Feature Extraction
sentence-transformers
Safetensors
Transformers
English
qwen3
text-embeddings-inference
Instructions to use codefuse-ai/F2LLM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codefuse-ai/F2LLM-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codefuse-ai/F2LLM-4B") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use codefuse-ai/F2LLM-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="codefuse-ai/F2LLM-4B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/F2LLM-4B") model = AutoModel.from_pretrained("codefuse-ai/F2LLM-4B") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -12,7 +12,7 @@ F2LLM (Foundation to Feature Large Language Models) are foundation models direct
|
|
| 12 |
|
| 13 |
To evaluate F2LLMs on MTEB:
|
| 14 |
|
| 15 |
-
```
|
| 16 |
import mteb
|
| 17 |
import logging
|
| 18 |
logging.basicConfig(level=logging.INFO)
|
|
|
|
| 12 |
|
| 13 |
To evaluate F2LLMs on MTEB:
|
| 14 |
|
| 15 |
+
```python
|
| 16 |
import mteb
|
| 17 |
import logging
|
| 18 |
logging.basicConfig(level=logging.INFO)
|