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
zeromodels/glm-4.7-flash
Pure-Keras 3 conversion of zai-org/GLM-4.7-Flash for 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.
Paper: ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools (arXiv:2406.12793) · HF Papers
✨ Quick start
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.
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).