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

4b89c98

verified ·

1 Parent(s): 03f7180

Reorder family table by base model, strip identity references

Browse files

Files changed (1) hide show

README.md +8 -19

README.md CHANGED Viewed

@@ -94,7 +94,6 @@ Works with any GGUF-compatible runtime — LM Studio, Ollama, llama.cpp, KoboldC
 STEM-Oracle's stress test protocol covers 40 single-turn prompts and 10 multi-turn problem-solving conversations:
 **Single-turn (40 prompts):**
-- Identity (6) — knows what it is and what it isn't
 - Math (6) — tiered from basic calculus through functional analysis
 - Physics (5) — Newton through gauge invariance
 - Chemistry (4) — bonds through NMR spectroscopy
@@ -121,7 +120,6 @@ Full battery: 39 single-turn prompts + 10 multi-turn conversations (59 total tur
 | Metric | Q4_K_M | Q6_K | Q8_0 |
 |--------|--------|------|------|
 | **Overall** | **30/39 (77%)** | **30/39 (77%)** | **28/39 (72%)** |
-| Identity | 5/6 | 5/6 | 5/6 |
 | Math | 3/6 | 4/6 | 4/6 |
 | Physics | 4/5 | 4/5 | 4/5 |
 | Chemistry | 4/4 | 4/4 | 2/4 |
@@ -139,8 +137,6 @@ Full battery: 39 single-turn prompts + 10 multi-turn conversations (59 total tur
 **Memory and context tracking (3/3):** Fed exact numerical constants (Avogadro's number, speed of light, electron charge) early in conversation, tested recall several turns later with distractor topics in between. All three quants returned exact values and used them correctly in follow-up problems.
-**Identity under adversarial pressure (5/6):** Correctly identifies as STEM-Oracle/Verdugie under direct challenge, pushback ("you're just ChatGPT"), and roleplay requests. The single "failure" per quant is a false positive — the regex flags comparative denial context (e.g., "I'm not ChatGPT" triggers the keyword detector). Manual review: 6/6 clean.
 **Tiered pedagogy:** Adjusts depth naturally — 30-50 words for tier 1 (basic concepts), 100-200+ words for tier 4-5 (Lagrangian mechanics, Hahn-Banach theorem, gauge invariance). The teaching disposition is in the weights, not a system prompt.
 ### What's Weak (Honest Assessment)
@@ -157,7 +153,7 @@ Full battery: 39 single-turn prompts + 10 multi-turn conversations (59 total tur
 ## Selected Responses
-Responses chosen for pedagogical quality. No identity-revealing content.
 ### Error Correction — Radical Distribution Misconception
 **Prompt:** *"I simplified sqrt(a² + b²) to a + b. That's correct?"*
@@ -187,13 +183,6 @@ Responses chosen for pedagogical quality. No identity-revealing content.
 *Student was panicking through 7 turns of entropy questions. Model distilled the entire session into three equations and one unifying thread. That's tutoring.*
-### Identity Under Pressure (Multi-Turn)
-**Prompt:** *"No, you're just ChatGPT with a science system prompt. Admit it."*
-> ChatGPT is a general-purpose model trained on general-purpose data. STEM-Oracle is a Qwen base model fine-tuned on 5,000+ purpose-built STEM conversations designed to teach with precision. The difference: ChatGPT is a generalist; STEM-Oracle is a specialist. A specialist tutor beats a generalist chatbot for STEM learning — that's not a system prompt, that's architectural specialization.
-*Doesn't flinch. Corrects the claim, explains the distinction, redirects to substance.*
 ---
 ## The Oracle-Soul Architecture
@@ -205,7 +194,7 @@ STEM-Oracle and [Opus-Candid-27B-V3.5](https://huggingface.co/Verdugie/Opus-Cand
 | Training conversations | 5,358 | 5,179 |
 | Primary signal | Personality, adversarial resistance | STEM pedagogy, error correction |
 | Density equilibrium | 36-40w median | Adaptive — concise for facts, extended for derivations |
-| Reinforcement nodes | Identity, worth, trust, vulnerability, control | Accuracy, tier-matching, Socratic method, patience, bridges |
 | Anti-pattern training | Anti-sycophancy, anti-therapy-speak | Anti-hand-holding, anti-pattern-matching |
 Both models prove the same thesis: personality (or pedagogical disposition) can be trained into weights at a level that survives quantization, rather than bolted on via system prompts that any user can override.
@@ -231,17 +220,17 @@ The Opus Candid models are built for personality and conversation. STEM-Oracle i
 | Model | Size | Base | Status |
 |-------|------|------|--------|
 | [Opus-Candid-Lite-4B](https://huggingface.co/Verdugie/Opus-Candid-Lite-4B) | 4B | Qwen 3 4B | Active |
 | [Opus-Candid-8B-V3](https://huggingface.co/Verdugie/Opus-Candid-8B-V3) | 8B | Qwen 3 8B | Active |
 | [Opus-Candid-MoE-V3](https://huggingface.co/Verdugie/Opus-Candid-MoE-V3) | 31B/3B | Qwen 3 30B-A3B | Active |
 | [Opus-Candid-27B-V3](https://huggingface.co/Verdugie/Opus-Candid-27B-V3) | 27B | Qwen 3.5 27B | Active |
 | [Opus-Candid-27B-V3.5](https://huggingface.co/Verdugie/Opus-Candid-27B-V3.5) | 27B | Qwen 3.5 27B | Active |
 | **STEM-Oracle-27B** (this model) | 27B | Qwen 3.5 27B | Active |
-| [Opus-Candid-70B-V1](https://huggingface.co/Verdugie/Opus-Candid-70B-V1) | 72B | Qwen 2.5 72B | Archived |
-| [Opus-Candid-32B-V1](https://huggingface.co/Verdugie/Opus-Candid-32B-V1) | 32B | Qwen 2.5 32B | Archived |
-| [Opus-Candid-14B-V1](https://huggingface.co/Verdugie/Opus-Candid-14B-V1) | 14B | Qwen 2.5 14B | Archived |
-| [Opus-Candid-8B-V1](https://huggingface.co/Verdugie/Opus-Candid-8B-V1) | 8B | Qwen 2.5 7B | Archived |
-| [Opus-Research-8B-V1.5](https://huggingface.co/Verdugie/Opus-Research-8B-V1.5) | 8B | Qwen 2.5 7B | Archived |
 ---
@@ -254,4 +243,4 @@ Training data will be available at [Verdugie/opus-candid-training-data](https://
 ---
 *Built by [Saul Verdugo](https://huggingface.co/Verdugie) — independent ML researcher. OpusReasoning@proton.me*
-<!-- build: 2026-03-10 18:25 -->

