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Qlora Self Instruct 65B by timdettmers

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  Arxiv:2302.13971   Arxiv:2305.14314   Adapter   Finetuned   Instruct   Lora   Region:us
Model Card on HF 🤗: https://huggingface.co/timdettmers/qlora-self-instruct-65b 

Qlora Self Instruct 65B Benchmarks

LLME Score: 0.08403
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qlora Self Instruct 65B (timdettmers/qlora-self-instruct-65b)
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Qlora Self Instruct 65B Parameters and Internals

Model Type 
causal language model
Use Cases 
Areas:
research
Additional Notes 
Models utilize NormalFloat4 datatype for base model and LoRA adapters on all linear layers with BFloat16 for computation.
Training Details 
Data Sources:
Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2
Methodology:
Quantized Low-Rank Adapter (QLoRA) tuning
Model Architecture:
LoRA adapters on top of LLaMA models
Input Output 
Input Format:
Expected as text prompts formatted for dialogue.
Accepted Modalities:
text
Output Format:
Textual responses as a language model.
Performance Tips:
Loading in 16-bit may improve inference speed. Active development for efficient 4-bit inference kernels.
LLM NameQlora Self Instruct 65B
Repository 🤗https://huggingface.co/timdettmers/qlora-self-instruct-65b 
Model Size65b
Required VRAM3.2 GB
Updated2025-12-03
Maintainertimdettmers
Instruction-BasedYes
Model Files  3.2 GB
Model ArchitectureAdapter
Is Biasednone
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesgate_proj|down_proj|up_proj|q_proj|k_proj|v_proj|o_proj
LoRA Alpha16
LoRA Dropout0.05
R Param64

Rank the Qlora Self Instruct 65B 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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Release v20241124
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