metadata
pipeline_tag: text-generation
license: other
license_name: glm-4
license_link: https://huggingface.co/THUDM/glm-4-9b-chat-hf/blob/main/LICENSE
base_model: zai-org/glm-4-9b-chat-hf
library_name: kerasformers
language:
- en
- zh
tags:
- keras
- kerasformers
- glm
- glm-4
- text-generation
- pytorch
- jax
- tf
Run GLM-4 with Keras 3: JAX, PyTorch, or TensorFlow
kerasformers/glm-4-9b-chat
Pure-Keras 3 conversion of zai-org/glm-4-9b-chat-hf for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX. This is a GLM-4-9B 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
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from kerasformers.models.glm import GlmTextGenerate, GlmTokenizer
model = GlmTextGenerate.from_weights("kerasformers/glm-4-9b-chat")
tokenizer = GlmTokenizer.from_weights("kerasformers/glm-4-9b-chat")
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("kerasformers/<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: glm-4 (link).