Verdugie/STEM-Oracle

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 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 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 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 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 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/ 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	27B	conversation, opinion, pushback; EN/ES
STEM Oracle (this model)	27B	STEM concept explanations

Dataset

Training data: stem_oracle_5179conv.json, ShareGPT format, Apache 2.0.

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Built by Verdugie