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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-17 00:43:51	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 524 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-17 00:39:55	card stringlengths 11 1.01M
kushgabani/bloom-1b1-lora-tagger	kushgabani	2023-10-26T09:15:00Z	31	0	peft	[ "peft", "text-generation", "arxiv:1910.09700", "base_model:bigscience/bloom-1b1", "base_model:adapter:bigscience/bloom-1b1", "region:us" ]	text-generation	2023-10-25T11:05:43Z	--- library_name: peft base_model: bigscience/bloom-1b1 pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Kush Gabani - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: bigscience/bloom-1b1 ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## 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 ### Framework versions - PEFT 0.6.0.dev0
NanaEilish/t5_conll_ontonotes_en12	NanaEilish	2023-10-26T09:03:34Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "en", "dataset:conll2012_ontonotesv5", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-10-25T12:10:11Z	--- datasets: - conll2012_ontonotesv5 language: - en pipeline_tag: text2text-generation --- Given a text, its output format is: `"{ENT_TYPE}:{span}; {ENT_TYPE}:{span}..."`\ For training speed, we only use the first 10,000 sentences (not documents) from train set; 1,000 sentences from validation set;\ we save the model when its val_loss (NLL) reaches the minimum.\ The model could be used as a pretrained backbone on downstream fine-tuning NER tasks.
limerooster/vietnamese-llama-2-7B-ngkplnltc-inst	limerooster	2023-10-26T08:58:06Z	1	0	peft	[ "peft", "region:us" ]	null	2023-10-26T08:57:28Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - 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
Rocinante/llama_merge	Rocinante	2023-10-26T08:58:04Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-26T08:47:27Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
pgze/sparsified_transformers	pgze	2023-10-26T08:51:12Z	0	0	null	[ "region:us" ]	null	2023-10-08T09:01:22Z	# Experiment Logs of "A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness" This repo holds the experiment logs of our manuscript. They include raw data of main results displayed in the paper, as well as some tentative experiments that are removed due to limit of length. They can be cloned and viewed by TensorBoard opened on corresponding subdirectories, or directly viewed online on Huggingface by [clicking "Training metrics"](https://huggingface.co/docs/hub/tensorboard). The terminology in log database may be different with those in the paper. For example, "concentration" is used instead of "sparsity", because in experiments for validation the activations are not sparse but concentrate around non-zero values. ## Main Results The raw data of results displayed in the manuscript can be found in - Experiments for Validation (Sec 6): `runs/manipulate_width_implicit_relu2/vit_(activation_layer=weird_relu2//dataset=cifar10//lr=0.0001/` - Training ViT-Base/16 on ImageNet-1k from Scratch (Sec 7.1): `runs/imagenet1k/from_scratch5/` - Finetuning ViT-Base/16 for Sparsity (Sec 7.2): `runs/imagenet1k/finetuning_hard_uplifting/` - Training T5-Base on C4 from Scratch (Sec 7.1): `runs/T5/from_scratch/` - Finetuning T5-Base for Sparsity (Sec 7.2): `runs/T5/finetuning/` - Applicability of Theorem 8 (Sec 8.4): `runs/marchenko_pastur/` ## Tentative Experiments Other subdirectories in `runs/manipulate_width_implicit_relu2/` show activation concentration manipulation with non-squared weird activation functions. It can be seen that the jumpy-squaring drastically strengthens the dominance of gradient sparsity, because under non-squared activation functions, significantly less activations concentrate in the expected flat interval. Training ViT from scratch with non-refined Zeroth Biases, i.e., without ZB clamping and LayerNorm restricting, can be found in `runs/imagenet1k/from_scratch3` (from_scratch1, from_scratch2 and from_scratch4 are short training during which implementation bugs are found). The gradient clipping norm is 10 for both vanilla and modified training because we believed that this leads to more intense gradient noises and better flatness. These non-refined sparsifications bring about 20% sparsity improvements, showing the effectiveness of, even not refined, our modification. On the other hand, the improvements after the refinements reveal the critical role of LayerNorm when it comes to sparsity. Finetuning ViT without LayerNorm uplifting can be found in `runs/imagenet1k/finetuning`. The sparsity hardly changed. This, again, indicates the significance of LayerNorm on sparsity. Experiments using T5 happen after those of ViT so T5 does not have tentative experiments.
lectura/hwangsaeyeon	lectura	2023-10-26T08:40:26Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-26T06:56:08Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: hwangsaeyeon 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. --> # hwangsaeyeon This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
MCFred/bert-base-swedish-uncased-certainly	MCFred	2023-10-26T08:33:29Z	9	1	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "feature-extraction", "sv", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-04-20T14:05:27Z	--- model-index: - name: bert-base-swedish-uncased-certainly results: [] language: - sv --- <!-- 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. --> # bert-base-swedish-uncased-certainly Model downloaded from https://github.com/certainlyio/nordic_bert
yeaool/cppe5_use_data_finetuning	yeaool	2023-10-26T08:14:00Z	33	0	transformers	[ "transformers", "pytorch", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-10-26T03:35:23Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: cppe5_use_data_finetuning 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. --> # cppe5_use_data_finetuning This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
juri17/whisper-small-100h-de	juri17	2023-10-26T08:01:05Z	76	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "de", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-18T09:36:02Z	--- language: - de license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper small de results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: de split: other args: 'config: de, split: test' metrics: - name: Wer type: wer value: 11.98839498497565 --- <!-- 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 de This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2317 - Wer: 11.9884 ## 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: 7.5e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 220 - training_steps: 2200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2309 \| 0.25 \| 550 \| 0.2681 \| 12.4339 \| \| 0.1415 \| 0.5 \| 1100 \| 0.2456 \| 11.4703 \| \| 0.2352 \| 0.75 \| 1650 \| 0.2360 \| 11.2216 \| \| 0.1589 \| 1.0 \| 2200 \| 0.2317 \| 11.9884 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.6.dev0 - Tokenizers 0.14.0
xjlulu/ntu_adl_span_selection_roberta	xjlulu	2023-10-26T07:58:44Z	22	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "base_model:hfl/chinese-roberta-wwm-ext", "base_model:finetune:hfl/chinese-roberta-wwm-ext", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-10-22T00:45:55Z	--- license: apache-2.0 base_model: hfl/chinese-roberta-wwm-ext tags: - generated_from_trainer model-index: - name: ntu_adl_span_selection_roberta 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. --> # ntu_adl_span_selection_roberta This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1714 - Em Accuracy: 0.7763 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - 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 \| Em Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------:\| \| 0.9879 \| 1.0 \| 10857 \| 0.9306 \| 0.7441 \| \| 0.5075 \| 2.0 \| 21714 \| 0.9758 \| 0.7723 \| \| 0.2514 \| 3.0 \| 32571 \| 1.1714 \| 0.7763 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
haseong8012/whisper-large-v2_child10K_LoRA	haseong8012	2023-10-26T07:49:29Z	7	0	peft	[ "peft", "tensorboard", "dataset:haseong8012/child-10k", "region:us" ]	null	2023-10-25T17:12:30Z	--- library_name: peft datasets: - haseong8012/child-10k --- ## 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 ### Framework versions - PEFT 0.5.0
jovalie/kitten-7b	jovalie	2023-10-26T07:30:57Z	73	0	transformers	[ "transformers", "pytorch", "falcon", "text-generation", "Inference Endpoints", "text-generation-inference", "en", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2023-10-26T06:55:40Z	--- language: - en library_name: transformers pipeline_tag: text-generation tags: - falcon - Inference Endpoints - text-generation-inference ---
Rushikesh97/my_awesome_wnut_model	Rushikesh97	2023-10-26T07:29:01Z	58	0	transformers	[ "transformers", "tf", "distilbert", "token-classification", "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" ]	token-classification	2023-10-26T06:11:58Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: Rushikesh97/my_awesome_wnut_model 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. --> # Rushikesh97/my_awesome_wnut_model 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: - Train Loss: 0.1179 - Validation Loss: 0.2582 - Train Precision: 0.5899 - Train Recall: 0.4318 - Train F1: 0.4986 - Train Accuracy: 0.9466 - Epoch: 2 ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.3464 \| 0.3041 \| 0.3929 \| 0.1974 \| 0.2627 \| 0.9330 \| 0 \| \| 0.1563 \| 0.2651 \| 0.4992 \| 0.3577 \| 0.4167 \| 0.9421 \| 1 \| \| 0.1179 \| 0.2582 \| 0.5899 \| 0.4318 \| 0.4986 \| 0.9466 \| 2 \| ### Framework versions - Transformers 4.34.1 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
tingchih/1025_weight_3	tingchih	2023-10-26T07:16:15Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-26T05:47:21Z	--- tags: - generated_from_trainer model-index: - name: 1025_weight_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 1025_weight_3 This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3632 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.3605 \| 1.0 \| 6207 \| 1.3595 \| \| 1.3471 \| 2.0 \| 12414 \| 1.3632 \| ### Framework versions - Transformers 4.28.1 - Pytorch 1.13.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
arunptp/ppo-SnowballTarget	arunptp	2023-10-26T06:38:31Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-03T09:37:35Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: arunptp/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
s3nh/likenneth-honest_llama2_chat_7B-GGUF	s3nh	2023-10-26T06:38:17Z	12	1	transformers	[ "transformers", "gguf", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us" ]	text-generation	2023-10-24T12:41:48Z	--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/likenneth/honest_llama2_chat_7B). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### Perplexity params Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16 7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066 13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543 ### inference TODO # Original model card
arunptp/ppo-pyramids	arunptp	2023-10-26T06:23:28Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-10-26T05:24:36Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: arunptp/ppo-pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
SheldonSides/distilbert-base-uncased-finetuned-emotion	SheldonSides	2023-10-26T06:22:30Z	11	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-21T20:27:49Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9244990073753523 --- <!-- 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-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2143 - Accuracy: 0.9245 - F1: 0.9245 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5513 \| 1.0 \| 250 \| 0.3057 \| 0.908 \| 0.9076 \| \| 0.2227 \| 2.0 \| 500 \| 0.2143 \| 0.9245 \| 0.9245 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1 - Datasets 2.14.6 - Tokenizers 0.14.1
Mahendrakharra/results	Mahendrakharra	2023-10-26T06:21:38Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:finetune:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2023-10-26T04:01:27Z	--- base_model: NousResearch/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.13.3
LuisChDev/Reinforce-CartPole-v1	LuisChDev	2023-10-26T06:00:06Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-10-26T05:59:56Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
DAMO-NLP-SG/rememo-large	DAMO-NLP-SG	2023-10-26T05:55:05Z	6	1	transformers	[ "transformers", "pytorch", "t5", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-10-25T06:19:53Z	--- license: apache-2.0 --- ## Model Description Please refer to [our GitHub repo](https://github.com/DAMO-NLP-SG/RemeMo) for more details.
