llama3.1_mental

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Direct Use

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from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name = "Manbatang/llama3.1_mental", max_seq_length = 512, dtype = None, load_in_4bit = True, ) FastLanguageModel.for_inference(model) # Enable native 2x faster inference

alpaca_prompt

alpaca_prompt = """Berikut ini adalah instruksi yang menjelaskan tugas, dipasangkan dengan masukan yang memberikan konteks lebih lanjut. Tulis respons yang melengkapi permintaan dengan tepat.

Instruction:

{}

Response:

{}"""

inputs = tokenizer( [ alpaca_prompt.format( "tangan ku tergores pisau dan berdarah, cara mengobatinya gimana", # instruction "", # input "", # output ) ], return_tensors = "pt").to("cuda")

from transformers import TextStreamer text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 500) [More Information Needed]

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Training Details

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.15.2