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Codallama 7B Instruct Nf4 Fp16 Upscaled by arnavgrg

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Codallama 7B Instruct Nf4 Fp16 Upscaled Benchmarks

LLME Score: 0.16244
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Codallama 7B Instruct Nf4 Fp16 Upscaled Parameters and Internals

Model Type 
text generation, inference
Additional Notes 
Quantization operation to nf4 is not lossless; model weights for linear layers are lossy
Training Details 
Methodology:
Upscaled fp16 variant with nf4 4-bit quantization
Model Architecture:
Linear4bit layers upscaled to fp16
Input Output 
Accepted Modalities:
text
Performance Tips:
Upscaling linear4bit layers to fp16 reduces overhead from quantization/dequantization
LLM NameCodallama 7B Instruct Nf4 Fp16 Upscaled
Repository 🤗https://huggingface.co/arnavgrg/codallama-7b-instruct-nf4-fp16-upscaled 
Model Size7b
Required VRAM13.5 GB
Updated2024-11-21
Maintainerarnavgrg
Model Typellama
Instruction-BasedYes
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   3.6 GB: 3-of-3
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
Codallama 7B Instruct Nf4 Fp16 Upscaled (arnavgrg/codallama-7b-instruct-nf4-fp16-upscaled)

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Note: green Score (e.g. "73.2") means that the model is better than arnavgrg/codallama-7b-instruct-nf4-fp16-upscaled.

Rank the Codallama 7B 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241110
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