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README.md

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 This model was converted to GGUF format from [`THUDM/GLM-Z1-9B-0414`](https://huggingface.co/THUDM/GLM-Z1-9B-0414) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/THUDM/GLM-Z1-9B-0414) for more details on the model.
 
+---
+Introduction
+-
+The GLM family welcomes a new generation of open-source models, the GLM-4-32B-0414
+ series, featuring 32 billion parameters. Its performance is comparable 
+to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very
+ user-friendly local deployment features. GLM-4-32B-Base-0414 was 
+pre-trained on 15T of high-quality data, including a large amount of 
+reasoning-type synthetic data, laying the foundation for subsequent 
+reinforcement learning extensions. In the post-training stage, in 
+addition to human preference alignment for dialogue scenarios, we also 
+enhanced the model's performance in instruction following, engineering 
+code, and function calling using techniques such as rejection sampling 
+and reinforcement learning, strengthening the atomic capabilities 
+required for agent tasks. GLM-4-32B-0414 achieves good results in areas 
+such as engineering code, Artifact generation, function calling, 
+search-based Q&A, and report generation. Some benchmarks even rival 
+larger models like GPT-4o and DeepSeek-V3-0324 (671B).
+
+GLM-Z1-9B-0414 is a surprise. We employed the 
+aforementioned series of techniques to train a 9B small-sized model that
+ maintains the open-source tradition. Despite its smaller scale, 
+GLM-Z1-9B-0414 still exhibits excellent capabilities in mathematical 
+reasoning and general tasks. Its overall performance is already at a 
+leading level among open-source models of the same size. Especially in 
+resource-constrained scenarios, this model achieves an excellent balance
+ between efficiency and effectiveness, providing a powerful option for 
+users seeking lightweight deployment
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)