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CodeLlama 7B Instruct

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44.3KDescargas258Me gustaAug 2023Publicado16K tokensContextoCommunityLicencia56 BuenoCalidad

CodeLlama 7B Instruct (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 CodeLlama 7B Instruct on your machine.

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Quick specs

Parameters7B
Architecturedense
Context16K tokens
Modalitycode
Min RAM2.7 GB
Rec. RAM4.3 GB (Q4_K_M)
LicenseCommunity
FamilyCodeLlama
Code

About this model

Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.

  • [x] Code completion
  • [x] Instructions / chat
  • [ ] Python specialist

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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

Quality benchmarks

CodeLlama 7B Instruct benchmark scores

Benchmark verified

Coding

SWE-bench Verified
HumanEval+34.8%
Aider Polyglot
LiveCodeBench

Source: official · 2023-08-24

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 — CodeLlama 7B Instruct

Ver también