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Ollama: Qwen2.5 7B Instruct

Ollama
Vision
128K
Context
Free
Input /1M
Free
Output /1M
N/A
Max Output
Run API Call Open in Chat

Ollama: Qwen2.5 7B Instruct

Overview

Property Value
Model ID ollama/qwen2.5:7b-instruct
Provider Alibaba (Qwen Team)
Display Name Qwen2.5 7B Instruct
Parameters 7.6 Billion (7.6B)
Type Instruction-Tuned Language Model
Release Date 2024

Description

Qwen2.5 7B Instruct is Alibaba's latest-generation instruction-tuned language model with 7.6 billion parameters, representing a significant upgrade to the Qwen family. Built on 18 trillion tokens of diverse, high-quality training data, Qwen2.5 excels at multilingual understanding, long-context processing, and structured output generation. The model supports an impressive 128K token context window, enabling it to process entire documents, books, and complex conversations seamlessly.

Qwen2.5 is specifically optimized for enterprise use cases, offering excellent performance across multiple languages and specialized tasks including coding, mathematics, and reasoning. Its instruction-tuning makes it highly responsive to user requests while maintaining strong factuality.

Specifications

  • Parameters: 7.6 Billion (7.6B)
  • Context Window: 128,000 tokens (128K) - effectively 32K-128K depending on configuration
  • Quantization Formats: Q4, Q5, Q6, FP16
  • Architecture: Transformer with Group Query Attention (GQA)
  • Training Data: 18 Trillion tokens from diverse sources
  • Languages Supported: 29+ languages (Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, Polish, Dutch, Turkish, Hebrew, Swedish, Danish, Finnish, Norwegian, Catalan, Filipino, Indonesian, Romanian, Czech, Ukrainian, Bulgarian, Hindi)
  • Memory Required (Float16): 16GB VRAM
  • Memory Required (Quantized Q4): 5-6GB VRAM
  • Inference Speed: 25-35 tokens/second on high-end GPU

Use Cases

  • Multilingual Customer Support: Support in 29+ languages
  • Content Generation: Long-form articles, reports, documentation
  • Code Development: Code generation, debugging, documentation
  • Data Processing: Structured output, JSON generation, parsing
  • Research: Document analysis, extraction, synthesis
  • Enterprise: Business process automation, data extraction
  • Education: Multilingual tutoring, explanation generation
  • Legal/Finance: Document analysis, summarization

Limitations

  • Slightly slower than Mistral 7B for simple tasks
  • Not as specialized as domain-specific models
  • Requires 12-16GB RAM for smooth operation
  • Vision capabilities require separate model (Qwen2.5-VL)
  • Weaker at very complex mathematical reasoning
  • qwen2.5:7b - Base model (not instruction-tuned)
  • qwen2.5:14b-instruct - Larger variant (14B parameters)
  • qwen2.5:32b-instruct - Largest variant (32B parameters)
  • qwen2.5-vl:7b - Vision-capable variant (multimodal)
  • qwen2.5-coder:7b - Specialized for coding tasks

Key Capabilities

  • Long-context processing (128K tokens)
  • Multilingual understanding and generation
  • Instruction following and compliance
  • Code generation and understanding
  • Mathematical reasoning
  • Structured output (JSON, tables)
  • Long-form content generation (8K+ tokens)
  • Reasoning and problem-solving
  • Function calling and tool use
  • Multi-turn conversation
  • Named entity recognition
  • Text classification

Installation

Quick Start

# Pull and run the model directly
ollama pull qwen2.5:7b-instruct
ollama run qwen2.5:7b-instruct

With Specific Quantization

# Q4 Quantization (5-6GB RAM) - recommended for most users
ollama pull qwen2.5:7b-instruct:q4

# Q5 Quantization (7-8GB RAM) - better quality
ollama pull qwen2.5:7b-instruct:q5

# Q6 Quantization (9-10GB RAM) - near full precision quality
ollama pull qwen2.5:7b-instruct:q6

# Full Precision (Float16 - 16GB RAM) - maximum quality
ollama pull qwen2.5:7b-instruct:fp16

Usage

Basic Chat

ollama run qwen2.5:7b-instruct "Explain blockchain technology in simple terms"

Interactive Conversation

ollama run qwen2.5:7b-instruct
# Type your message in any supported language
# Type /quit to exit

Long Document Processing

# Process large documents with 128K context
ollama run qwen2.5:7b-instruct "Summarize the following book chapter: [paste entire chapter here]"

API Usage (Ollama server on port 11434)

curl -X POST https://api.langmart.ai/v1/generate \
  -d '{
    "model": "qwen2.5:7b-instruct",
    "prompt": "Write a Python async web scraper",
    "stream": false
  }' | jq