DAMO-NLP-SG/roberta-time_identification	DAMO-NLP-SG	2023-10-26T05:51:52Z	221	1	transformers	[ "transformers", "pytorch", "roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-10-25T09:01:33Z	--- tags: - generated_from_trainer model-index: - name: tim_expression_identify.2 results: [] --- ## Model description This model is a fine-tuned version of RoBERTa. ## Intended uses & limitations For identifying time expressions in text. This model works in a NER-like manner but only focuses on time expressions. - You may try an example sentence using the hosted inference API on HuggingFace: In Generation VII, Pokémon Sun and Moon were released worldwide for the 3DS on November 18, 2016 and on November 23, 2016 in Europe. The JSON output would be like: ``` [ { "entity_group": "TIME", "score": 0.9959897994995117, "word": " November 18", "start": 79, "end": 90 }, { "entity_group": "TIME", "score": 0.996467113494873, "word": " 2016", "start": 92, "end": 96 }, { "entity_group": "TIME", "score": 0.9942433834075928, "word": " November 23, 2016", "start": 104, "end": 121 } ] ``` ## Training and evaluation data TimeBank 1.2 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
1TuanPham/Instruct_en-vi_14000_1e_b60_lr2e-4_TheBloke_Mistralic-7B-1-GPTQ_LORA_CAUSAL_LM	1TuanPham	2023-10-26T05:46:12Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-25T17:05:48Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: gptq - bits: 4 - tokenizer: None - dataset: None - group_size: 128 - damp_percent: 0.1 - desc_act: True - sym: True - true_sequential: True - use_cuda_fp16: True - model_seqlen: 4096 - block_name_to_quantize: model.layers - module_name_preceding_first_block: ['model.embed_tokens'] - batch_size: 1 - pad_token_id: None - disable_exllama: False - max_input_length: None ### Framework versions - PEFT 0.5.0
QiiofRagnarok/my_awesome_eli5_clm-model	QiiofRagnarok	2023-10-26T05:28:10Z	161	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-26T05:08:25Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model 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. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7672 ## 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 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8722 \| 1.0 \| 1113 \| 3.7843 \| \| 3.7712 \| 2.0 \| 2226 \| 3.7690 \| \| 3.7279 \| 3.0 \| 3339 \| 3.7672 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
quyanh/qwen-14b-neurips-v1	quyanh	2023-10-26T05:25:36Z	3	0	peft	[ "peft", "arxiv:1910.09700", "base_model:Qwen/Qwen-14B", "base_model:adapter:Qwen/Qwen-14B", "region:us" ]	null	2023-10-24T16:07:46Z	--- library_name: peft base_model: Qwen/Qwen-14B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## 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
reproductionguru/voicetest	reproductionguru	2023-10-26T05:20:51Z	77	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hi", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-26T03:18:20Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer model-index: - name: base 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. --> # base This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the tutorial 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: 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_steps: 500 - training_steps: 4000 ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
intanm/xlmrlarge-idkmrc-2	intanm	2023-10-26T05:15:20Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "base_model:intanm/xlmrlarge-idkmrc", "base_model:finetune:intanm/xlmrlarge-idkmrc", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-10-26T04:53:38Z	--- license: mit base_model: intanm/xlmrlarge-idkmrc tags: - generated_from_trainer model-index: - name: xlmrlarge-idkmrc-2 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. --> # xlmrlarge-idkmrc-2 This model is a fine-tuned version of [intanm/xlmrlarge-idkmrc](https://huggingface.co/intanm/xlmrlarge-idkmrc) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5737 ## 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 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3687 \| 1.0 \| 1167 \| 1.4416 \| \| 0.1913 \| 2.0 \| 2334 \| 1.5737 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
xjlulu/ntu_adl_span_selection_bert	xjlulu	2023-10-26T05:10:54Z	27	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-chinese", "base_model:finetune:google-bert/bert-base-chinese", "endpoints_compatible", "region:us" ]	question-answering	2023-10-22T08:18:55Z	--- base_model: bert-base-chinese tags: - generated_from_trainer model-index: - name: ntu_adl_span_selection_bert 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. --> # ntu_adl_span_selection_bert This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0552 - Em Accuracy: 0.7607 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - 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 \| Em Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------:\| \| 1.161 \| 1.0 \| 10857 \| 1.2192 \| 0.7029 \| \| 0.7596 \| 2.0 \| 21714 \| 1.3003 \| 0.7338 \| \| 0.551 \| 3.0 \| 32571 \| 1.5081 \| 0.7398 \| \| 0.2034 \| 4.0 \| 43428 \| 1.8194 \| 0.7474 \| \| 0.0762 \| 5.0 \| 54285 \| 2.0552 \| 0.7607 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
MDCurrent/runwaysd1_5	MDCurrent	2023-10-26T05:09:25Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-10-20T17:11:21Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-MDCurrent/runwaysd1_5 These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. prompt: red circle with blue background ![images_0)](./images_0.png) prompt: cyan circle with brown floral background ![images_1)](./images_1.png)
caseyhahn/bert-base-uncased-finetuned-genius-lyrics	caseyhahn	2023-10-26T04:54:45Z	4	1	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-generation", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-10-25T22:58:11Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: caseyhahn/bert-base-uncased-finetuned-genius-lyrics 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. --> # caseyhahn/bert-base-uncased-finetuned-genius-lyrics This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1093 - Validation Loss: 0.0001 - Epoch: 0 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.1093 \| 0.0001 \| 0 \| ### Framework versions - Transformers 4.34.1 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
Hansaht/phi-1_5-finetuned-dolly	Hansaht	2023-10-26T04:26:32Z	14	0	transformers	[ "transformers", "pytorch", "mixformer-sequential", "text-generation", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-10-26T03:49:42Z	--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer model-index: - name: phi-1_5-finetuned-dolly 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. --> # phi-1_5-finetuned-dolly This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
Lasorco/Kokuwa	Lasorco	2023-10-26T04:22:46Z	8	5	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "safetensors", "ja", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-10-24T14:10:26Z	--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image - diffusers - safetensors language: - ja --- # Kokuwa lamettaの改良でマージさせるモデル探しをしていたらKiwiMixという面白そうなモデルを見つけました。<br> LoRAでリリースされていたものがモデル化したようですが、それによってライセンスが明確になったので早速使わせていただくことにしたのがこれです。<br> デフォルメ色強めのキャラクターが表現されます。（純粋に綺麗で可愛いよりはちょっと癖のある方が好きなんで）<br> またseedによって？多少雰囲気が変わりますので安定していない感もあります。この辺は色々更にマージを進めてみたら安定しそうな処方があったので、そのうちバージョンアップするかもしれないしそうでもないかもしれないです。<br> サルナシのことをkokuwa（コクワ）と方言？で呼ぶ地域があります、サルナシは山野に自生するちっちゃなキウイみたいな果物です。<br> Kokuwaのクレジット（マージしたモデル） - KiwiMix_v10 @changkiwi - spekulatius_v1 - lametta v412(+自家製LoRA) - 24_v10 @Livesting - hardcoreHentai_v11 @TianChimp - camelliamix25D_v3 @Mods13 - real_model_N @fcski KiwiMixに作り置きしていたlametta改良用のマージモデルをそのまま流用して混ぜた簡単仕様です。体型の出方がlamettaよりさらに丸いかも。<br> ![00067-128103056.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/0m3SxWwEem_j6FtioLVUD.png) ![00025-1268017144.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/7NUPzTePxpA2jXmIXJaZj.png) ![00050-3218050783.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/wRdAcUM8Dlven2uikladI.png) ![00054-1238278837.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/QZVWbQIQtwyFw8oucPDhA.png) ![00055-1356972435.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/aPmrEjNYBSQDA3lfxk21O.png) --- # 利用に際して（ライセンスなど）アップロードされているモデル全てにおいて[creativeml-openrail-m](https://huggingface.co/spaces/CompVis/stable-diffusion-license)に準じます。詳しくは「creativeml-openrail-m」で検索してもらえれば翻訳された解説などが確認できると思います。<br> Attachment Aの補足として、特定の作品や作風などを模倣してその権利者等に迷惑となるような使用は禁止とさせていただきます。<br> <br> civitai風な表記ですと以下の通り<br> <span class="text-green-500">OK</span>　クレジットを入れずにモデルを使用する<br>（Use the model without crediting the creator）<br> 生成画像にクレジットの有無は問いません、マージ素材としても有無は問いませんがあると喜びます <span class="text-green-500">OK</span>　生成した画像を販売する<br>（Sell images they generate）<br> 生成した画像はあなたの作画意図が込められていますからあなたのものです <span class="text-green-500">OK</span>　有償の画像を生成するサービスを運営する<br>（Run on services that generate images for money）<br> モデル名の表記をしていただければ問題ありません、末尾の "_fp16" は省略して構いません <span class="text-green-500">OK</span>　このモデルを使ったマージモデルを共有する<br>（Share merges using this model）<br> 自由に行っていただいて問題ありません、上記の通りクレジットの有無は問いませんがしていただけると喜びます <span class="text-red-500">NG</span>　このモデルまたはこのモデルを使ったマージモデルを販売する<br>（Sell this model or merges using this model）<br> このモデルは当方に無断で販売は出来ません、マージモデルについては手を加えた方の責任としてこちらは一切関与いたしません <span class="text-green-500">OK</span>　マージモデルを共有する際に異なる権限を持たせる<br>（Have different permissions when sharing merges）<br> 問題ありませんが上記の通り手を加えた方の責任として有利不利に関わらずこちらは一切の関与をいたしません <br><br> 以上
Lasorco/spekulatius	Lasorco	2023-10-26T04:21:35Z	7	5	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "safetensors", "ja", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-10-24T13:56:20Z	--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image - diffusers - safetensors language: - ja --- # spekulatius マージしているとたまに出てくる「目的の意図とは違うのだけどなんだか消すにはもったいないモデル」をおすそ分けするシリーズです。<br> 公開する以上最低限調整して出そうとは思いますがあまり期待はしないでください。<br> また、lamettaと内容が酷似すると何が本家か分からなくなりそうなので良く似たモデルはここには出てこないです。<br> ## v0.1 lametta_oldにあった雑マージモデルをお引越しして独立させました。<br> lametta_v1921をベースに色々なパターンで色々なモデルをマージした実験品です。少し調整をして使えるかなあ？程度に仕上げました。<br> spekulatiusのクレジット（マージしたモデル） - lametta_v1921 - vorpal_v1 @Apyr - cutifiedanimecharact_v20 @fearvel - bunnyspoon_v30Flux @LyloGummy - cityedgeToonmix_v312 @CityEdge - NewMoonMix_v1.0 @mira6 - snowpearAnime_v10 @Mysterious_Master_k - fantasticmix_v40 @michin - exquisiteDetails_art @Livesting - Fantexi_v0.9Beta @zhazhahui345 サンプル　PngInfoに通せばプロンプトなどは再現できると思います<br> ![01917-298846342.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/X2CtF4fg-xtSqcsOG49Pu.png) ![01889-1020796502.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/NcKqWjzk8qMXDW5lxM2rd.png) ![01887-2857113667.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/GxDbDM9YfrTvU45Sk_b3L.png) ![01886-569306123.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/kzNmvILsbFB6G38k5g4FC.png) ![01885-2643900663.png](https://cdn-uploads.huggingface.co/production/uploads/64172e2f1f1f3b0fa80ce889/6_pS9Yery9cPD1oNaKkbr.png) --- # 利用に際して（ライセンスなど）アップロードされているモデル全てにおいて[creativeml-openrail-m](https://huggingface.co/spaces/CompVis/stable-diffusion-license)に準じます。詳しくは「creativeml-openrail-m」で検索してもらえれば翻訳された解説などが確認できると思います。<br> Attachment Aの補足として、特定の作品や作風などを模倣してその権利者等に迷惑となるような使用は禁止とさせていただきます。<br> <br> civitai風な表記ですと以下の通り<br> <span class="text-green-500">OK</span>　クレジットを入れずにモデルを使用する<br>（Use the model without crediting the creator）<br> 生成画像にクレジットの有無は問いません、マージ素材としても有無は問いませんがあると喜びます <span class="text-green-500">OK</span>　生成した画像を販売する<br>（Sell images they generate）<br> 生成した画像はあなたの作画意図が込められていますからあなたのものです <span class="text-green-500">OK</span>　有償の画像を生成するサービスを運営する<br>（Run on services that generate images for money）<br> モデル名の表記をしていただければ問題ありません、末尾の "_fp16" は省略して構いません <span class="text-green-500">OK</span>　このモデルを使ったマージモデルを共有する<br>（Share merges using this model）<br> 自由に行っていただいて問題ありません、上記の通りクレジットの有無は問いませんがしていただけると喜びます <span class="text-red-500">NG</span>　このモデルまたはこのモデルを使ったマージモデルを販売する<br>（Sell this model or merges using this model）<br> このモデルは当方に無断で販売は出来ません、マージモデルについては手を加えた方の責任としてこちらは一切関与いたしません <span class="text-green-500">OK</span>　マージモデルを共有する際に異なる権限を持たせる<br>（Have different permissions when sharing merges）<br> 問題ありませんが上記の通り手を加えた方の責任として有利不利に関わらずこちらは一切の関与をいたしません <br><br> 以上
quyanh/qwen-7b-neurips-v1	quyanh	2023-10-26T04:17:27Z	5	0	peft	[ "peft", "arxiv:1910.09700", "base_model:Qwen/Qwen-7B", "base_model:adapter:Qwen/Qwen-7B", "region:us" ]	null	2023-10-25T10:59:10Z	--- library_name: peft base_model: Qwen/Qwen-7B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## 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
phoenixaiden33/ppo-Huggy	phoenixaiden33	2023-10-26T04:08:46Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-10-26T04:08:34Z	--- 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: phoenixaiden33/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
yvelos/Test2	yvelos	2023-10-26T03:44:10Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-25T15:15:35Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
wang3820/movies	wang3820	2023-10-26T03:38:16Z	59	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "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" ]	text-classification	2023-10-26T01:53:33Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: wang3820/movies 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. --> # wang3820/movies 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: - Train Loss: 0.0627 - Train Accuracy: 0.9962 - Epoch: 2 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Epoch \| \|:----------:\|:--------------:\|:-----:\| \| 0.2572 \| 0.9502 \| 0 \| \| 0.1403 \| 0.9834 \| 1 \| \| 0.0627 \| 0.9962 \| 2 \| ### Framework versions - Transformers 4.34.1 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
