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CodeLlama 34B Python Fp16 by TheBloke

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  Autotrain compatible   Codegen   Codellama   Custom code   Endpoints compatible   Fp16   Llama   Llama2   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TheBloke/CodeLlama-34B-Python-fp16 

CodeLlama 34B Python Fp16 Benchmarks

ARC: 38.14 vs 96.7 (so35)-60.6%
HellaSwag: 34.8 vs 95.3 (gpt4)-63.5%
MMLU: 32.95 vs 88.3 (so35)-62.7%
TruthfulQA: 43.57 vs 59 (gpt4)-26.2%
WinoGrande: 72.14 vs 87.5 (gpt4)-17.6%
GSM8K: 20.02 vs 96.4 (so35)-79.2%
HumanEvalA: 33.87
Python Eva: 53.29 vs 92 (so35)-42.1%
Java Eval: 39.46
Javascript: 44.72
CPP Eval: 39.09
PHP Eval: 39.78
Julia Eval: 31.37
D Eval: 17.29
Lua Eval: 31.9
R Eval: 22.35
Racket Eva: 13.19
Rust Eval: 39.67
Swift Eval: 34.3
LLME Score: 0.15766
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CodeLlama 34B Python Fp16 (TheBloke/CodeLlama-34B-Python-fp16)
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CodeLlama 34B Python Fp16 Parameters and Internals

Model Type 
text generation, code synthesis
Use Cases 
Areas:
Commercial, Research
Applications:
Code synthesis, Code understanding, Python specific tasks
Primary Use Cases:
Commercial code generation, Research projects in programming and AI
Limitations:
Not for non-English languages, Should not violate laws or regulations
Considerations:
Adhere to legal compliances.
Additional Notes 
Intended for Python code tasks; requires specific handling for different operations.
Supported Languages 
English (proficient), Python (proficient)
Training Details 
Data Sources:
Same data as Llama 2 with different weights
Methodology:
Fine-tuned with additional instruction data for CodeLlama - Instruct
Context Length:
100000
Training Time:
January 2023 - July 2023
Hardware Used:
Meta’s Research Super Cluster, A100-80GB GPUs
Model Architecture:
Autoregressive language model using optimized transformer architectures
Responsible Ai Considerations 
Fairness:
Tested primarily in English.
Transparency:
Model's potential outputs cannot be predicted in advance.
Accountability:
Developers should perform safety testing and tuning tailored to their specific applications before deployment.
Mitigation Strategies:
Please see the Responsible Use Guide for more details.
Input Output 
Input Format:
Text only
Accepted Modalities:
text
Output Format:
Text only
Performance Tips:
Use 'trust_remote_code=True' for optimal operation due to a change in the RoPE Theta value
LLM NameCodeLlama 34B Python Fp16
Repository 🤗https://huggingface.co/TheBloke/CodeLlama-34B-Python-fp16 
Base Model(s)  CodeLlama 34B Python Hf   codellama/CodeLlama-34b-Python-hf
Model Size34b
Required VRAM67.5 GB
Updated2025-10-08
MaintainerTheBloke
Model Typellama
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
Quantization Typefp16
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length16384
Model Max Length16384
Transformers Version4.32.0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

Best Alternatives to CodeLlama 34B Python Fp16

Best Alternatives
Context / RAM
Downloads
Likes
CodeLlama 34B Instruct Fp1616K / 67.5 GB28196
Codellama Extraction16K / 67.6 GB50
Phind Codellama 34B V2 EXL216K /  GB616
Codellama 34B Bnb 4bit16K / 18.2 GB10874
CodeLlama 34B Instruct Hf 4bit16K / 19.4 GB382
CodeLlama 34B Fp1616K / 67.5 GB74
XwinCoder 34B 4.0bpw H6 EXL216K / 17.4 GB61
...Codellama 34B V2 Megacode EXL216K /  GB710
...gpt 32K Codellama 34B Instruct32K / 67.5 GB892
CodeLlama 34B Instruct Hf16K / 67.5 GB12225298
Note: green Score (e.g. "73.2") means that the model is better than TheBloke/CodeLlama-34B-Python-fp16.

Rank the CodeLlama 34B Python Fp16 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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