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Norobara ZLoss 8x7B AWQ by TheBloke

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  4-bit   Autotrain compatible   Awq Base model:doctor-shotgun/noro... Base model:quantized:doctor-sh...   Conversational Dataset:doctor-shotgun/capybar... Dataset:doctor-shotgun/no-robo... Dataset:huggingfaceh4/no robot...   Dataset:ldjnr/capybara   Dataset:ldjnr/verified-camel Dataset:unalignment/toxic-dpo-...   En   Mixtral   Moe   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TheBloke/Norobara-ZLoss-8x7B-AWQ 

Norobara ZLoss 8x7B AWQ Benchmarks

LLME Score: 0.12718
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Norobara ZLoss 8x7B AWQ (TheBloke/Norobara-ZLoss-8x7B-AWQ)
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Norobara ZLoss 8x7B AWQ Parameters and Internals

Model Type 
mixtral
Use Cases 
Primary Use Cases:
Uncensored general instruction following model
Limitations:
No ethical alignment was applied to prevent the generation of toxic or harmful outputs.
Additional Notes 
This model was part of an experiment to create an effective uncensored instruction-following model while exploring various loss techniques. The use of toxic data indicates a focus on uncovering potential biases and limitations in model generation.
Training Details 
Data Sources:
LDJnr/Capybara, unalignment/toxic-dpo-v0.1, LDJnr/Verified-Camel, HuggingFaceH4/no_robots, Doctor-Shotgun/no-robots-sharegpt, Doctor-Shotgun/capybara-sharegpt
Methodology:
This is an experimental instruct-tuned model based on ZLoss and Megablocks-based fork of transformers. It is trained as a QLora adapter for 3 epochs using a single H100 GPU for around 13 hours.
Training Time:
13 hours
Hardware Used:
single H100 GPU
Responsible Ai Considerations 
Fairness:
The model will show biases present in the base model.
Input Output 
Input Format:
### Instruction: {system_message} ### Input: {prompt} ### Response: '
LLM NameNorobara ZLoss 8x7B AWQ
Repository 🤗https://huggingface.co/TheBloke/Norobara-ZLoss-8x7B-AWQ 
Model NameNorobara ZLoss 8X7B
Model CreatorDoctor Shotgun
Base Model(s)  Norobara ZLoss 8x7B   Doctor-Shotgun/Norobara-ZLoss-8x7B
Model Size6.5b
Required VRAM24.7 GB
Updated2025-09-23
MaintainerTheBloke
Model Typemixtral
Model Files  10.0 GB: 1-of-3   10.0 GB: 2-of-3   4.7 GB: 3-of-3
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMixtralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Norobara-ZLoss-8x7B-AWQ.

Rank the Norobara ZLoss 8x7B AWQ 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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