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Viking SlimSonnet V0.2 7B by mpasila

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  Autotrain compatible Base model:finetune:lumiopen/v...   Base model:lumiopen/viking-7b   Conversational   Da Dataset:gryphe/sonnet3.5-slimo... Dataset:mpasila/sonnet3.5-slim...   En   Endpoints compatible   Fi   Is   Llama   Nn   No   Region:us   Safetensors   Sft   Sharded   Sv   Tensorflow   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/mpasila/Viking-SlimSonnet-v0.2-7B 

Viking SlimSonnet V0.2 7B Benchmarks

LLME Score: 0.21258
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Viking SlimSonnet V0.2 7B (mpasila/Viking-SlimSonnet-v0.2-7B)

Viking SlimSonnet V0.2 7B Parameters and Internals

Model Type 
text-generation-inference, transformers, unsloth, llama, trl, sft
Additional Notes 
This is a test model indicating a previous failed attempt. Trained incorrectly due to wrong dataset formatting. Set to be retrained.
Supported Languages 
en (English), fi (Finnish), sv (Swedish), no (Norwegian), da (Danish), is (Icelandic), nn (Nynorsk)
Training Details 
Data Sources:
Gryphe/Sonnet3.5-SlimOrcaDedupCleaned, mpasila/Sonnet3.5-SlimOrcaDedupCleaned-4k-context
Methodology:
Trained with regular LoRA, LoRA rank 128, Alpha 32.
Model Architecture:
Llama
LLM NameViking SlimSonnet V0.2 7B
Repository 🤗https://huggingface.co/mpasila/Viking-SlimSonnet-v0.2-7B 
Base Model(s)  Viking 7B   LumiOpen/Viking-7B
Model Size7b
Required VRAM15.1 GB
Updated2025-02-05
Maintainermpasila
Model Typellama
Model Files  4.9 GB: 1-of-4   5.0 GB: 2-of-4   4.1 GB: 3-of-4   1.1 GB: 4-of-4
Supported Languagesen fi sv no da is nn
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.44.2
Tokenizer ClassBloomTokenizer
Padding Token<pad>
Vocabulary Size131072
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than mpasila/Viking-SlimSonnet-v0.2-7B.

Rank the Viking SlimSonnet V0.2 7B Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
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