Create README.md

README.md

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+---
+base_model:
+- Qwen/Qwen2.5-Coder-1.5B
+license: cc-by-nc-4.0
+---
+
+<br><br>
+
+<p align="center">
+<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
+</p>
+
+<p align="center">
+<b>The code embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
+</p>
+
+# Jina Embeddings c1: A Small but Performant Code Embedding Model
+
+## Intended Usage & Model Info
+`jina-embeddings-c1` is an embedding model for code retrieval. 
+The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages. 
+
+
+Built on [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B), `jina-embeddings-c1-1.5B` features:
+
+- **Multilingual support** (15+ programming languages) and compatibility with a wide range of domains, including web development, software development, machine learning, data science, and educational coding problems.
+- **Task-specific instruction prefixes** for NL2Code, Code2Code, Code2NL, Code2Completion, and Technical QA, which can be selected at inference time.
+- **Flexible embedding size**: dense embeddings are 896-dimensional by default but can be truncated to as low as 64 with minimal performance loss.
+
+
+Summary of features:
+
+| Feature   | Jina Embeddings C1 1.5B   |
+|------------|------------|
+| Base Model | Qwen2.5-Coder-1.5B |
+| Supported Tasks | `nl2code`, `code2code`, `code2nl`, `code2completion`, `qa` |
+| Model DType | BFloat 16 |
+| Max Sequence Length | 32768 |
+| Embedding Vector Dimension | 1536 |
+| Matryoshka dimensions | 128, 256, 512, 1024, 1536 |
+| 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.
+- **sentence-transformers**: If you want to use the model via the `sentence-transformers` interface, install this package as well.
+</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-c1-1.5B", 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>
+
+<details>
+  <summary>via <a href="https://sbert.net/">sentence-transformers</a></summary>
+
+```python
+# !pip install sentence_transformers>=5.0.0 torch>=2.7.1
+
+import torch
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model = SentenceTransformer(
+    "jinaai/jina-embeddings-c1-1.5B",
+    model_kwargs={
+        "torch_dtype": torch.bfloat16,
+        "attn_implementation": "flash_attention_2",
+        "device_map": "auto"
+    }
+)
+
+# The queries and documents to embed
+queries = [
+    "print hello world in python",
+    "initialize array of 5 zeros in c++"
+]
+documents = [
+    "print('Hello World!')",
+    "int arr[5] = {0, 0, 0, 0, 0};"
+]
+
+query_embeddings = model.encode(queries, prompt_name="nl2code_query")
+document_embeddings = model.encode(documents, prompt_name="nl2code_document")
+
+# Compute the (cosine) similarity between the query and document embeddings
+similarity = model.similarity(query_embeddings, document_embeddings)
+print(similarity)
+# tensor([[0.8157, 0.1222],
+#         [0.1201, 0.5500]])
+```
+</details>
+
+## Training & Evaluation
+
+Please refer to our technical report of jina-embeddings-c1 for training details and benchmarks.
+
+## Contact
+
+Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.