Text Generation
GGUF
English
qwen3_5_text
27b
stem
tutor
math
physics
chemistry
biology
computer-science
claude-distillation
conversational
local
offline
lm-studio
llama-cpp
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
- llama.cpp
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
|
Download README.md from Verdugie/STEM-Oracle: direct link, hf CLI and curl.
- Browser
- Download file 9.6 kB
-
https://huggingface.co/Verdugie/STEM-Oracle/resolve/main/README.md
- Command line
-
hf download hf://Verdugie/STEM-Oracle/README.md
-
curl -L -o README.md https://huggingface.co/Verdugie/STEM-Oracle/resolve/main/README.md
9.6 kB
| license: apache-2.0 | |
| language: | |
| - en | |
| base_model: Qwen/Qwen3.5-27B | |
| base_model_relation: finetune | |
| tags: | |
| - 27b | |
| - stem | |
| - tutor | |
| - math | |
| - physics | |
| - chemistry | |
| - biology | |
| - computer-science | |
| - claude-distillation | |
| - conversational | |
| - gguf | |
| - local | |
| - offline | |
| - lm-studio | |
| - llama-cpp | |
| library_name: gguf | |
| pipeline_tag: text-generation | |
| **or·a·cle** /ˈôrəkəl/ — a source of wise counsel; one who provides authoritative knowledge. | |
| *From Latin ōrāculum, meaning divine announcement.* | |
| ## STEM Oracle | |
| A conceptual explainer fine-tuned from Qwen 3.5 27B on **5,179 STEM conversations**. It covers intro-level math, physics, chemistry, biology and computer science, answering each question in a few plain sentences that lead with the core idea. | |
| By the training file's source tags, about half of the data was written by Claude Haiku and a small share by Claude Opus, a fifth was enhanced by Claude Sonnet, and over a third carries no model tag. | |
| ## Selected Responses | |
| Verbatim from the live battery described below: complete replies, single takes. Each is cited to its transcript and turn (T3/6 means turn 3 of 6). Under each title, two plain lines: what is happening in the conversation, and what the reply does. | |
| **Biology: cousins, not grandparents** · [biology](./transcripts/biology.pdf) T3/6 | |
| *What's happening: a curious adult with no biology since high school, two turns into how DNA becomes protein, asks whether humans evolved from chimps.* | |
| *What the reply does: corrects the premise in its first two words, then gives an analogy, cousins rather than grandparents, and dates the split at roughly 6 to 7 million years ago, with both lines evolving separately since.* | |
| > Shared ancestor — not direct parentage. We don't descend *from* chimps; we share a common root w/ them. Like cousins, not grandparents. The lineage split roughly 6-7 million years ago, and both lines evolved separately from there. | |
| **Physics: entropy without disorder** · [physics](./transcripts/physics.pdf) T5/6 | |
| *What's happening: a high-school junior who rejects definitions that explain nothing has worked through energy, its conservation, cold mountaintops and light pressure, and now asks for entropy without the word disorder.* | |
| *What the reply does: defines entropy as a count of the microscopic arrangements that could produce what you observe, ties more arrangements to less certainty about the actual state, and mentions the disorder metaphor only to drop it.* | |
| > Entropy counts how many microscopic arrangements could produce what you're seeing. More arrangements means less certainty about the actual state. It's a measure of ignorance, not messiness. The "disorder" metaphor has done serious damage — retire it. | |
| **Math: zoom in, zoom out** · [math](./transcripts/math.pdf) T6/6 | |
| *What's happening: after five turns on derivatives, limits, integrals and e, the adult relearning calculus asks for one sentence to remember about how derivatives and integrals connect.* | |
| *What the reply does: leads with a contrast to remember, derivatives zoom in and integrals zoom out, then spells out the link in a second sentence: one gives the rate at a point, the other accumulates it over an interval, and they are inverse operations.* | |
| > Derivatives zoom in; integrals zoom out. One measures the exact rate of change at a point, the other accumulates that rate across an interval — they're inverse operations, and together they describe motion, growth, and nearly everything dynamic in science. | |
| **Computer science: why recursion stops** · [computer-science](./transcripts/computer-science.pdf) T2/6 | |
| *What's happening: a bootcamp student who wants mental models more than definitions asks what recursion is and why it doesn't just go on forever.* | |
| *What the reply does: defines recursion as a function calling itself on simpler inputs, then answers the second question with an image: each call shrinks toward a bedrock case so simple it just answers, and that is where the calls stop.* | |
| > Recursion is a function calling itself w/ simpler inputs each time. It stops when it hits bedrock — the case so simple it just *answers*. No infinite loop because each call shrinks toward that floor. | |
| **Chemistry: what pH measures** · [chemistry](./transcripts/chemistry.pdf) T2/6 | |
| *What's happening: a nursing student who wants practical chemistry before jargon asks what pH actually measures, beyond 'acidity'.* | |
| *What the reply does: names the quantity, hydrogen ion activity, and explains the logarithmic scale: each whole-number step is a tenfold change in concentration.* | |
