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A comprehensive fine-tuning framework based on Meta Code Llama, supporting code large models with parameter scales from 7B to 70B, and providing complete inference, code completion, and instruction-following capabilities
Section 01
A comprehensive fine-tuning framework based on Meta Code Llama, supporting code large models with parameter scales from 7B to 70B, and providing complete inference, code completion, and instruction-following capabilities
Section 02
Section 03
Code Llama is a family of code large language models developed by Meta based on Llama 2, offering state-of-the-art performance among open-source models. This model has code completion capabilities, supports large input contexts (up to 100,000 tokens), and zero-shot instruction-following ability. The BigCodeLLM-FT-Proj project introduced here provides developers with a minimal example for loading the Code Llama model and running inference.
Section 04
Code Llama offers multiple variants to adapt to different application scenarios:
Section 05
A general-purpose code generation model supporting multiple programming languages.
Section 06
Specially trained and optimized for Python code.
Section 07
Fine-tuned to understand and execute natural language instructions.
Section 08
| Model Scale | VRAM Requirement | Model Parallelism Value (MP) |
|---|---|---|
| 7B | ~12.55GB | 1 |
| 13B | 24GB | 2 |
| 34B | 63GB | 4 |
| 70B | 131GB | 8 |
All models are trained on 16k token sequences and show improvements on inputs up to 100,000 tokens. The 7B and 13B versions support code completion based on surrounding content.
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