the embedding and `3740/kiryuu_tsukasa_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 3740**, with the score of 0.993. The trigger words are: 1. `kiryuu_tsukasa_idolmastercinderellagirls` 2. `blonde_hair, purple_eyes, long_hair, jewelry, earrings, smile, bangs, breasts, blush` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.984 | [Download](5100/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.png) | ![pattern_7-5100](5100/previews/pattern_7.png) | ![pattern_8-5100](5100/previews/pattern_8.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.985 | [Download](4760/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![pattern_8-4760](4760/previews/pattern_8.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.982 | [Download](4420/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.png) | ![pattern_7-4420](4420/previews/pattern_7.png) | ![pattern_8-4420](4420/previews/pattern_8.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.989 | [Download](4080/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![pattern_8-4080](4080/previews/pattern_8.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | **3740** | **0.993** | [**Download**](3740/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.png) | ![pattern_7-3740](3740/previews/pattern_7.png) | ![pattern_8-3740](3740/previews/pattern_8.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.990 | [Download](3400/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![pattern_8-3400](3400/previews/pattern_8.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.964 | [Download](3060/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.png) | ![pattern_7-3060](3060/previews/pattern_7.png) | ![pattern_8-3060](3060/previews/pattern_8.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.988 | [Download](2720/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![pattern_8-2720](2720/previews/pattern_8.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.976 | [Download](2380/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.png) | ![pattern_7-2380](2380/previews/pattern_7.png) | ![pattern_8-2380](2380/previews/pattern_8.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.966 | [Download](2040/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![pattern_8-2040](2040/previews/pattern_8.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.964 | [Download](1700/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.png) | ![pattern_7-1700](1700/previews/pattern_7.png) | ![pattern_8-1700](1700/previews/pattern_8.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.975 | [Download](1360/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![pattern_8-1360](1360/previews/pattern_8.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.980 | [Download](1020/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.png) | ![pattern_7-1020](1020/previews/pattern_7.png) | ![pattern_8-1020](1020/previews/pattern_8.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.969 | [Download](680/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![pattern_8-680](680/previews/pattern_8.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.852 | [Download](340/kiryuu_tsukasa_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.png) | ![pattern_7-340](340/previews/pattern_7.png) | ![pattern_8-340](340/previews/pattern_8.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |
muvva/opus-mt-en-hi-finetuned-en-to-hi
muvva
2023-09-19T08:19:55Z
25
1
transformers
[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-hi", "base_model:finetune:Helsinki-NLP/opus-mt-en-hi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-09-12T10:39:45Z
--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-hi tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-en-hi-finetuned-en-to-hi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-hi-finetuned-en-to-hi This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) on the [hind_encorp](https://huggingface.co/datasets/hind_encorp) dataset. It achieves the following results on the evaluation set: - Loss: 3.1807 - Bleu: 14.0103 - Gen Len: 24.1149 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 3.3667 | 1.0 | 13695 | 3.1807 | 14.0103 | 24.1149 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
urbija/ner-bio-annotated-7
urbija
2023-09-19T08:19:00Z
118
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-09-19T06:55:53Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ner-bio-annotated-7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ner-bio-annotated-7 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3098 - Precision: 0.6877 - Recall: 0.7570 - F1: 0.7207 - Accuracy: 0.8997 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 67 | 0.5051 | 0.4822 | 0.6199 | 0.5425 | 0.8298 | | No log | 2.0 | 134 | 0.3376 | 0.6670 | 0.7173 | 0.6913 | 0.8889 | | No log | 3.0 | 201 | 0.3098 | 0.6877 | 0.7570 | 0.7207 | 0.8997 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0+cpu - Datasets 2.1.0 - Tokenizers 0.13.3
dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_qlora_merged
dhmeltzer
2023-09-19T08:14:05Z
8
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-16T18:36:39Z
trained for 3 epochs on ELI5 + simple wiki datasets
Carve/cascadepsp
Carve
2023-09-19T08:04:38Z
0
3
null
[ "license:apache-2.0", "region:us" ]
null
2023-01-26T15:19:48Z
--- license: apache-2.0 --- Trained on MSRA-10K, DUT-OMRON, ECSSD and FSS-1000 datasets. This model is used to refine the segmentation mask after seg. network. `cascadepsp_finetuned_carveset.pth` - Finetuned on CarveSet.
