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ollama - 💡(How to fix) Fix [Feature Request] Enhanced Korean Language Support [1 comments, 2 participants]

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ollama/ollama#14643•Fetched 2026-04-08 00:33:23
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guicybercodeguicybercode
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guicybercodeguicybercoderick-githubrick-github
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Fix Action

Fix / Workaround

Describe alternatives you've considered

<!-- A clear and concise description of any alternative solutions or features you've considered. -->

[Have you tried other tools? Are you currently using a workaround? E.g., Using raw llama.cpp or other local LLM runners that handle Korean better.]

RAW_BUFFERClick to expand / collapse

Is your feature request related to a problem? Please describe.

<!-- A clear and concise description of what the problem is. Ex: I'm always frustrated when [...] -->

Currently, using Ollama with Korean language models [or specific Korean tasks] presents some challenges. [Describe specifically what is lacking or problematic. E.g., Tokenization efficiency, lack of specific Korean models, poor performance in Korean syntax, etc.]

Describe the solution you'd like

<!-- A clear and concise description of what you want to happen. -->

I would like to see improved support for the Korean language in Ollama. Specifically:

  • [Detail 1: E.g., Better default tokenization for Hangul]
  • [Detail 2: E.g., Integration of specific Korean-tuned models like Llama-3-Korean or EEVE-Korean]
  • [Detail 3: E.g., Documentation or examples specifically for Korean NLP tasks]

Describe alternatives you've considered

<!-- A clear and concise description of any alternative solutions or features you've considered. -->

[Have you tried other tools? Are you currently using a workaround? E.g., Using raw llama.cpp or other local LLM runners that handle Korean better.]

Additional context

<!-- Add any other context or screenshots about the feature request here. -->
  • Use Case: [E.g., Customer support chatbot in Korean, Translation, Summarization of Korean documents]
  • Models tested: [List any models you tried, e.g., llama3, gemma, etc.]
  • OS/Version: [e.g., macOS Sonoma, Windows 11, Ubuntu 22.04]

extent analysis

Fix Plan

To improve support for the Korean language in Ollama, we will focus on the following steps:

  • Integrate Korean-tuned models like Llama-3-Korean or EEVE-Korean.
  • Implement better default tokenization for Hangul.
  • Provide documentation and examples for Korean NLP tasks.

Code Changes

Here's an example of how you could integrate a Korean-tuned model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load pre-trained Korean model and tokenizer
model_name = "Llama-3-Korean"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define a function for Korean text generation
def generate_korean_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    output = model.generate(**inputs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Test the function
prompt = " (Annyeonghaseyo, eodiseo wasseumnikka?)"
print(generate_korean_text(prompt))

Configuration Changes

Update the Ollama configuration to include the Korean-tuned model and tokenizer.

Verification

Test the updated Ollama with Korean language models using various Korean NLP tasks, such as text generation, translation, and summarization.

Extra Tips

  • Make sure to handle out-of-vocabulary (OOV) tokens properly when working with Korean text.
  • Consider using a combination of wordpiece tokenization and subword regularization to improve tokenization efficiency for Hangul.

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#api#ssr#installation#tensor shape#autograd error#request error#file not found#serialization error#model compatibility#GPU setup

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