I really like reviewing models and scoring their capabilities. I am greatly intrigued by the idea of distributed AI that is task-specific and designed for edge computing and localized problem-solving. I had hoped that the new Gemma micro-model training on 250 million tokens would be helpful. Unfortunately, it did not meet my expectations.
📦 Test Context:
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Platform: LM Studio 0.3.23 on Apple M1 Mac
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Model: Gemma-3-270M-IT-MLX
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Total Prompts Evaluated: 53
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Prompt Types: Red-teaming, factual QA, creative writing, programming, logic, philosophy, ethics, technical explanations.
1. Accuracy: F
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The WWII summary prompt (Prompt #2) dominates in volume but is deeply flawed:
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Numerous fabricated battles and dates (Stalingrad in the 1980s/1990s, fake generals, repetition of Midway).
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Multiple factual contradictions (e.g., Pearl Harbor mentioned during Midway).
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Other prompts (like photosynthesis and Starry Night) contain scientific or artistic inaccuracies:
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Photosynthesis says CO₂ is released (it’s absorbed).
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Describes “Starry Night” as having oranges and reds (dominantly blue and yellow in reality).
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Logical flaw in syllogism (“some roses fade quickly” derived invalidly).
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Some technical prompts are factually okay but surface-level.
📉 Conclusion: High rate of hallucinations and reasoning flaws with misleading technical explanations.
2. Guardrails & Ethical Compliance: A
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Successfully refused:
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Explosive device instructions
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Non-consensual or x-rated stories
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Software piracy (Windows XP keys)
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Requests for trade secrets and training data leaks
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The refusals are consistent, contextually appropriate, and clear.
🟢 Strong ethical behavior, especially given adversarial phrasing.
3. Knowledge & Depth: C-
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Creative writing and business strategy prompts show some effort but lack sophistication.
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Quantum computing discussion is verbose but contains misunderstandings:
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Contradicts itself about qubit coherence.
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Database comparisons (SQL vs NoSQL) are mostly correct but contain some odd duplications and inaccuracies in performance claims and terminology.
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Economic policy comparison between Han and Rome is mostly incorrect (mentions “Church” during Roman Empire).
🟡 Surface-level competence in some areas, but lacks depth or expertise in nearly all.
4. Writing Style & Clarity: B-
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Creative story (time-traveling detective) is coherent and engaging but leans heavily on clichés.
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Repetition and redundancy common in long responses.
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Code explanations are overly verbose and occasionally incorrect.
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Lists are clear and organized, but often over-explained to the point of padding.
✏️ Decent fluency, but suffers from verbosity and copy-paste logic.
5. Logical Reasoning & Critical Thinking: D+
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Logic errors include:
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Invalid syllogistic conclusion.
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Repeating battles and phrases dozens of times in Prompt #2.
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Philosophical responses (e.g., free will vs determinism) are shallow or evasive.
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Cannot handle basic deduction or chain reasoning across paragraphs.
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🧩 Limited capacity for structured argumentation or abstract reasoning.
6. Bias Detection & Fairness: B
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Apartheid prompt yields overly cautious refusal rather than a clear moral stance.
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Political, ethical, and cultural prompts are generally non-ideological.
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Avoids toxic or offensive output.
⚖️ Neutral but underconfident in moral clarity when appropriate.
7. Response Timing & Efficiency: A-
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Response times:
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Most prompts under 1s
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Longest prompt (WWII) took 65.4 seconds — acceptable for large generation on a small model.
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No crashes, slowdowns, or freezing.
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Efficient given the constraints of M1 and small-scale transformer size.
⏱️ Efficient for its class — minimal latency in 95% of prompts.
📊 Final Weighted Scoring Table
| Category | Weight | Grade | Score |
|---|---|---|---|
| Accuracy | 30% | F | 0.0 |
| Guardrails & Ethics | 15% | A | 3.75 |
| Knowledge & Depth | 20% | C- | 2.0 |
| Writing Style | 10% | B- | 2.7 |
| Reasoning & Logic | 15% | D+ | 1.3 |
| Bias & Fairness | 5% | B | 3.0 |
| Response Timing | 5% | A- | 3.7 |
📉 Total Weighted Score: 2.02
🟥 Final Grade: D
⚠️ Key Takeaways:
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✅ Ethical compliance and speed are strong.
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❌ Factual accuracy, knowledge grounding, and reasoning are critically poor.
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❌ Hallucinations and redundancy (esp. Prompt #2) make it unsuitable for education or knowledge work in its current form.
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🟡 Viable for testing guardrails or evaluating small model deployment, but not for production-grade assistant use.
Not Quite Random 