CyberHarem/hanabata_nohkins_futokunoguild
CyberHarem
2023-09-19T07:56:39Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/hanabata_nohkins_futokunoguild", "license:mit", "region:us" ]
text-to-image
2023-09-19T07:37:00Z
--- license: mit datasets: - CyberHarem/hanabata_nohkins_futokunoguild pipeline_tag: text-to-image tags: - art --- # Lora of hanabata_nohkins_futokunoguild This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5400, you need to download `5400/hanabata_nohkins_futokunoguild.pt` as the embedding and `5400/hanabata_nohkins_futokunoguild.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 5400**, with the score of 0.920. The trigger words are: 1. `hanabata_nohkins_futokunoguild` 2. `pink_hair, short_hair, blush, green_eyes, open_mouth, breasts, large_breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 8100 | 0.917 | [Download](8100/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-8100](8100/previews/pattern_1.png) | ![pattern_2-8100](8100/previews/pattern_2.png) | ![pattern_3-8100](8100/previews/pattern_3.png) | ![pattern_4-8100](8100/previews/pattern_4.png) | ![pattern_5-8100](8100/previews/pattern_5.png) | ![pattern_6-8100](8100/previews/pattern_6.png) | ![pattern_7-8100](8100/previews/pattern_7.png) | ![pattern_8-8100](8100/previews/pattern_8.png) | ![pattern_9-8100](8100/previews/pattern_9.png) | ![pattern_10-8100](8100/previews/pattern_10.png) | ![pattern_11-8100](8100/previews/pattern_11.png) | ![pattern_12-8100](8100/previews/pattern_12.png) | ![pattern_13-8100](8100/previews/pattern_13.png) | ![pattern_14-8100](8100/previews/pattern_14.png) | [<NSFW, click to see>](8100/previews/bikini.png) | [<NSFW, click to see>](8100/previews/bondage.png) | ![free-8100](8100/previews/free.png) | ![maid-8100](8100/previews/maid.png) | ![miko-8100](8100/previews/miko.png) | [<NSFW, click to see>](8100/previews/nude.png) | [<NSFW, click to see>](8100/previews/nude2.png) | ![suit-8100](8100/previews/suit.png) | ![yukata-8100](8100/previews/yukata.png) | | 7560 | 0.859 | [Download](7560/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-7560](7560/previews/pattern_1.png) | ![pattern_2-7560](7560/previews/pattern_2.png) | ![pattern_3-7560](7560/previews/pattern_3.png) | ![pattern_4-7560](7560/previews/pattern_4.png) | ![pattern_5-7560](7560/previews/pattern_5.png) | ![pattern_6-7560](7560/previews/pattern_6.png) | ![pattern_7-7560](7560/previews/pattern_7.png) | ![pattern_8-7560](7560/previews/pattern_8.png) | ![pattern_9-7560](7560/previews/pattern_9.png) | ![pattern_10-7560](7560/previews/pattern_10.png) | ![pattern_11-7560](7560/previews/pattern_11.png) | ![pattern_12-7560](7560/previews/pattern_12.png) | ![pattern_13-7560](7560/previews/pattern_13.png) | ![pattern_14-7560](7560/previews/pattern_14.png) | [<NSFW, click to see>](7560/previews/bikini.png) | [<NSFW, click to see>](7560/previews/bondage.png) | ![free-7560](7560/previews/free.png) | ![maid-7560](7560/previews/maid.png) | ![miko-7560](7560/previews/miko.png) | [<NSFW, click to see>](7560/previews/nude.png) | [<NSFW, click to see>](7560/previews/nude2.png) | ![suit-7560](7560/previews/suit.png) | ![yukata-7560](7560/previews/yukata.png) | | 7020 | 0.866 | [Download](7020/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-7020](7020/previews/pattern_1.png) | ![pattern_2-7020](7020/previews/pattern_2.png) | ![pattern_3-7020](7020/previews/pattern_3.png) | ![pattern_4-7020](7020/previews/pattern_4.png) | ![pattern_5-7020](7020/previews/pattern_5.png) | ![pattern_6-7020](7020/previews/pattern_6.png) | ![pattern_7-7020](7020/previews/pattern_7.png) | ![pattern_8-7020](7020/previews/pattern_8.png) | ![pattern_9-7020](7020/previews/pattern_9.png) | ![pattern_10-7020](7020/previews/pattern_10.png) | ![pattern_11-7020](7020/previews/pattern_11.png) | ![pattern_12-7020](7020/previews/pattern_12.png) | ![pattern_13-7020](7020/previews/pattern_13.png) | ![pattern_14-7020](7020/previews/pattern_14.png) | [<NSFW, click to see>](7020/previews/bikini.png) | [<NSFW, click to see>](7020/previews/bondage.png) | ![free-7020](7020/previews/free.png) | ![maid-7020](7020/previews/maid.png) | ![miko-7020](7020/previews/miko.png) | [<NSFW, click to see>](7020/previews/nude.png) | [<NSFW, click to see>](7020/previews/nude2.png) | ![suit-7020](7020/previews/suit.png) | ![yukata-7020](7020/previews/yukata.png) | | 6480 | 0.920 | [Download](6480/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-6480](6480/previews/pattern_1.png) | ![pattern_2-6480](6480/previews/pattern_2.png) | ![pattern_3-6480](6480/previews/pattern_3.png) | ![pattern_4-6480](6480/previews/pattern_4.png) | ![pattern_5-6480](6480/previews/pattern_5.png) | ![pattern_6-6480](6480/previews/pattern_6.png) | ![pattern_7-6480](6480/previews/pattern_7.png) | ![pattern_8-6480](6480/previews/pattern_8.png) | ![pattern_9-6480](6480/previews/pattern_9.png) | ![pattern_10-6480](6480/previews/pattern_10.png) | ![pattern_11-6480](6480/previews/pattern_11.png) | ![pattern_12-6480](6480/previews/pattern_12.png) | ![pattern_13-6480](6480/previews/pattern_13.png) | ![pattern_14-6480](6480/previews/pattern_14.png) | [<NSFW, click to