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Notux 8x7b V1 by argilla

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  Autotrain compatible Base model:finetune:mistralai/... Base model:mistralai/mixtral-8...   Conversational Dataset:argilla/ultrafeedback-...   De   Dpo   En   Endpoints compatible   Es   Fr   Instruct   It   Mixtral   Moe   Preference   Region:us   Rlaif   Safetensors   Sharded   Tensorboard   Tensorflow   Ultrafeedback
Model Card on HF 🤗: https://huggingface.co/argilla/notux-8x7b-v1 

Notux 8x7b V1 Benchmarks

MMLU Pro: 29.56
GPQA: 7.83
MUSR: 10.53
BBH: 34.76
IFEval: 54.22 vs 88 (so35)-38.4%
ARC: 70.65 vs 96.7 (so35)-26.9%
HellaSwag: 87.72 vs 95.3 (gpt4)-8%
MMLU: 71.39 vs 88.3 (so35)-19.2%
TruthfulQA: 66.21 vs 59 (gpt4)12.2%
WinoGrande: 80.74 vs 87.5 (gpt4)-7.7%
GSM8K: 61.11 vs 96.4 (so35)-36.6%
MATH Lvl 5: 9.97
LLME Score: 0.23827
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Notux 8x7b V1 (argilla/notux-8x7b-v1)

Notux 8x7b V1 Parameters and Internals

Model Type 
Pretrained generative Sparse Mixture of Experts
Supported Languages 
en (N/A), de (N/A), es (N/A), fr (N/A), it (N/A)
Training Details 
Data Sources:
argilla/ultrafeedback-binarized-preferences-cleaned
Methodology:
Preference tuning using Direct Preference Optimization (DPO) and distilled DPO (dDPO).
Training Time:
~10hr
Hardware Used:
8 x H100 80GB GPUs hosted in runpod.io
LLM NameNotux 8x7b V1
Repository 🤗https://huggingface.co/argilla/notux-8x7b-v1 
Base Model(s)  mistralai/Mixtral-8x7B-Instruct-v0.1   mistralai/Mixtral-8x7B-Instruct-v0.1
Model Size46.7b
Required VRAM93.6 GB
Updated2025-03-22
Maintainerargilla
Model Typemixtral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-19   5.0 GB: 2-of-19   5.0 GB: 3-of-19   4.9 GB: 4-of-19   5.0 GB: 5-of-19   5.0 GB: 6-of-19   4.9 GB: 7-of-19   5.0 GB: 8-of-19   5.0 GB: 9-of-19   4.9 GB: 10-of-19   5.0 GB: 11-of-19   5.0 GB: 12-of-19   5.0 GB: 13-of-19   4.9 GB: 14-of-19   5.0 GB: 15-of-19   5.0 GB: 16-of-19   4.9 GB: 17-of-19   5.0 GB: 18-of-19   4.2 GB: 19-of-19   0.0 GB
Supported Languagesen de es fr it
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

Quantized Models of the Notux 8x7b V1

Model
Likes
Downloads
VRAM
Notux 8x7b V1 GGUF564115 GB
Notux 8x7b V1 GGUF07617 GB
Notux 8x7b V1 AWQ31624 GB
Notux 8x7b V1 GPTQ2823 GB

Best Alternatives to Notux 8x7b V1

Best Alternatives
Context / RAM
Downloads
Likes
Mixtral 8x7B Instruct V0.132K / 93.6 GB5354074352
Mixtral 8x7B Instruct V0.1 FP832K / 47.1 GB15870
...rkrautLM Mixtral 8x7B Instruct32K / 93.6 GB219922
Merge Mixtral Prometheus 8x7B32K / 91.9 GB72
Dolphin 2.5 Mixtral 8x7b32K / 93.6 GB64571231
BagelMIsteryTour V2 8x7B32K / 93.5 GB2316
Karakuri Lm 8x7b Instruct V0.132K / 93.6 GB63519
Autotrain Xva0j Mixtral8x7b32K / 93.6 GB50
Chinese Mixtral Instruct32K / 93.6 GB169420
XLAM V0.1 R32K / 93.6 GB19553
Note: green Score (e.g. "73.2") means that the model is better than argilla/notux-8x7b-v1.

Rank the Notux 8x7b V1 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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Release v20241227
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