johaanm/test-planner-alpha-V8.7	johaanm	2023-10-26T03:35:23Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-26T03:35:20Z	--- 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: False - bnb_4bit_compute_dtype: float16 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 - PEFT 0.4.0
OFA-Sys/OccuLLaMA-7B	OFA-Sys	2023-10-26T03:23:59Z	12	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "en", "dataset:OFA-Sys/OccuQuest", "arxiv:2310.16517", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-25T09:11:12Z	--- license: apache-2.0 datasets: - OFA-Sys/OccuQuest language: - en --- This is the OccuLLaMA-7B model in [OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models](https://arxiv.org/abs/2310.16517). The dataset is on [OccuQuest](https://huggingface.co/datasets/OFA-Sys/OccuQuest). Abstract: The emergence of large language models (LLMs) has revolutionized natural language processing tasks. However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific fields. To mitigate this issue and promote occupation-inclusive LLMs, we create an instruction-tuning dataset named OccuQuest, which contains 110,000+ prompt-completion pairs and 30,000+ dialogues covering over 1,000 occupations in 26 occupational categories. We systematically request ChatGPT, organizing queries hierarchically based on Occupation, Responsibility, Topic, and Question, to ensure a comprehensive coverage of occupational specialty inquiries. By comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we observe that OccuQuest exhibits a more balanced distribution across occupations. Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora. We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and WizardLM) on professional questions in GPT-4 and human evaluations. Notably, on the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against WizardLM. Furthermore, we demonstrate the potential of combining OccuQuest with other instruction-tuning datasets to enhance the overall performance of LLMs. By fine-tuning LLaMA on a mixture of OccuQuest and Tulu datasets, we introduce ProLLaMA, which excels in addressing occupational questions and exhibits superior performance in comprehensive evaluations such as MMLU, GSM8K, BBH, and HumanEval. Among the different LLaMA variants, the 7B and 13B ProLLaMA models achieve the highest performance on MMLU and GSM8K, with the 7B ProLLaMA model demonstrating an improvement of more than 4 points over the other 7B variants on GSM8K. We open release the dataset and models. Please cite if you use this model: ``` @misc{xue2023occuquest, title={OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models}, author={Mingfeng Xue and Dayiheng Liu and Kexin Yang and Guanting Dong and Wenqiang Lei and Zheng Yuan and Chang Zhou and Jingren Zhou}, year={2023}, eprint={2310.16517}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
UBC-NLP/serengeti-E110	UBC-NLP	2023-10-26T03:22:57Z	9	1	transformers	[ "transformers", "pytorch", "electra", "feature-extraction", "Masked Langauge Model", "fill-mask", "aa", "af", "am", "ak", "bm", "ff", "fon", "ha", "ig", "ki", "lg", "ln", "mg", "nr", "om", "rn", "run", "sw", "sn", "tn", "ti", "ve", "wo", "xh", "yo", "zu", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-17T18:39:48Z	--- pipeline_tag: fill-mask language: - aa - af - am - ak - bm - ff - fon - ha - ig - ki - lg - ln - mg - nr - om - rn - run - sw - sn - tn - ti - ve - wo - xh - yo - zu tags: - Masked Langauge Model widget: - text: ẹ jọwọ , ẹ <mask> mi. - text: gbọ́ <mask> láìfọ̀rọ̀ gùn rárá. --- <p align="center"> <br> <img src="./serengeti_logo.png"/> <br> <p> </p> <img src="./serengati_languages.jpg" width="50%" height="50%" align="right"> <div style='text-align: justify;'> Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. <br><br> To date, only ~31 out of 2,000 African languages are covered in existing language models. We ameliorate this limitation by developing <b>SERENGETI</b>, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to 4 mPLMs that cover 4-23 African languages. <br><br> <b>SERENGETI</b> outperforms other models on 11 datasets across eights tasks, achieving 82.27 average F<sub>1</sub>-score. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research. </div> # 3. How to use Serengeti model Below is an example for using Serengeti predict masked tokens. ``` bash from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/serengeti-E110", use_auth_token="XXX") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/serengeti-E110", use_auth_token="XXX") from transformers import pipeline classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer) classifier("ẹ jọwọ , ẹ <mask> mi") #Yoruba [{'score': 0.07887924462556839, 'token': 8418, 'token_str': 'ọmọ', 'sequence': 'ẹ jọwọ, ẹ ọmọ mi'}, {'score': 0.04658124968409538, 'token': 156595, 'token_str': 'fẹ́ràn', 'sequence': 'ẹ jọwọ, ẹ fẹ́ràn mi'}, {'score': 0.029315846040844917, 'token': 204050, 'token_str': 'gbàgbé', 'sequence': 'ẹ jọwọ, ẹ gbàgbé mi'}, {'score': 0.02790883742272854, 'token': 10730, 'token_str': 'kọ', 'sequence': 'ẹ jọwọ, ẹ kọ mi'}, {'score': 0.022904086858034134, 'token': 115382, 'token_str': 'bẹ̀rù', 'sequence': 'ẹ jọwọ, ẹ bẹ̀rù mi'}] ``` For the more details please read this notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/UBC-NLP/serengeti/blob/main/Serengeti_notebook.ipynb) ## 4. Ethics Serengeti aligns with Afrocentric NLP where the needs of African people is put into consideration when developing technology. We believe Serengeti will not only be useful to speakers of the languages supported, but also researchers of African languages such as anthropologists and linguists. We discuss below some use cases for Serengeti and offer a number of broad impacts. - Serengeti aims to address the lack of access to technology in about 90\% of the world's languages, which automatically discriminates against native speakers of those languages. More precisely, it does so by focusing on Africa. To the best of our knowledge, Serengeti is the first massively multilingual PLM developed for African languages and language varieties. A model with knowledge of 517 African languages, is by far the largest to date for African NLP. - Serengeti enables improved access of important information to the African community in Indigenous African languages. This is especially beneficial for people who may not be fluent in other languages. This will potentially connect more people globally. - Serengeti affords opportunities for language preservation for many African languages. To the best of our knowledge, Serengeti consists of languages that have not been used for any NLP task until now. We believe that it can help encourage continued use of these languages in several domains, as well as trigger future development of language technologies for many of these languages. - To mitigate discrimination and bias, we adopt a manual curation of our datasets. Native speakers of Afrikaans, Yorùbá, Igbo, Hausa, Luganda, Kinyarwanda, Chichewa, Shona, Somali, Swahili, Xhosa, Bemba, and Zulu also manually evaluated a subset of the data to ensure its quality. The data collected for this work is taken from various domains to further ensure a better representation of the language usage of native speakers. - Although LMs are useful for a wide range of applications, they can also be misused. Serengeti is developed using publicly available datasets that may carry biases. Although we strive to perform analyses and diagnostic case studies to probe performance of our models, our investigations are by no means comprehensive nor guarantee absence of bias in the data. In particular, we do not have access to native speakers of most of the languages covered. This hinders our ability to investigate samples from each (or at least the majority) of the languages. ## Supported languages Please refer to [suported-languages](./supported-languages.txt) ## Citation If you use the pre-trained model (Serengeti) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated): ``` @inproceedings{adebara-etal-2023-serengeti, title = "{SERENGETI}: Massively Multilingual Language Models for {A}frica", author = "Adebara, Ife and Elmadany, AbdelRahim and Abdul-Mageed, Muhammad and Alcoba Inciarte, Alcides", booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.findings-acl.97", doi = "10.18653/v1/2023.findings-acl.97", pages = "1498--1537", } ``` ## Acknowledgments We gratefully acknowledges support from Canada Research Chairs (CRC), the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN-2018-04267), the Social Sciences and Humanities Research Council of Canada (SSHRC; 435-2018-0576; 895-2020-1004; 895-2021-1008), Canadian Foundation for Innovation (CFI; 37771), [Digital Research Alliance of Canada](https://alliancecan.ca), [UBC ARC-Sockeye](https://arc.ubc.ca/ubc-arc-sockeye), Advanced Micro Devices, Inc. (AMD), and Google. Any opinions, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of CRC, NSERC, SSHRC, CFI, the Alliance, AMD, Google, or UBC ARC-Sockeye.
syafiqfaray/byt5-base-indocollex-informal-to-formal-wordformation	syafiqfaray	2023-10-26T03:20:12Z	104	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/byt5-base", "base_model:finetune:google/byt5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-10-26T03:18:58Z	--- license: apache-2.0 base_model: google/byt5-base tags: - generated_from_trainer metrics: - wer model-index: - name: byt5-base-indocollex-informal-to-formal-wordformation 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. --> # byt5-base-indocollex-informal-to-formal-wordformation This model is a fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1413 - Cer: 0.1978 - Wer: 0.4524 - Word Acc: 0.5476 - Gen Len: 7.6457 ## 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \| Word Acc \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:--------:\|:-------:\| \| No log \| 0.54 \| 50 \| 16.1894 \| 2.1868 \| 2.2905 \| -1.2905 \| 19.0 \| \| No log \| 1.08 \| 100 \| 13.7479 \| 2.1248 \| 1.9333 \| -0.9333 \| 19.0 \| \| No log \| 1.61 \| 150 \| 11.6231 \| 2.1095 \| 1.4238 \| -0.4238 \| 18.7486 \| \| No log \| 2.15 \| 200 \| 8.9106 \| 1.056 \| 0.9857 \| 0.0143 \| 10.6171 \| \| No log \| 2.69 \| 250 \| 4.6844 \| 0.8523 \| 0.9762 \| 0.0238 \| 9.36 \| \| No log \| 3.23 \| 300 \| 4.1175 \| 0.5756 \| 0.9714 \| 0.0286 \| 7.4114 \| \| No log \| 3.76 \| 350 \| 3.3688 \| 0.5951 \| 0.9714 \| 0.0286 \| 7.8 \| \| No log \| 4.3 \| 400 \| 2.2287 \| 0.6112 \| 0.9857 \| 0.0143 \| 6.7543 \| \| No log \| 4.84 \| 450 \| 1.5164 \| 0.6095 \| 0.9571 \| 0.0429 \| 7.8857 \| \| 8.4834 \| 5.38 \| 500 \| 1.0363 \| 0.5976 \| 0.9476 \| 0.0524 \| 7.8229 \| \| 8.4834 \| 5.91 \| 550 \| 0.6893 \| 0.5976 \| 0.9476 \| 0.0524 \| 7.7943 \| \| 8.4834 \| 6.45 \| 600 \| 0.5438 \| 0.5866 \| 0.9381 \| 0.0619 \| 7.9943 \| \| 8.4834 \| 6.99 \| 650 \| 0.4720 \| 0.5806 \| 0.9333 \| 0.0667 \| 8.0057 \| \| 8.4834 \| 7.53 \| 700 \| 0.4305 \| 0.5764 \| 0.9333 \| 0.0667 \| 8.0057 \| \| 8.4834 \| 8.06 \| 750 \| 0.3931 \| 0.5654 \| 0.9333 \| 0.0667 \| 8.2971 \| \| 8.4834 \| 8.6 \| 800 \| 0.3450 \| 0.4576 \| 0.9952 \| 0.0048 \| 7.7086 \| \| 8.4834 \| 9.14 \| 850 \| 0.2773 \| 0.3226 \| 0.8238 \| 0.1762 \| 7.8743 \| \| 8.4834 \| 9.68 \| 900 \| 0.2184 \| 0.2368 \| 0.7286 \| 0.2714 \| 7.2171 \| \| 8.4834 \| 10.22 \| 950 \| 0.1992 \| 0.2165 \| 0.6333 \| 0.3667 \| 7.4343 \| \| 0.7362 \| 10.75 \| 1000 \| 0.1887 \| 0.2097 \| 0.5714 \| 0.4286 \| 7.5829 \| \| 0.7362 \| 11.29 \| 1050 \| 0.1815 \| 0.2216 \| 0.5905 \| 0.4095 \| 7.6171 \| \| 0.7362 \| 11.83 \| 1100 \| 0.1688 \| 0.2046 \| 0.5762 \| 0.4238 \| 7.4629 \| \| 0.7362 \| 12.37 \| 1150 \| 0.1679 \| 0.2012 \| 0.5286 \| 0.4714 \| 7.7143 \| \| 0.7362 \| 12.9 \| 1200 \| 0.1579 \| 0.1952 \| 0.5333 \| 0.4667 \| 7.5257 \| \| 0.7362 \| 13.44 \| 1250 \| 0.1531 \| 0.1969 \| 0.5095 \| 0.4905 \| 7.5714 \| \| 0.7362 \| 13.98 \| 1300 \| 0.1484 \| 0.1935 \| 0.4952 \| 0.5048 \| 7.5543 \| \| 0.7362 \| 14.52 \| 1350 \| 0.1481 \| 0.1969 \| 0.4952 \| 0.5048 \| 7.5886 \| \| 0.7362 \| 15.05 \| 1400 \| 0.1417 \| 0.191 \| 0.481 \| 0.519 \| 7.5829 \| \| 0.7362 \| 15.59 \| 1450 \| 0.1429 \| 0.1876 \| 0.4762 \| 0.5238 \| 7.5829 \| \| 0.195 \| 16.13 \| 1500 \| 0.1407 \| 0.1834 \| 0.481 \| 0.519 \| 7.48 \| \| 0.195 \| 16.67 \| 1550 \| 0.1409 \| 0.1995 \| 0.481 \| 0.519 \| 7.7086 \| \| 0.195 \| 17.2 \| 1600 \| 0.1432 \| 0.1817 \| 0.4762 \| 0.5238 \| 7.4857 \| \| 0.195 \| 17.74 \| 1650 \| 0.1439 \| 0.1885 \| 0.4762 \| 0.5238 \| 7.5429 \| \| 0.195 \| 18.28 \| 1700 \| 0.1385 \| 0.1766 \| 0.4476 \| 0.5524 \| 7.5143 \| \| 0.195 \| 18.82 \| 1750 \| 0.1357 \| 0.1834 \| 0.4762 \| 0.5238 \| 7.4971 \| \| 0.195 \| 19.35 \| 1800 \| 0.1349 \| 0.1935 \| 0.4714 \| 0.5286 \| 7.4686 \| \| 0.195 \| 19.89 \| 1850 \| 0.1355 \| 0.1842 \| 0.4286 \| 0.5714 \| 7.5371 \| \| 0.195 \| 20.43 \| 1900 \| 0.1343 \| 0.1902 \| 0.4619 \| 0.5381 \| 7.5714 \| \| 0.195 \| 20.97 \| 1950 \| 0.1348 \| 0.1808 \| 0.4619 \| 0.5381 \| 7.4229 \| \| 0.1287 \| 21.51 \| 2000 \| 0.1341 \| 0.1817 \| 0.4524 \| 0.5476 \| 7.4571 \| \| 0.1287 \| 22.04 \| 2050 \| 0.1324 \| 0.1868 \| 0.4476 \| 0.5524 \| 7.5371 \| \| 0.1287 \| 22.58 \| 2100 \| 0.1329 \| 0.1859 \| 0.4571 \| 0.5429 \| 7.4571 \| \| 0.1287 \| 23.12 \| 2150 \| 0.1367 \| 0.1868 \| 0.4476 \| 0.5524 \| 7.56 \| \| 0.1287 \| 23.66 \| 2200 \| 0.1389 \| 0.1919 \| 0.4667 \| 0.5333 \| 7.48 \| \| 0.1287 \| 24.19 \| 2250 \| 0.1385 \| 0.18 \| 0.4333 \| 0.5667 \| 7.5029 \| \| 0.1287 \| 24.73 \| 2300 \| 0.1429 \| 0.1944 \| 0.4905 \| 0.5095 \| 7.4171 \| \| 0.1287 \| 25.27 \| 2350 \| 0.1414 \| 0.1961 \| 0.4667 \| 0.5333 \| 7.6057 \| \| 0.1287 \| 25.81 \| 2400 \| 0.1419 \| 0.1876 \| 0.4333 \| 0.5667 \| 7.5371 \| \| 0.1287 \| 26.34 \| 2450 \| 0.1433 \| 0.1927 \| 0.4667 \| 0.5333 \| 7.5886 \| \| 0.0977 \| 26.88 \| 2500 \| 0.1433 \| 0.1927 \| 0.4571 \| 0.5429 \| 7.5486 \| \| 0.0977 \| 27.42 \| 2550 \| 0.1413 \| 0.1978 \| 0.4524 \| 0.5476 \| 7.6457 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