Structured Output (JSON)

curl -X POST https://api.langmart.ai/v1/generate \
  -d '{
    "model": "qwen2.5:7b-instruct",
    "prompt": "Extract user info from this text as JSON: {name, email, phone}. Text: John Smith, john@example.com, 555-1234",
    "stream": false
  }'

Multilingual Usage

# Chinese
ollama run qwen2.5:7b-instruct "用简单的语言解释什么是人工智能"

# French
ollama run qwen2.5:7b-instruct "Expliquez l'apprentissage automatique"

# Japanese
ollama run qwen2.5:7b-instruct "AIについて説明してください"

OpenAI Compatible API

curl https://api.langmart.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5:7b-instruct",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant"},
      {"role": "user", "content": "Hello!"}
    ]
  }'

Performance & Hardware Requirements

Minimum Requirements

  • CPU: 4+ cores, 2.5GHz+
  • RAM: 12GB minimum (16GB recommended)
  • VRAM: 5-6GB for quantized versions, 16GB for full precision
  • Disk: 6-10GB free space
  • CPU: 8+ cores, 3.0GHz+
  • RAM: 16GB or more
  • GPU: NVIDIA (8GB+ VRAM), AMD Radeon, or Apple Silicon
  • Quantization: Q4 or Q5 for best balance

Inference Speed

  • GPU (RTX 3090): 25-35 tokens/second
  • GPU (RTX 4090): 40-50 tokens/second
  • GPU (A100): 60-80 tokens/second
  • CPU Only: 1-2 tokens/second (not recommended)

Memory by Format

Quantization VRAM Required Quality Speed Best For
Context Window 128,000 tokens
Q4 (GGML) 5-6 GB Good Fast Consumer GPUs
Q5 (GGML) 7-8 GB Very Good Medium Balanced
Q6 (GGML) 9-10 GB Excellent Slower Quality-first
FP16 16 GB Maximum Slowest Server GPUs

Multilingual Support

Qwen2.5 supports 29+ languages with equal quality:

  • CJK Languages: Chinese (Simplified/Traditional), Japanese, Korean
  • European: English, French, Spanish, German, Italian, Portuguese
  • Others: Russian, Arabic, Hindi, Vietnamese, Thai, Turkish, Hebrew, Polish, Ukrainian, Bulgarian, Indonesian, Romanian, Czech, Filipino, Catalan, Dutch, Danish, Finnish, Norwegian, Swedish

Long Context Features

128K Context Window Benefits

  • Process entire books or long documents
  • Maintain coherent multi-turn conversations
  • Reference multiple documents simultaneously
  • Generate long-form content (10K+ tokens)

Usage Example

# Load a 50K token document
curl -X POST https://api.langmart.ai/v1/generate \
  -d '{
    "model": "qwen2.5:7b-instruct",
    "prompt": "Document:\n[50000 tokens of content]\n\nQuestion: Summarize the main points",
    "stream": false
  }'

Performance Comparison

Model Parameters Context Languages Speed Memory
Qwen2.5 7B 7.6B 128K 29+ Fast 5-16GB
Llama 3.1 8B 8B 128K 8 Fast 4-16GB
Mistral 7B 7B 32K 1 Fastest 4-15GB
Qwen1.5 7B 7B 32K 25+ Fast 5-16GB

Advantages

  • Exceptional multilingual support (29+ languages)
  • Extended context window (128K tokens)
  • Strong performance for 7.6B parameters
  • Excellent long-text generation capability
  • Structured output generation
  • Strong instruction following
  • Good reasoning abilities
  • Enterprise-ready quality
  • Active development and updates
  • Competitive with larger models

Troubleshooting

Out of Memory Error

# Use Q4 quantization (smallest)
ollama pull qwen2.5:7b-instruct:q4

# Set GPU memory limit
OLLAMA_GPU_MEMORY_PERCENT=0.75 ollama serve

Poor Multilingual Output

# Use higher precision quantization
ollama pull qwen2.5:7b-instruct:q5

# Ensure prompt includes language hints
ollama run qwen2.5:7b-instruct "Answer in French: ..."

Context Window Not Working

# Verify Ollama version supports 128K
ollama version

# Update if needed
ollama pull qwen2.5:7b-instruct:latest

Optimization Tips

For Best Quality

  • Use Q5 or Q6 quantization
  • Fully utilize 128K context window
  • Use system prompts for consistency
  • Enable higher temperature for creativity (0.7-0.8)

For Speed

  • Use Q4 quantization
  • Enable streaming responses
  • Reduce context usage if possible
  • Batch similar requests

For Multilingual Tasks

  • Include language hints in prompts
  • Use consistent formatting
  • Test different quantizations for specific languages

Pricing

Type Price
Input Free
Output Free

Note: Ollama models run locally on your own hardware. No API costs apply - the model is free and open-source.

Resources & Documentation

Version History

  • Qwen2.5 7B: Latest version (2024)
  • Qwen1.5 7B: Previous generation
  • Qwen 7B: Original Qwen generation