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Neural Chat 7B V3.3 by Intel

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Neural Chat 7B V3.3 is an open-source language model by Intel. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, HF Score: 69.8, LLM Explorer Score: 0.23, Arc: 66.9, HellaSwag: 85.3, MMLU: 63.1, TruthfulQA: 63, WinoGrande: 79.6, GSM8K: 61.1.

  Arxiv:2309.12284   Autotrain compatible Base model:finetune:intel/neur... Base model:intel/neural-chat-7...   Conversational   Endpoints compatible   Intel   Math   Mistral   Model-index   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Intel/neural-chat-7b-v3-3 

Neural Chat 7B V3.3 Benchmarks

MMLU Pro: 18.05
GPQA: 5.26
MUSR: 20.58
BBH: 27.75
IFEval: 47.63 vs 88 (so35)-45.9%
ARC: 66.89 vs 96.7 (so35)-30.8%
HellaSwag: 85.26 vs 95.3 (gpt4)-10.5%
MMLU: 63.07 vs 88.3 (so35)-28.6%
TruthfulQA: 63.01 vs 59 (gpt4)6.8%
WinoGrande: 79.64 vs 87.5 (gpt4)-9%
GSM8K: 61.11 vs 96.4 (so35)-36.6%
MATH Lvl 5: 4.08
LLME Score: 0.23496
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 (Intel/neural-chat-7b-v3-3)
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Neural Chat 7B V3.3 Parameters and Internals

Model Type 
Large Language Model
Use Cases 
Areas:
Research, Commercial applications
Applications:
Inference on language-related tasks
Primary Use Cases:
Language-related tasks
Limitations:
May produce factually incorrect or biased outputs, requires fine-tuning for specific tasks
Additional Notes 
The model should not be used to intentionally create hostile or alienating environments.
Training Details 
Data Sources:
meta-math/MetaMathQA
Methodology:
Aligned using Direct Performance Optimization (DPO) method with Intel/orca_dpo_pairs
Context Length:
8192
Hardware Used:
Intel Gaudi 2 processor (8 cards)
Model Architecture:
Fine-tuned 7B parameter LLM on the Intel Gaudi 2 processor
Safety Evaluation 
Methodologies:
Safety testing before deployment
Ethical Considerations:
Can produce factually incorrect output and biases, risk of generating offensive outputs. Developers should perform safety testing.
Input Output 
Performance Tips:
Model performance varies based on inputs.
LLM NameNeural Chat 7B V3.3
Repository 🤗https://huggingface.co/Intel/neural-chat-7b-v3-3 
Base Model(s)  Neural Chat 7B V3.1   Intel/neural-chat-7b-v3-1
Model Size7b
Required VRAM14.4 GB
Updated2025-09-23
MaintainerIntel
Model Typemistral
Model Files  9.9 GB: 1-of-2   4.5 GB: 2-of-2   9.9 GB: 1-of-2   4.5 GB: 2-of-2
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.34.0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

Quantized Models of the Neural Chat 7B V3.3

Model
Likes
Downloads
VRAM
Neural Chat 7B V3.3 GGUF01713 GB
Neural Chat 7B V3.3 GGUF144933 GB
Neural Chat 7B V3.3 GPTQ154 GB
Neural Chat 7B V3.3 GPTQ3274 GB
Neural Chat 7B V3.3 AWQ054 GB

Best Alternatives to Neural Chat 7B V3.3

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Context / RAM
Downloads
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...Nemo Instruct 2407 Abliterated1000K / 24.5 GB25420
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SpydazWeb AI HumanAI RP512K / 14.4 GB141
SpydazWeb AI HumanAI 002512K / 14.4 GB181
...daz Web AI ChatML 512K Project512K / 14.5 GB120
MegaBeam Mistral 7B 300K282K / 14.4 GB377916
MegaBeam Mistral 7B 300K282K / 14.4 GB785016
Hebrew Mistral 7B 200K256K / 30 GB125115
Astral 256K 7B V2250K / 14.4 GB50
Astral 256K 7B250K / 14.4 GB50
Note: green Score (e.g. "73.2") means that the model is better than Intel/neural-chat-7b-v3-3.

Rank the Neural Chat 7B V3.3 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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