PaliGemma 2 10B
Mid-tier PaliGemma 2 fine-tuning base. Better baseline for complex vision tasks.
Overview
Mid-tier PaliGemma 2 fine-tuning base. Better baseline for complex vision tasks.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Strong fine-tuning base
Weaknesses
- Same base-only caveat as 3B
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| BF16 | 20.0 GB | 24 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of PaliGemma 2 10B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run PaliGemma 2 10B?
Can I use PaliGemma 2 10B commercially?
What's the context length of PaliGemma 2 10B?
Does PaliGemma 2 10B support images?
Source: huggingface.co/google/paligemma2-10b-pt-224
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify PaliGemma 2 10B runs on your specific hardware before committing money.