F2LLM 1.7B vs F2LLM v2 1.7B
MTEB multilingual v2 · Mean over tasks · multilingual (1037 languages) Embeddings assign examples to categories, group similar examples, estimate semantic similarity between pairs, classify relationships between pairs, rank relevant documents for queries, find translated sentence pairs, rerank documents according to instructions, assign multiple labels to each example, reorder candidate documents by relevance on MTEB Multilingual v2 · mean over tasks in 1037 languages including 'Auhelawa, Abau, Achi, Achinese, Achuar-Shiwiar, Adzera, Afrikaans, Agarabi; tasks receive equal weight
F2LLM 1.7B—
F2LLM v2 1.7B65.2
| Benchmark | F2LLM 1.7B | F2LLM v2 1.7B |
|---|---|---|
| MTEB multilingual v2 · Mean over tasks · multilingual (1037 languages) | — | 65.2 |
Specs
| Spec | F2LLM 1.7B | F2LLM v2 1.7B |
|---|---|---|
| Context | 8K | 41K |
| Weights | Open | Open |
| Released | Sep 2025 | Mar 2026 |
| Dimensions | 2,048 | 2,048 |
| Max input | 41K tokens | 41K tokens |
| Languages | en | en, es, Estonian +80 |
| Memory (4-bit) | 1 GB | 1 GB |
| Licence | Apache-2.0 | Apache-2.0 |
| Commercial use | Allowed | Allowed |
What breaks when you switch
F2LLM 1.7B → F2LLM v2 1.7B
Re-embed your whole corpus›
Vectors from different models can't be mixed.
F2LLM v2 1.7B → F2LLM 1.7B
Re-embed your whole corpus›
Vectors from different models can't be mixed.