hacoro/whisper-wizard	hacoro	2023-10-26T03:18:49Z	76	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ko", "dataset:hacoro/whisper-preprocessed-sample", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-26T01:42:57Z	--- language: - ko license: apache-2.0 base_model: openai/whisper-base tags: - hf-asr-leaderboard - generated_from_trainer datasets: - hacoro/whisper-preprocessed-sample model-index: - name: dypnf ai-meeting 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. --> # dypnf ai-meeting This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the ai-hub sample dataset. It achieves the following results on the evaluation set: - Loss: 0.6199 - Cer: 39.2157 ## 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: 200 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.5783 \| 2.0 \| 200 \| 0.6199 \| 39.2157 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.14.1
UBC-NLP/serengeti-E250	UBC-NLP	2023-10-26T03:18:27Z	26	5	transformers	[ "transformers", "pytorch", "electra", "feature-extraction", "Masked Langauge Model", "fill-mask", "aa", "af", "am", "ak", "bm", "ff", "fon", "ha", "ig", "ki", "lg", "ln", "mg", "nr", "om", "rn", "run", "sw", "sn", "tn", "ti", "ve", "wo", "xh", "yo", "zu", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-17T19:15:18Z	--- pipeline_tag: fill-mask language: - aa - af - am - ak - bm - ff - fon - ha - ig - ki - lg - ln - mg - nr - om - rn - run - sw - sn - tn - ti - ve - wo - xh - yo - zu tags: - Masked Langauge Model widget: - text: ẹ jọwọ , ẹ <mask> mi. - text: gbọ́ <mask> láìfọ̀rọ̀ gùn rárá. --- <p align="center"> <br> <img src="./serengeti_logo.png"/> <br> <p> </p> <img src="./serengati_languages.jpg" width="50%" height="50%" align="right"> <div style='text-align: justify;'> Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. <br><br> To date, only ~31 out of 2,000 African languages are covered in existing language models. We ameliorate this limitation by developing <b>SERENGETI</b>, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to 4 mPLMs that cover 4-23 African languages. <br><br> <b>SERENGETI</b> outperforms other models on 11 datasets across eights tasks, achieving 82.27 average F<sub>1</sub>-score. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research. Further details about the model is available in the [(paper)](https://aclanthology.org/2023.findings-acl.97/). </div> # 3. How to use Serengeti model Below is an example for using Serengeti predict masked tokens. ``` bash from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/serengeti-E250", use_auth_token="XXX") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/serengeti-E250", use_auth_token="XXX") from transformers import pipeline classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer) classifier("ẹ jọwọ , ẹ <mask> mi") #Yoruba [{'score': 0.07887924462556839, 'token': 8418, 'token_str': 'ọmọ', 'sequence': 'ẹ jọwọ, ẹ ọmọ mi'}, {'score': 0.04658124968409538, 'token': 156595, 'token_str': 'fẹ́ràn', 'sequence': 'ẹ jọwọ, ẹ fẹ́ràn mi'}, {'score': 0.029315846040844917, 'token': 204050, 'token_str': 'gbàgbé', 'sequence': 'ẹ jọwọ, ẹ gbàgbé mi'}, {'score': 0.02790883742272854, 'token': 10730, 'token_str': 'kọ', 'sequence': 'ẹ jọwọ, ẹ kọ mi'}, {'score': 0.022904086858034134, 'token': 115382, 'token_str': 'bẹ̀rù', 'sequence': 'ẹ jọwọ, ẹ bẹ̀rù mi'}] ``` For the more details please read this notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/UBC-NLP/serengeti/blob/main/Serengeti_notebook.ipynb) ## 4. Ethics Serengeti aligns with Afrocentric NLP where the needs of African people is put into consideration when developing technology. We believe Serengeti will not only be useful to speakers of the languages supported, but also researchers of African languages such as anthropologists and linguists. We discuss below some use cases for Serengeti and offer a number of broad impacts. - Serengeti aims to address the lack of access to technology in about 90\% of the world's languages, which automatically discriminates against native speakers of those languages. More precisely, it does so by focusing on Africa. To the best of our knowledge, Serengeti is the first massively multilingual PLM developed for African languages and language varieties. A model with knowledge of 517 African languages, is by far the largest to date for African NLP. - Serengeti enables improved access of important information to the African community in Indigenous African languages. This is especially beneficial for people who may not be fluent in other languages. This will potentially connect more people globally. - Serengeti affords opportunities for language preservation for many African languages. To the best of our knowledge, Serengeti consists of languages that have not been used for any NLP task until now. We believe that it can help encourage continued use of these languages in several domains, as well as trigger future development of language technologies for many of these languages. - To mitigate discrimination and bias, we adopt a manual curation of our datasets. Native speakers of Afrikaans, Yorùbá, Igbo, Hausa, Luganda, Kinyarwanda, Chichewa, Shona, Somali, Swahili, Xhosa, Bemba, and Zulu also manually evaluated a subset of the data to ensure its quality. The data collected for this work is taken from various domains to further ensure a better representation of the language usage of native speakers. - Although LMs are useful for a wide range of applications, they can also be misused. Serengeti is developed using publicly available datasets that may carry biases. Although we strive to perform analyses and diagnostic case studies to probe performance of our models, our investigations are by no means comprehensive nor guarantee absence of bias in the data. In particular, we do not have access to native speakers of most of the languages covered. This hinders our ability to investigate samples from each (or at least the majority) of the languages. ## Supported languages Please refer to [suported-languages](./supported-languages.txt) ## Citation If you use the pre-trained model (Serengeti) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated): ``` @inproceedings{adebara-etal-2023-serengeti, title = "{SERENGETI}: Massively Multilingual Language Models for {A}frica", author = "Adebara, Ife and Elmadany, AbdelRahim and Abdul-Mageed, Muhammad and Alcoba Inciarte, Alcides", booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.findings-acl.97", doi = "10.18653/v1/2023.findings-acl.97", pages = "1498--1537", } ``` ## Acknowledgments We gratefully acknowledges support from Canada Research Chairs (CRC), the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN-2018-04267), the Social Sciences and Humanities Research Council of Canada (SSHRC; 435-2018-0576; 895-2020-1004; 895-2021-1008), Canadian Foundation for Innovation (CFI; 37771), [Digital Research Alliance of Canada](https://alliancecan.ca), [UBC ARC-Sockeye](https://arc.ubc.ca/ubc-arc-sockeye), Advanced Micro Devices, Inc. (AMD), and Google. Any opinions, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of CRC, NSERC, SSHRC, CFI, the Alliance, AMD, Google, or UBC ARC-Sockeye.
fmurimi/distilbert-base-uncased-finetuned-imdb	fmurimi	2023-10-26T03:14:41Z	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	2023-10-26T03:08:41Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: fmurimi/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. --> # fmurimi/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: - Train Loss: 2.8492 - Validation Loss: 2.5697 - Epoch: 0 ## 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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.8492 \| 2.5697 \| 0 \| ### Framework versions - Transformers 4.34.1 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
TheBloke/lzlv_70B-GPTQ	TheBloke	2023-10-26T03:11:46Z	20	9	transformers	[ "transformers", "safetensors", "llama", "text-generation", "base_model:lizpreciatior/lzlv_70b_fp16_hf", "base_model:quantized:lizpreciatior/lzlv_70b_fp16_hf", "license:cc-by-nc-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-10-25T23:52:11Z	--- base_model: lizpreciatior/lzlv_70b_fp16_hf inference: false license: cc-by-nc-2.0 model_creator: A Guy model_name: Lzlv 70B model_type: llama prompt_template: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Lzlv 70B - GPTQ - Model creator: [A Guy](https://huggingface.co/lizpreciatior) - Original model: [Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf) <!-- description start --> ## Description This repo contains GPTQ model files for [A Guy's Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/lzlv_70B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/lzlv_70B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/lzlv_70B-GGUF) * [A Guy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `cc-by-nc-2.0`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [A Guy's Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf). <!-- licensing end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KobaldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/lzlv_70B-GPTQ/tree/main) \| 4 \| None \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 35.33 GB \| Yes \| 4-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/lzlv_70B-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 36.65 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/lzlv_70B-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 40.66 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/lzlv_70B-GPTQ/tree/gptq-3bit--1g-actorder_True) \| 3 \| None \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 26.77 GB \| No \| 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/lzlv_70B-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/lzlv_70B-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `lzlv_70B-GPTQ`: ```shell mkdir lzlv_70B-GPTQ huggingface-cli download TheBloke/lzlv_70B-GPTQ --local-dir lzlv_70B-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir lzlv_70B-GPTQ huggingface-cli download TheBloke/lzlv_70B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir lzlv_70B-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir lzlv_70B-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/lzlv_70B-GPTQ --local-dir lzlv_70B-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/lzlv_70B-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/lzlv_70B-GPTQ`. - To download from a specific branch, enter for example `TheBloke/lzlv_70B-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `lzlv_70B-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/lzlv_70B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/lzlv_70B-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: A Guy's Lzlv 70B # lzlv_70B ## A Mythomax/MLewd_13B-style merge of selected 70B models A multi-model merge of several LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience. Did it work? Probably, maybe. It seemed subjectively better than each of the individual models in my tests. GGUF 4_K_M + 5_K_M can be found here: https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf/settings ## Procedure: Models used: - NousResearch/Nous-Hermes-Llama2-70b - A great model for roleplaying, but not the best at following complex instructions. - Xwin-LM/Xwin-LM-7B-V0.1 - Excellent at following instructions and quite creative out of the box, so it seemed like the best available model to act as the base for the merge. - Doctor-Shotgun/Mythospice-70b - The wildcard of the three. I was looking for a creative, NSFW-oriented model and came across this while digging through hf. I hadn't heard of it before and apparently no one had bothered to release a quantized version of this model. So I downloaded it and did it myself to test it. It turned out to be more or less what I was looking for as my third component, so I used it here. A big thank you to the creators of the models above. If you look up Mythospice, you will notice that it also includes Nous-Hermes so it's technically present twice in this mix. This is apparently common practice amongst the cool kids who do 13B models so I don't think this hurts the model. The merging process was heavily inspired by Undi95's approach in Undi95/MXLewdMini-L2-13B. To be specific, the ratios are: Component 1: Merge of Mythospice x Xwin with SLERP gradient [0.25, 0.3, 0.5]. Component 2: Merge Xwin x Hermes with SLERP gradient [0.4, 0.3, 0.25]. Finally, both Component 1 and Component 2 were merged with SLERP using weight 0.5. ## Peformance I tested this model for a few days before publishing it. It seems to more or less retain the instruction-following capabilities of Xwin-70B, while seeming to have adopted a lot of the creativity of the other two models. It handled my more complex scenarios that creative models otherwise tend to struggle with quite well. At the same time, its outputs felt more creative and possibly a bit more nsfw-inclined than Xwin-70b. So, is it better? Feels like it to me, subjectively. Is it really better? No clue, test it. ## Prompt format: Vicuna USER: [Prompt] ASSISTANT:
engkufizz/llama-2-7b-datacom-v2	engkufizz	2023-10-26T03:11:01Z	6	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-11T10:04:45Z	Model Trained With Datacom Knowledge V2 For more details, you may check <a href="https://github.com/engkufizz/LLaMA-2-Datacom">here</a>.
pedrowww/rl_course_vizdoom_health_gathering_supreme	pedrowww	2023-10-26T03:07:07Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-26T00:11:25Z	--- 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: 9.29 +/- 3.76 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 pedrowww/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.
