From 9f0313da34764f145f4fffb3042f837e5a1d8238 Mon Sep 17 00:00:00 2001 From: kelseye Date: Thu, 11 Sep 2025 12:37:22 +0000 Subject: [PATCH] Upload to Wan-AI/Wan2.2-Animate-14B on ModelScope hub --- .gitattributes | 22 + Wan2.1_VAE.pth | 3 + config.json | 30 + ...n_pytorch_model-00001-of-00004.safetensors | 3 + ...n_pytorch_model-00002-of-00004.safetensors | 3 + ...n_pytorch_model-00003-of-00004.safetensors | 3 + ...n_pytorch_model-00004-of-00004.safetensors | 3 + ...usion_pytorch_model.safetensors.index.json | 1448 +++++++++ google/umt5-xxl/special_tokens_map.json | 308 ++ google/umt5-xxl/spiece.model | 3 + google/umt5-xxl/tokenizer.json | 3 + google/umt5-xxl/tokenizer_config.json | 2748 +++++++++++++++++ ...pen-clip-xlm-roberta-large-vit-huge-14.pth | 3 + models_t5_umt5-xxl-enc-bf16.pth | 3 + relighting_lora.ckpt | 3 + relighting_lora/README.md | 201 ++ relighting_lora/adapter_config.json | 41 + relighting_lora/adapter_model.safetensors | 3 + xlm-roberta-large/README.md | 200 ++ 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https://git-lfs.github.com/spec/v1 +oid sha256:7cace0da2b446bbbbc57d031ab6cf163a3d59b366da94e5afe36745b746fd81d +size 11361920418 diff --git a/relighting_lora.ckpt b/relighting_lora.ckpt new file mode 100644 index 0000000..67ab134 --- /dev/null +++ b/relighting_lora.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49fe0a5a5af97c3f410598ddb146b5f4b3f30b2a526ec8cc32e9f032a1d328f7 +size 2873429974 diff --git a/relighting_lora/README.md b/relighting_lora/README.md new file mode 100644 index 0000000..8114b38 --- /dev/null +++ b/relighting_lora/README.md @@ -0,0 +1,201 @@ +--- +library_name: peft +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.14.0 \ No newline at end of file diff --git a/relighting_lora/adapter_config.json b/relighting_lora/adapter_config.json new file mode 100644 index 0000000..af85e2e --- /dev/null +++ b/relighting_lora/adapter_config.json @@ -0,0 +1,41 @@ +{ + "alpha_pattern": {}, + "auto_mapping": { + "base_model_class": "WanxiangI2VHumanOmniArch", + "parent_library": "hfm.archs.wanxiang_i2v.wanxiang_i2v_human_omni_arch" + }, + "base_model_name_or_path": null, + "bias": "none", + "eva_config": null, + "exclude_modules": null, + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": "gaussian", + "layer_replication": null, + "layers_pattern": null, + "layers_to_transform": null, + "loftq_config": {}, + "lora_alpha": 128, + "lora_bias": false, + "lora_dropout": 0.0, + "megatron_config": null, + "megatron_core": "megatron.core", + "modules_to_save": null, + "peft_type": "LORA", + "r": 128, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "ffn.0", + "k", + "q", + "v", + "ffn.2", + "v_img", + "o", + "k_img" + ], + "task_type": null, + "use_dora": false, + "use_rslora": false +} \ No newline at end of file diff --git a/relighting_lora/adapter_model.safetensors b/relighting_lora/adapter_model.safetensors new file mode 100644 index 0000000..2bc4afe --- /dev/null +++ b/relighting_lora/adapter_model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e821a68a66964c4c00dc8b5d06bfc8cc7e24ab77316c547eb41174097394899c +size 2873221376 diff --git a/xlm-roberta-large/README.md b/xlm-roberta-large/README.md new file mode 100644 index 0000000..353e24d --- /dev/null +++ b/xlm-roberta-large/README.md @@ -0,0 +1,200 @@ +--- +tags: +- exbert +language: +- multilingual +- af +- am +- ar +- as +- az +- be +- bg +- bn +- br +- bs +- ca +- cs +- cy +- da +- de +- el +- en +- eo +- es +- et +- eu +- fa +- fi +- fr +- fy +- ga +- gd +- gl +- gu +- ha +- he +- hi +- hr +- hu +- hy +- id +- is +- it +- ja +- jv +- ka +- kk +- km +- kn +- ko +- ku +- ky +- la +- lo +- lt +- lv +- mg +- mk +- ml +- mn +- mr +- ms +- my +- ne +- nl +- no +- om +- or +- pa +- pl +- ps +- pt +- ro +- ru +- sa +- sd +- si +- sk +- sl +- so +- sq +- sr +- su +- sv +- sw +- ta +- te +- th +- tl +- tr +- ug +- uk +- ur +- uz +- vi +- xh +- yi +- zh +license: mit +--- + +# XLM-RoBERTa (large-sized model) + +XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/xlmr). + +Disclaimer: The team releasing XLM-RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team. + +## Model description + +XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. + +RoBERTa is a transformers model pretrained on a large corpus in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. + +More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. + +This way, the model learns an inner representation of 100 languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the XLM-RoBERTa model as inputs. + +## Intended uses & limitations + +You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=xlm-roberta) to look for fine-tuned versions on a task that interests you. + +Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at models like GPT2. + +## Usage + +You can use this model directly with a pipeline for masked language modeling: + +```python +>>> from transformers import pipeline +>>> unmasker = pipeline('fill-mask', model='xlm-roberta-large') +>>> unmasker("Hello I'm a model.") + +[{'score': 0.10563907772302628, + 'sequence': "Hello I'm a fashion model.", + 'token': 54543, + 'token_str': 'fashion'}, + {'score': 0.08015287667512894, + 'sequence': "Hello I'm a new model.", + 'token': 3525, + 'token_str': 'new'}, + {'score': 0.033413201570510864, + 'sequence': "Hello I'm a model model.", + 'token': 3299, + 'token_str': 'model'}, + {'score': 0.030217764899134636, + 'sequence': "Hello I'm a French model.", + 'token': 92265, + 'token_str': 'French'}, + {'score': 0.026436051353812218, + 'sequence': "Hello I'm a sexy model.", + 'token': 17473, + 'token_str': 'sexy'}] +``` + +Here is how to use this model to get the features of a given text in PyTorch: + +```python +from transformers import AutoTokenizer, AutoModelForMaskedLM + +tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-large') +model = AutoModelForMaskedLM.from_pretrained("xlm-roberta-large") + +# prepare input +text = "Replace me by any text you'd like." +encoded_input = tokenizer(text, return_tensors='pt') + +# forward pass +output = model(**encoded_input) +``` + +### BibTeX entry and citation info + +```bibtex +@article{DBLP:journals/corr/abs-1911-02116, + author = {Alexis Conneau and + Kartikay Khandelwal and + Naman Goyal and + Vishrav Chaudhary and + Guillaume Wenzek and + Francisco Guzm{\'{a}}n and + Edouard Grave and + Myle Ott and + Luke Zettlemoyer and + Veselin Stoyanov}, + title = {Unsupervised Cross-lingual Representation Learning at Scale}, + journal = {CoRR}, + volume = {abs/1911.02116}, + year = {2019}, + url = {http://arxiv.org/abs/1911.02116}, + eprinttype = {arXiv}, + eprint = {1911.02116}, + timestamp = {Mon, 11 Nov 2019 18:38:09 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + + + diff --git a/xlm-roberta-large/config.json b/xlm-roberta-large/config.json new file mode 100644 index 0000000..8e5fb14 --- /dev/null +++ b/xlm-roberta-large/config.json @@ -0,0 +1,25 @@ +{ + "architectures": [ + "XLMRobertaForMaskedLM" + ], + "attention_probs_dropout_prob": 0.1, + "bos_token_id": 0, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": "xlm-roberta", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "output_past": true, + "pad_token_id": 1, + "position_embedding_type": "absolute", + "transformers_version": "4.17.0.dev0", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 250002 +} diff --git a/xlm-roberta-large/configuration.json 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