GLM 4 9B 0414 GGUF
Model Overview
Model Features
Model Capabilities
Use Cases
🚀 GLM-4-9B-0414
The GLM-4-9B-0414 is a remarkable addition to the GLM family, offering powerful text generation capabilities. It has comparable performance to well - known models and supports user - friendly local deployment.
🚀 Quick Start
The provided README doesn't contain specific quick - start steps. If you want to start using this model, you can refer to the official documentation of the transformers
library as the library_name
is specified as transformers
.
✨ Features
- High - Performance Model: The GLM family welcomes new members, the GLM-4-32B-0414 series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series.
- Local Deployment: It supports very user - friendly local deployment features.
- Extensive Pre - training: GLM-4-32B-Base-0414 was pre - trained on 15T of high - quality data, including substantial reasoning - type synthetic data, laying the foundation for subsequent reinforcement learning extensions.
- Human Preference Alignment: In the post - training stage, human preference alignment was employed for dialogue scenarios.
- Enhanced Capabilities: Using techniques like rejection sampling and reinforcement learning, the model's performance in instruction following, engineering code, and function calling was enhanced, strengthening the atomic capabilities required for agent tasks.
- Good Results in Multiple Tasks: GLM-4-32B-0414 achieves good results in engineering code, Artifact generation, function calling, search - based Q&A, and report generation.
- Specialized Reasoning Models:
- GLM-Z1-32B-0414: A reasoning model with deep thinking capabilities, significantly improving mathematical abilities and the capability to solve complex tasks compared to the base model.
- GLM-Z1-Rumination-32B-0414: A deep reasoning model with rumination capabilities, capable of deeper and longer thinking to solve more open - ended and complex problems.
- GLM-Z1-9B-0414: A small model (9B) that exhibits excellent capabilities in mathematical reasoning and general tasks, achieving an excellent balance between efficiency and effectiveness, especially in resource - constrained scenarios.
📚 Documentation
Model Information
Property | Details |
---|---|
Tags | unsloth |
Base Model | THUDM/GLM-4-9B-0414 |
License | mit |
Language | zh, en |
Pipeline Tag | text - generation |
Library Name | transformers |
Introduction
The GLM family welcomes new members, the GLM-4-32B-0414 series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series. It also supports very user - friendly local deployment features. GLM-4-32B-Base-0414 was pre - trained on 15T of high - quality data, including substantial reasoning - type synthetic data. This lays the foundation for subsequent reinforcement learning extensions. In the post - training stage, we employed human preference alignment for dialogue scenarios. Additionally, using techniques like rejection sampling and reinforcement learning, we enhanced the model’s performance in instruction following, engineering code, and function calling, thus strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in engineering code, Artifact generation, function calling, search - based Q&A, and report generation. In particular, on several benchmarks, such as code generation or specific Q&A tasks, GLM-4-32B-Base-0414 achieves comparable performance with those larger models like GPT - 4o and DeepSeek - V3 - 0324 (671B).
GLM-Z1-32B-0414 is a reasoning model with deep thinking capabilities. This was developed based on GLM-4-32B-0414 through cold start, extended reinforcement learning, and further training on tasks including mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During training, we also introduced general reinforcement learning based on pairwise ranking feedback, which enhances the model's general capabilities.
GLM-Z1-Rumination-32B-0414 is a deep reasoning model with rumination capabilities (against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model is capable of deeper and longer thinking 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). Z1 - Rumination is trained through scaling end - to - end reinforcement learning with responses graded by the ground truth answers or rubrics and can make use of search tools during its deep thinking process to handle complex tasks. The model shows significant improvements in research - style writing and complex tasks.
Finally, GLM-Z1-9B-0414 is a surprise. We employed all the aforementioned techniques to train a small model (9B). GLM-Z1-9B-0414 exhibits excellent capabilities in mathematical reasoning and general tasks. Its overall performance is top - ranked among all 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.
Showcase
Animation Generation
Model | Video | Prompt |
---|---|---|
GLM-Z1-32B-0414 | write a Python program that shows a ball bouncing inside a spinning hexagon. The ball should be affected by gravity and friction, and it must bounce off the rotating walls realistically | |
GLM-4-32B-0414 | Use HTML to simulate the scenario of a small ball released from the center of a rotating hexagon. Consider the collision between the ball and the hexagon's edges, the gravity acting on the ball, and assume all collisions are perfectly elastic. (Prompt translated from Chinese) |
Web Design
Model | Image | Prompt |
---|---|---|
GLM-4-32B-0414 | Design a drawing board that supports custom function plotting, allowing adding and deleting custom functions, and assigning colors to functions. (Prompt translated from Chinese) | |
GLM-4-32B-0414 | Design a UI for a mobile machine learning platform, which should include interfaces for training tasks, storage management, and personal statistics. The personal statistics interface should use charts to display the user's resource usage over a period. Use Tailwind CSS to style the page, and display these 3 mobile interfaces tiled on a single HTML page. (Prompt translated from Chinese) |
SVG Generation
Model | Image | Prompt |
---|---|---|
GLM-4-32B-0414 | Create a misty Jiangnan scene using SVG. (Prompt translated from Chinese) | |
GLM-4-32B-0414 | Use SVG to illustrate the training process of an LLM. (Prompt translated from Chinese) |
Search - Based Writing
For search - based writing tasks, we use the following system prompt to have the model respond based on search results:
Please answer the user's question based on the provided search results.
