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Danube2 Upscale 1.7 by Lambent

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  Arxiv:2203.05482   Autotrain compatible Base model:lambent/danube2-ups... Base model:lambent/danube2-ups... Base model:lambent/danube2-ups... Base model:lambent/danube2-ups... Base model:merge:lambent/danub... Base model:merge:lambent/danub... Base model:merge:lambent/danub... Base model:merge:lambent/danub... Dataset:huggingfacetb/cosmoped... Dataset:nampdn-ai/tiny-bridged... Dataset:severian/internal-know... Dataset:severian/internal-know... Dataset:sordonia/redpajama-sam... Dataset:teknium/gpteacher-gene... Dataset:vezora/tested-22k-pyth...   Endpoints compatible   Merge   Mergekit   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Lambent/danube2-upscale-1.7 

Danube2 Upscale 1.7 Benchmarks

ARC: 43.34 vs 96.7 (so35)-55.2%
HellaSwag: 68.96 vs 95.3 (gpt4)-27.6%
MMLU: 40.38 vs 88.3 (so35)-54.3%
TruthfulQA: 42.2 vs 59 (gpt4)-28.5%
WinoGrande: 64.25 vs 87.5 (gpt4)-26.6%
GSM8K: 11.3 vs 96.4 (so35)-88.3%
LLME Score: 0.22088
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Danube2 Upscale 1.7 (Lambent/danube2-upscale-1.7)

Danube2 Upscale 1.7 Parameters and Internals

Model Type 
pre-trained, language model
Additional Notes 
Training methodology is experimental, not easy to replicate.
Training Details 
Data Sources:
HuggingFaceTB/cosmopedia-100k, Vezora/Tested-22k-Python-Alpaca, sordonia/redpajama-sample_from_valid_all, nampdn-ai/tiny-bridgedict, teknium/GPTeacher-General-Instruct, Severian/Internal-Knowledge-Map, Severian/Internal-Knowledge-Map-StoryWriter-RolePlaying
Methodology:
Linear merge of layers, duplicating layers 16-21, and attempting various repair methods. Merge achieved using EQ-Bench benchmark.
LLM NameDanube2 Upscale 1.7
Repository 🤗https://huggingface.co/Lambent/danube2-upscale-1.7 
Base Model(s)  Lambent/danube2-upscale-1.53lisa   Lambent/danube2-upscale-1.51galore   Lambent/danube2-upscale-1.531qlora   Lambent/danube2-upscale-1.51qlora   Lambent/danube2-upscale-1.53lisa   Lambent/danube2-upscale-1.51galore   Lambent/danube2-upscale-1.531qlora   Lambent/danube2-upscale-1.51qlora
Model Size2.2b
Required VRAM4.5 GB
Updated2025-02-05
MaintainerLambent
Model Typemistral
Model Files  4.5 GB: 1-of-1
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length8192
Model Max Length8192
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typefloat16

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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