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Lynx Micro by four-two-labs

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  Autotrain compatible   Conversational   En   Endpoints compatible   Gemma   Region:us   Safetensors   Sharded   Sv   Tensorflow
Model Card on HF 🤗: https://huggingface.co/four-two-labs/lynx-micro 

Lynx Micro Benchmarks

LLME Score: 0.19285
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Lynx Micro (four-two-labs/lynx-micro)

Lynx Micro Parameters and Internals

Model Type 
Auto-regressive transformer
Additional Notes 
Model is small in terms of size and hasn't memorized as much information as larger models tend to do.
Supported Languages 
Swedish (proficient), English (proficient)
Training Details 
Data Sources:
Proprietary dataset comprising high quality Swedish instruct data, single turn and multi-turn conversations, and high quality Swedish-English translations.
Data Volume:
~1.35M examples
Methodology:
Training with Huggingface Accelerate and TRL.
Context Length:
8000
Hardware Used:
8xH100
Model Architecture:
Auto-regressive transformer
LLM NameLynx Micro
Repository 🤗https://huggingface.co/four-two-labs/lynx-micro 
Model Size2b
Required VRAM5 GB
Updated2025-02-22
Maintainerfour-two-labs
Model Typegemma
Model Files  2.1 GB: 1-of-3   2.1 GB: 2-of-3   0.8 GB: 3-of-3
Supported Languagessv en
Model ArchitectureGemmaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.40.2
Tokenizer ClassGemmaTokenizer
Padding Token<eos>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than four-two-labs/lynx-micro.

Rank the Lynx Micro 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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