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CodeLlama 70B Instruct Hf by codellama

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CodeLlama 70B Instruct Hf is an open-source language model by codellama. Features: 70b LLM, VRAM: 72.3GB, Context: 4K, License: llama2, Instruction-Based, Code Generating, HF Score: 60, LLM Explorer Score: 0.17, Arc: 55, HellaSwag: 77.2, MMLU: 56.4, TruthfulQA: 50.4, WinoGrande: 74.5, GSM8K: 46.3, HumanEval: 75.6.

  Arxiv:2308.12950   Code   Codegen   Conversational   Endpoints compatible   Instruct   Llama   Llama2   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf 

CodeLlama 70B Instruct Hf Benchmarks

ARC: 55.03 vs 96.7 (so35)-43.1%
HellaSwag: 77.24 vs 95.3 (gpt4)-19%
MMLU: 56.4 vs 88.3 (so35)-36.1%
TruthfulQA: 50.44 vs 59 (gpt4)-14.5%
WinoGrande: 74.51 vs 87.5 (gpt4)-14.8%
GSM8K: 46.25 vs 96.4 (so35)-52%
HumanEvalA: 42.64
Python Eva: 75.6 vs 92 (so35)-17.8%
Java Eval: 47.2
Javascript: 57.76
CPP Eval: 48.45
PHP Eval: 57.14
Julia Eval: 42.24
D Eval: 19.88
Lua Eval: 44.1
R Eval: 29.19
Rust Eval: 47.2
Swift Eval: 42.86
LLME Score: 0.16879
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CodeLlama 70B Instruct Hf (codellama/CodeLlama-70b-Instruct-hf)
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CodeLlama 70B Instruct Hf Parameters and Internals

Model Type 
text-generation, code synthesis
Use Cases 
Areas:
Commercial use, Research
Primary Use Cases:
Code synthesis and understanding, Python programming, Instruction following
Considerations:
Use in English and relevant programming languages.
Supported Languages 
English (General proficiency, focused on code synthesis and understanding)
Training Details 
Context Length:
16000
Hardware Used:
Meta’s Research Super Cluster, A100-80GB GPUs
Model Architecture:
Auto-regressive language model with an optimized transformer architecture.
Input Output 
Input Format:
Text only
Output Format:
Text only
LLM NameCodeLlama 70B Instruct Hf
Repository 🤗https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf 
Model Size70b
Required VRAM72.3 GB
Updated2026-04-11
Maintainercodellama
Model Typellama
Instruction-BasedYes
Model Files  4.7 GB: 1-of-29   4.7 GB: 2-of-29   5.0 GB: 3-of-29   5.0 GB: 4-of-29   4.7 GB: 5-of-29   4.7 GB: 6-of-29   4.7 GB: 7-of-29   5.0 GB: 8-of-29   5.0 GB: 9-of-29   4.7 GB: 10-of-29   4.7 GB: 11-of-29   4.7 GB: 12-of-29   5.0 GB: 13-of-29   5.0 GB: 14-of-29   4.7 GB: 15-of-29   4.7 GB: 16-of-29   4.7 GB: 17-of-29   5.0 GB: 18-of-29   5.0 GB: 19-of-29   4.7 GB: 20-of-29   4.7 GB: 21-of-29   4.7 GB: 22-of-29   5.0 GB: 23-of-29   5.0 GB: 24-of-29   4.7 GB: 25-of-29   4.7 GB: 26-of-29   4.7 GB: 27-of-29   5.0 GB: 28-of-29   3.8 GB: 29-of-29   4.7 GB: 1-of-29   4.7 GB: 2-of-29   5.0 GB: 3-of-29   5.0 GB: 4-of-29   4.7 GB: 5-of-29   4.7 GB: 6-of-29   4.7 GB: 7-of-29   5.0 GB: 8-of-29   5.0 GB: 9-of-29   4.7 GB: 10-of-29   4.7 GB: 11-of-29   4.7 GB: 12-of-29   5.0 GB: 13-of-29   5.0 GB: 14-of-29   4.7 GB: 15-of-29
Supported Languagescode
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.37.1
Vocabulary Size32016
Torch Data Typebfloat16

Quantized Models of the CodeLlama 70B Instruct Hf

Model
Likes
Downloads
VRAM
CodeLlama 70B Instruct GGUF60215925 GB
CodeLlama 70B Instruct AWQ1310436 GB
CodeLlama 70B Instruct Hf GGUF215025 GB
CodeLlama 70B Instruct GPTQ143235 GB

Best Alternatives to CodeLlama 70B Instruct Hf

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Context / RAM
Downloads
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CodeLlama 70B Instruct Hf4K / 72.3 GB36224
Code Llama 70B Python Instruct4K / 138.1 GB51
CodeLlama 70B Instruct Neuron4K /  GB121
BigCodeLlama 92B2K / 184 GB910
...Llama 70B Instruct Hf 4bit MLX4K / 39.1 GB33725
...70B Instruct Nf4 Fp16 Upscaled4K / 138.7 GB12
...70B Instruct Hf 5.0bpw H6 EXL22K / 43.6 GB36
...0B Instruct Hf 2.65bpw H6 EXL22K / 23.4 GB33
...70B Instruct Hf 2.4bpw H6 EXL22K / 21.3 GB11
...70B Instruct Hf 4.0bpw H6 EXL22K / 35.1 GB01
Note: green Score (e.g. "73.2") means that the model is better than codellama/CodeLlama-70b-Instruct-hf.

Rank the CodeLlama 70B 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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