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Instruct Igel 001 by philschmid

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  Autotrain compatible   Bloom   De   Instruct   Lora   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/philschmid/instruct-igel-001 

Instruct Igel 001 Benchmarks

LLME Score: 0.10222
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Instruct Igel 001 (philschmid/instruct-igel-001)

Instruct Igel 001 Parameters and Internals

Model Type 
text generation, instruction-tuned
Additional Notes 
Designed to evaluate instruction-based model creation with minimal training and naive translated data.
Supported Languages 
German (Proficient)
Training Details 
Data Sources:
naive translated instruction datasets
Methodology:
LoRA tuning with merged weights
Model Architecture:
BLOOM-CLP German
LLM NameInstruct Igel 001
Repository 🤗https://huggingface.co/philschmid/instruct-igel-001 
Required VRAM12.9 GB
Updated2024-12-21
Maintainerphilschmid
Instruction-BasedYes
Model Files  0.0 GB   9.9 GB: 1-of-2   3.0 GB: 2-of-2
Supported Languagesde
Model ArchitectureAutoModelForCausalLM
Licensebigscience-openrail-m
Is Biasednone
Tokenizer ClassGPT2Tokenizer
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesquery_key_value
LoRA Alpha32
LoRA Dropout0.05
R Param8

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Note: green Score (e.g. "73.2") means that the model is better than philschmid/instruct-igel-001.

Rank the Instruct Igel 001 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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 v20241217
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