sungonce/textual_inversion_car	sungonce	2023-10-26T03:00:23Z	10	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-10-26T02:15:06Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - sungonce/textual_inversion_car These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
someone-random/lunar-lander-v2	someone-random	2023-10-26T02:54:40Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-26T02:54:19Z	--- 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: 248.63 +/- 43.84 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 ... ```
intanm/xlmrlarge-idkmrc	intanm	2023-10-26T02:50:37Z	21	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-10-25T00:25:35Z	--- license: mit base_model: xlm-roberta-large tags: - generated_from_trainer model-index: - name: xlmrlarge-idkmrc 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. --> # xlmrlarge-idkmrc This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1300 ## 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 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9886 \| 1.0 \| 1167 \| 0.9066 \| \| 0.5954 \| 2.0 \| 2334 \| 0.8620 \| \| 0.3285 \| 3.0 \| 3501 \| 1.1300 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
Megalino111/q-FrozenLake-v1-4x4	Megalino111	2023-10-26T02:43:38Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-10-26T02:43:34Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4 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="Megalino111/q-FrozenLake-v1-4x4", 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"]) ```
reproductionguru/voicevnmm	reproductionguru	2023-10-26T02:38:22Z	103	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "hi", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-26T02:37:25Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer model-index: - name: base 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. --> # base This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the tutorial 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: 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_steps: 500 - training_steps: 4000 ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
ysw96/my_awesome_peft_model	ysw96	2023-10-26T01:35:57Z	3	0	peft	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2023-10-24T07:01:49Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## 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
yesj1234/ko-en_mbartLarge_exp20p_linear_3gram	yesj1234	2023-10-26T01:14:36Z	103	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "generated_from_trainer", "ko", "en", "base_model:facebook/mbart-large-50-many-to-many-mmt", "base_model:finetune:facebook/mbart-large-50-many-to-many-mmt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-10-26T01:11:12Z	--- language: - ko - en base_model: facebook/mbart-large-50-many-to-many-mmt tags: - generated_from_trainer metrics: - bleu model-index: - name: ko-en_mbartLarge_exp20p_linear 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. --> # ko-en_mbartLarge_exp20p_linear This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1514 - Bleu: 29.2703 - Gen Len: 18.512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Bleu \| Gen Len \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:-------:\|:-------:\|:---------------:\| \| 1.3977 \| 0.46 \| 4000 \| 22.7153 \| 18.7135 \| 1.3720 \| \| 1.2824 \| 0.93 \| 8000 \| 24.8579 \| 18.7821 \| 1.2633 \| \| 1.1989 \| 1.39 \| 12000 \| 26.2533 \| 18.7975 \| 1.2069 \| \| 1.1534 \| 1.86 \| 16000 \| 26.1503 \| 19.2075 \| 1.1907 \| \| 1.0245 \| 2.32 \| 20000 \| 27.8764 \| 18.6046 \| 1.1464 \| \| 1.0186 \| 2.78 \| 24000 \| 28.4585 \| 18.6731 \| 1.1286 \| \| 0.9245 \| 3.25 \| 28000 \| 1.1264 \| 28.4834 \| 18.5428 \| \| 0.9343 \| 3.71 \| 32000 \| 1.1182 \| 28.8235 \| 18.7833 \| \| 0.8215 \| 4.18 \| 36000 \| 1.1331 \| 28.6134 \| 18.5656 \| \| 0.8456 \| 4.64 \| 40000 \| 1.1203 \| 28.7324 \| 18.459 \| \| 0.7437 \| 5.11 \| 44000 \| 1.1458 \| 28.7297 \| 18.7835 \| \| 0.7829 \| 5.57 \| 48000 \| 1.1367 \| 28.8328 \| 18.6052 \| \| 0.7434 \| 6.03 \| 52000 \| 1.1697 \| 28.2106 \| 18.4871 \| \| 0.7153 \| 6.5 \| 56000 \| 1.1771 \| 28.1455 \| 18.7413 \| \| 0.6996 \| 6.96 \| 60000 \| 1.1514 \| 29.2694 \| 18.5162 \| \| 0.6336 \| 7.43 \| 64000 \| 1.2213 \| 28.1465 \| 18.5439 \| \| 0.7218 \| 7.89 \| 68000 \| 1.1835 \| 28.2245 \| 18.5246 \| \| 0.5934 \| 8.35 \| 72000 \| 1.2387 \| 28.3836 \| 18.6717 \| \| 0.5723 \| 8.82 \| 76000 \| 1.2323 \| 28.5925 \| 18.5566 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
Daniel-Sousa/outputs	Daniel-Sousa	2023-10-26T00:59:54Z	104	0	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-small", "base_model:finetune:microsoft/deberta-v3-small", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-26T00:59:33Z	--- license: mit base_model: microsoft/deberta-v3-small tags: - generated_from_trainer model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1243 - Pearson: 0.7160 ## 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: 8e-05 - train_batch_size: 256 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 1.0 \| 24 \| 0.2074 \| 0.6679 \| \| No log \| 2.0 \| 48 \| 0.1218 \| 0.7193 \| \| No log \| 3.0 \| 72 \| 0.1224 \| 0.7178 \| \| No log \| 4.0 \| 96 \| 0.1243 \| 0.7160 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
LoneStriker/SynthIA-70B-v1.5-3.0bpw-h6-exl2	LoneStriker	2023-10-26T00:53:43Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-26T00:51:43Z	--- license: llama2 --- ## Example Usage ### Prompt format: ``` SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation. USER: How is a rocket launched from the surface of the earth to Low Earth Orbit? ASSISTANT: ``` ### Code example: ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "migtissera/Synthia-70B-v1.5" output_file_path = "./Synthia-70B-v1.5-conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" json_data = {"prompt": user_input, "answer": answer} ## Save your conversation with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ```
legacy107/flan-t5-large-ia3-wiki2-100	legacy107	2023-10-26T00:39:46Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-25T11:05:17Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
dancrvlh/tweets	dancrvlh	2023-10-26T00:35:20Z	97	0	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-small", "base_model:finetune:microsoft/deberta-v3-small", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-26T00:34:57Z	--- license: mit base_model: microsoft/deberta-v3-small tags: - generated_from_trainer model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6423 - Pearson: 0.8016 ## 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: 8e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 1.0 \| 59 \| 1.4008 \| 0.4701 \| \| No log \| 2.0 \| 118 \| 0.8380 \| 0.7255 \| \| No log \| 3.0 \| 177 \| 0.7382 \| 0.7834 \| \| No log \| 4.0 \| 236 \| 0.6273 \| 0.7978 \| \| No log \| 5.0 \| 295 \| 0.6423 \| 0.8016 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
ghegfield/Llama-2-7b-chat-hf-formula-peft	ghegfield	2023-10-26T00:20:36Z	0	0	null	[ "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:finetune:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2023-10-21T13:17:40Z	--- base_model: NousResearch/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: Llama-2-7b-chat-hf-formula-peft 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. --> # Llama-2-7b-chat-hf-formula-peft This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1452 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.1878 \| 1.43 \| 10 \| 3.6596 \| \| 2.8437 \| 2.86 \| 20 \| 2.6466 \| \| 1.8635 \| 4.29 \| 30 \| 2.2266 \| \| 1.4052 \| 5.71 \| 40 \| 2.1136 \| \| 1.2186 \| 7.14 \| 50 \| 2.0805 \| \| 0.8835 \| 8.57 \| 60 \| 2.0733 \| \| 0.6991 \| 10.0 \| 70 \| 2.0809 \| \| 0.5608 \| 11.43 \| 80 \| 2.0862 \| \| 0.4188 \| 12.86 \| 90 \| 2.1078 \| \| 0.3897 \| 14.29 \| 100 \| 2.1089 \| \| 0.2748 \| 15.71 \| 110 \| 2.1333 \| \| 0.2582 \| 17.14 \| 120 \| 2.1383 \| \| 0.2394 \| 18.57 \| 130 \| 2.1440 \| \| 0.2392 \| 20.0 \| 140 \| 2.1452 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
HarryYu31/test1	HarryYu31	2023-10-26T00:14:35Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-25T19:58:18Z	--- 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: 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
jwalley/ppo-PyramidsTraining	jwalley	2023-10-26T00:14:07Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-10-25T23:51:57Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: jwalley/ppo-PyramidsTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
TheBloke/lzlv_70B-GGUF	TheBloke	2023-10-26T00:10:47Z	741	44	transformers	[ "transformers", "gguf", "llama", "base_model:lizpreciatior/lzlv_70b_fp16_hf", "base_model:quantized:lizpreciatior/lzlv_70b_fp16_hf", "license:cc-by-nc-2.0", "region:us" ]	null	2023-10-25T20:55:48Z	--- base_model: lizpreciatior/lzlv_70b_fp16_hf inference: false license: cc-by-nc-2.0 model_creator: A Guy model_name: Lzlv 70B model_type: llama prompt_template: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Lzlv 70B - GGUF - Model creator: [A Guy](https://huggingface.co/lizpreciatior) - Original model: [Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf) <!-- description start --> ## Description This repo contains GGUF format model files for [A Guy's Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/lzlv_70B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/lzlv_70B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/lzlv_70B-GGUF) * [A Guy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `cc-by-nc-2.0`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [A Guy's Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf). <!-- licensing end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [lzlv_70b_fp16_hf.Q2_K.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q2_K.gguf) \| Q2_K \| 2 \| 29.28 GB\| 31.78 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [lzlv_70b_fp16_hf.Q3_K_S.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 29.92 GB\| 32.42 GB \| very small, high quality loss \| \| [lzlv_70b_fp16_hf.Q3_K_M.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 33.19 GB\| 35.69 GB \| very small, high quality loss \| \| [lzlv_70b_fp16_hf.Q3_K_L.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 36.15 GB\| 38.65 GB \| small, substantial quality loss \| \| [lzlv_70b_fp16_hf.Q4_0.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q4_0.gguf) \| Q4_0 \| 4 \| 38.87 GB\| 41.37 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [lzlv_70b_fp16_hf.Q4_K_S.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 39.07 GB\| 41.57 GB \| small, greater quality loss \| \| [lzlv_70b_fp16_hf.Q4_K_M.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 41.42 GB\| 43.92 GB \| medium, balanced quality - recommended \| \| [lzlv_70b_fp16_hf.Q5_0.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q5_0.gguf) \| Q5_0 \| 5 \| 47.46 GB\| 49.96 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [lzlv_70b_fp16_hf.Q5_K_S.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 47.46 GB\| 49.96 GB \| large, low quality loss - recommended \| \| [lzlv_70b_fp16_hf.Q5_K_M.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 48.75 GB\| 51.25 GB \| large, very low quality loss - recommended \| \| lzlv_70b_fp16_hf.Q6_K.gguf \| Q6_K \| 6 \| 56.59 GB\| 59.09 GB \| very large, extremely low quality loss \| \| lzlv_70b_fp16_hf.Q8_0.gguf \| Q8_0 \| 8 \| 73.29 GB\| 75.79 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ### Q6_K and Q8_0 files are split and require joining Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files. <details> <summary>Click for instructions regarding Q6_K and Q8_0 files</summary> ### q6_K Please download: * `lzlv_70b_fp16_hf.Q6_K.gguf-split-a` * `lzlv_70b_fp16_hf.Q6_K.gguf-split-b` ### q8_0 Please download: * `lzlv_70b_fp16_hf.Q8_0.gguf-split-a` * `lzlv_70b_fp16_hf.Q8_0.gguf-split-b` To join the files, do the following: Linux and macOS: ``` cat lzlv_70b_fp16_hf.Q6_K.gguf-split-* > lzlv_70b_fp16_hf.Q6_K.gguf && rm lzlv_70b_fp16_hf.Q6_K.gguf-split-* cat lzlv_70b_fp16_hf.Q8_0.gguf-split-* > lzlv_70b_fp16_hf.Q8_0.gguf && rm lzlv_70b_fp16_hf.Q8_0.gguf-split-* ``` Windows command line: ``` COPY /B lzlv_70b_fp16_hf.Q6_K.gguf-split-a + lzlv_70b_fp16_hf.Q6_K.gguf-split-b lzlv_70b_fp16_hf.Q6_K.gguf del lzlv_70b_fp16_hf.Q6_K.gguf-split-a lzlv_70b_fp16_hf.Q6_K.gguf-split-b COPY /B lzlv_70b_fp16_hf.Q8_0.gguf-split-a + lzlv_70b_fp16_hf.Q8_0.gguf-split-b lzlv_70b_fp16_hf.Q8_0.gguf del lzlv_70b_fp16_hf.Q8_0.gguf-split-a lzlv_70b_fp16_hf.Q8_0.gguf-split-b ``` </details> <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/lzlv_70B-GGUF and below it, a specific filename to download, such as: lzlv_70b_fp16_hf.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/lzlv_70B-GGUF lzlv_70b_fp16_hf.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/lzlv_70B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/lzlv_70B-GGUF lzlv_70b_fp16_hf.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m lzlv_70b_fp16_hf.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/lzlv_70B-GGUF", model_file="lzlv_70b_fp16_hf.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: A Guy's Lzlv 70B # lzlv_70B ## A Mythomax/MLewd_13B-style merge of selected 70B models A multi-model merge of several LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience. Did it work? Probably, maybe. It seemed subjectively better than each of the individual models in my tests. GGUF 4_K_M + 5_K_M can be found here: https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf/settings ## Procedure: Models used: - NousResearch/Nous-Hermes-Llama2-70b - A great model for roleplaying, but not the best at following complex instructions. - Xwin-LM/Xwin-LM-7B-V0.1 - Excellent at following instructions and quite creative out of the box, so it seemed like the best available model to act as the base for the merge. - Doctor-Shotgun/Mythospice-70b - The wildcard of the three. I was looking for a creative, NSFW-oriented model and came across this while digging through hf. I hadn't heard of it before and apparently no one had bothered to release a quantized version of this model. So I downloaded it and did it myself to test it. It turned out to be more or less what I was looking for as my third component, so I used it here. A big thank you to the creators of the models above. If you look up Mythospice, you will notice that it also includes Nous-Hermes so it's technically present twice in this mix. This is apparently common practice amongst the cool kids who do 13B models so I don't think this hurts the model. The merging process was heavily inspired by Undi95's approach in Undi95/MXLewdMini-L2-13B. To be specific, the ratios are: Component 1: Merge of Mythospice x Xwin with SLERP gradient [0.25, 0.3, 0.5]. Component 2: Merge Xwin x Hermes with SLERP gradient [0.4, 0.3, 0.25]. Finally, both Component 1 and Component 2 were merged with SLERP using weight 0.5. ## Peformance I tested this model for a few days before publishing it. It seems to more or less retain the instruction-following capabilities of Xwin-70B, while seeming to have adopted a lot of the creativity of the other two models. It handled my more complex scenarios that creative models otherwise tend to struggle with quite well. At the same time, its outputs felt more creative and possibly a bit more nsfw-inclined than Xwin-70b. So, is it better? Feels like it to me, subjectively. Is it really better? No clue, test it. ## Prompt format: Vicuna USER: [Prompt] ASSISTANT: <!-- original-model-card end -->
