Phind-70b

Phind-70B is based on the CodeLlama-70B model and is fine-tuned on an additional 50 billion tokens, yielding significant improvements. It also supports a context window of 32K tokens. Phind-70B scores 82.3% on HumanEval, beating the latest GPT-4 Turbo (gpt-4-0125-preview) score of 81.1% in our evaluation. On Meta's CRUXEval dataset, Phind-70B scores 59% to GPT-4's reported score of 62% on the output prediction benchmark. However, neither of these public datasets fully captures how our users use Phind for real-world workloads. We find that Phind-70B is in the same quality realm as GPT-4 Turbo for code generation and exceeds it on some tasks. Phind-70B is also less “lazy” than GPT-4 Turbo and doesn't hesistate to generate detailed code examples. Phind-70B is significantly faster than GPT-4 Turbo, running at 80+ tokens per second to GPT-4 Turbo's ~20 tokens per second. We're able to achieve this by running NVIDIA's TensorRT-LLM library on H100 GPUs, and we're working on optimizations to f

Phind-70B is based on the CodeLlama-70B model and is fine-tuned on an additional 50 billion tokens, yielding significant improvements. It also supports a context window of 32K tokens. Phind-70B scores 82.3% on HumanEval, beating the latest GPT-4 Turbo (gpt-4-0125-preview) score of 81.1% in our evaluation. On Meta's CRUXEval dataset, Phind-70B scores 59% to GPT-4's reported score of 62% on the output prediction benchmark. However, neither of these public datasets fully captures how our users use Phind for real-world workloads. We find that Phind-70B is in the same quality realm as GPT-4 Turbo f