Instructions to use nagi1012/llm-jp-3-13b-finetune2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nagi1012/llm-jp-3-13b-finetune2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nagi1012/llm-jp-3-13b-finetune2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use nagi1012/llm-jp-3-13b-finetune2 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 nagi1012/llm-jp-3-13b-finetune2 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 nagi1012/llm-jp-3-13b-finetune2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nagi1012/llm-jp-3-13b-finetune2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="nagi1012/llm-jp-3-13b-finetune2", max_seq_length=2048, )
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- tokenizer_config.json +1 -1
tokenizer_config.json
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@@ -76,7 +76,7 @@
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"mask_token": "<MASK|LLM-jp>",
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"model_max_length": 4096,
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"pad_token": "<PAD|LLM-jp>",
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"padding_side": "
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"sep_token": "<SEP|LLM-jp>",
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"sp_model_kwargs": {},
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"tokenizer_class": "PreTrainedTokenizerFast",
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"mask_token": "<MASK|LLM-jp>",
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"model_max_length": 4096,
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"pad_token": "<PAD|LLM-jp>",
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"padding_side": "right",
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"sep_token": "<SEP|LLM-jp>",
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"sp_model_kwargs": {},
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"tokenizer_class": "PreTrainedTokenizerFast",
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