1

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2025 zAI
+Copyright (c) 2025 Zhipu AI
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,51 +1,45 @@
----
-license: mit
-language:
-- zh
-- en
-pipeline_tag: text-generation
-library_name: transformers
----
-
-# GLM-4-Z1-32B-0414
-
-## Introduction
-
-Based on our latest technological advancements, we have trained a `GLM-4-0414` series model. During pretraining, we incorporated more code-related and reasoning-related data. In the alignment phase, we optimized the model specifically for agent capabilities. As a result, the model's performance in agent tasks such as tool use, web search, and coding has been significantly improved.
-
-## Installation
-
-Install the transformers library from the source code:
-
-```shell
-pip install git+https://github.com/huggingface/transformers.git
-```
-
-## Inference Code
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-MODEL_PATH = "THUDM/GLM-4-Z1-32B-0414"
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
-model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")
-
-message = [{"role": "user", "content": "Let a, b be positive real numbers such that ab = a + b + 3. Determine the range of possible values for a + b."}]
-
-inputs = tokenizer.apply_chat_template(
-    message,
-    return_tensors="pt",
-    add_generation_prompt=True,
-    return_dict=True,
-).to(model.device)
-
-generate_kwargs = {
-    "input_ids": inputs["input_ids"],
-    "attention_mask": inputs["attention_mask"],
-    "max_new_tokens": 4096,
-    "do_sample": False,
-}
-out = model.generate(**generate_kwargs)
-print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+---
+license: mit
+language:
+- zh
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# GLM-4-Z1-32B-0414
+
+## Introduction
+
+Based on our latest technological advancements, we have trained a `GLM-4-0414` series model. During pretraining, we incorporated more code-related and reasoning-related data. In the alignment phase, we optimized the model specifically for agent capabilities. As a result, the model's performance in agent tasks such as tool use, web search, and coding has been significantly improved.
+
+## Inference Code
+
+Make Sure Using `transforemrs>=4.51.3`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_PATH = "THUDM/GLM-4-Z1-32B-0414"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")
+
+message = [{"role": "user", "content": "Let a, b be positive real numbers such that ab = a + b + 3. Determine the range of possible values for a + b."}]
+
+inputs = tokenizer.apply_chat_template(
+    message,
+    return_tensors="pt",
+    add_generation_prompt=True,
+    return_dict=True,
+).to(model.device)
+
+generate_kwargs = {
+    "input_ids": inputs["input_ids"],
+    "attention_mask": inputs["attention_mask"],
+    "max_new_tokens": 4096,
+    "do_sample": False,
+}
+out = model.generate(**generate_kwargs)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
 ```