Overview

The Code Generation LLM LoRA Combined Model is a specialized adapter-based solution designed to enhance standard LLM performance across diverse programming languages. By merging multiple Low-Rank Adaptation (LoRA) weights, this model offers a refined balance between general reasoning and domain-specific syntax accuracy without the computational overhead of a full parameter fine-tune. It is particularly effective for boilerplate generation, refactoring legacy code, and implementing complex algorithmic patterns. For developers, this means easier integration into existing pipelines via PEFT libraries, allowing for high-efficiency inference and lower VRAM requirements compared to monolithic code models. It serves as a practical middle ground for those needing specialized coding capabilities while maintaining the flexibility of a base model.

Highlights

Merged LoRA weights for enhanced multi-language coding proficiency
Reduced VRAM footprint via parameter-efficient fine-tuning
Optimized for boilerplate generation and complex refactoring tasks
Apache-2.0 license ensures flexible commercial integration
Seamless compatibility with PEFT and Hugging Face ecosystems

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Rabinovich/Code-Generation-LLM-LoRA-Combined-Model")
tokenizer = AutoTokenizer.from_pretrained("Rabinovich/Code-Generation-LLM-LoRA-Combined-Model")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download Rabinovich/Code-Generation-LLM-LoRA-Combined-Model

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download Rabinovich/Code-Generation-LLM-LoRA-Combined-Model config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Rabinovich/Code-Generation-LLM-LoRA-Combined-Model')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/Rabinovich/Code-Generation-LLM-LoRA-Combined-Model

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Rabinovich/Code-Generation-LLM-LoRA-Combined-Model

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Rabinovich/Code-Generation-LLM-LoRA-Combined-Model')
tokenizer = AutoTokenizer.from_pretrained('Rabinovich/Code-Generation-LLM-LoRA-Combined-Model')