see>](6480/previews/bikini.png) | [<NSFW, click to see>](6480/previews/bondage.png) | ![free-6480](6480/previews/free.png) | ![maid-6480](6480/previews/maid.png) | ![miko-6480](6480/previews/miko.png) | [<NSFW, click to see>](6480/previews/nude.png) | [<NSFW, click to see>](6480/previews/nude2.png) | ![suit-6480](6480/previews/suit.png) | ![yukata-6480](6480/previews/yukata.png) | | 5940 | 0.912 | [Download](5940/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-5940](5940/previews/pattern_1.png) | ![pattern_2-5940](5940/previews/pattern_2.png) | ![pattern_3-5940](5940/previews/pattern_3.png) | ![pattern_4-5940](5940/previews/pattern_4.png) | ![pattern_5-5940](5940/previews/pattern_5.png) | ![pattern_6-5940](5940/previews/pattern_6.png) | ![pattern_7-5940](5940/previews/pattern_7.png) | ![pattern_8-5940](5940/previews/pattern_8.png) | ![pattern_9-5940](5940/previews/pattern_9.png) | ![pattern_10-5940](5940/previews/pattern_10.png) | ![pattern_11-5940](5940/previews/pattern_11.png) | ![pattern_12-5940](5940/previews/pattern_12.png) | ![pattern_13-5940](5940/previews/pattern_13.png) | ![pattern_14-5940](5940/previews/pattern_14.png) | [<NSFW, click to see>](5940/previews/bikini.png) | [<NSFW, click to see>](5940/previews/bondage.png) | ![free-5940](5940/previews/free.png) | ![maid-5940](5940/previews/maid.png) | ![miko-5940](5940/previews/miko.png) | [<NSFW, click to see>](5940/previews/nude.png) | [<NSFW, click to see>](5940/previews/nude2.png) | ![suit-5940](5940/previews/suit.png) | ![yukata-5940](5940/previews/yukata.png) | | **5400** | **0.920** | [**Download**](5400/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-5400](5400/previews/pattern_1.png) | ![pattern_2-5400](5400/previews/pattern_2.png) | ![pattern_3-5400](5400/previews/pattern_3.png) | ![pattern_4-5400](5400/previews/pattern_4.png) | ![pattern_5-5400](5400/previews/pattern_5.png) | ![pattern_6-5400](5400/previews/pattern_6.png) | ![pattern_7-5400](5400/previews/pattern_7.png) | ![pattern_8-5400](5400/previews/pattern_8.png) | ![pattern_9-5400](5400/previews/pattern_9.png) | ![pattern_10-5400](5400/previews/pattern_10.png) | ![pattern_11-5400](5400/previews/pattern_11.png) | ![pattern_12-5400](5400/previews/pattern_12.png) | ![pattern_13-5400](5400/previews/pattern_13.png) | ![pattern_14-5400](5400/previews/pattern_14.png) | [<NSFW, click to see>](5400/previews/bikini.png) | [<NSFW, click to see>](5400/previews/bondage.png) | ![free-5400](5400/previews/free.png) | ![maid-5400](5400/previews/maid.png) | ![miko-5400](5400/previews/miko.png) | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) | ![suit-5400](5400/previews/suit.png) | ![yukata-5400](5400/previews/yukata.png) | | 4860 | 0.905 | [Download](4860/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-4860](4860/previews/pattern_1.png) | ![pattern_2-4860](4860/previews/pattern_2.png) | ![pattern_3-4860](4860/previews/pattern_3.png) | ![pattern_4-4860](4860/previews/pattern_4.png) | ![pattern_5-4860](4860/previews/pattern_5.png) | ![pattern_6-4860](4860/previews/pattern_6.png) | ![pattern_7-4860](4860/previews/pattern_7.png) | ![pattern_8-4860](4860/previews/pattern_8.png) | ![pattern_9-4860](4860/previews/pattern_9.png) | ![pattern_10-4860](4860/previews/pattern_10.png) | ![pattern_11-4860](4860/previews/pattern_11.png) | ![pattern_12-4860](4860/previews/pattern_12.png) | ![pattern_13-4860](4860/previews/pattern_13.png) | ![pattern_14-4860](4860/previews/pattern_14.png) | [<NSFW, click to see>](4860/previews/bikini.png) | [<NSFW, click to see>](4860/previews/bondage.png) | ![free-4860](4860/previews/free.png) | ![maid-4860](4860/previews/maid.png) | ![miko-4860](4860/previews/miko.png) | [<NSFW, click to see>](4860/previews/nude.png) | [<NSFW, click to see>](4860/previews/nude2.png) | ![suit-4860](4860/previews/suit.png) | ![yukata-4860](4860/previews/yukata.png) | | 4320 | 0.889 | [Download](4320/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | ![pattern_2-4320](4320/previews/pattern_2.png) | ![pattern_3-4320](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | ![pattern_5-4320](4320/previews/pattern_5.png) | ![pattern_6-4320](4320/previews/pattern_6.png) | ![pattern_7-4320](4320/previews/pattern_7.png) | ![pattern_8-4320](4320/previews/pattern_8.png) | ![pattern_9-4320](4320/previews/pattern_9.png) | ![pattern_10-4320](4320/previews/pattern_10.png) | ![pattern_11-4320](4320/previews/pattern_11.png) | ![pattern_12-4320](4320/previews/pattern_12.png) | ![pattern_13-4320](4320/previews/pattern_13.png) | ![pattern_14-4320](4320/previews/pattern_14.png) | [<NSFW, click to see>](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | ![free-4320](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | 3780 | 0.918 | [Download](3780/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-3780](3780/previews/pattern_1.png) | ![pattern_2-3780](3780/previews/pattern_2.png) | ![pattern_3-3780](3780/previews/pattern_3.png) | ![pattern_4-3780](3780/previews/pattern_4.png) | ![pattern_5-3780](3780/previews/pattern_5.png) | ![pattern_6-3780](3780/previews/pattern_6.png) | ![pattern_7-3780](3780/previews/pattern_7.png) | ![pattern_8-3780](3780/previews/pattern_8.png) | ![pattern_9-3780](3780/previews/pattern_9.png) | ![pattern_10-3780](3780/previews/pattern_10.png) | ![pattern_11-3780](3780/previews/pattern_11.png) | ![pattern_12-3780](3780/previews/pattern_12.png) | ![pattern_13-3780](3780/previews/pattern_13.png) | ![pattern_14-3780](3780/previews/pattern_14.png) | [<NSFW, click