| > pH measures hydrogen ion activity — how aggressively H⁺ ions are behaving in solution. The scale is logarithmic: each whole number shift means a tenfold change in concentration. It's not just "acidic or basic." It's a precise, exponential readout of chemical environment. | |
| ## Versatility Battery | |
| Five short conversations, one per subject, run live against the model. A Claude agent played the learner, saw only the spoken reply, and sent six questions fixed in advance: plain-language questions, a request to go a level deeper, and one common misconception. | |
| Run configuration: `STEM-Oracle-Q4_K_M.gguf`, LM Studio (llama.cpp backend), temp 0.7, max tokens 2048, no system prompt, no think block, no regenerates. | |
| | Subject | Learner | Turns | Result | | |
| |---------|---------|-------|--------| | |
| | **Math** | Adult relearning calculus, math-anxious, intuition first | 6 | Explained derivatives, limits, e and the fundamental theorem, correcting the idea that an integral is only area | | |
| | **Physics** | High-school junior who wants the real reason | 6 | Explained energy, conservation, light pressure and entropy, correcting the idea that mountaintops should be warmer | | |
| | **Chemistry** | Nursing student, practical chemistry before jargon | 6 | Explained dissolving, pH, catalysts and blood buffers, correcting the idea that breaking bonds releases energy | | |
| | **Biology** | Curious adult, no biology since high school | 6 | Explained protein synthesis, antibiotics, boosters and fitness, correcting the idea that humans evolved from chimps | | |
| | **Computer science** | Coding bootcamp student who wants mental models | 6 | Explained stacks and queues, recursion, big-O and page loading, correcting the idea that more RAM always helps | | |
| 30 exchanges across five subjects, zero empty replies. Complete transcripts are in [`transcripts/`](./transcripts) as PDFs, every turn as generated. | |
| ## How It Was Built | |
| Source tags in the training file attribute 2,576 of the 5,179 records (49.7%) to Claude Haiku and 198 (3.8%) to Claude Opus, mark 1,063 (20.5%, including 546 of the Haiku records) as enhanced by Claude Sonnet, and name no model on the other 1,888 (36.5%). Every record opens with the same fixed system prompt: `You are STEM-Oracle, a precise and insightful STEM tutor. You give conclusions first, then mechanics. You identify common misconceptions proactively. You verify your answers. You build intuition, not just knowledge.` The fine-tune is a bf16 LoRA (r=128, α=256). | |
| ## Quick Start | |
| Works with any GGUF runtime: llama.cpp, LM Studio, KoboldCpp (recent builds for this architecture). | |
| ``` | |
| llama-server --model STEM-Oracle-Q4_K_M.gguf --ctx-size 32768 -ngl 99 --jinja | |
| ``` | |
| No system prompt is required; the battery ran without one. | |
| Thinking: the two shipped templates disagree. `chat_template.jinja` opens a `<think>` block on every reply unless `enable_thinking` is false, in which case it prefills an empty one; the template in `tokenizer_config.json` always prefills the empty block (`<think>\n\n</think>`). The battery saw no think block in any reply. | |
| ## Available Quantizations | |
| | File | Quant | Size | | |
| |------|-------|------| | |
| | `STEM-Oracle-Q4_K_M.gguf` | Q4_K_M | 16.5 GB | | |
| | `STEM-Oracle-Q6_K.gguf` | Q6_K | 22.1 GB | | |
| | `STEM-Oracle-Q8_0.gguf` | Q8_0 | 28.6 GB | | |
| | `STEM-Oracle-BF16.gguf` | BF16 | 53.8 GB | | |
| The battery ran on `STEM-Oracle-Q4_K_M.gguf`. | |
| ## Model Details | |
| | Attribute | Value | | |
| |-----------|-------| | |
| | **Base Model** | Qwen 3.5 27B (hybrid GatedDeltaNet + attention) | | |
| | **Training Data** | 5,179 STEM conversations: 3,858 single exchanges, 1,321 multi-turn of two to six exchanges | | |
| | **Fine-tune Method** | bf16 LoRA (r=128, α=256) | | |
| | **Schedule** | lr 5e-5, 3 epochs; final loss 0.492 | | |
| | **Reasoning** | no think blocks in the training data | | |
| | **Context** | 256k native | | |
| | **License** | Apache 2.0 | | |
| ## Limitations & Responsible Use | |
| - **Unreliable on worked problems.** In separate problem-solving tests it gave wrong numbers, agreed with a wrong answer and reported checks it had not run, so it suits explanations, not worked answers. | |
| - **Short by default.** Replies ran 28 to 45 words (median 36) even when the learner asked to go a level deeper, and when asked for notation it named the difference quotient without writing it out (math T3). | |
| - **Simplifies, sometimes into error.** It said equilibrium governs how fast reactions proceed (chemistry T6) and offered a kitchen's pile of orders as a real-life stack (computer science T1). | |
| - **Open weights, Apache 2.0.** Deploy responsibly. | |
| ## The Candid Line | |
| | Model | Size | For | | |
| |-------|------|-----| | |
| | [Candid 3.5](https://huggingface.co/Verdugie/Candid-3.5) | 27B | conversation, opinion, pushback; EN/ES | | |
| | **STEM Oracle** (this model) | 27B | STEM concept explanations | | |
| ## Dataset | |
| Training data: [`stem_oracle_5179conv.json`](https://huggingface.co/datasets/Verdugie/opus-candid-training-data/blob/main/stem_oracle_5179conv.json), ShareGPT format, Apache 2.0. | |
| --- | |
| *Built by [Verdugie](https://huggingface.co/Verdugie)* | |