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Phixtral 4x2 8 by mlabonne

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Phixtral 4x2 8 is an open-source language model by mlabonne. Features: 7.8b LLM, VRAM: 15.7GB, License: mit, LLM Explorer Score: 0.13.

  Code Cognitivecomputations/dolphin-...   Conversational   Custom code   En   Lxuechen/phi-2-dpo   Moe   Mrm8488/phi-2-coder   Phi-msft   Region:us   Safetensors   Sharded   Tensorflow Yhyu13/phi-2-sft-dpo-gpt4 en-e...
Model Card on HF 🤗: https://huggingface.co/mlabonne/phixtral-4x2_8 

Phixtral 4x2 8 Benchmarks

LLME Score: 0.12828
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phixtral 4x2 8 (mlabonne/phixtral-4x2_8)
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Phixtral 4x2 8 Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
Research, Commercial applications
Additional Notes 
Inspired by mistralai/Mixtral-8x7B-v0.1 architecture.
Supported Languages 
en (English)
Training Details 
Methodology:
Custom version of the mergekit library (mixtral branch)
LLM NamePhixtral 4x2 8
Repository 🤗https://huggingface.co/mlabonne/phixtral-4x2_8 
Model Size7.8b
Required VRAM15.7 GB
Updated2026-03-29
Maintainermlabonne
Model Typephi-msft
Model Files  10.0 GB: 1-of-2   5.7 GB: 2-of-2
Supported Languagesen
Model ArchitecturePhiForCausalLM
Licensemit
Model Max Length2048
Transformers Version4.35.2
Tokenizer ClassCodeGenTokenizer
Padding Token<|endoftext|>
Vocabulary Size51200
Torch Data Typefloat16
Activation Functiongelu_new

Quantized Models of the Phixtral 4x2 8

Model
Likes
Downloads
VRAM
Phixtral 4x2 8 GPTQ24184 GB

Best Alternatives to Phixtral 4x2 8

Best Alternatives
Context / RAM
Downloads
Likes
Merlin1.22K / 5.6 GB2280
Merlin1.32K / 5.6 GB2560
Merlin1.52K / 5.6 GB1260
Merlin1.42K / 5.6 GB500
Phi1.5 Quantized 22K /  GB50
Tinyllama Quant2K / 0.8 GB141
Phi1.5 Update 42K /  GB60
Phi 2 Fp82K / 3.3 GB890
Phi 2 Fp82K / 3.3 GB60
Phi 2 Int8 Ov2K / 0 GB220
Note: green Score (e.g. "73.2") means that the model is better than mlabonne/phixtral-4x2_8.

Rank the Phixtral 4x2 8 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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