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InternVL2 8B

Atual
468.2KDownloads187CurtidasJul 2024Publicado8K tokensContextoMITLicença76 ForteQualidade

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.

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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
Vision Chat

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,...

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Opções de quantização

Estimativas de VRAM por nível de quantização

No hardware detected — fit column shows raw VRAM estimates

QuantBitsVRAMQualityFit
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

Compatibilidade de hardware

Estimativas de compatibilidade para todo o hardware

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Computing compatibility...

Detalhamento de memória

Reference: RTX 2060 6GB

Weights4.9 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom0.6 GB

Perguntas frequentes

FAQ — InternVL2 8B

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