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CodeLlama 7B Instruct
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18.2KDownloads258LikesAug 2023Released16K tokensContextCommunityLicense56 GoodQuality
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.
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— copy & paste to run locallyCopy-paste commands to run CodeLlama 7B Instruct on your machine.
Run
lms load CodeLlama-7b-Instruct-hf && lms server startQuick specs
Parameters7B
Architecturedense
Context16K tokens
Modalitycode
Min RAM2.7 GB
Rec. RAM4.3 GB (Q4_K_M)
LicenseCommunity
FamilyCodeLlama
✓ Code
About this model
- •[x] Code completion
- •[x] Instructions / chat
- •[ ] Python specialist
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Quantization options
VRAM estimates by quant level
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
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
Coding
SWE-bench Verified—
HumanEval+34.8%
Aider Polyglot—
LiveCodeBench—
Source: official · 2023-08-24
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom0.6 GB
Frequently asked questions
FAQ — CodeLlama 7B Instruct
See also