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Codellama 13B Instruct Nf4 Fp16 Upscaled by arnavgrg

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Codellama 13B Instruct Nf4 Fp16 Upscaled is an open-source language model by arnavgrg. Features: 13b LLM, VRAM: 26GB, Context: 16K, License: apache-2.0, Quantized, Instruction-Based, Code Generating, LLM Explorer Score: 0.1.

  Codegen   Endpoints compatible   Fp16   Instruct   Llama   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/arnavgrg/codellama-13b-instruct-nf4-fp16-upscaled 

Codellama 13B Instruct Nf4 Fp16 Upscaled Benchmarks

LLME Score: 0.10418
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Codellama 13B Instruct Nf4 Fp16 Upscaled (arnavgrg/codellama-13b-instruct-nf4-fp16-upscaled)
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Codellama 13B Instruct Nf4 Fp16 Upscaled Parameters and Internals

Model Type 
text generation
Additional Notes 
Quantization to nf4 is not lossless, and model weights for linear layers are lossy compared to the official base model.
Training Details 
Methodology:
Upscaled fp16 variant after nf4 4-bit quantization
Input Output 
Accepted Modalities:
text
Performance Tips:
Upscaling helps avoid quantization/dequantization costs for each inference pass.
LLM NameCodellama 13B Instruct Nf4 Fp16 Upscaled
Repository 🤗https://huggingface.co/arnavgrg/codellama-13b-instruct-nf4-fp16-upscaled 
Model Size13b
Required VRAM26 GB
Updated2026-05-25
Maintainerarnavgrg
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-6   5.0 GB: 2-of-6   5.0 GB: 3-of-6   4.9 GB: 4-of-6   4.9 GB: 5-of-6   1.2 GB: 6-of-6
Quantization Typefp16
Generates CodeYes
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length16384
Model Max Length16384
Transformers Version4.35.2
Tokenizer ClassCodeLlamaTokenizer
Padding Token[PAD]
Vocabulary Size32016
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than arnavgrg/codellama-13b-instruct-nf4-fp16-upscaled.

Rank the Codellama 13B Instruct Nf4 Fp16 Upscaled 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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