llama-3.1-8b-q4-expert : GGUF

Meta-Llama-3.1-8B-Instruct — Ways of Thinking Finetune

A specialized finetune of Meta-Llama-3.1-8B-Instruct trained on a synthetic dataset of four structured thinking modes: astute, logical, pragmatic, and systemic. Quantized to Q4_K_M.

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Model Description

This finetune targets reasoning quality over response volume. Where the base model tends to enumerate possibilities and hedge conclusions, this model is trained to identify the load-bearing variable in a problem and reason outward from it. The result is shorter, more decisive answers with a higher signal-to-noise ratio on problems that have a dominant causal explanation.

The tradeoff is intentional: the model compresses aggressively, which means it can occasionally prune relevant constraints alongside noise on problems that require holding multiple interdependent variables simultaneously.

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Recommended System Prompt

First focus on the problem, and then try to find the best way to solve it without adding unnecessary information (occam's razor).

This prompt was found to best activate the model's trained behavior. Adding be verbose or systemic keywords shifts the balance toward the base model's strengths and partially negates the finetune's compression advantage.

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The Four Thinking Modes

Mode	Behavior	Activates on
Logical	Eliminates by contradiction; uses key clues to rule out options	Problems with a provably weaker alternative
Pragmatic	Answers the actionable question; drops everything else	Problems where one variable dominates the outcome
Astute	Reads domain context embedded in the problem; catches implicit signals	Problems where surface framing hides the real structure
Systemic	Traces how variables interact and cascade	Multi-loop or feedback-dependent problems

In practice, the model defaults most strongly toward logical and pragmatic modes. Astute activates reliably when domain vocabulary is present. Systemic is the weakest mode and benefits most from explicit prompting.

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Observed Behavior vs Base Model

Where the finetune outperforms

Direct causal reasoning. When a problem has one dominant cause, the finetune identifies it and stops. The base model lists all plausible causes with equal weight and defers to the reader.

Example — three hats sold as premier, one likely fake:

• Base model: "Each description could apply to either a genuine or a fake item. The likelihood is neutral."
• Finetune: "The first hat is most likely the fake, because the other two have clear and natural explanations for their quality. The first hat's 'premier' label is a claim that lacks evidence."

Domain knowledge activation. When the problem contains implicit domain context, the finetune uses it rather than treating the problem as purely abstract.

Example — bakery scheduling with one oven and one baker:

• Base model: treated baking time as fully blocking the baker, produced a suboptimal schedule
• Finetune: correctly identified that oven time and prep time overlap, allowing parallel task progression

Instruction following. The base model partially complies with the system prompt on the surface but consistently adds hedges and unnecessary alternatives. The finetune's brevity is structural, not a style choice.

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Where the base model outperforms

Counterintuitive or indirect causation. When the correct answer requires reasoning through feedback loops or non-obvious cascades, the finetune's compression bias strips out the indirect effects that produce the answer.

Example — wolf reintroduction causing deer population increase (trophic cascade):

• Base model: correctly identified vegetation recovery, mesopredator suppression, and behavior change as mechanisms
• Finetune: "prey population increases because wolves regulate the population of deer through predation" — a contradiction that ignores the question's premise

Multi-variable systems with interacting constraints. Problems where no single variable dominates and the answer emerges from sequencing or resource allocation across several constraints tend to exceed the finetune's pruning threshold.

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Failure mode profile

Failure type	Base model	Finetune
Over-hedges into non-answers	Common	Rare
Confident wrong answer from bad anchor	Occasional	Occasional
Drops load-bearing constraint	Rare	Occasional
Misses indirect / cascading causation	Rare	Common
Performs reasoning structure without depth	Rare	Occasional on systemic problems

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System Prompt Sensitivity

The model's behavior shifts meaningfully with system prompt wording:

Addition	Effect
occam's razor	Reinforces compression; reduces base model verbosity more than finetune
systemic / dynamic	Nudges finetune toward indirect causation; partially compensates for its weakest mode
be verbose	Unlocks base model's full reasoning chain; reduces finetune's compression advantage
consider all relevant information	Reduces constraint-dropping on multi-variable problems

The recommended prompt above reflects the configuration where the finetune's trained behavior most consistently outperforms the base model across a range of problem types.

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Addendum: Systemic Reasoning — Partial Fix via System Prompt

The finetune's weakest mode — systemic / indirect causation — shows meaningful improvement when the system prompt is adjusted to include conditional holistic reasoning:

First focus on the problem not only locally but holistically if required, and then try to find the best way to solve it without adding unnecessary information (occam's razor) or use systems thinking when appropriate.

The key addition is conditionality: "when appropriate" and "if required" allow the model to self-assess whether a problem warrants higher reasoning cost before applying compression. This partially compensates for the model's tendency to prune indirect causal chains alongside unnecessary information.

Observed result: On a trophic cascade problem (wolf reintroduction increasing deer population) that the model previously answered with a direct contradiction, this prompt produced a response correctly identifying habitat recovery from overgrazing and the keystone species mechanism — the core of the cascade — though stopping short of fully tracing the ecology-of-fear pathway.

Limitation: The fix is incomplete. The model reaches the correct neighborhood but may describe the mechanism at lower resolution than the problem requires. Additional training examples of indirect / feedback-loop causation are likely needed to fully close the gap. The system prompt adjustment is a viable workaround in the meantime.

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Base Model

meta-llama/Meta-Llama-3.1-8B-Instruct — Q4_K_M quantization

License

Follows the base model's license: Meta Llama 3.1 Community License

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

• For text only LLMs: llama-cli -hf JPQ24/llama-3.1-8b-q4-expert --jinja
• For multimodal models: llama-mtmd-cli -hf JPQ24/llama-3.1-8b-q4-expert --jinja

Available Model files:

• Meta-Llama-3.1-8B-Instruct.Q4_K_M.gguf

Ollama

An Ollama Modelfile is included for easy deployment. This was trained 2x faster with Unsloth

useful system prompt: "first focus on the problem, and then try to find the best way to solve it without adding unnecessary information (occam's razor)."