The GLM-4-9B-Chat is a powerful large language model that enables developers to create advanced chat applications. In this guide, we’ll explore how to implement it, akin to assembling a LEGO set where each piece contributes to a magnificent structure.

Requirements

• Python installed on your system
• Pip for package management
• Torch library for tensor operations
• Transformers library by Hugging Face
• VLLM library for better efficiency

Installation Steps

To create your own chat application using GLM-4-9B-Chat, follow these steps:

• Install necessary packages by running the following command in your terminal:

pip install torch transformers vllm

• Import the required libraries:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

• Load the model and tokenizer:

device = "cuda"  # Use GPU for better performance
tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat", trust_remote_code=True)

• Create a query input structure:

query = inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}],
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
return_dict=True)

• Generate a response from the model.

inputs = inputs.to(device)
model = AutoModelForCausalLM.from_pretrained(
    "THUDM/glm-4-9b-chat",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True).to(device).eval()

gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1}
with torch.no_grad():
    outputs = model.generate(**inputs, **gen_kwargs)
    outputs = outputs[:, inputs["input_ids"].shape[1]:]
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Explaining Code with an Analogy

Using GLM-4-9B-Chat is like baking a cake. First, you gather all your ingredients (libraries and models). Next, you mix them in the right order (load the model, prepare the inputs). You need to adjust the oven temperature and timing carefully (set generation parameters) to ensure the cake rises perfectly (get a coherent response). Finally, when the cake is ready, you slice and serve it (decode and print the generated text).

Troubleshooting

If you encounter issues while implementing GLM-4-9B-Chat, consider the following tips:

• Ensure that you have the latest versions of dependencies installed.
• Check that your hardware meets the model’s requirements, especially if using GPU.
• Pay attention to the format of the input data to avoid errors in processing.
• Clear your Python cache or restart your environment if you face unexpected errors.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following this guide, you can harness the power of the GLM-4-9B-Chat model for your applications. Experiment with different inputs to see the model’s flexibility and creativity in generating responses.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.