Instructions to use keras/qwen2.5_coder_instruct_1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/qwen2.5_coder_instruct_1.5b with KerasHub:
import keras_hub # Load CausalLM model (optional: use half precision for inference) causal_lm = keras_hub.models.CausalLM.from_preset("hf://keras/qwen2.5_coder_instruct_1.5b", dtype="bfloat16") causal_lm.compile(sampler="greedy") # (optional) specify a sampler # Generate text causal_lm.generate("Keras: deep learning for", max_length=64)import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/qwen2.5_coder_instruct_1.5b") - Keras
How to use keras/qwen2.5_coder_instruct_1.5b with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/qwen2.5_coder_instruct_1.5b") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81107f1ce5d399361a10415cf156fd3abcce10a50901e52db7e9db24190675f1
- Size of remote file:
- 6.18 GB
- SHA256:
- e30e5af3ef287e8db168b1a751171a4fa3c35912e170cfd08e260d4b7f72d815
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.