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Dolphin 2.9.1 Phi 3 Kensho 4.5B Abliterated V3 by cognitivecomputations

 »  All LLMs  »  cognitivecomputations  »  Dolphin 2.9.1 Phi 3 Kensho 4.5B Abliterated V3   URL Share it on

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  Autotrain compatible Base model:finetune:unsloth/ph... Base model:unsloth/phi-3-mini-... Dataset:cognitivecomputations/... Dataset:cognitivecomputations/... Dataset:cognitivecomputations/...   Dataset:internlm/agent-flan Dataset:locutusque/function-ca... Dataset:m-a-p/codefeedback-fil... Dataset:microsoft/orca-math-wo...   Dataset:teknium/openhermes-2.5   En   Endpoints compatible   Instruct   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/cognitivecomputations/Dolphin-2.9.1-Phi-3-Kensho-4.5B-abliterated-v3 

Dolphin 2.9.1 Phi 3 Kensho 4.5B Abliterated V3 Benchmarks

LLME Score: 0.18591
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Dolphin 2.9.1 Phi 3 Kensho 4.5B Abliterated V3 (cognitivecomputations/Dolphin-2.9.1-Phi-3-Kensho-4.5B-abliterated-v3)

Dolphin 2.9.1 Phi 3 Kensho 4.5B Abliterated V3 Parameters and Internals

Model Type 
Instruction, Conversational, Coding
Use Cases 
Areas:
Research, Commercial Applications
Primary Use Cases:
Instruction skills, Conversational skills, Coding skills, Initial agentic abilities, Function calling
Limitations:
Removed alignment and bias, Requires own alignment layer for safe deployment
Considerations:
Enjoy responsibly.
Additional Notes 
Model weights adjusted to ablate and inhibit refusals. Supports use of both merged model and adapter method for replication.
Supported Languages 
en (Proficient)
Training Details 
Data Sources:
Cognitive Computations, teknium/OpenHermes-2.5, m-a-p/CodeFeedback-Filtered-Instruction, cognitivecomputations/dolphin-coder, cognitivecomputations/samantha-data, microsoft/orca-math-word-problems-200k, Locutusque/function-calling-chatml, internlm/Agent-FLAN
Methodology:
PEFT layer replication, qLoRA fine-tuning, ablation to inhibit refusals based on 'Refusal in LLMs is mediated by a single direction'.
Context Length:
4000
Training Time:
2.5 days
Hardware Used:
8xL40S node from Crusoe Cloud
Model Architecture:
Based on Phi-3-Mini-Instruct-4k
Input Output 
Input Format:
ChatML prompt template
LLM NameDolphin 2.9.1 Phi 3 Kensho 4.5B Abliterated V3
Repository 🤗https://huggingface.co/cognitivecomputations/Dolphin-2.9.1-Phi-3-Kensho-4.5B-abliterated-v3 
Base Model(s)  Phi 3 Mini 4K Instruct   unsloth/Phi-3-mini-4k-instruct
Model Size4.5b
Required VRAM13.1 GB
Updated2025-03-14
Maintainercognitivecomputations
Model Typemistral
Instruction-BasedYes
Model Files  5.0 GB: 1-of-3   5.0 GB: 2-of-3   3.1 GB: 3-of-3
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licensemit
Context Length4096
Model Max Length4096
Transformers Version4.40.2
Vocabulary Size32064
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than cognitivecomputations/Dolphin-2.9.1-Phi-3-Kensho-4.5B-abliterated-v3.

Rank the Dolphin 2.9.1 Phi 3 Kensho 4.5B Abliterated V3 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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