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MultiVerse 70B AWQ by MTSAIR

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  4-bit   Autotrain compatible   Awq   Conversational   Endpoints compatible   Llama   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/MTSAIR/MultiVerse_70B_AWQ 

MultiVerse 70B AWQ Benchmarks

ARC: 78.58 vs 96.7 (so35)-18.7%
HellaSwag: 89.74 vs 95.3 (gpt4)-5.8%
MMLU: 78.27 vs 88.3 (so35)-11.4%
TruthfulQA: 75.09 vs 59 (gpt4)27.3%
WinoGrande: 87.37 vs 87.5 (gpt4)-0.1%
GSM8K: 76.8 vs 96.4 (so35)-20.3%
LLME Score: 0.14652
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MultiVerse 70B AWQ (MTSAIR/MultiVerse_70B_AWQ)
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MultiVerse 70B AWQ Parameters and Internals

Additional Notes 
The multiverse training method is not related to the multiverse paper; it's a new technique with potential for publication.
Training Details 
Methodology:
(AWQ quantization method I)
LLM NameMultiVerse 70B AWQ
Repository 🤗https://huggingface.co/MTSAIR/MultiVerse_70B_AWQ 
Base Model(s)  MultiVerse 70B   MTSAIR/MultiVerse_70B
Model Size70b
Required VRAM41.3 GB
Updated2025-10-13
MaintainerMTSAIR
Model Typellama
Model Files  5.0 GB: 1-of-9   5.0 GB: 2-of-9   5.0 GB: 3-of-9   5.0 GB: 4-of-9   5.0 GB: 5-of-9   5.0 GB: 6-of-9   5.0 GB: 7-of-9   3.8 GB: 8-of-9   2.5 GB: 9-of-9
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.39.3
Tokenizer ClassGPT2Tokenizer
Padding Token<
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than MTSAIR/MultiVerse_70B_AWQ.

Rank the MultiVerse 70B AWQ Capabilities

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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