Phind-CodeLlama-34B-v1

Phind-CodeLlama-34B-v1

Phind

A powerful 34B parameter code generation model fine-tuned from CodeLlama, achieving 67.6% pass@1 on HumanEval, matching GPT-4's performance. Trained on 80k programming problems.

PropertyValue
LicenseLlama 2
Training Infrastructure32x A100-80GB GPUs
Training Time90 GPU-hours
HumanEval Score67.6% pass@1

What is Phind-CodeLlama-34B-v1?

Phind-CodeLlama-34B-v1 is a sophisticated code generation model that represents a significant advancement in AI-powered programming assistance. Fine-tuned from CodeLlama-34B, this model matches GPT-4's performance on the HumanEval benchmark, achieving an impressive 67.6% pass@1 rate. The model was trained on a proprietary dataset of approximately 80,000 high-quality programming problems and solutions, focusing on instruction-answer pairs rather than traditional code completion examples.

Implementation Details

The model utilizes state-of-the-art training techniques, including DeepSpeed ZeRO 3 and Flash Attention 2, enabling efficient training on 32 A100-80GB GPUs in just three hours. The training process involved a sequence length of 4096 tokens and a complete native finetune without using LoRA adaptations.

  • Native finetune implementation without LoRA
  • Trained for 2 epochs (~160k total examples)
  • Utilizes DeepSpeed ZeRO 3 and Flash Attention 2
  • 4096 token sequence length

Core Capabilities

  • High-performance code generation matching GPT-4's capabilities
  • Instruction-tuned for programming tasks
  • Efficient processing of programming problems and solutions
  • Supports various programming languages with focus on Python

Frequently Asked Questions

Q: What makes this model unique?

The model's distinctive feature is its ability to match GPT-4's performance on HumanEval while being specifically optimized for code generation tasks. It's trained on a carefully curated dataset of programming problems and uses advanced training techniques for optimal performance.

Q: What are the recommended use cases?

The model excels at code generation tasks and is particularly well-suited for programming assistance, code completion, and solving programming problems. It's recommended to use simple prompts followed by "\n: " rather than complex chat markup.

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