Manbatang/llama3.1_mental

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Model Description

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Uses

Direct Use

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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Bias, Risks, and Limitations

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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

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Training Details

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

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