Update README.md with new model card content

README.md

@@ -2,25 +2,84 @@
 library_name: keras-hub
 pipeline_tag: text-generation
 ---
-This is a [`Qwen` model](https://keras.io/api/keras_hub/models/qwen) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-This model is related to a `CausalLM` task.
-
-Model config:
-* **name:** qwen_backbone
-* **trainable:** True
-* **dtype:** {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}
-* **vocabulary_size:** 152064
-* **num_layers:** 64
-* **num_query_heads:** 40
-* **hidden_dim:** 5120
-* **intermediate_dim:** 27648
-* **rope_max_wavelength:** 1000000.0
-* **rope_scaling_factor:** 1.0
-* **num_key_value_heads:** 8
-* **layer_norm_epsilon:** 1e-06
-* **dropout:** 0
-* **tie_word_embeddings:** False
-* **use_sliding_window_attention:** False
-* **sliding_window_size:** 131072
-
-This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.
+### Model Overview
+# Model Summary
+
+Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, **0.5, 1.5, 3, 7, 14, 32** billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
+
+Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
+
+A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
+
+**Long-context Support** up to 128K tokens.
+
+For more details, please refer to Qwen [Blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/keras-team/keras-hub/tree/master/keras_hub/src/models/qwen), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+
+Weights are released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE) . Keras model code is released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
+
+## Links
+
+* [Qwen 2.5 Coder Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/qwen2-5-coder-quickstart-notebook)
+* [Qwen 2.5 Coder API Documentation](https://keras.io/keras_hub/api/models/qwen/)
+* [Qwen 2.5 Coder Model Card](https://qwenlm.github.io/blog/qwen2.5/)
+* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
+* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
+
+## Installation
+
+Keras and KerasHub can be installed with:
+
+```
+pip install -U -q keras-hub
+pip install -U -q keras
+```
+
+Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.
+
+## Presets
+
+The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
+
+| Preset name                            | Parameters | Description                                                                                                  |
+|---------------------------------------|------------|--------------------------------------------------------------------------------------------------------------|
+| qwen2.5_coder_0.5b      | 0.5B      | 24-layer with 0.5 billion parameters. Code-Specific large language models base on the strong Qwen2.5 |
+| qwen2.5_coder_instruct_0.5b    | 0.5B      | 24-layer  with 0.5 billion parameters. Code-Specific large language models  base on the strong Qwen2.5. |
+| qwen2.5_coder_1.5b      | 1.5B      | 28-layer with 1.5 billion parameters. Code-Specific large language models  base on the strong Qwen2.5. |
+| qwen2.5_coder_instruct_1.5b     | 1.5B      | 28-layer  with 1.5 billion parameters.  Code-Specific large language models  base on the strong Qwen2.5.|
+| qwen2.5_coder_3b      | 3B      | 36-layer with 3 billion parameters.  Code-Specific large language models  base on the strong Qwen2.5.|
+| qwen2.5_coder_instruct_3b     | 3B      | 36-layer with 3 billion parameters. Code-Specific large language models  base on the strong Qwen2.5. |
+| qwen2.5_coder_7b      | 7B      | 28-layer  with 7B billion parameters. Code-Specific large language models  base on the strong Qwen2.5.|
+| qwen2.5_coder_instruct_7b     | 7B      | 28-layer with 7B billion parameters. Code-Specific large language models  base on the strong Qwen2.5.|
+| qwen2.5_coder_14b      | 14B      | 48-layer with 14B billion parameters. Code-Specific large language models  base on the strong Qwen2.5.|
+| qwen2.5_coder_instruct_14b     | 14B      | 48-layer with 14B billion parameters. Code-Specific large language models  base on the strong Qwen2.5.|
+| qwen2.5_coder_32b      | 32B      | 64-layer with 32B billion parameters. Code-Specific large language models  base on the strong Qwen2.5.|
+| qwen2.5_coder_instruct_32b     | 32B      | 64-layer with 32B billion parameters. Code-Specific large language models  base on the strong Qwen2.5.|
+
+## Example Usage
+```Python
+
+import keras
+import keras_hub
+import numpy as np
+
+# Use generate() to do code generation.
+qwen_lm = keras_hub.models.QwenCausalLM.from_preset("qwen2.5_coder_32b")
+qwen_lm.generate(" write a quick sort algorithm in python.", max_length=512)
+
+
+```
+
+## Example Usage with Hugging Face URI
+
+```Python
+
+import keras
+import keras_hub
+import numpy as np
+
+# Use generate() to do code generation.
+qwen_lm = keras_hub.models.QwenCausalLM.from_preset("hf://keras/qwen2.5_coder_32b")
+qwen_lm.generate(" write a quick sort algorithm in python.", max_length=512)
+
+
+```