to see>](3780/previews/bikini.png) | [<NSFW, click to see>](3780/previews/bondage.png) | ![free-3780](3780/previews/free.png) | ![maid-3780](3780/previews/maid.png) | ![miko-3780](3780/previews/miko.png) | [<NSFW, click to see>](3780/previews/nude.png) | [<NSFW, click to see>](3780/previews/nude2.png) | ![suit-3780](3780/previews/suit.png) | ![yukata-3780](3780/previews/yukata.png) | | 3240 | 0.884 | [Download](3240/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-3240](3240/previews/pattern_1.png) | ![pattern_2-3240](3240/previews/pattern_2.png) | ![pattern_3-3240](3240/previews/pattern_3.png) | ![pattern_4-3240](3240/previews/pattern_4.png) | ![pattern_5-3240](3240/previews/pattern_5.png) | ![pattern_6-3240](3240/previews/pattern_6.png) | ![pattern_7-3240](3240/previews/pattern_7.png) | ![pattern_8-3240](3240/previews/pattern_8.png) | ![pattern_9-3240](3240/previews/pattern_9.png) | ![pattern_10-3240](3240/previews/pattern_10.png) | ![pattern_11-3240](3240/previews/pattern_11.png) | ![pattern_12-3240](3240/previews/pattern_12.png) | ![pattern_13-3240](3240/previews/pattern_13.png) | ![pattern_14-3240](3240/previews/pattern_14.png) | [<NSFW, click to see>](3240/previews/bikini.png) | [<NSFW, click to see>](3240/previews/bondage.png) | ![free-3240](3240/previews/free.png) | ![maid-3240](3240/previews/maid.png) | ![miko-3240](3240/previews/miko.png) | [<NSFW, click to see>](3240/previews/nude.png) | [<NSFW, click to see>](3240/previews/nude2.png) | ![suit-3240](3240/previews/suit.png) | ![yukata-3240](3240/previews/yukata.png) | | 2700 | 0.850 | [Download](2700/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-2700](2700/previews/pattern_1.png) | ![pattern_2-2700](2700/previews/pattern_2.png) | ![pattern_3-2700](2700/previews/pattern_3.png) | ![pattern_4-2700](2700/previews/pattern_4.png) | ![pattern_5-2700](2700/previews/pattern_5.png) | ![pattern_6-2700](2700/previews/pattern_6.png) | ![pattern_7-2700](2700/previews/pattern_7.png) | ![pattern_8-2700](2700/previews/pattern_8.png) | ![pattern_9-2700](2700/previews/pattern_9.png) | ![pattern_10-2700](2700/previews/pattern_10.png) | ![pattern_11-2700](2700/previews/pattern_11.png) | ![pattern_12-2700](2700/previews/pattern_12.png) | ![pattern_13-2700](2700/previews/pattern_13.png) | ![pattern_14-2700](2700/previews/pattern_14.png) | [<NSFW, click to see>](2700/previews/bikini.png) | [<NSFW, click to see>](2700/previews/bondage.png) | ![free-2700](2700/previews/free.png) | ![maid-2700](2700/previews/maid.png) | ![miko-2700](2700/previews/miko.png) | [<NSFW, click to see>](2700/previews/nude.png) | [<NSFW, click to see>](2700/previews/nude2.png) | ![suit-2700](2700/previews/suit.png) | ![yukata-2700](2700/previews/yukata.png) | | 2160 | 0.890 | [Download](2160/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-2160](2160/previews/pattern_1.png) | ![pattern_2-2160](2160/previews/pattern_2.png) | ![pattern_3-2160](2160/previews/pattern_3.png) | ![pattern_4-2160](2160/previews/pattern_4.png) | ![pattern_5-2160](2160/previews/pattern_5.png) | ![pattern_6-2160](2160/previews/pattern_6.png) | ![pattern_7-2160](2160/previews/pattern_7.png) | ![pattern_8-2160](2160/previews/pattern_8.png) | ![pattern_9-2160](2160/previews/pattern_9.png) | ![pattern_10-2160](2160/previews/pattern_10.png) | ![pattern_11-2160](2160/previews/pattern_11.png) | ![pattern_12-2160](2160/previews/pattern_12.png) | ![pattern_13-2160](2160/previews/pattern_13.png) | ![pattern_14-2160](2160/previews/pattern_14.png) | [<NSFW, click to see>](2160/previews/bikini.png) | [<NSFW, click to see>](2160/previews/bondage.png) | ![free-2160](2160/previews/free.png) | ![maid-2160](2160/previews/maid.png) | ![miko-2160](2160/previews/miko.png) | [<NSFW, click to see>](2160/previews/nude.png) | [<NSFW, click to see>](2160/previews/nude2.png) | ![suit-2160](2160/previews/suit.png) | ![yukata-2160](2160/previews/yukata.png) | | 1620 | 0.861 | [Download](1620/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-1620](1620/previews/pattern_1.png) | ![pattern_2-1620](1620/previews/pattern_2.png) | ![pattern_3-1620](1620/previews/pattern_3.png) | ![pattern_4-1620](1620/previews/pattern_4.png) | ![pattern_5-1620](1620/previews/pattern_5.png) | ![pattern_6-1620](1620/previews/pattern_6.png) | ![pattern_7-1620](1620/previews/pattern_7.png) | ![pattern_8-1620](1620/previews/pattern_8.png) | ![pattern_9-1620](1620/previews/pattern_9.png) | ![pattern_10-1620](1620/previews/pattern_10.png) | ![pattern_11-1620](1620/previews/pattern_11.png) | ![pattern_12-1620](1620/previews/pattern_12.png) | ![pattern_13-1620](1620/previews/pattern_13.png) | ![pattern_14-1620](1620/previews/pattern_14.png) | [<NSFW, click to see>](1620/previews/bikini.png) | [<NSFW, click to