What are the hardware requirements for CodeGen RE?

CodeGen RE requires approximately 28.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed CodeGen RE and how large is it?

CodeGen RE is developed by shailja, a model with 6b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate CodeGen RE?

CodeGen RE is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

CodeGen RE by shailja — VRAM 28.8GB

Name: CodeGen RE
Author: shailja

CodeGen RE is an open-source language model by shailja. Features: 6b LLM, VRAM: 28.8GB, License: mit, Code Generating, LLM Explorer Score: 0.05.

Codegen Endpoints compatible Pytorch Region:us

Model Card on HF 🤗: https://huggingface.co/shailja/CodeGen-RE

CodeGen RE Parameters and Internals

Model Type

code understanding, question answering

Additional Notes

This model is trained to understand code semantics and can answer questions related to the purpose and language of C or C++ code, particularly decompiled code from Ghidra.

Training Details

Data Sources:

decompiled ghidra C source code

Input Output

Accepted Modalities:

text

Output Format:

text

LLM Name	CodeGen RE
Repository 🤗	https://huggingface.co/shailja/CodeGen-RE
Model Size	6b
Required VRAM	28.8 GB
Updated	2026-07-30
Maintainer	shailja
Model Type	codegen
Model Files	28.8 GB 0.0 GB
Generates Code	Yes
Model Architecture	CodeGenForCausalLM
License	mit
Model Max Length	2048
Transformers Version	4.25.0.dev0
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	50295
Torch Data Type	float16
Activation Function	gelu_new

Rank the CodeGen RE capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to CodeGen RE

Best Alternatives	Context / RAM	Downloads	Likes
Nsql 6B	0K / 28.4 GB	23	52
...egen 6B Nl Lora Adapter Merged	0K / 14.3 GB	3	1
...en 6B Mono Lora Adapter Merged	0K / 14.3 GB	5	0
Codegen 6B Multi	0K / 14.3 GB	951	20
Diff Codegen 6B V2	0K / 14.3 GB	20	40
...en 6B Mono Instruct Py Revised	0K / 28.4 GB	5	2
...n 6B Mono Instruct Py Critique	0K / 28.4 GB	4	2
...gen 6B Nl Instruct Py Critique	0K / 28.4 GB	6	1
Codegen 6B Nl	0K / 14.3 GB	146	4
Fine Tuned Codegen 6B Verilog	0K / 28.4 GB	14	2

Note: green Score (e.g. "73.2") means that the model is better than shailja/CodeGen-RE.