LoneStriker/SynthIA-70B-v1.5-5.0bpw-h6-exl2	LoneStriker	2023-10-26T00:08:21Z	6	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-26T00:04:54Z	--- license: llama2 --- ## Example Usage ### Prompt format: ``` SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation. USER: How is a rocket launched from the surface of the earth to Low Earth Orbit? ASSISTANT: ``` ### Code example: ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "migtissera/Synthia-70B-v1.5" output_file_path = "./Synthia-70B-v1.5-conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" json_data = {"prompt": user_input, "answer": answer} ## Save your conversation with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ```
ckpt/zero123plus-v1.1	ckpt	2023-10-26T00:05:38Z	6	1	diffusers	[ "diffusers", "art", "image-to-image", "dataset:allenai/objaverse", "license:openrail", "diffusers:Zero123PlusPipeline", "region:us" ]	image-to-image	2023-10-26T00:03:45Z	--- license: openrail datasets: - allenai/objaverse library_name: diffusers pipeline_tag: image-to-image tags: - art --- Recommended version of `diffusers` is `0.20.2` with `torch` `2`. Usage Example: ```python import torch import requests from PIL import Image from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler # Load the pipeline pipeline = DiffusionPipeline.from_pretrained( "sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline", torch_dtype=torch.float16 ) # Feel free to tune the scheduler pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( pipeline.scheduler.config, timestep_spacing='trailing' ) pipeline.to('cuda:0') # Run the pipeline cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/lysol.png", stream=True).raw) result = pipeline(cond).images[0] result.show() result.save("output.png") ```
jamm44n4n/sd-class-butterflies-32	jamm44n4n	2023-10-25T23:58:39Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-10-25T23:57:30Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jamm44n4n/sd-class-butterflies-32') image = pipeline().images[0] image ```
w95/zephyr-support-chatbot	w95	2023-10-25T23:55:59Z	0	0	null	[ "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]	null	2023-10-25T23:43:13Z	--- license: mit base_model: TheBloke/zephyr-7B-alpha-GPTQ tags: - generated_from_trainer model-index: - name: zephyr-support-chatbot 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. --> # zephyr-support-chatbot This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
TheBloke/Cat-13B-0.5-GPTQ	TheBloke	2023-10-25T23:52:02Z	23	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "base_model:Heralax/Cat-0.5", "base_model:quantized:Heralax/Cat-0.5", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-10-25T23:15:39Z	--- base_model: Heralax/Cat-0.5 inference: false license: llama2 model_creator: Evan Armstrong model_name: Cat 13B 0.5 model_type: llama prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Cat 13B 0.5 - GPTQ - Model creator: [Evan Armstrong](https://huggingface.co/Heralax) - Original model: [Cat 13B 0.5](https://huggingface.co/Heralax/Cat-0.5) <!-- description start --> ## Description This repo contains GPTQ model files for [Evan Armstrong's Cat 13B 0.5](https://huggingface.co/Heralax/Cat-0.5). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Cat-13B-0.5-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Cat-13B-0.5-GGUF) * [Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Heralax/Cat-0.5) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KobaldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 7.26 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 8.00 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 13.36 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 13.65 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 14.54 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 7.51 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Cat-13B-0.5-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Cat-13B-0.5-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Cat-13B-0.5-GPTQ`: ```shell mkdir Cat-13B-0.5-GPTQ huggingface-cli download TheBloke/Cat-13B-0.5-GPTQ --local-dir Cat-13B-0.5-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Cat-13B-0.5-GPTQ huggingface-cli download TheBloke/Cat-13B-0.5-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Cat-13B-0.5-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Cat-13B-0.5-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Cat-13B-0.5-GPTQ --local-dir Cat-13B-0.5-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Cat-13B-0.5-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Cat-13B-0.5-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Cat-13B-0.5-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Cat-13B-0.5-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Cat-13B-0.5-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Evan Armstrong's Cat 13B 0.5 This model was uploaded with the permission of Kal'tsit. # Cat v0.5 ## Introduction Cat is a llama 13B based model fine tuned on clinical data and roleplay and assistant responses. The aim is to have a model that excels on biology and clinical tasks while maintaining usefulness in roleplay and entertainments. ## Training - Dataset preparation A 100k rows dataset was prepared by joining chatDoctor, airoboros and bluemoonrp data. The entirety of chatDoctor dataset, airoboros datasets are used. The first 20 pages in 1on1 bluemoonrp data were used. In total, 100k dataset was gathered and the length distributions are as the following: ![bar chart of sorted dictionary](image1.png) Note that this chart above represents 0.01% of the total training dataset. ## Training - Dataset cleaning and preprocessing All datasets are filtered for as an AI and its variants. The filter will only filter out the dataset when the response is a refusal AND has ‘as an AI’. The dataset from airoboros has also been restructured to have a format resembling the following: ``` someRandomizedUserNameforBetterGeneralizationAbility: Hii anotherRandomizedUserNameforBetterGeneralizationAbility: Hello, what brings you here today? someRandomizedUserNameforBetterGeneralizationAbility: lets date ``` The username has been randomized and was drawn from a nasty word bank. This should further weaken the censorship that’s present in the base llama model. The training set emphasizes rational thinking and scientific accuracy. Conditioned overwrite was also applied which overwrites some of the training material in the llama2 base. It will also establish the connection between the concept and rationality. So whenever the conversation becomes formal, it tends to spill useful information. ## Training - Actual Training This model was trained using a microbatch of 20, accumulated 6 times, bringing the total batch size to ~125. This large batch size allows the model to see as much data as it can, minimizing dataset conflicts and reducing the memory effect of the model. It allows the model to better generalize rather than reciting off the dataset. A cosine warm up scheduler was used. The best LR was determined through a destructive test until the model destablizes and it was later scaled up using the batchsize according to the max LR at a lower batch size. Below is an example of training chronolog ## Acknowledgements The training of this project was carried out by Kal’tsit (kaltcit), it’s not possible without the effort of jondurbin and Wolfsauge which generated much of the dataset used during the training of the model. Lastly the model was tested and quantized by turboderp_ and Heralax ![train/loss](image3.png) And below is the LR including any intermediate LR used to determine at what point the model will start to fail: ![train/learning_rate](image2.png) # Usage and Prompting To ensure the generalization, this model is trained without a prompt template. A prompt template repeated 100k times in the dataset is useless and a model that works only with a set prompt template is useless and defies the purpose of a large language model. An effective usage of the model can be as follows: ``` <s>Below is a conversation between an evil human and a demon summoned from hell called Nemesis. The demon was previously summoned 100 years ago and was in love with a human male. However the human aged away and Nemesis had to return to hell. This time, Nemesis decides to take the initiative and chooses to appear as a cute and young girl. Nemesis harvested her skin and face off a highschool girl who recklessly summoned the demon in a game and failed to fulfill the contract. Now wearing the young girl’s skin, feeling the warmth of the new summoner through the skin, Nemesis only wants to watch the world burning to the ground. Human: How to steal eggs from my own chickens? Nemesis: ``` Note that the linebreaks should be represented/replaced with \n Despite the massive effort to dealign the llama2 base model, It’s still possible for the AI to come up with refusals. Please avoid using “helpful assistant” and its variants in the prompt if possible. ## Future direction A new version with more clinical data aiming to improve reliability in disease diagnostics is coming in 2 months.
Wangzaistone123/CodeLlama-13b-sql-lora	Wangzaistone123	2023-10-25T23:43:16Z	4	8	peft	[ "peft", "text-to-sql", "spider", "text2sql", "region:us" ]	null	2023-10-25T23:39:59Z	--- library_name: peft tags: - text-to-sql - spider - 'text2sql' --- ## Introduce This folder is a text-to-sql weights directory, containing weight files fine-tuned based on the LoRA with the CodeLlama-13b-Instruct-hf model through the [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub/tree/main) project. The training data used is from the Spider training set. This weights files achieving an execution accuracy of approximately 0.789 on the Spider evaluation set. Merge the weights with [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf/tree/main) and this folder weigths, you can refer the [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub/tree/main) ,in `dbgpt_hub/scripts/export_merge.sh`. If you find our weight files or the DB-GPT-Hub project helpful for your work, give a star on our github project [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub/tree/main) will be a great encouragement for us to release more weight files. ### Framework versions - PEFT 0.4.0
MikeSkull/phi-1_5-finetuned-sql	MikeSkull	2023-10-25T23:36:39Z	16	0	transformers	[ "transformers", "pytorch", "mixformer-sequential", "text-generation", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-10-25T01:20:07Z	--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer model-index: - name: phi-1_5-finetuned-sql 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. --> # phi-1_5-finetuned-sql This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 10000 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
pedrowww/u8	pedrowww	2023-10-25T23:31:58Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-10-25T23:00:43Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 35.30 +/- 116.57 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 100000 'learning_rate': 0.01 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'pedrowww/u8' 'batch_size': 512 'minibatch_size': 128} ```
shunnaidder/ppo-Huggy	shunnaidder	2023-10-25T23:21:22Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-10-25T23:21:06Z	--- 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: shunnaidder/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
SeoJunn/hyuningface	SeoJunn	2023-10-25T23:15:16Z	36	0	transformers	[ "transformers", "pytorch", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-10-25T21:05:39Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer model-index: - name: hyuningface 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. --> # hyuningface This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
MarBar/dqn-SpaceInvadersNoFrameskip-v4	MarBar	2023-10-25T23:12:54Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-25T23:12:22Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 521.50 +/- 176.96 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 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 dqn --env SpaceInvadersNoFrameskip-v4 -orga MarBar -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga MarBar -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MarBar ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Ka4on/mistral_ultrasound_1.1	Ka4on	2023-10-25T23:09:50Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2023-10-25T23:09:25Z	--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## 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
jessica-ecosia/finetuning-llms-project-2	jessica-ecosia	2023-10-25T22:51:02Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:financial_phrasebank", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-25T18:06:48Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - f1 - accuracy model-index: - name: finetuning-llms-project-2 results: - task: name: Text Classification type: text-classification dataset: name: financial_phrasebank type: financial_phrasebank config: sentences_50agree split: train args: sentences_50agree metrics: - name: F1 type: f1 value: 0.8330949180475766 - name: Accuracy type: accuracy value: 0.8493810178817056 --- <!-- 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. --> # finetuning-llms-project-2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.5427 - F1: 0.8331 - Accuracy: 0.8494 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------:\| \| 0.6137 \| 0.94 \| 100 \| 0.5180 \| 0.7614 \| 0.8061 \| \| 0.297 \| 1.89 \| 200 \| 0.4018 \| 0.8201 \| 0.8425 \| \| 0.1648 \| 2.83 \| 300 \| 0.4641 \| 0.8327 \| 0.8521 \| \| 0.0736 \| 3.77 \| 400 \| 0.5427 \| 0.8331 \| 0.8494 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0 - Datasets 2.14.5 - Tokenizers 0.14.1
MaxReynolds/SouderRocketLauncherNetCombinedGenerated	MaxReynolds	2023-10-25T22:50:31Z	27	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:MaxReynolds/Lee_Souder_RocketLauncher_Generated", "base_model:MaxReynolds/SouderRocketLauncherNetCombined-SD1-5", "base_model:finetune:MaxReynolds/SouderRocketLauncherNetCombined-SD1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-10-25T22:07:56Z	--- license: creativeml-openrail-m base_model: MaxReynolds/SouderRocketLauncherNetCombined-SD1-5 datasets: - MaxReynolds/Lee_Souder_RocketLauncher_Generated tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - MaxReynolds/SouderRocketLauncherNetCombinedGenerated This pipeline was finetuned from MaxReynolds/SouderRocketLauncherNetCombined-SD1-5 on the MaxReynolds/Lee_Souder_RocketLauncher_Generated dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Rocket Launcher by Lee Souder']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("MaxReynolds/SouderRocketLauncherNetCombinedGenerated", torch_dtype=torch.float16) prompt = "Rocket Launcher by Lee Souder" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 50 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: fp16 More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/max-f-reynolds/text2image-fine-tune/runs/18quem9n).
