Update README.md

README.md

@@ -40,6 +40,51 @@ Summary of features:
 | Pooling Strategy | Last-token pooling |
 | Attention Mechanism | FlashAttention2 |
 
+## Usage
+
+<details>
+  <summary>Requirements</a></summary>
+  
+The following Python packages are required:
+
+- `transformers>=4.53.0`
+- `torch>=2.7.1`
+  
+### Optional / Recommended
+- **flash-attention**: Installing [flash-attention](https://github.com/Dao-AILab/flash-attention) is recommended for improved inference speed and efficiency, but not mandatory.
+</details>
+
+<details>
+  <summary>via <a href="https://huggingface.co/docs/transformers/en/index">transformers</a></summary>
+
+```python
+# !pip install transformers>=4.53.0 torch>=2.7.1
+
+from transformers import AutoModel
+import torch
+
+# Initialize the model
+model = AutoModel.from_pretrained("jinaai/jina-embeddings-c1-0.5B", trust_remote_code=True)
+model.to("cuda")
+
+# Configure truncate_dim, max_length, batch_size in the encode function if needed
+
+# Encode query
+query_embeddings = model.encode(
+    ["print hello world in python"],
+    task="nl2code",
+    prompt_name="query",
+)
+
+# Encode passage
+passage_embeddings = model.encode(
+    ["print('Hello World!')"],
+    task="nl2code",
+    prompt_name="passage",
+)
+```
+</details>
+
 ## Training & Evaluation
 
 Please refer to our technical report of jina-embeddings-c1 for training details and benchmarks.