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Synthia MoE V3 Mixtral 8x7B 3.5bpw H6 EXL2 2 by LoneStriker

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  Autotrain compatible   Endpoints compatible   Exl2   Mixtral   Moe   Pytorch   Quantized   Region:us   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/LoneStriker/Synthia-MoE-v3-Mixtral-8x7B-3.5bpw-h6-exl2-2 

Synthia MoE V3 Mixtral 8x7B 3.5bpw H6 EXL2 2 Benchmarks

LLME Score: 0.16052
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Synthia MoE V3 Mixtral 8x7B 3.5bpw H6 EXL2 2 (LoneStriker/Synthia-MoE-v3-Mixtral-8x7B-3.5bpw-h6-exl2-2)

Synthia MoE V3 Mixtral 8x7B 3.5bpw H6 EXL2 2 Parameters and Internals

Use Cases 
Primary Use Cases:
Answering complex questions with reasoning
Additional Notes 
Model might be overfitted due to higher learning rate, which is expected to be fixed in the next release.
Training Details 
Data Sources:
Synthia-v3.0 dataset
Data Volume:
~10K super high-quality GPT-4-Turbo generated samples
Methodology:
Trained on the Orca-2 principle of replacing the system context with one message. No system context included.
LLM NameSynthia MoE V3 Mixtral 8x7B 3.5bpw H6 EXL2 2
Repository 🤗https://huggingface.co/LoneStriker/Synthia-MoE-v3-Mixtral-8x7B-3.5bpw-h6-exl2-2 
Required VRAM20.7 GB
Updated2024-12-21
MaintainerLoneStriker
Model Typemixtral
Model Files  8.6 GB: 1-of-3   8.6 GB: 2-of-3   3.5 GB: 3-of-3
Quantization Typeexl2
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/Synthia-MoE-v3-Mixtral-8x7B-3.5bpw-h6-exl2-2.

Rank the Synthia MoE V3 Mixtral 8x7B 3.5bpw H6 EXL2 2 Capabilities

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Instruction Following and Task Automation  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217
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