🤖 Foundation Models

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Leading open-source LLMs, vision models, and multimodal systems (2026).

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📋 Table of Contents

• Overview
• Model List
• Selection Guide
• Performance Benchmarks

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Overview

Foundation models are the core of modern AI applications. This curated list includes the top open-source models of 2026, selected based on: - Performance benchmarks (HumanEval, SWE-bench, MMLU) - License (Apache 2.0, MIT preferred) - Community adoption (GitHub stars, downloads) - Production readiness

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Model List

Repo	Description	Stars
huggingface/transformers	State-of-the-art ML models for PyTorch, TensorFlow, and JAX
QwenLM/Qwen2.5	Premier multilingual model (29+ languages), 235B MoE with 22B active - Apache 2.0
deepseek-ai/DeepSeek-V3	685B MoE activating 37B per token, beats GPT-5 on reasoning - MIT license
meta-llama/llama	Llama 4 foundation models (16B-400B params) for general reasoning
mistralai/mistral-src	Mistral Large - optimized for speed and efficiency, runs on mobile
THUDM/GLM-4	GLM-4 (744B) - S-tier model, 94.2% HumanEval, 73.8% SWE-bench
google/gemma	Gemma 4 (26B) - frontier intelligence at laptop scale, 85 tokens/sec on consumer hardware
openai/whisper	Robust speech recognition via large-scale weak supervision

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Selection Guide

By Use Case

🎯 General Purpose & Reasoning - Qwen 2.5 - Best for multilingual tasks (29+ languages), Apache 2.0 license - DeepSeek V3 - Best for reasoning tasks, beats GPT-5, MIT license - Llama 4 - Established ecosystem, wide adoption, 16B-400B variants

⚡ Speed & Efficiency - Mistral Large - Runs on mobile devices, sub-500ms response times - Gemma 4 - 85 tokens/sec on consumer hardware, laptop-optimized

💻 Consumer Hardware - Gemma 4 26B - Runs on single RTX 4090 (24GB VRAM) - Llama 3.2 8B - Best for 8-16GB VRAM setups

🔧 Coding & Development - GLM-4 - 94.2% HumanEval, 73.8% SWE-bench - Qwen 2.5 Coder - Dominates coding benchmarks at every size

🌍 Multilingual - Qwen 2.5 - Premier choice for 29+ languages - GLM-4 - Strong bilingual (English/Chinese) support

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Performance Benchmarks

HumanEval (Code Generation)

1. GLM-4: 94.2%
2. Qwen 2.5 Coder: 92.8%
3. DeepSeek V3: 91.5%

SWE-bench (Real-world Coding)

1. GLM-4: 73.8%
2. Qwen 2.5: 69.2%
3. DeepSeek V3: 67.4%

MMLU (General Knowledge)

1. DeepSeek V3: 89.7%
2. Qwen 2.5: 88.3%
3. Llama 4: 87.1%

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License Comparison

Model	License	Commercial Use	Restrictions
Qwen 2.5	Apache 2.0	✅ Full	None
DeepSeek V3	MIT	✅ Full	None
Gemma 4	Custom (Permissive)	✅ Full	Terms of Use apply
Llama 4	Custom	⚠️ Limited	700M+ user restriction
GLM-4	Custom	✅ Full	Attribution required
Mistral	Apache 2.0	✅ Full	None

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Quick Deployment

Using Ollama (Easiest)

# Gemma 4
ollama run gemma4

# Qwen 2.5
ollama run qwen2.5

# Llama 4
ollama run llama4

Using vLLM (Production)

pip install vllm
vllm serve QwenLM/Qwen2.5-72B

Using LM Studio (GUI)

Download from lmstudio.ai - best for non-developers

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Related Resources

• Infrastructure - Serving and optimization tools
• Deployment & Serving - Production deployment
• Developer Tools - AI coding assistants

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