Instructions to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("AXIOMCORE/Axiom-2B-Logic-Density-v1", set_active=True) - llama-cpp-python
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AXIOMCORE/Axiom-2B-Logic-Density-v1", filename="axiom3.5_2b_ep1_Q6_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K # Run inference directly in the terminal: llama-cli -hf AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K # Run inference directly in the terminal: llama-cli -hf AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
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 AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K # Run inference directly in the terminal: ./llama-cli -hf AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
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 AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
Use Docker
docker model run hf.co/AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
- LM Studio
- Jan
- Ollama
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with Ollama:
ollama run hf.co/AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
- Unsloth Studio new
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 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 AXIOMCORE/Axiom-2B-Logic-Density-v1 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 AXIOMCORE/Axiom-2B-Logic-Density-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AXIOMCORE/Axiom-2B-Logic-Density-v1 to start chatting
- Pi new
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
Run Hermes
hermes
- Docker Model Runner
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with Docker Model Runner:
docker model run hf.co/AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
- Lemonade
How to use AXIOMCORE/Axiom-2B-Logic-Density-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AXIOMCORE/Axiom-2B-Logic-Density-v1:Q6_K
Run and chat with the model
lemonade run user.Axiom-2B-Logic-Density-v1-Q6_K
List all available models
lemonade list
Evaluation Results
20-Question Extreme Stress Test (Zero-Error Evidence Chain)
Tested across high-entropy vertical domains:
- Legal Logic Reconstruction
- Personal Information Protection Law (dynamic anonymization)
- AI Omission Crime Liability
- Algorithmic Discrimination Weight Proof
- CRISPR-Cas9 Off-target Logical Entropy
- Tau Prion-like Propagation Kinetics
- Topological Data Analysis (Homology Groups)
Key Metrics:
- Δ_mean_abs ≈ 0.03
- CosSim > 0.99999
- Entropy: 2.3 – 3.8
The model demonstrates near-zero hallucination and strict "no-addition-no-subtraction" logic with closed evidence chains in extreme tasks.
Full English and Chinese reports are available in the repository.
Axiom-2B-Logic-Density-v1 (Q6_K GGUF)
"这是 2B 模型,但它不负责陪你聊天。"
🧩 逻辑主权宣言
当所有模型都在追求“像人一样说话”时,AXIOM-2B 追求的是逻辑的绝对闭环。基于 Axiom Core v5.93 动态架构,我们成功在 2B 的参数空间内,压缩了对标 14B 级别的逻辑推理深度。
🛠️ 极客属性
- 参数量: 2B (实际逻辑表现:14B Class)
- 量化: Q6_K GGUF (极致精度保留,拒绝逻辑漂移)
- 硬件: 原生适配 Tesla P100 / GTX 1080。在 2GB 显存以下即可开启深度审计。
- 铁律: 遵循“不加不减”原则,专为法律存证、代码审计、生物逻辑推导设计。
📈 战报结论
在最新的压力测试中,本模型在法律程序正义逻辑重构、CNN 拓扑特征分析等高阶命题中表现极其稳定。这证明了:算力不是逻辑的边界,架构才是。
📊 逻辑审计战报实测 (Audit Visuals)
⚖️ Q1: 法律逻辑链重构
🧬 Q20: TDA 拓扑数据分析
💻 环境验证 (Tesla P100)
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