Instructions to use Ash0k0723/mms-1b-npi-finetuned-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ash0k0723/mms-1b-npi-finetuned-lora with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("facebook/mms-1b-all") model = PeftModel.from_pretrained(base_model, "Ash0k0723/mms-1b-npi-finetuned-lora") - Transformers
How to use Ash0k0723/mms-1b-npi-finetuned-lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ash0k0723/mms-1b-npi-finetuned-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +55 -0
- adapter_config.json +37 -0
- adapter_model.safetensors +3 -0
- preprocessor_config.json +10 -0
- training_args.bin +3 -0
README.md
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---
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library_name: peft
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license: cc-by-nc-4.0
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base_model: facebook/mms-1b-all
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tags:
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- base_model:adapter:facebook/mms-1b-all
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- lora
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- transformers
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model-index:
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- name: mms-1b-npi-finetuned-lora
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# mms-1b-npi-finetuned-lora
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 32
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- total_train_batch_size: 128
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 5
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### Framework versions
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- PEFT 0.17.1
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- Transformers 4.57.1
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- Pytorch 2.8.0+cu126
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- Datasets 4.4.1
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- Tokenizers 0.22.1
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "facebook/mms-1b-all",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"qalora_group_size": 16,
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"q_proj"
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],
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"target_parameters": null,
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"task_type": "FEATURE_EXTRACTION",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f6da2fb55e629cc6505e03b2bfb4356a46e1d17237ac9e6d80ff08e4d0b311d1
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size 25371944
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preprocessor_config.json
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{
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"do_normalize": true,
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0,
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"processor_class": "Wav2Vec2Processor",
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:35f971c6af82683735ae795e14847c0aef238226e7a5d1dbfd1ad1091d25808a
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size 5841
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