Instructions to use Tohrumi/MistralAI_iwslt15_en_vi_manual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Tohrumi/MistralAI_iwslt15_en_vi_manual with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Tohrumi/MistralAI_iwslt15_en_vi_manual") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Tohrumi/MistralAI_iwslt15_en_vi_manual with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Tohrumi/MistralAI_iwslt15_en_vi_manual to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Tohrumi/MistralAI_iwslt15_en_vi_manual to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Tohrumi/MistralAI_iwslt15_en_vi_manual to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Tohrumi/MistralAI_iwslt15_en_vi_manual", max_seq_length=2048, )
Model save
Browse files- README.md +2 -6
- adapter_config.json +2 -2
- adapter_model.safetensors +1 -1
- runs/Apr14_22-29-34_ubuntu/events.out.tfevents.1713108584.ubuntu +3 -0
- runs/Apr14_22-32-20_ubuntu/events.out.tfevents.1713108755.ubuntu +3 -0
- runs/Apr15_10-46-36_ubuntu/events.out.tfevents.1713152828.ubuntu +3 -0
- runs/Apr15_11-06-02_ubuntu/events.out.tfevents.1713153964.ubuntu +3 -0
- tokenizer_config.json +1 -0
- training_args.bin +1 -1
README.md
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed:
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 1
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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### Training results
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### Framework versions
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- PEFT 0.10.0
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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: 8
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- eval_batch_size: 8
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- seed: 4269
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 1
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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### Framework versions
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- PEFT 0.10.0
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adapter_config.json
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"rank_pattern": {},
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"target_modules": [
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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adapter_model.safetensors
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runs/Apr14_22-29-34_ubuntu/events.out.tfevents.1713108584.ubuntu
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tokenizer_config.json
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "</s>",
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"sp_model_kwargs": {},
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"max_seq_length": 1024,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "</s>",
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"sp_model_kwargs": {},
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