PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kLicense:llama3Architecture:Transformer0.0K Warm

Waktaverse-Llama-3-KO-8B-Instruct is an 8 billion parameter Korean language model developed by Waktaverse AI, fine-tuned from Meta-Llama-3-8B-Instruct. This model specializes in Korean natural language processing tasks, designed to handle complex instructions and generate coherent, contextually appropriate responses in Korean. It is optimized for text completion, summarization, and question answering in Korean, while also supporting English. The model was trained using LoRA on the MarkrAI/KoCommercial-Dataset, focusing on commercial texts.

Loading preview...

Waktaverse-Llama-3-KO-8B-Instruct Overview

Waktaverse-Llama-3-KO-8B-Instruct is an 8 billion parameter large language model developed by Waktaverse AI, specifically tailored for Korean natural language processing. It is a specialized version of Meta-Llama-3-8B-Instruct, designed to process complex instructions and generate contextually relevant responses in Korean, while also supporting English.

Key Capabilities

  • Korean Language Specialization: Optimized for various Korean NLP tasks, including text completion, summarization, and question answering.
  • Instruction Following: Capable of handling complex instructions to produce coherent outputs.
  • Efficient Fine-tuning: Trained using LoRA (Low-Rank Adaptation) on the MarkrAI/KoCommercial-Dataset, focusing on commercial Korean texts.
  • Llama 3 Base: Leverages the architecture and capabilities of the Meta-Llama-3-8B-Instruct model.

Good For

  • Direct Use: Suitable for immediate deployment in applications requiring text completion, summarization, and question answering without additional fine-tuning.
  • Korean NLP Applications: Ideal for developers building applications that require robust Korean language understanding and generation.

Limitations

This model shares common limitations of machine learning models, including potential biases from training data, vulnerability to adversarial attacks, and unpredictable behavior in edge cases. It is not intended for high-stakes decision-making in medical, legal, or safety-critical domains.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p