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Cypher Mini 1.8B by aloobun

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Cypher Mini 1.8B is an open-source language model by aloobun. Features: 1.8b LLM, VRAM: 3.7GB, Context: 16K, License: apache-2.0, LLM Explorer Score: 0.15, Arc: 39.6, HellaSwag: 67.5, MMLU: 31.1, GSM8K: 14.5.

  Conversational   Custom code Dataset:locutusque/hercules-v3...   Endpoints compatible   Finetuned   Gpt4   H2oai   Mistral   Region:us   Safetensors   Sharded   Synthetic data   Tensorflow
Model Card on HF 🤗: https://huggingface.co/aloobun/Cypher-Mini-1.8B 

Cypher Mini 1.8B Benchmarks

ARC: 39.59 vs 96.7 (so35)-59.1%
HellaSwag: 67.45 vs 95.3 (gpt4)-29.2%
MMLU: 31.14 vs 88.3 (so35)-64.7%
GSM8K: 14.48 vs 96.4 (so35)-85%
LLME Score: 0.1542
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Cypher Mini 1.8B (aloobun/Cypher-Mini-1.8B)
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Cypher Mini 1.8B Parameters and Internals

Model Type 
text generation
Additional Notes 
This is an experimental model finetuned on a combination of Hercules v3 dataset and a private dataset.
Training Details 
Data Sources:
Locutusque/Hercules-v3.0, Private dataset
Methodology:
Mixture of experts approach
Input Output 
Input Format:
<|system|>~~<|prompt|>~~<|answer|>
Accepted Modalities:
text
Performance Tips:
Hyperparameters include adamw optimizer with eps of 1e-8, cosine decay with 20% warmup, and learning rate of 2e-5.
LLM NameCypher Mini 1.8B
Repository 🤗https://huggingface.co/aloobun/Cypher-Mini-1.8B 
Model Size1.8b
Required VRAM3.7 GB
Updated2026-05-25
Maintaineraloobun
Model Typemistral
Model Files  3.0 GB: 1-of-2   0.7 GB: 2-of-2
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length16384
Model Max Length16384
Transformers Version4.37.0
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than aloobun/Cypher-Mini-1.8B.

Rank the Cypher Mini 1.8B 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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