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BigCodeLlama 92B by nisten

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  Autotrain compatible Base model:codellama/codellama... Base model:finetune:codellama/...   Code   Codegen   Conversational   Endpoints compatible   Instruct   Llama   Merge   Mergekit   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/nisten/BigCodeLlama-92b 

BigCodeLlama 92B Benchmarks

ARC: 54.78 vs 96.7 (so35)-43.4%
HellaSwag: 77.84 vs 95.3 (gpt4)-18.3%
MMLU: 55.4 vs 88.3 (so35)-37.3%
TruthfulQA: 51.34 vs 59 (gpt4)-13%
WinoGrande: 73.09 vs 87.5 (gpt4)-16.5%
GSM8K: 44.96 vs 96.4 (so35)-53.4%
LLME Score: 0.15403
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
BigCodeLlama 92B (nisten/BigCodeLlama-92b)
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BigCodeLlama 92B Parameters and Internals

Model Type 
conversational
Additional Notes 
This is an experimental 92B parameter model constructed by merging with `../CodeLlama-70b-Python-hf` and `../CodeLlama-70b-Instruct-hf` models using a specific YAML configuration. The model's type is conversational.
LLM NameBigCodeLlama 92B
Repository 🤗https://huggingface.co/nisten/BigCodeLlama-92b 
Base Model(s)  CodeLlama 70B Instruct Hf   codellama/CodeLlama-70b-Instruct-hf
Model Size70b
Required VRAM184 GB
Updated2025-09-23
Maintainernisten
Model Typellama
Instruction-BasedYes
Model Files  9.8 GB: 1-of-19   9.8 GB: 2-of-19   9.6 GB: 3-of-19   9.8 GB: 4-of-19   9.9 GB: 5-of-19   9.7 GB: 6-of-19   10.0 GB: 7-of-19   9.9 GB: 8-of-19   9.6 GB: 9-of-19   10.0 GB: 10-of-19   9.8 GB: 11-of-19   9.9 GB: 12-of-19   9.8 GB: 13-of-19   9.8 GB: 14-of-19   10.0 GB: 15-of-19   9.9 GB: 16-of-19   9.6 GB: 17-of-19   9.7 GB: 18-of-19   7.4 GB: 19-of-19
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licensemit
Context Length2048
Model Max Length2048
Transformers Version4.37.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size32016
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than nisten/BigCodeLlama-92b.

Rank the BigCodeLlama 92B Capabilities

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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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