solar-chatbot-final (LoRA Adapter)

Summary: A Korean conversational LoRA adapter fine-tuned on Upstage's SOLAR-10.7B-Instruct-v1.0
Designed for natural, responsive dialogue with capabilities in general Q&A, summarization, and text generation.

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Model Details

• Developed by: Jihee Cho
• Model type: Causal Language Model (chat/instruction-tuned), LoRA adapter for SOLAR-10.7B
• Languages: Korean (primary), English (basic support)
• License: Apache-2.0
• Finetuned from: Upstage/SOLAR-10.7B-Instruct-v1.0

Model Sources

• Base Model: https://huggingface.co/Upstage/SOLAR-10.7B-Instruct-v1.0
• Repository: https://huggingface.co/Jay1121/solar-chatbot-final

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Quick Start

Requirements

pip install transformers peft torch

Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model and adapter
base_model_name = "Upstage/SOLAR-10.7B-Instruct-v1.0"
adapter_name = "Jay1121/solar-chatbot-final"

tokenizer = AutoTokenizer.from_pretrained(base_model_name, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    base_model_name, 
    torch_dtype="auto", 
    device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter_name)

# Generate response
prompt = "안녕! 오늘 뭐 하지?"  # "Hi! What should I do today?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs, 
    max_new_tokens=256, 
    do_sample=True, 
    temperature=0.8,
    pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

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Intended Use

Primary Use Cases

• Casual Korean conversation and chatbot demos
• General question-answering systems
• Text summarization and draft writing
• Prototyping and educational purposes

Out-of-Scope Use

• Professional advice in medical, legal, or financial domains
• Fact-checking or authoritative information provision
• Production-level commercial services
• Processing personal or sensitive data

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Limitations and Risks

Known Limitations

• No factual accuracy guarantee: The model cannot ensure the correctness of generated information
• Hallucination: May generate false or non-existent content
• Bias: Training data biases may be reflected in outputs
• Consistency issues: May struggle to maintain consistency in long conversations

Ethical Considerations

• Users should critically review all generated content
• Always consult reliable sources for important decisions
• Do not blindly trust model outputs; verification is essential

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Technical Details

Training Details

• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Target Modules: Query, Key, Value projection layers
• Rank: 16
• Alpha: 32
• Training Data: Korean conversation datasets
• Training Framework: Transformers + PEFT + TRL

Performance

This model is optimized for natural conversational experiences rather than benchmark performance, focusing on engaging and contextually appropriate Korean dialogue.

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Disclaimer and Usage Terms

No Impersonation Policy

This project is not affiliated with or endorsed by any person or organization. Do not use this model to impersonate specific individuals or create systems that could mislead others about official endorsements.

Branding and Attribution

When using this model, please provide appropriate attribution and avoid using it in ways that could be mistaken for official statements from the original author or related organizations.

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Citation

If you use this model in your research or projects, please cite:

@misc{solar-chatbot-final,
  author = {Jihee Cho},
  title = {solar-chatbot-final: Korean Conversational LoRA Adapter for SOLAR-10.7B},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Jay1121/solar-chatbot-final}}
}

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Contact

For questions or feedback, please use the Discussion tab on the Hugging Face model page.