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Mixtral 8x22B Instruct V0.1 FP8 V1 by comaniac

 »  All LLMs  »  comaniac  »  Mixtral 8x22B Instruct V0.1 FP8 V1   URL Share it on

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  Autotrain compatible   Conversational   Endpoints compatible   Fp8   Instruct   Mixtral   Moe   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/comaniac/Mixtral-8x22B-Instruct-v0.1-FP8-v1 

Mixtral 8x22B Instruct V0.1 FP8 V1 Benchmarks

LLME Score: 0.18468
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mixtral 8x22B Instruct V0.1 FP8 V1 (comaniac/Mixtral-8x22B-Instruct-v0.1-FP8-v1)

Mixtral 8x22B Instruct V0.1 FP8 V1 Parameters and Internals

Training Details 
Data Sources:
Ultrachat (mgoin/ultrachat_2k)
Context Length:
8192
Model Architecture:
Weights and activations are per-tensor quantized to float8_e4m3; Quantization with AutoFP8
LLM NameMixtral 8x22B Instruct V0.1 FP8 V1
Repository 🤗https://huggingface.co/comaniac/Mixtral-8x22B-Instruct-v0.1-FP8-v1 
Model Size140.6b
Required VRAM140.9 GB
Updated2025-03-12
Maintainercomaniac
Model Typemixtral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-29   4.9 GB: 2-of-29   4.9 GB: 3-of-29   4.9 GB: 4-of-29   4.9 GB: 5-of-29   4.9 GB: 6-of-29   4.9 GB: 7-of-29   4.9 GB: 8-of-29   4.9 GB: 9-of-29   4.9 GB: 10-of-29   4.9 GB: 11-of-29   4.9 GB: 12-of-29   4.9 GB: 13-of-29   4.9 GB: 14-of-29   4.9 GB: 15-of-29   4.9 GB: 16-of-29   4.9 GB: 17-of-29   4.9 GB: 18-of-29   4.9 GB: 19-of-29   4.9 GB: 20-of-29   5.0 GB: 21-of-29   5.0 GB: 22-of-29   5.0 GB: 23-of-29   4.9 GB: 24-of-29   4.9 GB: 25-of-29   4.9 GB: 26-of-29   4.9 GB: 27-of-29   4.9 GB: 28-of-29   3.4 GB: 29-of-29
Model ArchitectureMixtralForCausalLM
Context Length65536
Model Max Length65536
Transformers Version4.40.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size32768
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than comaniac/Mixtral-8x22B-Instruct-v0.1-FP8-v1.

Rank the Mixtral 8x22B Instruct V0.1 FP8 V1 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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