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CodeLlama 34B 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-34b-Instruct-hf 

CodeLlama 34B Instruct Hf Benchmarks

LMSys ELO: 1058 vs 1272 (so35)-16.8%
ARC: 40.78 vs 96.7 (so35)-57.8%
HellaSwag: 35.66 vs 95.3 (gpt4)-62.6%
MMLU: 39.72 vs 88.3 (so35)-55%
TruthfulQA: 44.29 vs 59 (gpt4)-24.9%
WinoGrande: 74.51 vs 87.5 (gpt4)-14.8%
GSM8K: 31.01 vs 96.4 (so35)-67.8%
HumanEvalA: 35.09
Python Eva: 50.79 vs 92 (so35)-44.8%
Java Eval: 41.53
Javascript: 45.85
CPP Eval: 41.53
PHP Eval: 36.98
Julia Eval: 32.65
D Eval: 13.63
Lua Eval: 38.87
R Eval: 24.25
Racket Eva: 18.09
Rust Eval: 39.26
Swift Eval: 37.63
LLME Score: 0.26321
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CodeLlama 34B Instruct Hf (codellama/CodeLlama-34b-Instruct-hf)
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CodeLlama 34B Instruct Hf Parameters and Internals

Model Type 
text generation, code synthesis, instruction following
Use Cases 
Areas:
Commercial use, Research
Applications:
Code synthesis, Understanding tasks
Primary Use Cases:
Code assistant, Code generation
Limitations:
Use only in English, Potential production of inaccurate or objectionable responses
Supported Languages 
Programming Languages (Python), Human Languages (English)
Training Details 
Methodology:
Uses an optimized transformer architecture. Trained on similar data as Llama 2.
Training Time:
January 2023 - July 2023
Hardware Used:
A100-80GB GPUs
Model Architecture:
Auto-regressive language model
Safety Evaluation 
Methodologies:
Safety evaluations include tests in English.
Ethical Considerations:
Use in languages other than English may produce unpredictable results.
Responsible Ai Considerations 
Accountability:
Developers should perform safety testing tailored to specific applications.
Mitigation Strategies:
Detailed in Responsible Use Guide.
Input Output 
Input Format:
Text input only
Accepted Modalities:
text
Output Format:
Generates text only
Performance Tips:
Ensure proper safety testing and tuning before deploying applications.
LLM NameCodeLlama 34B Instruct Hf
Repository 🤗https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf 
Model Size34b
Required VRAM67.5 GB
Updated2025-09-23
Maintainercodellama
Model Typellama
Instruction-BasedYes
Model Files  9.8 GB: 1-of-7   9.7 GB: 2-of-7   9.7 GB: 3-of-7   9.7 GB: 4-of-7   9.7 GB: 5-of-7   9.7 GB: 6-of-7   9.2 GB: 7-of-7   9.8 GB: 1-of-7   9.7 GB: 2-of-7   9.7 GB: 3-of-7   9.7 GB: 4-of-7   9.7 GB: 5-of-7   9.7 GB: 6-of-7   9.4 GB: 7-of-7
Supported Languagescode
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length16384
Model Max Length16384
Transformers Version4.32.0.dev0
Tokenizer ClassCodeLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

Quantized Models of the CodeLlama 34B Instruct Hf

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VRAM
CodeLlama 34B Instruct Hf GGUF121912 GB

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Rank the CodeLlama 34B 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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Release v20241124
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