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Mixtral 8x7B Instruct V0.1 LimaRP ZLoss AWQ by TheBloke

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Mixtral 8x7B Instruct V0.1 LimaRP ZLoss AWQ is an open-source language model by TheBloke. Features: 6.5b LLM, VRAM: 24.7GB, Context: 32K, License: apache-2.0, MoE, Quantized, Instruction-Based, LLM Explorer Score: 0.11.

  4-bit   Autotrain compatible   Awq Base model:doctor-shotgun/mixt... Base model:quantized:doctor-sh...   Conversational   Dataset:lemonilia/limarp   En   Instruct   Mixtral   Moe   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-AWQ 

Mixtral 8x7B Instruct V0.1 LimaRP ZLoss AWQ Benchmarks

LLME Score: 0.11144
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mixtral 8x7B Instruct V0.1 LimaRP ZLoss AWQ (TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-AWQ)
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Mixtral 8x7B Instruct V0.1 LimaRP ZLoss AWQ Parameters and Internals

Model Type 
mixtral
Use Cases 
Areas:
roleplaying
Applications:
roleplaying chat
Primary Use Cases:
roleplaying chat interactions
Limitations:
known biases from niche roleplaying forums
Considerations:
Not intended for factual information or advice.
Additional Notes 
The model shows similar biases to niche roleplaying forums. It may repeat or exhibit high confidence with repeated interactions.
Supported Languages 
en (proficient)
Input Output 
Input Format:
Instruction-Input-Response
Accepted Modalities:
text
LLM NameMixtral 8x7B Instruct V0.1 LimaRP ZLoss AWQ
Repository 🤗https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-AWQ 
Model NameMixtral 8X7B Instruct v0.1 LimaRP ZLoss
Model CreatorDoctor Shotgun
Base Model(s)  Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss   Doctor-Shotgun/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss
Model Size6.5b
Required VRAM24.7 GB
Updated2025-10-05
MaintainerTheBloke
Model Typemixtral
Instruction-BasedYes
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
Licenseapache-2.0
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/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-AWQ.

Rank the Mixtral 8x7B Instruct V0.1 LimaRP ZLoss 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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