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OLMo 3 7B
By Allen AI · United States
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general
Overview
Allen AI's fully open 7B model releasing weights, training data, and code under Apache 2.0. The reference choice for reproducible LLM research.
When to pick this model
- Academic and reproducibility-focused research
- Auditing training data for compliance or bias
- Teaching LLM internals end-to-end
- Apache-licensed commercial baselines
- Regulatory environments demanding full traceability
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 14 GB |
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.
Strengths
- Weights, data, and code all Apache 2.0
- Full traceability from corpus to checkpoint
- Backed by Allen AI's research credibility
Limitations
- Quality trails the best closed-data 7B models
- 8K context is restrictive for modern workloads
- Not tuned for top leaderboard scores
Architecture & training
Architecture: Dense 7B · 100% open
Training: Allen AI.
Verdict
The clearest choice when full training transparency matters more than peak benchmark scores.
Quick start
ollama run olmo-3:7bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.