Komodo_6B_v3.0.0

Base Model

beomi/Yi-Ko-6B

Model Details

Model Description

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Uses

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "TeamUNIVA/Komodo_6B_v3.0.0"

model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)


text = '''<|system|>
당신은 사용자의 질문에 친절하게 답변을 하는 챗봇입니다.
<|user|>
안녕하세요?
<|bot|>
'''

inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

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Factors

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Results

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Summary

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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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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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