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CodeGenForCausalLM

As explained in Domain-Specific Small Language Models

The Hugging Face auto class specialized for loading CodeGen causal language models.

CodeGenForCausalLM is the Hugging Face auto class used to load CodeGen causal language models from pretrained checkpoints. It is shown with the CodeGen 350M mono model on CPU, alongside tokenizer loading and standard generation calls. section 7.2.1

The class sits in a code-generation workflow that starts from a prompt, calls generate, and decodes the result into code. The same model family is also used for code verification and unit-test generation. section 7.2.1

Typical uses shown here include:

  • Generating a function body from a function definition. section 7.2.1
  • Generating code from natural-language or comment-based prompts. section 7.2.1
  • Producing unit tests from code plus an instruction comment. section 7.2.1

Minimal loading and generation

Load the tokenizer and model with AutoTokenizer and CodeGenForCausalLM, move the model to CPU, then generate from a short function stub such as def hello_world():.

How Domain-Specific Small Language Models covers it

How deep it goes
Defines the loader and demonstrates end-to-end prompting, but defers model internals and broader optimization details.
Examples
Uses hello_world code completion, matplotlib plotting, Dash app generation, and create_empty_array unit tests.
What it assumes
Assumes basic Transformers loading and prompt-based text generation; it introduces CodeGenForCausalLM and CPU-only use from there.
What you can do after
Apply it via Transformers to generate code from prompts on CPU and compare vanilla, ONNX, and quantized inference paths.
How it connects
Connects to Transformers, Hugging Face Hub, AutoTokenizer, generate, and Optimum ONNX conversion.
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This explainer was generated by Manning's knowledge graph from the text of Domain-Specific Small Language Models. The section links point to the parts of the book it draws on.