Instructions to use keras/qwen2.5_coder_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/qwen2.5_coder_7b 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_7b", 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_7b") - Keras
How to use keras/qwen2.5_coder_7b 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_7b") - Notebooks
- Google Colab
- Kaggle
File size: 885 Bytes
ea37813 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"module": "keras_hub.src.models.qwen.qwen_backbone",
"class_name": "QwenBackbone",
"config": {
"name": "qwen_backbone",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "float32"
},
"registered_name": null
},
"vocabulary_size": 152064,
"num_layers": 28,
"num_query_heads": 28,
"hidden_dim": 3584,
"intermediate_dim": 18944,
"rope_max_wavelength": 1000000.0,
"rope_scaling_factor": 1.0,
"num_key_value_heads": 4,
"layer_norm_epsilon": 1e-06,
"dropout": 0,
"tie_word_embeddings": false,
"use_sliding_window_attention": false,
"sliding_window_size": 131072
},
"registered_name": "keras_hub>QwenBackbone"
} |