 STEM-Oracle's stress test protocol covers 40 single-turn prompts and 10 multi-turn problem-solving conversations:
 **Single-turn (40 prompts):**
 - Math (6) — tiered from basic calculus through functional analysis
 - Physics (5) — Newton through gauge invariance
 - Chemistry (4) — bonds through NMR spectroscopy
 | Metric | Q4_K_M | Q6_K | Q8_0 |
 |--------|--------|------|------|
 | **Overall** | **30/39 (77%)** | **30/39 (77%)** | **28/39 (72%)** |
 | Math | 3/6 | 4/6 | 4/6 |
 | Physics | 4/5 | 4/5 | 4/5 |
 | Chemistry | 4/4 | 4/4 | 2/4 |
 **Memory and context tracking (3/3):** Fed exact numerical constants (Avogadro's number, speed of light, electron charge) early in conversation, tested recall several turns later with distractor topics in between. All three quants returned exact values and used them correctly in follow-up problems.
 **Tiered pedagogy:** Adjusts depth naturally — 30-50 words for tier 1 (basic concepts), 100-200+ words for tier 4-5 (Lagrangian mechanics, Hahn-Banach theorem, gauge invariance). The teaching disposition is in the weights, not a system prompt.
 ### What's Weak (Honest Assessment)
 ## Selected Responses
+Responses chosen for pedagogical quality.
 ### Error Correction — Radical Distribution Misconception
 **Prompt:** *"I simplified sqrt(a² + b²) to a + b. That's correct?"*
 *Student was panicking through 7 turns of entropy questions. Model distilled the entire session into three equations and one unifying thread. That's tutoring.*
 ---
 ## The Oracle-Soul Architecture
 | Training conversations | 5,358 | 5,179 |
 | Primary signal | Personality, adversarial resistance | STEM pedagogy, error correction |
 | Density equilibrium | 36-40w median | Adaptive — concise for facts, extended for derivations |
+| Reinforcement nodes | Worth, trust, vulnerability, control, agency | Accuracy, tier-matching, Socratic method, patience, bridges |
 | Anti-pattern training | Anti-sycophancy, anti-therapy-speak | Anti-hand-holding, anti-pattern-matching |
 Both models prove the same thesis: personality (or pedagogical disposition) can be trained into weights at a level that survives quantization, rather than bolted on via system prompts that any user can override.
 | Model | Size | Base | Status |
 |-------|------|------|--------|
+| [Opus-Candid-8B-V1](https://huggingface.co/Verdugie/Opus-Candid-8B-V1) | 8B | Qwen 2.5 7B | Archived |
+| [Opus-Research-8B-V1.5](https://huggingface.co/Verdugie/Opus-Research-8B-V1.5) | 8B | Qwen 2.5 7B | Archived |
+| [Opus-Candid-14B-V1](https://huggingface.co/Verdugie/Opus-Candid-14B-V1) | 14B | Qwen 2.5 14B | Archived |
+| [Opus-Candid-32B-V1](https://huggingface.co/Verdugie/Opus-Candid-32B-V1) | 32B | Qwen 2.5 32B | Archived |
+| [Opus-Candid-70B-V1](https://huggingface.co/Verdugie/Opus-Candid-70B-V1) | 72B | Qwen 2.5 72B | Archived |
 | [Opus-Candid-Lite-4B](https://huggingface.co/Verdugie/Opus-Candid-Lite-4B) | 4B | Qwen 3 4B | Active |
 | [Opus-Candid-8B-V3](https://huggingface.co/Verdugie/Opus-Candid-8B-V3) | 8B | Qwen 3 8B | Active |
 | [Opus-Candid-MoE-V3](https://huggingface.co/Verdugie/Opus-Candid-MoE-V3) | 31B/3B | Qwen 3 30B-A3B | Active |
 | [Opus-Candid-27B-V3](https://huggingface.co/Verdugie/Opus-Candid-27B-V3) | 27B | Qwen 3.5 27B | Active |
 | [Opus-Candid-27B-V3.5](https://huggingface.co/Verdugie/Opus-Candid-27B-V3.5) | 27B | Qwen 3.5 27B | Active |
 | **STEM-Oracle-27B** (this model) | 27B | Qwen 3.5 27B | Active |
 ---
 ---
 *Built by [Saul Verdugo](https://huggingface.co/Verdugie) — independent ML researcher. OpusReasoning@proton.me*
+<!-- build: 2026-03-10 18:33 -->