Compare
Florence 2 Base Ft vs Florence 2 Large Ft
Put two to four models side by side on the fields that decide a deployment: context, price, region, license, languages, hosting, and what is verified.
| Florence 2 Base Ft | Florence 2 Large Ft | |
|---|---|---|
| Provider | Microsoft | Microsoft |
| Parameters | 0.2B | 0.8B |
| Context window | Not verified: the provider has not documented this claim. | Not verified: the provider has not documented this claim. |
| License | mit | mit |
| Modalities | text, vision | text, vision |
| Release date | 15 Jun 2024 | 15 Jun 2024 |
| Languages | Not verified: the provider has not documented this claim. | Not verified: the provider has not documented this claim. |
| Open weights | yes | yes |
| Tasks | Vision, Low cost | Vision |
Where two models differ, this table shows the difference and stops. LLM EU publishes no winner badge without a documented score for that task, and a missing figure stays missing instead of becoming a zero.
When to use it
Florence 2 Base Ft
Strengths
- open weights are published on Hugging Face and can be downloaded without a vendor agreement
- mit licence, so commercial self-hosting is permitted without a separate agreement
- at 0.2B parameters it fits on a single accelerator
Limits
- catalogue only — LLM EU does not run this model, and nothing on this card implies an LLM EU endpoint
- no evaluation has been run by LLM EU
- the published configuration records no context window, so the card carries none
- at 0.2B parameters, the weights fit on a single modest accelerator
- a base checkpoint: it answers nothing useful without fine-tuning or a task head
- vision checkpoints are heavier to serve than their parameter count alone suggests, because the image encoder and pre-processing run alongside the language model
Tasks
Florence 2 Large Ft
Strengths
- open weights are published on Hugging Face and can be downloaded without a vendor agreement
- mit licence, so commercial self-hosting is permitted without a separate agreement
- at 0.8B parameters it fits on a single accelerator
Limits
- catalogue only — LLM EU does not run this model, and nothing on this card implies an LLM EU endpoint
- no evaluation has been run by LLM EU
- the published configuration records no context window, so the card carries none
- at 0.8B parameters, the weights fit on a single modest accelerator
- vision checkpoints are heavier to serve than their parameter count alone suggests, because the image encoder and pre-processing run alongside the language model
Tasks
Tasks: Vision