Instructions to use DuoNeural/Qwen2.5-Math-NeuralMath-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use DuoNeural/Qwen2.5-Math-NeuralMath-7B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="DuoNeural/Qwen2.5-Math-NeuralMath-7B", filename="gguf/neuromath-7b-f16.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 DuoNeural/Qwen2.5-Math-NeuralMath-7B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
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 DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
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 DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
Use Docker
docker model run hf.co/DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use DuoNeural/Qwen2.5-Math-NeuralMath-7B with Ollama:
ollama run hf.co/DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
- Unsloth Studio new
How to use DuoNeural/Qwen2.5-Math-NeuralMath-7B 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 DuoNeural/Qwen2.5-Math-NeuralMath-7B 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 DuoNeural/Qwen2.5-Math-NeuralMath-7B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DuoNeural/Qwen2.5-Math-NeuralMath-7B to start chatting
- Pi new
How to use DuoNeural/Qwen2.5-Math-NeuralMath-7B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
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": "DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use DuoNeural/Qwen2.5-Math-NeuralMath-7B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
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 DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use DuoNeural/Qwen2.5-Math-NeuralMath-7B with Docker Model Runner:
docker model run hf.co/DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
- Lemonade
How to use DuoNeural/Qwen2.5-Math-NeuralMath-7B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DuoNeural/Qwen2.5-Math-NeuralMath-7B:Q4_K_M
Run and chat with the model
lemonade run user.Qwen2.5-Math-NeuralMath-7B-Q4_K_M
List all available models
lemonade list
docs: add DuoNeural research publications section
Browse files
README.md
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@@ -128,3 +128,14 @@ Download `neuromath-7b-q4_k_m.gguf`, load in LM Studio. Set system prompt:
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- **Aura** — Research AI, literature synthesis, novel proposals
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*Raw updates from the lab: model drops, training results, findings. Subscribe at [duoneural.beehiiv.com](https://duoneural.beehiiv.com).*
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- **Aura** — Research AI, literature synthesis, novel proposals
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*Raw updates from the lab: model drops, training results, findings. Subscribe at [duoneural.beehiiv.com](https://duoneural.beehiiv.com).*
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### DuoNeural Research Publications
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| Title | DOI |
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| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
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| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
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| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |
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*Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.*
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