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Felix 8B by ontocord

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  Autotrain compatible   Conversational   En   Endpoints compatible   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/ontocord/Felix-8B 

Felix 8B Benchmarks

ARC: 65.02 vs 96.7 (so35)-32.8%
HellaSwag: 84.61 vs 95.3 (gpt4)-11.2%
MMLU: 61.05 vs 88.3 (so35)-30.9%
TruthfulQA: 64.23 vs 59 (gpt4)8.9%
WinoGrande: 75.93 vs 87.5 (gpt4)-13.2%
GSM8K: 51.78 vs 96.4 (so35)-46.3%
LLME Score: 0.18783
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Felix 8B (ontocord/Felix-8B)

Felix 8B Parameters and Internals

Model Type 
language model
Additional Notes 
Felix-8B is experimental and of a research work product with a strong focus on safety and truthfulness.
Supported Languages 
en (English)
Training Details 
Data Sources:
ontocord/sft-4e-exp2, OpenHermes 2.5 dataset
Methodology:
auto-purpleteaming
Responsible Ai Considerations 
Fairness:
Efforts to mitigate potential biases during the training process.
Mitigation Strategies:
Designed with principles of lawfulness and equal access.
LLM NameFelix 8B
Repository 🤗https://huggingface.co/ontocord/Felix-8B 
Model Size8b
Required VRAM17.9 GB
Updated2025-03-24
Maintainerontocord
Model Typemistral
Model Files  4.9 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   3.1 GB: 4-of-4   0.0 GB
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.39.1
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32002
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than ontocord/Felix-8B.

Rank the Felix 8B 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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