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Llama 3.1 8B Stheno V3.4 Abliterated by v000000

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Llama 3.1 8B Stheno V3.4 Abliterated is an open-source language model by v000000. Features: 8b LLM, VRAM: 32.1GB, Context: 128K, Instruction-Based, Merged, LLM Explorer Score: 0.15.

  Merged Model   Arxiv:2203.05482 Base model:grimjim/llama-3-ins... Base model:sao10k/llama-3.1-8b...   Conversational   Endpoints compatible   Instruct   Llama   Lora   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/v000000/Llama-3.1-8B-Stheno-v3.4-abliterated 

Llama 3.1 8B Stheno V3.4 Abliterated Benchmarks

LLME Score: 0.14715
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 3.1 8B Stheno V3.4 Abliterated (v000000/Llama-3.1-8B-Stheno-v3.4-abliterated)
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Llama 3.1 8B Stheno V3.4 Abliterated Parameters and Internals

Additional Notes 
Thanks to mradermacher for the quants. The model uses abliteration adaption.
Training Details 
Methodology:
Merge Method: linear
LLM NameLlama 3.1 8B Stheno V3.4 Abliterated
Repository 🤗https://huggingface.co/v000000/Llama-3.1-8B-Stheno-v3.4-abliterated 
Base Model(s)  Sao10K/Llama-3.1-8B-Stheno-v3.4   ... Instruct Abliteration LoRA 8B   Sao10K/Llama-3.1-8B-Stheno-v3.4   grimjim/Llama-3-Instruct-abliteration-LoRA-8B
Merged ModelYes
Model Size8b
Required VRAM32.1 GB
Updated2026-05-25
Maintainerv000000
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-7   4.9 GB: 2-of-7   5.0 GB: 3-of-7   5.0 GB: 4-of-7   4.8 GB: 5-of-7   5.0 GB: 6-of-7   2.4 GB: 7-of-7
Model ArchitectureLlamaForCausalLM
Context Length131072
Model Max Length131072
Transformers Version4.44.1
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|finetune_right_pad_id|>
Vocabulary Size128256
LoRA ModelYes
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than v000000/Llama-3.1-8B-Stheno-v3.4-abliterated.

Rank the Llama 3.1 8B Stheno V3.4 Abliterated 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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