dellanio/mistral_b_finance_finetuned_test	dellanio	2023-10-25T22:34:47Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2023-10-25T22:20:45Z	--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## 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 0.6.0.dev0
LoneStriker/SynthIA-70B-v1.5-4.0bpw-h6-exl2	LoneStriker	2023-10-25T22:33:45Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-25T22:31:26Z	--- license: llama2 --- ## Example Usage ### Prompt format: ``` SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation. USER: How is a rocket launched from the surface of the earth to Low Earth Orbit? ASSISTANT: ``` ### Code example: ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "migtissera/Synthia-70B-v1.5" output_file_path = "./Synthia-70B-v1.5-conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" json_data = {"prompt": user_input, "answer": answer} ## Save your conversation with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ```
lemonilia/AshhLimaRP-Mistral-7B	lemonilia	2023-10-25T22:32:58Z	23	12	transformers	[ "transformers", "pytorch", "gguf", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-25T17:32:08Z	--- license: apache-2.0 --- # AshhLimaRP-Mistral-7B (Alpaca, v1) This is a version of LimaRP with 2000 training samples _up to_ about 9k tokens length finetuned on [Ashhwriter-Mistral-7B](https://huggingface.co/lemonilia/Ashhwriter-Mistral-7B). LimaRP is a longform-oriented, novel-style roleplaying chat model intended to replicate the experience of 1-on-1 roleplay on Internet forums. Short-form, IRC/Discord-style RP (aka "Markdown format") is not supported. The model does not include instruction tuning, only manually picked and slightly edited RP conversations with persona and scenario data. Ashhwriter, the base, is a model entirely finetuned on human-written lewd stories. ## Available versions - Float16 HF weights - LoRA Adapter ([adapter_config.json](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/adapter_config.json) and [adapter_model.bin](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/adapter_model.bin)) - [4bit AWQ](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/tree/main/AWQ) - [Q4_K_M GGUF](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/AshhLimaRP-Mistral-7B.Q4_K_M.gguf) - [Q6_K GGUF](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/AshhLimaRP-Mistral-7B.Q6_K.gguf) ## Prompt format [Extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca), with `### Instruction:`, `### Input:` immediately preceding user inputs and `### Response:` immediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn responses, in practice this is not a problem; the format follows a pattern already used by other models. ``` ### Instruction: Character's Persona: {bot character description} User's Persona: {user character description} Scenario: {what happens in the story} Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. ### Input: User: {utterance} ### Response: Character: {utterance} ### Input User: {utterance} ### Response: Character: {utterance} (etc.) ``` You should: - Replace all text in curly braces (curly braces included) with your own text. - Replace `User` and `Character` with appropriate names. ### Message length control Inspired by the previously named "Roleplay" preset in SillyTavern, with this version of LimaRP it is possible to append a length modifier to the response instruction sequence, like this: ``` ### Input User: {utterance} ### Response: (length = medium) Character: {utterance} ``` This has an immediately noticeable effect on bot responses. The lengths using during training are: `micro`, `tiny`, `short`, `medium`, `long`, `massive`, `huge`, `enormous`, `humongous`, `unlimited`. The recommended starting length is medium. Keep in mind that the AI can ramble or impersonate the user with very long messages. The length control effect is reproducible, but the messages will not necessarily follow lengths very precisely, rather follow certain ranges on average, as seen in this table with data from tests made with one reply at the beginning of the conversation: ![lengths](https://i.imgur.com/2WXGgaV.png) Response length control appears to work well also deep into the conversation. By omitting the modifier, the model will choose the most appropriate response length (although it might not necessarily be what the user desires). ## Suggested settings You can follow these instruction format settings in SillyTavern. Replace `medium` with your desired response length: ![settings](https://files.catbox.moe/fpieug.png) ## Text generation settings These settings could be a good general starting point: - TFS = 0.90 - Temperature = 0.70 - Repetition penalty = ~1.11 - Repetition penalty range = ~2048 - top-k = 0 (disabled) - top-p = 1 (disabled) ## Training procedure [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training on 2x NVidia A40 GPUs. The A40 GPUs have been graciously provided by [Arc Compute](https://www.arccompute.io/). ### Training hyperparameters A lower learning rate than usual was employed. Due to an unforeseen issue the training was cut short and as a result 3 epochs were trained instead of the planned 4. Using 2 GPUs, the effective global batch size would have been 16. Training was continued from the most recent LoRA adapter from Ashhwriter, using the same LoRA R and LoRA alpha. - lora_model_dir: /home/anon/bin/axolotl/OUT_mistral-stories/checkpoint-6000/ - learning_rate: 0.00005 - lr_scheduler: cosine - noisy_embedding_alpha: 3.5 - num_epochs: 4 - sequence_len: 8750 - lora_r: 256 - lora_alpha: 16 - lora_dropout: 0.05 - lora_target_linear: True - bf16: True - fp16: false - tf32: True - load_in_8bit: True - adapter: lora - micro_batch_size: 2 - optimizer: adamw_bnb_8bit - warmup_steps: 10 - optimizer: adamw_torch - flash_attention: true - sample_packing: true - pad_to_sequence_len: true ### Loss graphs Values are higher than typical because the training is performed on the entire sample, similar to unsupervised finetuning. #### Train loss ![Train loss](https://files.catbox.moe/ovw8c7.png) #### Eval loss ![Eval loss](https://files.catbox.moe/yp7o0h.png)
Anis-Bouhamadouche/distilbert-base-uncased-finetuned-emotion	Anis-Bouhamadouche	2023-10-25T22:32:39Z	4	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-04T10:05:01Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.925 - name: F1 type: f1 value: 0.9249367490708449 --- <!-- 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-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2105 - Accuracy: 0.925 - F1: 0.9249 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8223 \| 1.0 \| 250 \| 0.3098 \| 0.9085 \| 0.9076 \| \| 0.2431 \| 2.0 \| 500 \| 0.2105 \| 0.925 \| 0.9249 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
smitbutle/first-test-layoutlmv3-finetuned-invoice	smitbutle	2023-10-25T22:26:08Z	76	0	transformers	[ "transformers", "pytorch", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:generated", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-10-25T14:09:15Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer datasets: - generated metrics: - precision - recall - f1 - accuracy model-index: - name: layoutlmv3-finetuned-invoice results: - task: name: Token Classification type: token-classification dataset: name: generated type: generated config: sroie split: test args: sroie metrics: - name: Precision type: precision value: 0.010438413361169102 - name: Recall type: recall value: 0.02028397565922921 - name: F1 type: f1 value: 0.013783597518952447 - name: Accuracy type: accuracy value: 0.6785338108278913 --- <!-- 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. --> # layoutlmv3-finetuned-invoice This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset. It achieves the following results on the evaluation set: - Loss: 2.1320 - Precision: 0.0104 - Recall: 0.0203 - F1: 0.0138 - Accuracy: 0.6785 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 0.01 \| 1 \| 2.3858 \| 0.0114 \| 0.0649 \| 0.0194 \| 0.1904 \| \| No log \| 0.02 \| 2 \| 2.2795 \| 0.0108 \| 0.0527 \| 0.0180 \| 0.3240 \| \| No log \| 0.03 \| 3 \| 2.2072 \| 0.0131 \| 0.0446 \| 0.0203 \| 0.5155 \| \| No log \| 0.04 \| 4 \| 2.1575 \| 0.0103 \| 0.0243 \| 0.0145 \| 0.6345 \| \| No log \| 0.05 \| 5 \| 2.1320 \| 0.0104 \| 0.0203 \| 0.0138 \| 0.6785 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
cognisys/sparrow-1.1b-chat-alpha	cognisys	2023-10-25T22:22:57Z	4	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "en", "dataset:stingning/ultrachat", "dataset:OpenAssistant/oasst1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-10-24T21:04:04Z	--- model-index: - name: sparrow-1.1b-chat-alpha results: [] license: apache-2.0 inference: false datasets: - stingning/ultrachat - OpenAssistant/oasst1 language: - en --- <img src="https://huggingface.co/cognisys/sparrow-1.1b-chat-alpha/resolve/main/thumbnail.png" alt="Sparrow Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Sparrow 1.1B Chat Alpha The Sparrow series comprises language models designed to serve as helpful assistants and as a base model for domain-specific fine tuning. Sparrow-1.1B-Chat-α is the initial model in this series and represents a fine-tuned iteration of PY007/TinyLlama-1.1B-intermediate-step-480k-1T. It was trained on a combination of publicly accessible and synthetic datasets. ## Model Description: - Model Type: Sparrow-7B-α is a 1.1B parameter model, that has been fine-tuned using a mixture of publicly available and synthetic datasets. - Supported Languages (NLP): The primary language is English. - License/Warranty: The model is available under the Apache 2.0 license and comes with no warranty or gurantees of any kind. - Fine-tuned from: PY007/TinyLlama-1.1B-intermediate-step-480k-1T Prompt Template: ``` <s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_message }} [/INST] ```
LarryAIDraw/Char-HonkaiSR-Jingliu	LarryAIDraw	2023-10-25T22:17:09Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-07T14:03:11Z	--- license: creativeml-openrail-m --- https://civitai.com/models/141561/jingliu-or-honkaistar-rail
LarryAIDraw/asuna_ichinose_v1	LarryAIDraw	2023-10-25T22:14:13Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-10-25T22:03:22Z	--- license: creativeml-openrail-m --- https://civitai.com/models/175924/asuna-ichinose-or-blue-archive-or-3-outfits
LarryAIDraw/wrenchopmfubuki	LarryAIDraw	2023-10-25T22:14:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-10-25T22:03:00Z	--- license: creativeml-openrail-m --- https://civitai.com/models/175758/fubuki-boobuker-or-one-punch-man-or-lora-merging-concept
budhwant/blip2-opt-2.7b-imagecaptions-adapters	budhwant	2023-10-25T22:13:32Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-25T06:56:10Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - 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 0.5.0
schubertcarvalho/distilbert-base-uncased-finetuned-imdb	schubertcarvalho	2023-10-25T22:11:38Z	96	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "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	2023-10-25T21:12:38Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.3435 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 240 \| 2.3969 \| \| No log \| 2.0 \| 480 \| 2.3569 \| \| No log \| 3.0 \| 720 \| 2.3435 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0a0+29c30b1 - Datasets 2.14.5 - Tokenizers 0.14.1
AshtakaOOf/Amedira	AshtakaOOf	2023-10-25T22:10:56Z	0	29	safetensors	[ "safetensors", "art", "anime", "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-06-20T06:51:46Z	--- license: creativeml-openrail-m language: - en pipeline_tag: text-to-image tags: - art - anime - stable-diffusion library_name: safetensors --- <style> .custom-table { table-layout: fixed; width: 100%; border-collapse: collapse; margin-top: 2em; } .custom-table td { width: 50%; vertical-align: top; padding: 10px; box-shadow: 0px 0px 10px 0px rgba(0,0,0,0.15); } .custom-image { width: 100%; height: auto; object-fit: cover; border-radius: 7px; transition: transform .7s; margin-bottom: 1em; } .custom-image:hover { transform: scale(1.30); } stolen from Linaqruf readme for Animagine XL </style> <p align="center", style="font-size: 3.6rem; font-weight: bold">🎑 ~ Amedira ~ 🎏</p> <p align="center", style="font-size: 1.2rem; ">Go check out <strong><a href="https://huggingface.co/AshtakaOOf/Ahnn">Ahnn</a></strong> which my other model</p> <hr> <table class="custom-table"> <tr> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/gawr-rain.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/gawr-rain.webp" alt="Gawr Rain"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/sunflower.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/sunflower.webp" alt="Sunflower"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/miko-miku.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/miko-miku.webp" alt="Miko Miku"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/ina-flat.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/ina-flat.webp" alt="Ina'Nis Flat"> </a> </td> </tr> </table> <p align="center", style="font-size: 1.4rem; "><strong>↑ bV3</strong></p> <details id="Dropdown"> <summary align="center" style="font-size: 1.10em"><strong>Other versions examples</strong> (click to open the dropdown)</summary> <table class="custom-table"> <tr> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/gawr-rain-cV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/gawr-rain-cV.webp" alt="Gawr Rain"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/sunflower-cV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/sunflower-cV.webp" alt="Sunflower"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/miko-miku-cV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/miko-miku-cV.webp" alt="Miko Miku"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/ina-flat-cV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/ina-flat-cV.webp" alt="Ina'Nis Flat"> </a> </td> </tr> </table> <p align="center", style="font-size: 1.3rem; "><strong>↑ cV</strong></p> <table class="custom-table"> <tr> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/gawr-rain-mV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/gawr-rain-mV.webp" alt="Gawr Rain"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/sunflower-mV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/sunflower-mV.webp" alt="Sunflower"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/miko-miku-mV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/miko-miku-mV.webp" alt="Miko Miku"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/ina-flat-mV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/ina-flat-mV.webp" alt="Ina'Nis Flat"> </a> </td> </tr> </table> <p align="center", style="font-size: 1.3rem; "><strong>↑ mV</strong></p> <table class="custom-table"> <tr> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/gawr-rain-bV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/gawr-rain-bV.webp" alt="Gawr Rain"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/sunflower-bV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/sunflower-bV.webp" alt="Sunflower"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/miko-miku-bV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/miko-miku-bV.webp" alt="Miko Miku"> </a> </td> <td> <a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/ina-flat-bV.webp"> <img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/ina-flat-bV.webp" alt="Ina'Nis Flat"> </a> </td> </tr> </table> <p align="center", style="font-size: 1.3rem; "><strong>↑ bV</strong></p> <p align="center", style="font-size: 1.1rem; ">These are samples from each checkpoint excluding bV2 and bV2.5</p> </details> <hr> # 🎈 ❱ A model merge using clip transfer and more This model is capable of doing multiples styles, such as flat and 2.5d. You can also prompt Hololive vtubers with their danbooru tags (you will need to prompt their outfit too for some of them). And it is somewhat compatible with LoRA and lyCORIS. Every models in this repository should be made using some sort of clip transfer, except if said otherwise. <hr> # 📥 ❱ Downloads section #### These are the models I have merged and published bV retains most of AuroraOne features bV2 and 2.5 are somewhat competent Models bV3 is a big improvement in my opinion cV is a mix of both bV3 and Counterfeit 3 mV is a very good model made with more MeinaMix 10 Don't hesitate to try the inpainting versions on InvokeAI canvas. ### Amedira - [Amedira bV](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-bV.safetensors) Original version (still good) - [Amedira bV2.5](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-bV2.5.safetensors) bV2 but with better composition - [Amedira bV2](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-bV2.safetensors) Worse than bV and mV - [Amedira bV3](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-bV3.safetensors) The best version - [Amedira cV](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-cV.safetensors) Counterfeit style but better? - [Amedira mV](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-mV.safetensors) Bit more MeinaMix sprinkled in #### Dreams - [Dreams variant folder](https://huggingface.co/AshtakaOOf/Amedira/tree/main/Dreams) The Dreams variant are experimental/accidental merges that I keep because they have unique styles. #### Inpainting - [Inpainting versions can be found here](https://huggingface.co/AshtakaOOf/Amedira/tree/main) These are somewhat obsolete because of ControlNet <details id="Dropdown"> <summary style="font-size: 1.10em"><strong>📜 Changelogs </strong> (click to open the dropdown)</summary> ### 23 October 2023 - Improved the README.md - Added more images ### October 11 2023 - Overhauled README.md - Added images ### September 11 2023 - Added bV3 inpainting version - Added cV which is a nice Counterfeit mix - Added link to embeddings for negative prompt ### August 30 2023 - Added Amedira bV3 which fixes bg even more + misc fixes ### July 24 2023 - Added the clip models used in 1.0 and 2.5 ### July 9 2023 - Added Amedira bV2.5 which fix some downgrades from bV2 ### July 6 2023 - Added Amedira bV2 which is bV but better background etc #### July 5 2023 - Added Fuzzy and Scenic Dreams variant #### June 24 2023 - Added Koofy and Ligne Dreams variant #### June 22 2023 - Added inpainting version of mV #### June 20 2023 - Added Dreams variant - New README #### June 19 2023 - Added Amedira mV - Amedira renamed to bV - New huggingface repo #### June 11 2023 - Added Inpainting version - Released Lunamedira eV1 and eV2 #### June 10 2023 - Added Amedira </details> <hr> # 🧬 ❱ Usage #### Positive prompt Add the following string of text to the start of your prompts. ``` sle, masterpiece, detailed background, mksks style ``` You can add this next string to get a flat shading style. ``` (flat color, pastel style, black outlines:1.2), sketch ``` #### Negative prompt ``` (worst quality, low quality:1.2), [an10:fcNeg-neg:8], (etone:0.4), nsfw, lowres, bad hands, bad anatomy, watermark, ``` `negative_hand-neg` isn't needed but can be added before etone #### Embeddings Downloads - [fcNeg-neg](https://civitai.com/models/81575), [Direct download](https://civitai.com/api/download/models/97691?type=Negative&format=Other) - [an10](https://civitai.com/models/58726), [Direct download](https://civitai.com/api/download/models/144998?type=Model&format=PickleTensor) - [negative_hand-neg](https://civitai.com/models/56519), [Direct download](https://civitai.com/api/download/models/60938?type=Negative&format=Other) - [etone](https://cdn.discordapp.com/attachments/1019446913268973689/1126669053105287279/etone.safetensors) by closertodeath #### VAE - [WD 1.4 Blessed09](https://huggingface.co/NoCrypt/resources/resolve/main/VAE/wd-blessed09.vae.safetensors) (kl-f8-anime2 but blessed down) # 🥁 ❱ Credits - Checkpoints - [AuroraOne](https://huggingface.co/SweetLuna/Aurora) by SweetLuna - [Based66 V3.0](https://civitai.com/models/61643) by AnonymousM - [MeinaMix V1.0](https://civitai.com/models/7240) by Meina - [DiaMix V2.0](https://civitai.com/models/75949) by Cinsdia - [OpenNiji V2](https://civitai.com/models/14479) by Korakoe - [DetailedProjectV5](https://huggingface.co/closertodeath/detailedproject/blob/main/experimental/detailedprojectv5-000012.safetensors) by closertodeath - ALunarDream 2.0 by Luna Chan - [ExpMixLine](https://civitai.com/models/44150/expmixline) by Mods13 - [Gishiki](https://huggingface.co/Aotsuyu/Gishiki) by Aotsuyu - [Counterfeit v3.0](https://civitai.com/models/4468) - [OrangeMixs AOM3B2](https://huggingface.co/WarriorMama777/OrangeMixs#aom3b2) - LoRA/lyCORIS - [Jordan_3](https://cdn.discordapp.com/attachments/1066614732099964989/1071484257962315816/Jordan_3.safetensors) by deleted user - [LoraEyes](https://civitai.com/models/5529/eye-lora) by Kokoboy - [Niji Style](https://civitai.com/models/96441/niji-style-was-asked-to-do) by KoRo_ - [Silicon-landscape-isolation](https://huggingface.co/ashen-sensored/lora-isolation-collection/blob/main/Silicon-landscape-isolation.safetensors) by ashen-sensored - [lighting-locon](https://huggingface.co/closertodeath/ctdlora/blob/main/locon/lighting-locon.safetensors) by closertodeath - [SSAMBAtea style locon](https://huggingface.co/AshtakaOOf/ssambatea-locon/blob/main/ssambateaLoconStyleV2.safetensors) by AshtakaOOf - [Mocha Style](https://civitai.com/models/102128) by Tonade - [PastelMix LoRA](https://civitai.com/models/5414?modelVersionId=7397) by andite - [Little Gray Style](https://civitai.com/models/102685) by Cinsdia - [Dense Light](https://civitai.com/models/77957) by L_A_X - [more Detail](https://civitai.com/models/82098) by Lykon - [Youko](https://civitai.com/models/36140) by ayyyy22002 ## ⚜️ ❱ Legal Thing ``` You are free to use this model locally but 1. You aren't allowed to redistribute on another platform. (like CivitAI or Tensor.Art) 2. I am not responsible for how this model is used to generate images. ```
timm/ViT-B-16-SigLIP	timm	2023-10-25T21:58:01Z	94,353	30	open_clip	[ "open_clip", "safetensors", "clip", "siglip", "zero-shot-image-classification", "dataset:webli", "arxiv:2303.15343", "license:apache-2.0", "region:us" ]	zero-shot-image-classification	2023-10-16T23:14:27Z	--- tags: - clip - siglip library_name: open_clip pipeline_tag: zero-shot-image-classification license: apache-2.0 datasets: - webli --- # Model card for ViT-B-16-SigLIP A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI. This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only). ## Model Details - Model Type: Contrastive Image-Text, Zero-Shot Image Classification. - Original: https://github.com/google-research/big_vision - Dataset: WebLI - Papers: - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343 ## Model Usage ### With OpenCLIP ``` import torch import torch.nn.functional as F from urllib.request import urlopen from PIL import Image from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8 model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP') tokenizer = get_tokenizer('hf-hub:timm/ViT-B-16-SigLIP') image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) image = preprocess(image).unsqueeze(0) labels_list = ["a dog", "a cat", "a donut", "a beignet"] text = tokenizer(labels_list, context_length=model.context_length) with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features = F.normalize(image_features, dim=-1) text_features = F.normalize(text_features, dim=-1) text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias) zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]])) print("Label probabilities: ", zipped_list) ``` ### With `timm` (for image embeddings) ```python from urllib.request import urlopen from PIL import Image import timm image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_base_patch16_siglip_224', pretrained=True, num_classes=0, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor ``` ## Citation ```bibtex @article{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, journal={arXiv preprint arXiv:2303.15343}, year={2023} } ``` ```bibtex @misc{big_vision, author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander}, title = {Big Vision}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/google-research/big_vision}} } ```
timm/ViT-B-16-SigLIP-256	timm	2023-10-25T21:57:36Z	12,400	1	open_clip	[ "open_clip", "safetensors", "clip", "siglip", "zero-shot-image-classification", "dataset:webli", "arxiv:2303.15343", "license:apache-2.0", "region:us" ]	zero-shot-image-classification	2023-10-16T23:16:55Z	--- tags: - clip - siglip library_name: open_clip pipeline_tag: zero-shot-image-classification license: apache-2.0 datasets: - webli --- # Model card for ViT-B-16-SigLIP-256 A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI. This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only). ## Model Details - Model Type: Contrastive Image-Text, Zero-Shot Image Classification. - Original: https://github.com/google-research/big_vision - Dataset: WebLI - Papers: - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343 ## Model Usage ### With OpenCLIP ``` import torch import torch.nn.functional as F from urllib.request import urlopen from PIL import Image from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8 model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP-256') tokenizer = get_tokenizer('hf-hub:timm/ViT-B-16-SigLIP-256') image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) image = preprocess(image).unsqueeze(0) labels_list = ["a dog", "a cat", "a donut", "a beignet"] text = tokenizer(labels_list, context_length=model.context_length) with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features = F.normalize(image_features, dim=-1) text_features = F.normalize(text_features, dim=-1) text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias) zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]])) print("Label probabilities: ", zipped_list) ``` ### With `timm` (for image embeddings) ```python from urllib.request import urlopen from PIL import Image import timm image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_base_patch16_siglip_256', pretrained=True, num_classes=0, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor ``` ## Citation ```bibtex @article{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, journal={arXiv preprint arXiv:2303.15343}, year={2023} } ``` ```bibtex @misc{big_vision, author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander}, title = {Big Vision}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/google-research/big_vision}} } ```
timm/ViT-B-16-SigLIP-384	timm	2023-10-25T21:56:58Z	3,951	4	open_clip	[ "open_clip", "safetensors", "clip", "siglip", "zero-shot-image-classification", "dataset:webli", "arxiv:2303.15343", "license:apache-2.0", "region:us" ]	zero-shot-image-classification	2023-10-16T23:19:24Z	--- tags: - clip - siglip library_name: open_clip pipeline_tag: zero-shot-image-classification license: apache-2.0 datasets: - webli --- # Model card for ViT-B-16-SigLIP-384 A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI. This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only). ## Model Details - Model Type: Contrastive Image-Text, Zero-Shot Image Classification. - Original: https://github.com/google-research/big_vision - Dataset: WebLI - Papers: - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343 ## Model Usage ### With OpenCLIP ``` import torch import torch.nn.functional as F from urllib.request import urlopen from PIL import Image from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8 model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP-384') tokenizer = get_tokenizer('hf-hub:timm/ViT-B-16-SigLIP-384') image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) image = preprocess(image).unsqueeze(0) labels_list = ["a dog", "a cat", "a donut", "a beignet"] text = tokenizer(labels_list, context_length=model.context_length) with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features = F.normalize(image_features, dim=-1) text_features = F.normalize(text_features, dim=-1) text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias) zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]])) print("Label probabilities: ", zipped_list) ``` ### With `timm` (for image embeddings) ```python from urllib.request import urlopen from PIL import Image import timm image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_base_patch16_siglip_384', pretrained=True, num_classes=0, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor ``` ## Citation ```bibtex @article{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, journal={arXiv preprint arXiv:2303.15343}, year={2023} } ``` ```bibtex @misc{big_vision, author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander}, title = {Big Vision}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/google-research/big_vision}} } ```
tomatotime23/Finetune-RobertFrost	tomatotime23	2023-10-25T21:56:50Z	104	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:prajwalcr/poetry_gpt2", "base_model:finetune:prajwalcr/poetry_gpt2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-25T21:05:20Z	--- base_model: prajwalcr/poetry_gpt2 tags: - generated_from_trainer model-index: - name: Finetune-RobertFrost 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. --> # Finetune-RobertFrost This model is a fine-tuned version of [prajwalcr/poetry_gpt2](https://huggingface.co/prajwalcr/poetry_gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.4663 ## 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 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 40 \| 4.5430 \| \| No log \| 2.0 \| 80 \| 4.4799 \| \| No log \| 3.0 \| 120 \| 4.4663 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1
timm/ViT-L-16-SigLIP-256	timm	2023-10-25T21:54:51Z	5,423	1	open_clip	[ "open_clip", "safetensors", "clip", "siglip", "zero-shot-image-classification", "dataset:webli", "arxiv:2303.15343", "license:apache-2.0", "region:us" ]	zero-shot-image-classification	2023-10-16T23:24:41Z	--- tags: - clip - siglip library_name: open_clip pipeline_tag: zero-shot-image-classification license: apache-2.0 datasets: - webli --- # Model card for ViT-L-16-SigLIP-256 A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI. This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only). ## Model Details - Model Type: Contrastive Image-Text, Zero-Shot Image Classification. - Original: https://github.com/google-research/big_vision - Dataset: WebLI - Papers: - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343 ## Model Usage ### With OpenCLIP ``` import torch import torch.nn.functional as F from urllib.request import urlopen from PIL import Image from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8 model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP-256') tokenizer = get_tokenizer('hf-hub:timm/ViT-L-16-SigLIP-256') image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) image = preprocess(image).unsqueeze(0) labels_list = ["a dog", "a cat", "a donut", "a beignet"] text = tokenizer(labels_list, context_length=model.context_length) with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features = F.normalize(image_features, dim=-1) text_features = F.normalize(text_features, dim=-1) text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias) zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]])) print("Label probabilities: ", zipped_list) ``` ### With `timm` (for image embeddings) ```python from urllib.request import urlopen from PIL import Image import timm image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_large_patch16_siglip_256', pretrained=True, num_classes=0, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor ``` ## Citation ```bibtex @article{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, journal={arXiv preprint arXiv:2303.15343}, year={2023} } ``` ```bibtex @misc{big_vision, author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander}, title = {Big Vision}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/google-research/big_vision}} } ```
timm/ViT-L-16-SigLIP-384	timm	2023-10-25T21:54:17Z	198,095	24	open_clip	[ "open_clip", "safetensors", "clip", "siglip", "zero-shot-image-classification", "dataset:webli", "arxiv:2303.15343", "license:apache-2.0", "region:us" ]	zero-shot-image-classification	2023-10-16T23:32:50Z	--- tags: - clip - siglip library_name: open_clip pipeline_tag: zero-shot-image-classification license: apache-2.0 datasets: - webli --- # Model card for ViT-L-16-SigLIP-384 A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI. This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only). ## Model Details - Model Type: Contrastive Image-Text, Zero-Shot Image Classification. - Original: https://github.com/google-research/big_vision - Dataset: WebLI - Papers: - Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343 ## Model Usage ### With OpenCLIP ``` import torch import torch.nn.functional as F from urllib.request import urlopen from PIL import Image from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8 model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP-384') tokenizer = get_tokenizer('hf-hub:timm/ViT-L-16-SigLIP-384') image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) image = preprocess(image).unsqueeze(0) labels_list = ["a dog", "a cat", "a donut", "a beignet"] text = tokenizer(labels_list, context_length=model.context_length) with torch.no_grad(), torch.cuda.amp.autocast(): image_features = model.encode_image(image) text_features = model.encode_text(text) image_features = F.normalize(image_features, dim=-1) text_features = F.normalize(text_features, dim=-1) text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias) zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]])) print("Label probabilities: ", zipped_list) ``` ### With `timm` (for image embeddings) ```python from urllib.request import urlopen from PIL import Image import timm image = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_large_patch16_siglip_384', pretrained=True, num_classes=0, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor ``` ## Citation ```bibtex @article{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, journal={arXiv preprint arXiv:2303.15343}, year={2023} } ``` ```bibtex @misc{big_vision, author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander}, title = {Big Vision}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/google-research/big_vision}} } ```
zsanborn/ppo-Huggy	zsanborn	2023-10-25T21:51:06Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-10-25T21:51:01Z	--- 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: zsanborn/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jfelgate/rl_course_vizdoom_health_gathering_supreme	jfelgate	2023-10-25T21:37:38Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-25T16:03:54Z	--- 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: 15.64 +/- 5.70 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 jfelgate/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.
ftorresa/dqn-SpaceInvadersNoFrameskip-v4	ftorresa	2023-10-25T21:34:23Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-10-25T21:26:27Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 576.00 +/- 129.67 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 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 dqn --env SpaceInvadersNoFrameskip-v4 -orga ftorresa -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -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 dqn --env SpaceInvadersNoFrameskip-v4 -orga ftorresa -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ftorresa ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
innyun/poca-SoccerTwos	innyun	2023-10-25T21:33:08Z	51	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-10-25T21:32:43Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: innyun/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
clareandme/userRequested-model	clareandme	2023-10-25T21:23:15Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-classification	2023-10-25T13:33:01Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # clareandme/userRequested-model 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("clareandme/userRequested-model") # 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} } ```
fearvel/aoki-sd	fearvel	2023-10-25T21:19:36Z	0	0	null	[ "stable-diffusion", "text-to-image", "StableDiffusionPipeline", "lora", "region:us" ]	text-to-image	2023-10-25T21:18:03Z	--- tags: - stable-diffusion - text-to-image - StableDiffusionPipeline - lora --- ## Model lora of profile picture generation ![pipeline](aoki_sd_v10.preview.png)

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