Model card
CodeLlama 34B is a specialized derivative of the Llama 2 architecture, specifically optimized for software engineering workflows. Unlike general-purpose models, this 34B parameter variant is fine-tuned on massive code repositories to excel in logic-heavy tasks such as code completion, debugging, and translating natural language requirements into executable scripts. For developers, the primary value lies in its balance between reasoning depth and inference efficiency. It supports multiple programming languages and provides a significant performance lift over standard LLMs when integrated into IDE extensions or automated CI/CD pipelines. While smaller versions exist for low-latency autocomplete, the 34B model is the sweet spot for complex refactoring and architectural reasoning. It is built on the Llama 2 framework, ensuring compatibility with existing deployment stacks and making it a reliable choice for local or cloud-based private deployments where data privacy is paramount.
Model files and versions
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We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.
codellama/CodeLlama-34b-InstructInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model codellama/CodeLlama-34b-InstructREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model codellama/CodeLlama-34b-Instruct README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('codellama/CodeLlama-34b-Instruct')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/codellama/CodeLlama-34b-Instruct.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/codellama/CodeLlama-34b-Instruct.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
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