see>](1620/previews/bondage.png) | ![free-1620](1620/previews/free.png) | ![maid-1620](1620/previews/maid.png) | ![miko-1620](1620/previews/miko.png) | [<NSFW, click to see>](1620/previews/nude.png) | [<NSFW, click to see>](1620/previews/nude2.png) | ![suit-1620](1620/previews/suit.png) | ![yukata-1620](1620/previews/yukata.png) | | 1080 | 0.818 | [Download](1080/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-1080](1080/previews/pattern_1.png) | ![pattern_2-1080](1080/previews/pattern_2.png) | ![pattern_3-1080](1080/previews/pattern_3.png) | ![pattern_4-1080](1080/previews/pattern_4.png) | ![pattern_5-1080](1080/previews/pattern_5.png) | ![pattern_6-1080](1080/previews/pattern_6.png) | ![pattern_7-1080](1080/previews/pattern_7.png) | ![pattern_8-1080](1080/previews/pattern_8.png) | ![pattern_9-1080](1080/previews/pattern_9.png) | ![pattern_10-1080](1080/previews/pattern_10.png) | ![pattern_11-1080](1080/previews/pattern_11.png) | ![pattern_12-1080](1080/previews/pattern_12.png) | ![pattern_13-1080](1080/previews/pattern_13.png) | ![pattern_14-1080](1080/previews/pattern_14.png) | [<NSFW, click to see>](1080/previews/bikini.png) | [<NSFW, click to see>](1080/previews/bondage.png) | ![free-1080](1080/previews/free.png) | ![maid-1080](1080/previews/maid.png) | ![miko-1080](1080/previews/miko.png) | [<NSFW, click to see>](1080/previews/nude.png) | [<NSFW, click to see>](1080/previews/nude2.png) | ![suit-1080](1080/previews/suit.png) | ![yukata-1080](1080/previews/yukata.png) | | 540 | 0.802 | [Download](540/hanabata_nohkins_futokunoguild.zip) | ![pattern_1-540](540/previews/pattern_1.png) | ![pattern_2-540](540/previews/pattern_2.png) | ![pattern_3-540](540/previews/pattern_3.png) | ![pattern_4-540](540/previews/pattern_4.png) | ![pattern_5-540](540/previews/pattern_5.png) | ![pattern_6-540](540/previews/pattern_6.png) | ![pattern_7-540](540/previews/pattern_7.png) | ![pattern_8-540](540/previews/pattern_8.png) | ![pattern_9-540](540/previews/pattern_9.png) | ![pattern_10-540](540/previews/pattern_10.png) | ![pattern_11-540](540/previews/pattern_11.png) | ![pattern_12-540](540/previews/pattern_12.png) | ![pattern_13-540](540/previews/pattern_13.png) | ![pattern_14-540](540/previews/pattern_14.png) | [<NSFW, click to see>](540/previews/bikini.png) | [<NSFW, click to see>](540/previews/bondage.png) | ![free-540](540/previews/free.png) | ![maid-540](540/previews/maid.png) | ![miko-540](540/previews/miko.png) | [<NSFW, click to see>](540/previews/nude.png) | [<NSFW, click to see>](540/previews/nude2.png) | ![suit-540](540/previews/suit.png) | ![yukata-540](540/previews/yukata.png) |
takumi12/id2pg_pattern2_en_batchsize8_epoch30
takumi12
2023-09-19T07:56:33Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-19T07:56:27Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0 - PEFT 0.6.0.dev0
kuldeepsingh-in/kd-project-google-03
kuldeepsingh-in
2023-09-19T07:43:12Z
1
1
diffusers
[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]
text-to-image
2023-09-19T07:43:10Z
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a person kdsingh1009 tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
shaowenchen/vicuna-7b-v1.5-16k-gguf
shaowenchen
2023-09-19T07:42:42Z
104
1
null
[ "gguf", "vicuna", "chinese", "text-generation", "zh", "en", "license:other", "region:us" ]
text-generation
2023-09-19T01:28:28Z
--- inference: false language: - zh - en license: other model_creator: lmsys model_link: https://huggingface.co/lmsys/vicuna-7b-v1.5-16k model_name: chinese-llama-2-7b-16k model_type: vicuna pipeline_tag: text-generation quantized_by: shaowenchen tasks: - text2text-generation tags: - gguf - vicuna - chinese --- ## Provided files | Name | Quant method | Size | | ------------------------------ | ------------ | ------ | | vicuna-7b-v1.5-16k.Q2_K.gguf | Q2_K | 2.6 GB | | vicuna-7b-v1.5-16k.Q3_K.gguf | Q3_K | 3.1 GB | | vicuna-7b-v1.5-16k.Q3_K_L.gguf | Q3_K_L | 3.3 GB | | vicuna-7b-v1.5-16k.Q3_K_S.gguf | Q3_K_S | 2.7 GB | | vicuna-7b-v1.5-16k.Q4_0.gguf | Q4_0 | 3.6 GB | | vicuna-7b-v1.5-16k.Q4_1.gguf | Q4_1 | 3.9 GB | | vicuna-7b-v1.5-16k.Q4_K.gguf | Q4_K | 3.8 GB | | vicuna-7b-v1.5-16k.Q4_K_S.gguf | Q4_K_S | 3.6 GB | | vicuna-7b-v1.5-16k.Q5_0.gguf | Q5_0 | 4.3 GB | | vicuna-7b-v1.5-16k.Q5_1.gguf | Q5_1 | 4.7 GB | | vicuna-7b-v1.5-16k.Q5_K.gguf | Q5_K | 4.5 GB | | vicuna-7b-v1.5-16k.Q5_K_S.gguf | Q5_K_S | 4.3 GB | | vicuna-7b-v1.5-16k.Q6_K.gguf | Q6_K | 5.1 GB | | vicuna-7b-v1.5-16k.Q8_0.gguf | Q8_0 | 6.7 GB | | vicuna-7b-v1.5-16k.gguf | full | 13 GB | Usage: ``` docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/gguf-model-name.gguf hubimage/llama-cpp-python:latest ``` and you can view http://localhost:8000/docs to see the swagger UI. ## Provided images | Name | Quant method | Compressed Size | | ------------------------------------------ | ------------ | --------------- | | `shaowenchen/vicuna-7b-v1.5-16k-gguf:Q2_K` | Q2_K | 2.88 GB | | `shaowenchen/vicuna-7b-v1.5-16k-gguf:Q3_K` | Q3_K | 3.3 GB | | `shaowenchen/vicuna-7b-v1.5-16k-gguf:Q4_K` | Q4_K | 4 GB | Usage: ``` docker run --rm -p 8000:8000 shaowenchen/vicuna-7b-v1.5-16k-gguf:Q2_K ``` and you can view http://localhost:8000/docs to see the swagger UI.
