Meta-Llama-3.1-8B-Instruct-FP8-dynamic

Model overview

This model is a quantized version of Meta-Llama-3.1-8B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. Meta-Llama-3.1-8B-Instruct-FP8-dynamic achieves 105.4% recovery for the Arena-Hard evaluation, 99.7% for OpenLLM v1 (using Meta's prompting when available), 101.2% for OpenLLM v2, 100.0% for HumanEval pass@1, and 101.0% for HumanEval+ pass@1. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis. LLM Compressor is used for quantization.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：llama3.1。模型介绍与第三方评测均以原来源的具体版本和方法为准。