## Note
1. Make full use of and organize the collected information, rather than simply copying and pasting, to generate a professional answer that meets the user's requirements and has depth.
2. When the provided information is sufficient, your answer should be as long as possible. Starting from the user's intention, provide a response with sufficient information and multiple perspectives.
3. Additionally, not all search results are closely related to the user's question. Please carefully甄别, filter, and utilize them.
4. The answers to objective questions are usually very short. You can appropriately add one or two related sentences to enrich the content.
5. Please ensure that your response is beautifully formatted and highly readable. For multi - entity comparisons or listings, make good use of the list format to help the user better understand the information.
6. Unless the user requests otherwise, please keep the language of your answer consistent with the language of the user's question.
7. In appropriate cases, use the format such as: 【0†source】 at the end of the sentence to reference the search results.
When using, you can obtain search results through methods such as RAG
or WebSearch
, and wrap them in observation
, for example:
[
{
"role": "user",
"content": "Explore the common characteristics of children's literature, with a focus on its narrative techniques and thematic tendencies. This includes narrative techniques: common approaches in children's literature such as first - person, third - person, omniscient narrator, and interactive narration, and their influence on young readers. It also includes thematic tendencies: recurring themes in children's literature such as growth, adventure, friendship, and family, with an analysis of how these themes impact children's cognitive and emotional development. Additionally, other universal features such as the use of personification, repetitive language, symbolism and metaphor, and educational value should be considered. Please provide a detailed analytical report based on academic research, classic examples of children's literature, and expert opinions."
},
{
"role": "observation",
"content": "【{id}†{title}†{url}】\n{content}"
},
...
]
For the above prompt, we use an internal or external search model to obtain the search results. Using the format shown above, we can generate the following analysis report:
Analysis Report on Common Characteristics of Children's Literature
Children's literature, as a literary genre specifically created for children, possesses unique artistic features and expressive techniques. This report will comprehensively analyze the common characteristics of children's literature from three dimensions: narrative methods, thematic tendencies, and other universal features, integrating academic research, classic examples of children's literature, and expert viewpoints.
Narrative Methods and Their Impact on Child Readers
The narrative methods of children's literature exhibit diverse characteristics, including first - person, third - person, narration, and interactive storytelling. These different narrative perspectives and methods profoundly influence children's reading experiences and cognitive development.
First - person narration is common in children's literature, unfolding the story from the perspective of a child protagonist. This narrative style bridges the gap between the reader and the story, making it easier for child readers to identify with the characters and develop emotional resonance. For example, many coming - of - age novels use first - person narration, allowing the protagonist to directly recount their experiences and feelings, making readers feel as though they are experiencing the joys and sorrows of growth alongside the protagonist. This narrative perspective lends the work a strong sense of authenticity and intimacy, helping to cultivate children's empathy【1†source】.
Third - person narration offers a broader perspective, allowing the author to flexibly switch between different characters' viewpoints and present richer layers of the story. In children's literature, third - person omniscient narration enables the author to control the narrative pace, revealing or concealing information as needed to guide children's attention. At the same time, third - person narration facilitates direct dialogue between the author and the reader, conveying values or explaining complex concepts through narration. This narrative method positively influences children's macro - thinking and comprehensive understanding【1†source】.
Narration (authorial intrusion) is a unique narrative technique in children's literature, where the author directly appears as the "storyteller," explaining the background, commenting on characters, or posing questions to the reader. This technique is particularly common in classic fairy tales, such as the opening lines of Andersen's Fairy Tales: "Once, there was a child..." Narration helps children understand the story's context, fills cognitive gaps, and conveys the author's educational intent. Research shows that appropriate authorial intrusion aids children in grasping the story's structure and improving reading comprehension【5†source】.
Interactive storytelling is a new trend in contemporary children's literature, especially prominent in the digital media era. Interactive storytelling breaks the traditional unidirectional author - reader relationship, encouraging child readers to participate in the story's creation, such as by choosing plot directions, character dialogues, or endings. This participatory reading enhances children's sense of agency and fosters decision - making skills and creative thinking. For example, some children's reading apps incorporate interactive elements, allowing children to influence the story's development through clicks, drag - and - drop actions, and other operations, thereby gaining a stronger sense of immersion and achievement【6†source】. Interactive storytelling transforms children from passive information recipients into active meaning - makers, uniquely contributing to the development of their subjectivity.
Table: Common Narrative Methods in Children's Literature and Their Effects
Narrative Method | Characteristics | Impact on Child Readers | Classic Examples |
---|---|---|---|
First - person narration | Unfolds the story from the perspective of a child protagonist | Helps children identify with characters and develop emotional resonance | Many coming - of - age novels |
Third - person narration | Offers a broader perspective, allows the author to switch viewpoints | Positively influences children's macro - thinking and comprehensive understanding | |
Narration (authorial intrusion) | The author directly appears as the "storyteller" | Helps children understand the story's context and improves reading comprehension | Andersen's Fairy Tales |
Interactive storytelling | Encourages child readers to participate in the story's creation | Enhances children's sense of agency and fosters decision - making and creative thinking | Some children's reading apps |
📄 License
The model is released under the mit
license.

