Instructions to use Aadeshisdoingsomething/llama-claude-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Aadeshisdoingsomething/llama-claude-reasoning with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Aadeshisdoingsomething/llama-claude-reasoning", filename="llama-3.2-3b-instruct.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Aadeshisdoingsomething/llama-claude-reasoning with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Aadeshisdoingsomething/llama-claude-reasoning: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 Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Aadeshisdoingsomething/llama-claude-reasoning: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 Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M
Use Docker
docker model run hf.co/Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Aadeshisdoingsomething/llama-claude-reasoning with Ollama:
ollama run hf.co/Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M
- Unsloth Studio
How to use Aadeshisdoingsomething/llama-claude-reasoning 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 Aadeshisdoingsomething/llama-claude-reasoning 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 Aadeshisdoingsomething/llama-claude-reasoning to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Aadeshisdoingsomething/llama-claude-reasoning to start chatting
- Pi
How to use Aadeshisdoingsomething/llama-claude-reasoning with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Aadeshisdoingsomething/llama-claude-reasoning: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": "Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Aadeshisdoingsomething/llama-claude-reasoning with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Aadeshisdoingsomething/llama-claude-reasoning: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 Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Aadeshisdoingsomething/llama-claude-reasoning with Docker Model Runner:
docker model run hf.co/Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M
- Lemonade
How to use Aadeshisdoingsomething/llama-claude-reasoning with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M
Run and chat with the model
lemonade run user.llama-claude-reasoning-Q4_K_M
List all available models
lemonade list
Llama-3.2-3B-claude-reasoning-GGUF : GGUF
Trained with: Unsloth Studio
This model was finetuned and converted to GGUF format using Unsloth.
Usecases
From my testing, the only good usecase is to mimic the style of output and reasoning of larger models like claude 4.5 opus. Beyond that, it keeps llama's "yes-man" style and has no clue what it is doing. Maybe it got a bit better but the fact its 3 billion parameters limits its ability. also it sometimes forgets to close thinking tags.
Benchmarks
coming soon to see if its any good
Datasets:
TeichAI/claude-4.5-opus-high-reasoning-250x
Example usage:
- For text only LLMs:
./llama.cpp/llama-cli -hf Aadeshisdoingsomething/llama-claude-reasoning --jinja - For multimodal models:
./llama.cpp/llama-mtmd-cli -hf Aadeshisdoingsomething/llama-claude-reasoning --jinja
Available Model files:
llama-3.2-3b-instruct.Q4_K_M.gguf
Unsloth
This was trained 2x faster with Unsloth

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