Text Generation
Safetensors
GGUF
English
Portuguese
qwen3_5_text
reasoning
structured-reasoning
experimental
conversational
Instructions to use KarlSage/oraculum_alpha_08b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use KarlSage/oraculum_alpha_08b 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 KarlSage/oraculum_alpha_08b:Q8_0 # Run inference directly in the terminal: llama cli -hf KarlSage/oraculum_alpha_08b:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf KarlSage/oraculum_alpha_08b:Q8_0 # Run inference directly in the terminal: llama cli -hf KarlSage/oraculum_alpha_08b: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 KarlSage/oraculum_alpha_08b:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf KarlSage/oraculum_alpha_08b: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 KarlSage/oraculum_alpha_08b:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf KarlSage/oraculum_alpha_08b:Q8_0
Use Docker
docker model run hf.co/KarlSage/oraculum_alpha_08b:Q8_0
- LM Studio
- Jan
- vLLM
How to use KarlSage/oraculum_alpha_08b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KarlSage/oraculum_alpha_08b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KarlSage/oraculum_alpha_08b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/KarlSage/oraculum_alpha_08b:Q8_0
- Ollama
How to use KarlSage/oraculum_alpha_08b with Ollama:
ollama run hf.co/KarlSage/oraculum_alpha_08b:Q8_0
- Unsloth Desktop
- Pi
How to use KarlSage/oraculum_alpha_08b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KarlSage/oraculum_alpha_08b:Q8_0
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": "KarlSage/oraculum_alpha_08b:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use KarlSage/oraculum_alpha_08b with Docker Model Runner:
docker model run hf.co/KarlSage/oraculum_alpha_08b:Q8_0
- Lemonade
How to use KarlSage/oraculum_alpha_08b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KarlSage/oraculum_alpha_08b:Q8_0
Run and chat with the model
lemonade run user.oraculum_alpha_08b-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use KarlSage/oraculum_alpha_08b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KarlSage/oraculum_alpha_08b:Q8_0
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 KarlSage/oraculum_alpha_08b:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use KarlSage/oraculum_alpha_08b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KarlSage/oraculum_alpha_08b:Q8_0
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 "KarlSage/oraculum_alpha_08b:Q8_0" \ --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"
| license: apache-2.0 | |
| base_model: Qwen/Qwen3.5-0.8B | |
| pipeline_tag: text-generation | |
| language: | |
| - en | |
| - pt | |
| tags: | |
| - reasoning | |
| - structured-reasoning | |
| - gguf | |
| - experimental | |
| # Oraculum Alpha 0.8B | |
| **Experimental public preview.** A 0.8B-parameter reasoning model fine-tuned | |
| from [Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B). | |
| Instead of free-form chain-of-thought, Oraculum writes its reasoning as a | |
| **structured XML trace**, discrete steps with explicit operations and | |
| dependencies, inside its thinking channel, and only then gives the final | |
| answer. The structured trace is the research subject of this release. | |
| ``` | |
| <thoughtml> | |
| <step id="s1" op="observe">The user asks for the sum of 2 and 2.</step> | |
| <step id="s2" op="deduce" depends_on="s1">2 + 2 = 4.</step> | |
| <step id="s3" op="answer" depends_on="s2">The answer is 4.</step> | |
| </thoughtml> | |
| The answer is 4. | |
| ``` | |
| ## Files | |
| | file | format | use | | |
| |---|---|---| | |
| | `model.safetensors` | bf16 | transformers / fine-tuning | | |
| | `oraculum_alpha_08b-bf16.gguf` | GGUF bf16 | llama.cpp, full precision | | |
| | `oraculum_alpha_08b-Q8_0.gguf` | GGUF Q8_0 | llama.cpp, recommended | | |
| ## Run it (llama.cpp) | |
| ```bash | |
| llama-server -m oraculum_alpha_08b-Q8_0.gguf -c 8192 | |
| # or | |
| llama-cli -m oraculum_alpha_08b-Q8_0.gguf -cnv | |
| ``` | |
| Use the built-in chat template. The reasoning trace appears in the thinking | |
| channel; the visible reply comes after it. | |
| ## Notes and limitations | |
| - **Alpha.** This is a quick public test of the format, not a polished | |
| assistant. Expect mistakes, it is a 0.8B model. | |
| - Reasoning is strongest in math, logic and step-by-step analysis; | |
| Portuguese and English are the primary languages. | |
| - The XML trace is machine-parseable by design; malformed traces can still | |
| occur under long generations. | |
| ## License | |
| Apache 2.0, same as the base model. | |