filipealmeida/open-llama-3b-v2-pii-transform
filipealmeida
2023-09-19T07:39:45Z
137
0
transformers
[ "transformers", "pytorch", "safetensors", "gguf", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-31T02:27:12Z
--- license: apache-2.0 widget: - text: "### Instruction:\nMy name is Filipe and my phone number is 555-121-2234. How are you?\n### Response:\n" example_title: "Example 1" --- # Open Llama based PII anonymizer ## Description This model, based on the `openlm-research/open_llama_3b_v2` architecture, is designed to automatically anonymize personal identifiable information (PII) from text data. Given a piece of text, the model can replace specific details such as names, addresses, dates, and other personal details with generic or randomized alternatives, thereby safeguarding the privacy of individuals while retaining the overall context of the text. ## Disclaimer This model is an experiment and, while it strives to maintain privacy, it may not capture or anonymize all instances of PII in every context. Users should always review and verify the output, especially when dealing with sensitive data.
albertengineer/lora-trained-xl-colab
albertengineer
2023-09-19T07:39:25Z
12
2
diffusers
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2023-09-18T07:31:05Z
--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - albertengineer/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
SalmonAI123/Question_answering_test
SalmonAI123
2023-09-19T07:31:25Z
113
0
transformers
[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-09-15T10:04:15Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - squad model-index: - name: Question_answering_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Question_answering_test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.8232 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.4127 | | 2.7467 | 2.0 | 500 | 1.9516 | | 2.7467 | 3.0 | 750 | 1.8232 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
sakuraumi/touka_nukitashi
sakuraumi
2023-09-19T07:30:34Z
4
0
transformers
[ "transformers", "audio-to-audio", "ja", "zh", "license:apache-2.0", "endpoints_compatible", "region:us" ]
audio-to-audio
2023-09-19T07:00:46Z
--- license: apache-2.0 language: - ja - zh pipeline_tag: audio-to-audio --- <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <div align="center"> <h1> 冷泉院桐香 So-VITS模型 </h1> </div> # 模型详情 - 数据集：拔作岛1~2所有桐香语音，并进行筛选 - steps数：100k
Laksitha/autotrain-tosdr_tldr_legal_summarisation_v1-1434353657
Laksitha
2023-09-19T07:28:04Z
106
2
transformers
[ "transformers", "pytorch", "safetensors", "autotrain", "summarization", "unk", "dataset:Laksitha/autotrain-data-tosdr_tldr_legal_summarisation_v1", "co2_eq_emissions", "endpoints_compatible", "region:us" ]
summarization
2022-09-11T23:56:10Z
--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - Laksitha/autotrain-data-tosdr_tldr_legal_summarisation_v1 co2_eq_emissions: emissions: 2.9024601099439225 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 1434353657 - CO2 Emissions (in grams): 2.9025 ## Validation Metrics - Loss: 2.821 - Rouge1: 32.961 - Rouge2: 10.761 - RougeL: 20.551 - RougeLsum: 30.094 - Gen Len: 92.222 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Laksitha/autotrain-tosdr_tldr_legal_summarisation_v1-1434353657 ```
prince99/model11
prince99
2023-09-19T07:22:51Z
1
0
peft
[ "peft", "region:us" ]
null
2023-09-19T07:22:42Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
Arabic-Clip-Archive/m-bert-base-ViT-B-32-trained-mclip-data
Arabic-Clip-Archive
2023-09-19T07:17:57Z
104
0
transformers
[ "transformers", "pytorch", "tf", "bert", "feature-extraction", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2023-09-19T06:21:14Z
The following checkpoint is for m-bert-base-ViT-B-32 trained on mclip dataset ( with try and execept)
dgbuzzer/test-upload
dgbuzzer
2023-09-19T07:16:47Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-19T07:07:44Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
ai-sherpa/llama-7b-test
ai-sherpa
2023-09-19T07:14:18Z
0
0
peft
[ "peft", "llama", "region:us" ]
null
2023-09-18T06:02:00Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0
checkiejan/prefix-paraphase-45-20-auto
checkiejan
2023-09-19T07:13:19Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-19T07:13:17Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
jackaduma/Baichuan2-7B-Chat-8bits
jackaduma
2023-09-19T07:09:16Z
108
0
transformers
[ "transformers", "pytorch", "baichuan", "text-generation", "custom_code", "zh", "en", "arxiv:1910.07467", "arxiv:2009.03300", "license:mit", "autotrain_compatible", "text-generation-inference", "8-bit", "bitsandbytes", "region:us" ]
text-generation
2023-09-08T07:12:15Z
--- license: mit library_name: transformers language: - zh - en pipeline_tag: text-generation inference: false --- # Baichuan-7B <!-- Provide a quick summary of what the model is/does. --> Baichuan-7B是由百川智能开发的一个开源的大规模预训练模型。基于Transformer结构，在大约1.2万亿tokens上训练的70亿参数模型，支持中英双语，上下文窗口长度为4096。在标准的中文和英文权威benchmark（C-EVAL/MMLU）上均取得同尺寸最好的效果。 如果希望使用Baichuan-7B（如进行推理、Finetune等），我们推荐使用配套代码库[Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B)。 