Instructions to use Aadeshisdoingsomething/llama-claude-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Aadeshisdoingsomething/llama-claude-reasoning with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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 Desktop

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 serve -hf Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

How to use Aadeshisdoingsomething/llama-claude-reasoning with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Aadeshisdoingsomething/llama-claude-reasoning:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

Downloads last month: 17

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Aadeshisdoingsomething
/

llama-claude-reasoning

Llama-3.2-3B-claude-reasoning-GGUF : GGUF

Usecases

Benchmarks

Datasets:

Available Model files:

Unsloth

Dataset used to train Aadeshisdoingsomething/llama-claude-reasoning

Collection including Aadeshisdoingsomething/llama-claude-reasoning

Llama-Claude