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Neural Chat 7B V3.3 GPTQ by TheBloke

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Neural Chat 7B V3.3 GPTQ is an open-source language model by TheBloke. Features: 7b LLM, VRAM: 4.2GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.1.

  4-bit Base model:intel/neural-chat-7... Base model:quantized:intel/neu...   Gptq   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/neural-chat-7B-v3-3-GPTQ 

Neural Chat 7B V3.3 GPTQ Benchmarks

LLME Score: 0.10314
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 V3.3 GPTQ (TheBloke/neural-chat-7B-v3-3-GPTQ)
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Neural Chat 7B V3.3 GPTQ Parameters and Internals

Model Type 
mistral
Use Cases 
Applications:
Language-related tasks inference
Limitations:
Model should be fine-tuned for specific tasks; cannot be used to create hostile environments
Additional Notes 
Know compatible clients/servers available for running models inferences.
Training Details 
Data Sources:
meta-math/MetaMathQA, Intel/orca_dpo_pairs
Methodology:
Aligned using Direct Performance Optimization (DPO) method
Hardware Used:
Intel Gaudi 2 processor
Model Architecture:
Fine-tuned from Mistral-7B-v-0.1
Input Output 
Input Format:
### System: {system_message} ### User: {prompt} ### Assistant:
Accepted Modalities:
text
LLM NameNeural Chat 7B V3.3 GPTQ
Repository 🤗https://huggingface.co/TheBloke/neural-chat-7B-v3-3-GPTQ 
Model NameNeural Chat 7B V3-3
Model CreatorIntel
Base Model(s)  Neural Chat 7B V3.3   Intel/neural-chat-7b-v3-3
Model Size7b
Required VRAM4.2 GB
Updated2026-06-26
MaintainerTheBloke
Model Typemistral
Model Files  4.2 GB
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.0.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/neural-chat-7B-v3-3-GPTQ.

Rank the Neural Chat 7B V3.3 GPTQ 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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