Day 10: Open-Source LLM Ecosystem OverviewGlobal Open-Source LLM ComparisonKorean-Specialized ModelsModel Selection GuideQwen 2.5 Execution ExampleKorean Model Comparison ExperimentOpen-Source LLM Trends SummaryToday’s Exercises
LLM 25-Day CourseDay 10pythonintermediate

LLM 25-Day Course - Day 10: Open-Source LLM Ecosystem Overview

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Day 10: Open-Source LLM Ecosystem Overview

Beyond Llama and Mistral, numerous open-source LLMs are competing in the space. Understanding each model’s characteristics helps you choose the optimal model for your project.

Global Open-Source LLM Comparison

ModelDeveloperSizeStrengthsLicense
Qwen 2.5Alibaba0.5B~72BMultilingual, coding, mathApache 2.0
DeepSeek V2DeepSeek236B (21B active)MoE, cost-effectiveMIT
DeepSeek-Coder V2DeepSeek236BCode-specialized, 338 languagesMIT
Phi-3Microsoft3.8B / 7B / 14BUltra-small, high performanceMIT
Yi-1.501.AI6B / 9B / 34BStrong in Chinese + EnglishApache 2.0
Command R+Cohere104BRAG-specialized, multilingualCC-BY-NC
Falcon 2TII (UAE)11BStrong in ArabicApache 2.0

Korean-Specialized Models

ModelDeveloperSizeFeatures
SOLARUpstage10.7BDUS (Depth Up-Scaling) technique
EXAONE 3.0LG AI Research7.8BKorean + English bilingual
HyperCLOVA XNAVERUndisclosedBest Korean performance, proprietary
Polyglot-KoEleutherAI1.3B~12.8BKorean pre-trained
KoAlpacaCommunityVariousAlpaca Korean fine-tuning

Model Selection Guide

def recommend_model(task, budget, korean_priority):
    """Recommend a model based on task and conditions"""
    recommendations = {
        ("coding", "low", False): "DeepSeek-Coder-V2-Lite (16B)",
        ("coding", "high", False): "DeepSeek-Coder-V2 (236B)",
        ("general", "low", False): "Phi-3 Mini (3.8B)",
        ("general", "medium", False): "Qwen 2.5 72B",
        ("general", "low", True): "EXAONE 3.0 7.8B",
        ("general", "medium", True): "SOLAR 10.7B + Korean fine-tuning",
        ("general", "high", True): "Qwen 2.5 72B (excellent Korean performance)",
        ("math", "low", False): "Qwen 2.5 Math 7B",
        ("math", "high", False): "DeepSeek-Math 7B",
        ("rag", "medium", False): "Command R+ (104B)",
    }

    key = (task, budget, korean_priority)
    return recommendations.get(key, "Qwen 2.5 or Llama 3.1 recommended")

# Usage examples
print(recommend_model("coding", "low", False))
print(recommend_model("general", "low", True))
print(recommend_model("general", "medium", False))

Qwen 2.5 Execution Example

# Ollama: ollama pull qwen2.5:7b
import ollama

response = ollama.chat(
    model="qwen2.5:7b",
    messages=[
        {"role": "system", "content": "Please answer in English."},
        {"role": "user", "content": "Write a generator that produces the Fibonacci sequence."},
    ],
)
print(response["message"]["content"])

# HuggingFace approach
# from transformers import AutoModelForCausalLM, AutoTokenizer
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

Korean Model Comparison Experiment

import ollama

# Korean performance comparison test
korean_prompts = [
    "Describe the characteristics of Korea's four seasons in one sentence each.",
    "Explain the meaning of the Korean proverb 'If the words you speak are kind, the words you hear will be kind too.'",
    "Briefly explain the civil service examination system of the Joseon Dynasty.",
]

models_to_test = ["llama3.1:8b", "qwen2.5:7b", "gemma2:9b"]

for prompt in korean_prompts:
    print(f"\nQuestion: {prompt}")
    print("=" * 60)
    for model_name in models_to_test:
        try:
            response = ollama.chat(
                model=model_name,
                messages=[{"role": "user", "content": f"Please answer in Korean. {prompt}"}],
            )
            answer = response["message"]["content"][:150]
            print(f"  [{model_name}] {answer}...")
        except Exception:
            print(f"  [{model_name}] Model not installed")
TrendDescription
Small model dominanceModels like Phi-3, Gemma 2 at 3~9B achieving 13B-level performance
MoE expansionGrowing number of efficient MoE models like DeepSeek, Mixtral
Code specializationCode-specific models like DeepSeek-Coder, CodeLlama
Multilingual enhancementImproved non-English language performance in Qwen, EXAONE, etc.
License relaxationExpanding commercially usable licenses like Apache 2.0, MIT

Today’s Exercises

  1. Install Qwen 2.5 7B via Ollama and send 3 coding questions. Compare with Llama 3.1 8B and evaluate which model performs better.
  2. Research what SOLAR’s DUS (Depth Up-Scaling) technique is and compare it with conventional model scaling methods.
  3. Select the most suitable open-source model for your project and document the reasoning. (Consider task, budget, hardware, and language requirements)

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