Instructions to use ProfessorCastillo/notConfucius.v2.llama3.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ProfessorCastillo/notConfucius.v2.llama3.1-8b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ProfessorCastillo/notConfucius.v2.llama3.1-8b", filename="notConfuciusV2-llama318b.Q8_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use ProfessorCastillo/notConfucius.v2.llama3.1-8b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0 # Run inference directly in the terminal: llama-cli -hf ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0 # Run inference directly in the terminal: llama-cli -hf ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0
Use Docker
docker model run hf.co/ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0
- LM Studio
- Jan
- Ollama
How to use ProfessorCastillo/notConfucius.v2.llama3.1-8b with Ollama:
ollama run hf.co/ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0
- Unsloth Studio new
How to use ProfessorCastillo/notConfucius.v2.llama3.1-8b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ProfessorCastillo/notConfucius.v2.llama3.1-8b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ProfessorCastillo/notConfucius.v2.llama3.1-8b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ProfessorCastillo/notConfucius.v2.llama3.1-8b to start chatting
- Docker Model Runner
How to use ProfessorCastillo/notConfucius.v2.llama3.1-8b with Docker Model Runner:
docker model run hf.co/ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0
- Lemonade
How to use ProfessorCastillo/notConfucius.v2.llama3.1-8b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ProfessorCastillo/notConfucius.v2.llama3.1-8b:Q8_0
Run and chat with the model
lemonade run user.notConfucius.v2.llama3.1-8b-Q8_0
List all available models
lemonade list
notConfucius
notConfucius.v2 is a fine-tuning experiment. llama3.1:8b base with a better designed dataset to reflect a particular cognitive persona - wiser, more coherent, less maddening, and still occasionally enlightening. it’s less a model and more a vibe.
This is the second, more functional iteration of a "cognitive persona" fine-tuning experiment. The first version was a maddening, character-locked notConfucius. This version attempts to fix that. Three different base models on this new FT dataset support that.
Technical Details
- Base Model: meta-llama/Meta-Llama-3.1-8B
- Technique: Parameter-Efficient Fine-Tuning (PEFT) using LoRA
- Framework: Trained using
unslothfor high-speed, memory-efficient training on a single GPU - Format: Q8_0 GGUF quantization, with the LoRA adapter fully merged
- Dataset: A large, custom dataset of ~1100 instruction-response pairs designed with a single, highly stylized persona, generated with multiple proprietary and open source LLMs
- Size: 8.5GB
- Context Length: 128K tokens
What Changed in V2: From Sledgehammer to Scalpel
The first version suffered from severe persona overfitting. A large, single-minded dataset of ~1100 examples didn't just teach the model a skill; it performed a personality transplant that left it unable to answer a direct question. It was a funhouse mirror, but not a very useful tool.
V2 was retrained on a smaller, more tactical dataset of ~300 examples with a completely different philosophy:
Mode Switching, Not Reprogramming: The dataset is now a balanced diet, not an overdose. It explicitly teaches the model to switch between three modes:
Direct Mode (Pragmatist): For factual questions. It's now trained to just give the damn answer.
Advisory Mode (Strategist): For decisions. It maps out tradeoffs instead of spouting philosophy.
Emergent Mode (Provocateur): For when you're genuinely stuck. This is the only place the old "notConfucius" is allowed out of its cage.
Pragmatism by Default: The model's new primary directive is utility, not depth. The metaphors and poetic reframing are now a specialized reponse, not the only repsonse.
How to Use This Model (v2)
You can now ask it factual questions. It should answer them. Mostly.
The model is designed to be a strategic advisor, not a default philosopher.
For a clear plan, ask it a tactical question.
For a decision framework, present it with a tradeoff.
If you're truly stuck, give it an ambiguous problem and see if the old spark is still there.
This version is less of a "funhouse mirror" and more of a "shop tool." It's still got a weird personality, but now it has an off-switch. Sometimes. It's still a vibe more than it is a model.
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Base model
meta-llama/Llama-3.1-8B