Text Generation
Keras
PyTorch
JAX
TensorFlow
English
Chinese
zeromodels
glm
glm4_moe_lite
glm-4.7
mixture-of-experts
Instructions to use zeromodels/glm-4.7-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use zeromodels/glm-4.7-flash with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://zeromodels/glm-4.7-flash") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: text-generation
license: mit
base_model: zai-org/GLM-4.7-Flash
library_name: zeromodels
language:
- en
- zh
tags:
- keras
- zeromodels
- glm
- glm4_moe_lite
- glm-4.7
- mixture-of-experts
- text-generation
- pytorch
- jax
- tf
---
# Run GLM-4.7-Flash with Keras 3: JAX, PyTorch, or TensorFlow
[](https://github.com/IMvision12/ZeroModels) [](https://imvision12.github.io/ZeroModels/glm4_moe_lite/) [](https://huggingface.co/collections/zeromodels/glm-6a82b8f9f753e8dcae3ff3f7)
# zeromodels/glm-4.7-flash
Pure-**Keras 3** conversion of [`zai-org/GLM-4.7-Flash`](https://huggingface.co/zai-org/GLM-4.7-Flash) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**. **GLM-4.7-Flash** is a mixture-of-experts model (MLA + DeepSeekMoE) served as **text -> text**; weights are stored in **bfloat16**, with the mixture-of-experts router correction bias kept in **float32** (matching the upstream mixed-precision checkpoint). See `zm_config.json` (`weight_dtype` + `weight_dtype_overrides`) for the exact layout.
For model details, license, and usage terms, see the upstream [model card](https://huggingface.co/zai-org/GLM-4.7-Flash).
Paper: [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools (arXiv:2406.12793)](https://arxiv.org/abs/2406.12793) · [HF Papers](https://huggingface.co/papers/2406.12793)
## ✨ Quick start
```python
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from zeromodels.models.glm4_moe_lite import Glm4MoeLiteTextGenerate, Glm4MoeLiteTokenizer
model = Glm4MoeLiteTextGenerate.from_weights("zeromodels/glm-4.7-flash")
tokenizer = Glm4MoeLiteTokenizer.from_weights("zeromodels/glm-4.7-flash")
messages = [{"role": "user", "content": "Name three prime numbers."}]
inputs = tokenizer(messages)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```
Load any GLM variant the same way with `from_weights("zeromodels/<variant>")`. Browse them all in the [GLM collection](https://huggingface.co/collections/zeromodels/glm-6a82b8f9f753e8dcae3ff3f7).
## Special Thanks
A huge thank you to the Zhipu AI / THUDM team for creating and releasing the GLM models.
License: `mit` (per the upstream model card).
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