Baichuan-7B is an open-source large-scale pre-trained model developed by Baichuan Intelligent Technology. Based on the Transformer architecture, it is a model with 7 billion parameters trained on approximately 1.2 trillion tokens. It supports both Chinese and English, with a context window length of 4096. It achieves the best performance of its size on standard Chinese and English authoritative benchmarks (C-EVAL/MMLU). If you wish to use Baichuan-7B (for inference, finetuning, etc.), we recommend using the accompanying code library [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B). ## Why use Baichuan-7B - 在同尺寸模型中Baichuan-7B达到了目前SOTA的水平，参考下面MMLU指标 - Baichuan-7B使用自有的中英文双语语料进行训练，在中文上进行优化，在C-Eval达到SOTA水平 - 不同于LLaMA完全禁止商业使用，Baichuan-7B使用更宽松的开源协议，允许用于商业目的 - Among models of the same size, Baichuan-7B has achieved the current state-of-the-art (SOTA) level, as evidenced by the following MMLU metrics. - Baichuan-7B is trained on proprietary bilingual Chinese-English corpora, optimized for Chinese, and achieves SOTA performance on C-Eval. - Unlike LLaMA, which completely prohibits commercial use, Baichuan-7B employs a more lenient open-source license, allowing for commercial purposes. ## How to Get Started with the Model inference code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("jackaduma/Baichuan2-7B-Chat-8bits", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("jackaduma/Baichuan2-7B-Chat-8bits", device_map="auto", trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("jackaduma/Baichuan2-7B-Chat-8bits") # non-streaming messages = [] messages.append({"role": "user", "content": "解释一下“温故而知新”"}) response = model.chat(tokenizer, messages) print(response) # streaming position = 0 for response in model.chat(tokenizer, messages, stream=True): # print(response) print(response[position:], end='', flush=True) position = len(response) ``` ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** 百川智能(Baichuan Intelligent Technology) - **Email**: [email protected] - **Language(s) (NLP):** Chinese/English - **License:** [Baichuan-7B License](https://huggingface.co/baichuan-inc/Baichuan-7B/blob/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) ### Model Sources <!-- Provide the basic links for the model. --> 整体模型基于标准的Transformer结构，我们采用了和LLaMA一样的模型设计 - **Position Embedding**：采用rotary-embedding，是现阶段被大多数模型采用的位置编码方案，具有很好的外推性。 - **Feedforward Layer**：采用SwiGLU，Feedforward变化为(8/3)倍的隐含层大小，即11008。 - **Layer Normalization**: 基于[RMSNorm](https://arxiv.org/abs/1910.07467)的Pre-Normalization。 具体参数和见下表 | Hyperparameter | Value | |----------------|-------| |n_parameters | 7000559616 | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 64000 | | sequence length | 4096 | The overall model is based on the standard Transformer structure, and we have adopted the same model design as LLaMA: - Position Embedding: We use rotary-embedding, which is the position encoding scheme adopted by most models at this stage, and it has excellent extrapolation capabilities. - Feedforward Layer: We use SwiGLU. The feedforward changes to (8/3) times the size of the hidden layer, that is, 11008. - Layer Normalization: Pre-Normalization based on [RMSNorm](https://arxiv.org/abs/1910.07467). The specific parameters are as follows: | Hyperparameter | Value | |----------------|-------| |n_parameters | 7000559616 | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 64000 | | sequence length | 4096 | ## 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. --> ### Downstream Use <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> 我们同时开源出了和本模型配套的训练代码，允许进行高效的Finetune用于下游任务，具体参见[Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B)。 We have also open-sourced the training code that accompanies this model, allowing for efficient finetuning for downstream tasks. For more details, please refer to [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> 在没有充分评估风险和采取缓解措施的情况下投入生产使用；任何可能被视为不负责任或有害的使用案例。 Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Baichuan-7B可能会产生事实上不正确的输出，不应依赖它产生事实上准确的信息。Baichuan-7B是在各种公共数据集上进行训练的。尽管我们已经做出了巨大的努力来清洗预训练数据，但这个模型可能会生成淫秽、偏见或其他冒犯性的输出。 Baichuan-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. Baichuan-7B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## Training Details 训练具体设置参见[Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B)。 For specific training settings, please refer to [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B). ## Evaluation ### 中文评测 #### C-Eval [CEval数据集](https://cevalbenchmark.com/index.html)是一个全面的中文基础模型评测数据集，涵盖了52个学科和四个难度的级别。我们使用该数据集的dev集作为few-shot的来源，在test集上进行了5-shot测试。 | Model 5-shot | Average | Avg(Hard) | STEM | Social Sciences | Humanities | Others | |-----------------------------|---------|-----------|------|-----------------|------------|--------| | GPT-4 | 68.7 | 54.9 | 67.1 | 77.6 | 64.5 | 67.8 | | ChatGPT | 54.4 | 41.4 | 52.9 | 61.8 | 50.9 | 53.6 | | Claude-v1.3 | 54.2 | 39.0 | 51.9 | 61.7 | 52.1 | 53.7 | | Claude-instant-v1.0 | 45.9 | 35.5 | 43.1 | 53.8 | 44.2 | 45.4 | | moss-moon-003-base (16B) | 27.4 | 24.5 | 27.0 | 29.1 | 27.2 | 26.9 | | Ziya-LLaMA-13B-pretrain | 30.2 | 22.7 | 27.7 | 34.4 | 32.0 | 28.9 | | LLaMA-7B-hf | 27.1 | 25.9 | 27.1 | 26.8 | 27.9 | 26.3 | | ChatGLM-6B | 34.5 | 23.1 | 30.4 | 39.6 | 37.4 | 34.5 | | Falcon-7B | 25.8 | 24.3 | 25.8 | 26.0 | 25.8 | 25.6 | | Open-LLaMA-v2-pretrain (7B) | 24.0 | 22.5 | 23.1 | 25.3 | 25.2 | 23.2 | | TigerBot-7B-base | 25.7 | 27.0 | 27.3 | 24.7 | 23.4 | 26.1 | | Aquila-7B<sup>*</sup> | 25.5 | 25.2 | 25.6 | 24.6 | 25.2 | 26.6 | | BLOOM-7B | 22.8 | 20.2 | 21.8 | 23.3 | 23.9 | 23.3 | | BLOOMZ-7B | 35.7 | 25.8 | 31.3 | 43.5 | 36.6 | 35.6 | | **Baichuan-7B** | 42.8 | 31.5 | 38.2 | 52.0 | 46.2 | 39.3 | #### Gaokao [Gaokao](https://github.com/ExpressAI/AI-Gaokao) 是一个以中国高考题作为评测大语言模型能力的数据集，用以评估模型的语言能力和逻辑推理能力。 我们只保留了其中的单项选择题，并对所有模型进行统一5-shot测试。 以下是测试的结果。 | Model | Average | |-------------------------|-----------------| | Open-LLaMA-v2-pretrain | 21.41 | | Ziya-LLaMA-13B-pretrain | 23.17 | | Falcon-7B | 23.98 | | TigerBot-7B-base | 25.94 | | LLaMA-7B | 27.81 | | ChatGLM-6B | 21.41 | | BLOOM-7B | 26.96 | | BLOOMZ-7B | 28.72 | | Aquila-7B<sup>*</sup> | 24.39 | | **Baichuan-7B** | **36.24** | #### AGIEval [AGIEval](https://github.com/microsoft/AGIEval) 旨在评估模型的认知和解决问题相关的任务中的一般能力。 