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kerasformers/
glm-4-32b-base-0414

Name: glm-4-32b-base-0414
Availability: InStock

zai-org/GLM-4-32B-Base-0414

finetune

mit

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Run GLM-4-0414 with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

HuggingFace

zeromodels/glm-4-32b-base-0414

Pure-Keras 3 conversion of zai-org/GLM-4-32B-Base-0414 for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX. This is a GLM-4-0414 checkpoint served as text -> text; weights are stored in bfloat16.

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

python

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from zeromodels.models.glm4 import Glm4TextGenerate, Glm4Tokenizer

model = Glm4TextGenerate.from_weights("zeromodels/glm-4-32b-base-0414")
tokenizer = Glm4Tokenizer.from_weights("zeromodels/glm-4-32b-base-0414")

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).

kerasformers/glm-4-32b-base-0414

Run GLM-4-0414 with Keras 3: JAX, PyTorch, or TensorFlow

zeromodels/glm-4-32b-base-0414

✨ Quick start

Special Thanks

kerasformers/
glm-4-32b-base-0414