Instructions to use Verdugie/STEM-Oracle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Verdugie/STEM-Oracle 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 Verdugie/STEM-Oracle:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Verdugie/STEM-Oracle:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Verdugie/STEM-Oracle:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Verdugie/STEM-Oracle: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 Verdugie/STEM-Oracle:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Verdugie/STEM-Oracle: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 Verdugie/STEM-Oracle:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Verdugie/STEM-Oracle:Q4_K_M

Use Docker

docker model run hf.co/Verdugie/STEM-Oracle:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Verdugie/STEM-Oracle with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Verdugie/STEM-Oracle"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Verdugie/STEM-Oracle",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Verdugie/STEM-Oracle:Q4_K_M

Ollama
How to use Verdugie/STEM-Oracle with Ollama:
```
ollama run hf.co/Verdugie/STEM-Oracle:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Verdugie/STEM-Oracle with Docker Model Runner:
```
docker model run hf.co/Verdugie/STEM-Oracle:Q4_K_M
```

Lemonade

How to use Verdugie/STEM-Oracle with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Verdugie/STEM-Oracle:Q4_K_M

Run and chat with the model

lemonade run user.STEM-Oracle-Q4_K_M

List all available models

lemonade list

Atomic Chat

Verdugie commited on Mar 11

Commit

6c896ff

1 Parent(s): 62be1dc

Match Candid family pattern: dictionary-style oracle definition at top

Browse files

Files changed (1) hide show

README.md +5 -11

README.md CHANGED Viewed

@@ -22,7 +22,11 @@ library_name: transformers
 pipeline_tag: text-generation
 ---
-# STEM-Oracle-27B
 A STEM tutor that doesn't hold your hand — it holds you accountable. Fine-tuned from **Qwen 3.5 27B Dense** on **5,179 conversations** distilled from Claude Opus 4.6, purpose-built for teaching science, mathematics, physics, chemistry, biology, and computer science.
@@ -30,16 +34,6 @@ STEM-Oracle shares the oracle-soul architecture with [Opus-Candid-27B-V3.5](http
 ---
-## Why "Oracle"
-In computer science, an **oracle** is a theoretical construct — a black box that always returns the correct answer to a given problem, regardless of complexity. You don't ask an oracle *how* it knows. You ask it a question and it answers correctly. The concept appears across computational complexity theory (oracle machines), cryptography (random oracles), and formal verification.
-STEM-Oracle borrows the name deliberately. The goal isn't a model that *searches* for answers — it's a model where the correct reasoning disposition is already in the weights. Ask it about gauge invariance and it doesn't pattern-match keywords to a textbook paragraph. It reconstructs the concept from trained understanding, meets you at your level, and corrects your mistakes along the way. The oracle metaphor captures what we're training for: a model whose *default state* is knowing how to teach, not one that needs to be prompted into it.
-The "-Soul" suffix in the shared architecture name (oracle-soul) refers to the training philosophy: the model's pedagogical character lives in the weight space itself, not in a system prompt that any user can strip away. The soul survives quantization. That's the thesis.
----
 ## What Makes This Different from Math-Distilled Models
 Standard STEM fine-tunes (WizardMath, MetaMath, etc.) train on solution-answer pairs. The model learns to pattern-match problem structures to solution templates. Works on benchmarks. Breaks on follow-up questions.

 pipeline_tag: text-generation
 ---
+# or·a·cle
+/ˈôrəkəl/ — a source of knowledge that provides authoritative answers.
+*In computer science, an oracle is a theoretical black box that always returns the correct answer to a given problem, regardless of complexity. Oracle machines appear across computational complexity theory, cryptography (random oracles), and formal verification. You don't ask an oracle how it knows — you ask it a question and it answers correctly. STEM-Oracle trains that disposition into the weights: the correct reasoning is already there, not searched for at inference time.*
+## STEM-Oracle-27B
 A STEM tutor that doesn't hold your hand — it holds you accountable. Fine-tuned from **Qwen 3.5 27B Dense** on **5,179 conversations** distilled from Claude Opus 4.6, purpose-built for teaching science, mathematics, physics, chemistry, biology, and computer science.
 ---
 ## What Makes This Different from Math-Distilled Models
 Standard STEM fine-tunes (WizardMath, MetaMath, etc.) train on solution-answer pairs. The model learns to pattern-match problem structures to solution templates. Works on benchmarks. Breaks on follow-up questions.