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InternLMInternLM

InternLM 7B

Legacy
1.8KDescargas96Me gustaJul 2023Publicado8K tokensContextoApache 2.0Licencia50 BuenoCalidad

InternLM 7B (7B parameters) requires approximately 13.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 16 GB of VRAM.

Comenzar

— copia y pega para ejecutar en local

Copy-paste commands to run InternLM 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "InternLM/InternLM-7B" \ --hf-file "InternLM-7B-Q4_K_M.gguf" \ -c 4096 -ngl 99

Quick specs

Parameters7B
Architecturedense
Context8K tokens
Modalitytext
Min RAM2.7 GB
Rec. RAM4.3 GB (Q4_K_M)
LicenseApache 2.0
FamilyInternLM
Chat Reasoning

About this model

InternLM has open-sourced a 7 billion parameter base model tailored for practical scenarios. The model has the following characteristics: - It leverages trillions of high-quality tokens for training to establish a powerful knowledge base. - It provides a versatile toolset for users to flexibly build their own workflows.

  • It leverages trillions of high-quality tokens for training to establish a powerful knowledge base
  • It provides a versatile toolset for users to flexibly build their own workflows

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Opciones de cuantización

Estimaciones de VRAM por nivel de cuantización

No hardware detected — fit column shows raw VRAM estimates

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
Low
Q3_K_S
3
3.4 GB
Low
NVFP4
4
3.9 GB
Medium
Q4_K_M
4
4.3 GB
Medium
Q5_K_M
5
5.0 GB
High
Q6_K
6
5.7 GB
High
Q8_0
8
7.5 GB
Very High
F16
16
14.3 GB
Maximum

Compatibilidad de hardware

Estimaciones de encaje en todo el hardware

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Computing compatibility...

Desglose de memoria

Reference: RTX 2060 6GB

Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom0.6 GB

Preguntas frecuentes

FAQ — InternLM 7B

Ver también