InternLM

InternVL2 8B

Name: InternVL2 8B
Rating: 81 (185 reviews)
Author: InternLM

当前

HuggingFace

468.2K下载量187点赞Jul 2024发布日期8K tokens上下文MIT许可证76 优秀质量

InternVL2 8B (8B parameters) requires approximately 8.6 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 10 GB of VRAM.

快速开始

— 复制粘贴即可本地运行

Copy-paste commands to run InternVL2 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "OpenGVLab/InternVL2-8B" \
  --hf-file "InternVL2-8B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Quick specs

Parameters8B

Architecturedense

Context8K tokens

Modalitytext+vision

Min RAM3.1 GB

Rec. RAM4.9 GB (Q4_K_M)

LicenseMIT

FamilyInternVL

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About this model

We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of instruction-tuned models, ranging from 1 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-8B model.

•For more details and evaluation reproduction, please refer to our Evaluation Guide
•We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA,...

快速推荐

最佳性价比A

Intel Arc B570 10GB~$219 — 45 tok/s

最佳综合S

RTX 3080 Ti 12GB~$1,199 — 112 tok/s

最佳硬件

InternVL2 8B 的最佳选择

运行此模型

InternVL2 8B on RTX 3080 Ti 12GB InternVL2 8B on RTX 5070 12GB InternVL2 8B on RTX 3080 12GB

量化选项

各量化级别的 VRAM 估算

No hardware detected — fit column shows raw VRAM estimates

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	—
Q3_K_S	3	3.9 GB	Low	—
NVFP4	4	4.5 GB	Medium	—
Q4_K_M	4	4.9 GB	Medium	—
Q5_K_M	5	5.8 GB	High	—
Q6_K	6	6.6 GB	High	—
Q8_0	8	8.6 GB	Very High	—
F16	16	16.4 GB	Maximum	—

硬件兼容性

全部硬件的适配估算

打开计算器

Computing compatibility...

内存详细分析

Reference: RTX 2060 6GB

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.6 GB

常见问题

FAQ — InternVL2 8B

另请参阅

量化指南评分方法打开计算器