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Neural Chat 7B V3.1 AWQ by TheBloke

 »  All LLMs  »  TheBloke  »  Neural Chat 7B V3.1 AWQ   URL Share it on

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  4-bit   Autotrain compatible   Awq Base model:intel/neural-chat-7... Base model:quantized:intel/neu...   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/neural-chat-7B-v3-1-AWQ 

Neural Chat 7B V3.1 AWQ Benchmarks

LLME Score: 0.17028
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Neural Chat 7B V3.1 AWQ (TheBloke/neural-chat-7B-v3-1-AWQ)

Neural Chat 7B V3.1 AWQ Parameters and Internals

Model Type 
mistral
Additional Notes 
neural-chat-7b-v3-1 can produce factually incorrect output and might generate lewd, biased or offensive content.
Training Details 
Data Sources:
Open-Orca/SlimOrca
Methodology:
fine-tuning based on mistralai/Mistral-7B-v0.1
Input Output 
Input Format:
### System: {system_message} ### User: {prompt} ### Assistant:
LLM NameNeural Chat 7B V3.1 AWQ
Repository 🤗https://huggingface.co/TheBloke/neural-chat-7B-v3-1-AWQ 
Model NameNeural Chat 7B v3-1
Model CreatorIntel
Base Model(s)  Neural Chat 7B V3.1   Intel/neural-chat-7b-v3-1
Model Size7b
Required VRAM4.2 GB
Updated2025-02-22
MaintainerTheBloke
Model Typemistral
Model Files  4.2 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.35.0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

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Rank the Neural Chat 7B V3.1 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 v20241227
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