Falcon 3 7B Instruct

Overview

TII's 7B trained on 14T tokens, hitting MMLU 70.5 — on par with Qwen2.5-7B — with native support for English, French, Spanish, German, and Portuguese.

When to pick this model

• Multilingual chat across the five supported European languages
• General-purpose 7B serving where Qwen licensing is a concern
• Workloads needing 32k context at small scale
• Sovereign deployments preferring a non-Chinese-origin model
• Knowledge-heavy QA at the 7B tier

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• MMLU 70.5 matches Qwen2.5-7B
• Trained on 14T tokens for broad knowledge coverage
• Five-language native support out of the box
• Permissive commercial license under TII Falcon-LLM 2.0
• 32k context covers most production needs

Limitations

• TII license is permissive but not Apache 2.0
• Smaller community than Llama or Qwen ecosystems
• No official multimodal variants

Architecture & training

Architecture: Dense 7B · GQA · 32k ctx

Training: 14T tokens.

Quick start

ollama run falcon3:7b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.