Update README.md

README.md

@@ -1,20 +1,51 @@
----
-base_model: THUDM/GLM-Z1-Rumination-32B-0414
-language:
-- zh
-- en
-library_name: transformers
-license: mit
-pipeline_tag: text-generation
-tags:
-- llama-cpp
-- gguf-my-repo
----
+---
+base_model: THUDM/GLM-Z1-Rumination-32B-0414
+language:
+- zh
+- en
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+tags:
+- llama-cpp
+- gguf-my-repo
+---
 
 # Triangle104/GLM-Z1-Rumination-32B-0414-Q6_K-GGUF
 This model was converted to GGUF format from [`THUDM/GLM-Z1-Rumination-32B-0414`](https://huggingface.co/THUDM/GLM-Z1-Rumination-32B-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-Rumination-32B-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-Rumination-32B-0414 is a deep reasoning model with rumination capabilities
+ (benchmarked against OpenAI's Deep Research). Unlike typical deep 
+thinking models, the rumination model employs longer periods of deep 
+thought to solve more open-ended and complex problems (e.g., writing a 
+comparative analysis of AI development in two cities and their future 
+development plans). The rumination model integrates search tools during 
+its deep thinking process to handle complex tasks and is trained by 
+utilizing multiple rule-based rewards to guide and extend end-to-end 
+reinforcement learning. Z1-Rumination shows significant improvements in 
+research-style writing and complex retrieval tasks.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)