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CodeLlama 7B Instruct Hf by codellama

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  Arxiv:2308.12950   Autotrain compatible   Code   Codegen   Conversational   Endpoints compatible   Instruct   Llama   Llama2   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf 

CodeLlama 7B Instruct Hf Benchmarks

ARC: 36.52 vs 96.7 (so35)-62.2%
HellaSwag: 55.44 vs 95.3 (gpt4)-41.8%
MMLU: 34.54 vs 88.3 (so35)-60.9%
TruthfulQA: 41.25 vs 59 (gpt4)-30.1%
WinoGrande: 64.56 vs 87.5 (gpt4)-26.2%
GSM8K: 7.96 vs 96.4 (so35)-91.7%
HumanEvalA: 26.45
Python Eva: 45.65 vs 92 (so35)-50.4%
Java Eval: 28.77
Javascript: 33.11
CPP Eval: 29.03
PHP Eval: 28.55
Julia Eval: 27.58
D Eval: 11.81
Lua Eval: 30.47
R Eval: 19.7
Racket Eva: 11.81
Rust Eval: 24.27
Swift Eval: 26.66
LLME Score: 0.21699
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CodeLlama 7B Instruct Hf (codellama/CodeLlama-7b-Instruct-hf)
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CodeLlama 7B Instruct Hf Parameters and Internals

Model Type 
generative, text-generation
Use Cases 
Areas:
Commercial, Research
Applications:
Code synthesis, Code understanding, Code assistant and generation applications
Primary Use Cases:
General code synthesis, Python programming, Instruction following
Limitations:
Output in languages other than English is not recommended., Use in any manner that violates applicable laws or regulations is prohibited.
Considerations:
Developers should perform safety testing and tuning tailored to their specific applications.
Additional Notes 
This is a static model trained on an offline dataset.
Supported Languages 
primary_language (English), specialties (Programming languages, primarily Python)
Training Details 
Data Sources:
Same data as Llama 2
Methodology:
Trained with custom libraries on Meta’s Research Super Cluster
Hardware Used:
A100-80GB GPUs
Model Architecture:
Auto-regressive language model using an optimized transformer architecture
Input Output 
Input Format:
Text
Accepted Modalities:
Text
Output Format:
Text
LLM NameCodeLlama 7B Instruct Hf
Repository 🤗https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf 
Model Size7b
Required VRAM13.5 GB
Updated2025-10-19
Maintainercodellama
Model Typellama
Instruction-BasedYes
Model Files  10.0 GB: 1-of-2   3.5 GB: 2-of-2   4.9 GB: 1-of-3   5.0 GB: 2-of-3   3.6 GB: 3-of-3
Supported Languagescode
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length16384
Model Max Length16384
Transformers Version4.33.0.dev0
Tokenizer ClassCodeLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32016
Torch Data Typebfloat16

Quantized Models of the CodeLlama 7B Instruct Hf

Model
Likes
Downloads
VRAM
...B Instruct Hf Bnb 4bit Smashed064 GB

Best Alternatives to CodeLlama 7B Instruct Hf

Best Alternatives
Context / RAM
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Deepseek Coder 6.7B Instruct16K / 13.5 GB51597448
GetCode Slerp16K / 13.6 GB7351
CodeLlama 7B Instruct Hf16K / 13.5 GB360753
Stack Codellama 7B Inst16K / 13.5 GB60
CodeV CL 7B16K / 13.5 GB1361
Glaive Coder 7B16K / 27 GB190855
Cdlm 7 Ko Nl2sql V1.016K / 13.5 GB7274
...ama 7B Instruct Hf Dequantized16K / 13.5 GB1970
...deLlama 7B Instruct Hf 8bits Q16K / 4.2 GB51
Llama 2 7B Chat Finetune516K / 13.8 GB51
Note: green Score (e.g. "73.2") means that the model is better than codellama/CodeLlama-7b-Instruct-hf.

Rank the CodeLlama 7B Instruct Hf Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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