我们只保留了其中的四选一单项选择题，随机划分后对所有模型进行了统一5-shot测试。 | Model | Average | |-------------------------|-----------------| | Open-LLaMA-v2-pretrain | 23.49 | | Ziya-LLaMA-13B-pretrain | 27.64 | | Falcon-7B | 27.18 | | TigerBot-7B-base | 25.19 | | LLaMA-7B | 28.17 | | ChatGLM-6B | 23.49 | | BLOOM-7B | 26.55 | | BLOOMZ-7B | 30.27 | | Aquila-7B<sup>*</sup> | 25.58 | | **Baichuan-7B** | **34.44** | <sup>*</sup>其中Aquila模型来源于[智源官方网站](https://model.baai.ac.cn/model-detail/100098)，仅做参考 ### English Leaderboard In addition to Chinese, we also tested the model's performance in English. #### MMLU [MMLU](https://arxiv.org/abs/2009.03300) is an English evaluation dataset that includes 57 multiple-choice tasks, covering elementary mathematics, American history, computer science, law, etc. The difficulty ranges from high school level to expert level, making it a mainstream LLM evaluation dataset. We adopted the [open-source]((https://github.com/hendrycks/test)) evaluation scheme, and the final 5-shot results are as follows: | Model | Humanities | Social Sciences | STEM | Other | Average | |----------------------------------------|-----------:|:---------------:|:----:|:-----:|:-------:| | LLaMA-7B<sup>2</sup> | 34.0 | 38.3 | 30.5 | 38.1 | 35.1 | | Falcon-7B<sup>1</sup> | - | - | - | - | 35.0 | | mpt-7B<sup>1</sup> | - | - | - | - | 35.6 | | ChatGLM-6B<sup>0</sup> | 35.4 | 41.0 | 31.3 | 40.5 | 36.9 | | BLOOM 7B<sup>0</sup> | 25.0 | 24.4 | 26.5 | 26.4 | 25.5 | | BLOOMZ 7B<sup>0</sup> | 31.3 | 42.1 | 34.4 | 39.0 | 36.1 | | moss-moon-003-base (16B)<sup>0</sup> | 24.2 | 22.8 | 22.4 | 24.4 | 23.6 | | moss-moon-003-sft (16B)<sup>0</sup> | 30.5 | 33.8 | 29.3 | 34.4 | 31.9 | | **Baichuan-7B<sup>0</sup>** | 38.4 | 48.9 | 35.6 | 48.1 | 42.3 | The superscript in the Model column indicates the source of the results. ``` 0:reimplemented 1:https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard 2:https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu ``` ## Our Group ![WeChat](https://github.com/baichuan-inc/Baichuan-13B/blob/main/media/wechat.jpeg?raw=true)
BanUrsus/rl_course_vizdoom_health_gathering_supreme
BanUrsus
2023-09-19T06:42:13Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-19T06:42:05Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 14.05 +/- 5.47 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r BanUrsus/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
lccllccc/textual_inversion_tangseng_sdxl_lora
lccllccc
2023-09-19T06:37:33Z
1
1
diffusers
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-18T06:16:02Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - lccllccc/textual_inversion_tangseng_sdxl_lora These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None dataset. You can find some example images in the following. LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
teknium/Phi-Hermes-1.3B
teknium
2023-09-19T06:29:14Z
68
43
transformers
[ "transformers", "pytorch", "safetensors", "mixformer-sequential", "text-generation", "custom_code", "en", "dataset:teknium/openhermes", "license:other", "autotrain_compatible", "region:us" ]
text-generation
2023-09-13T19:12:40Z
--- license: other language: - en pipeline_tag: text-generation datasets: - teknium/openhermes --- # Model Card for Puffin-Phi V2 Phi-1.5 fine tuned with Hermes Dataset ## Model Details ### Model Sources This model was trained on the OpenHermes Dataset, made by me, which is over 240,000 mostly GPT-4 generated synthetic datapoints ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/KFV00TWHS6E0z_l82QDxV.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/8M5xBh_ixVxdtPQnDuCkV.png) ## Uses Let me know! ## How to Get Started with the Model Phi does not support device_map "auto", and does not seem to want to inference in fp16, so use bf16. Here is working code to inference, though it can be improved: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda") tokenizer = AutoTokenizer.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16) inputs = tokenizer(f"### Instruction:\nWrite a negative review for the website, Twitter.\n### Response:\n", return_tensors="pt", return_attention_mask=False) outputs = model.generate(**inputs, max_length=128, do_sample=True, temperature=0.2, top_p=0.9, use_cache=True, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id) text = tokenizer.batch_decode(outputs)[0] print(text) ``` The prompt format is Alpaca, then is prompted like so: ``` ### Instruction: <prompt> ### Response: ``` ## Training Details ### Training Procedure Trained with Axolotl. View the wandb runs for all my puffin runs (this is puffin-phi-4 on wandb): https://wandb.ai/teknium1/hermes-phi/runs/hermes-phi-1 ## Evaluation ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/sQqgzk6dM7mxbyVloFMa1.png)
Solitary12138/Frozen-Lake
Solitary12138
2023-09-19T06:26:01Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-09-11T07:04:26Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: Frozen-Lake 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="Solitary12138/Frozen-Lake", 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"]) ```
Meta1408/llama2-qlora-finetunined-french
Meta1408
2023-09-19T06:22:24Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-19T04:32:18Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
yongwah/llama-2-7b-yw
yongwah
2023-09-19T06:16:56Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-15T00:21:29Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0