base-model-selector

name	base-model-selector
description	Use when starting a fine-tuning project to determine if fine-tuning is needed, or when evaluating whether a base model meets quality thresholds for a specific domain task

Base Model Selector + Baseline Evaluation

Overview

Evaluate base models against your domain rubric BEFORE committing to fine-tuning. Most projects skip this and waste resources fine-tuning models that were already good enough—or fine-tuning models that need fundamental architectural changes instead.

Core principle: Never fine-tune without baseline data. The baseline tells you if fine-tuning will help and by how much.

When to Use

digraph when_to_use {
    "Starting fine-tuning project?" [shape=diamond];
    "Have baseline metrics?" [shape=diamond];
    "Use this skill" [shape=box, style=filled, fillcolor=lightgreen];
    "Skip - you have data" [shape=box];
    "Proceed to data generation" [shape=box];

    "Starting fine-tuning project?" -> "Have baseline metrics?" [label="yes"];
    "Starting fine-tuning project?" -> "Skip - you have data" [label="no"];
    "Have baseline metrics?" -> "Proceed to data generation" [label="yes"];
    "Have baseline metrics?" -> "Use this skill" [label="no"];
}

Use when:

• Starting any fine-tuning project
• Switching base models mid-project
• Evaluating if existing model needs fine-tuning for new domain

Skip when:

• You already have baseline evaluation data
• Pure prompting project (no fine-tuning planned)

The Process

Step 1: Deep Research (EXHAUSTIVE)

This is NOT quick research. You must build a comprehensive comparison table covering ALL viable candidates for your target size and use case.

1a. Identify ALL Candidates

Search for models in your target parameter range. For 7-10B, check:

Family	Models to Evaluate
Qwen	Qwen 3 8B, Qwen 2.5 7B, Qwen 2.5 14B
Llama	Llama 3.1 8B, Llama 3.2 3B/8B
Mistral	Mistral 7B v0.3, Zephyr 7B, OpenChat 3.5
Google	Gemma 2 9B, Gemma 3 4B
Microsoft	Phi-4 14B, Phi-4-mini 3.8B
DeepSeek	DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Llama-8B
Specialized	Domain-specific fine-tunes on HuggingFace

Search queries to run:

• "best [size] parameter LLM 2025 [your domain]"
• "[model family] vs [model family] comparison 2025"
• "open source LLM [your domain] fine-tuned 2025"
• "best LLM for [specific capability] 2025" (e.g., empathy, coding, reasoning)

1b. Evaluate Each Factor

Factor	Questions to Answer
License	Apache 2.0 / MIT / Llama? Commercial use allowed?
Context length	Fits your longest input? (calculate: turns × 2 × ~200 tokens)
Architecture focus	Reasoning-focused or conversation-focused?
Domain fit	Research shows this model type works for your domain?
Recency	Latest generation? Newer models often outperform.
Quantization	GGUF available? Quality at Q4_K_M?
Community	Active development? Known issues?

1c. Domain-Specific Research

CRITICAL: Different domains need different model characteristics.

Domain	Prefer	Avoid
Therapeutic/Empathy	Chat-focused, dialogue-optimized	Reasoning-focused (R1, o1, Phi-4)
Coding	Code-trained, reasoning-capable	Pure chat models
Math/Logic	Reasoning models, thinking modes	Pure instruction-following
Creative Writing	High temperature tolerance, natural flow	Overly formal models
Factual Q&A	Knowledge-dense, grounded	Creative/hallucination-prone

Example research for therapeutic coaching:

"Models such as DeepSeek R1, OpenAI o3, and o1 excel in logical reasoning but fall short in conversational fluency and emotional responsiveness."

This means: avoid DeepSeek R1 distills for therapy, even though they're excellent models.

1d. Build Comparison Table

Create a comprehensive table with ALL candidates:

| Model | Params | License | Context | Release | Domain Fit | Notes |
|-------|--------|---------|---------|---------|------------|-------|
| Qwen 3 8B | 8.2B | Apache 2.0 | 128K | Apr 2025 | ⭐⭐⭐⭐ | Thinking/non-thinking modes |
| ... | ... | ... | ... | ... | ... | ... |

Rate domain fit on a scale (⭐ to ⭐⭐⭐⭐⭐) based on research findings.

Step 2: Select Primary Candidate

Based on research, select ONE candidate. Document:

• Why this model over alternatives
• Expected strengths for your domain
• Potential concerns to watch for

Also identify a backup candidate in case primary underperforms.

Step 3: Pull Model

# Ollama
ollama pull qwen3:8b

# Or llama.cpp with GGUF
huggingface-cli download Qwen/Qwen3-8B-Instruct-GGUF qwen3-8b-instruct-q4_k_m.gguf

# Verify it runs
llama-cli -m model.gguf -p "Hello, testing."

Step 4: Generate Evaluation Scenarios

Create ~50 diverse scenarios from your input taxonomy. These are INPUTS only (user messages), not full conversations.

# Generate opening messages covering your taxonomy
scenarios = []
for topic in taxonomy["topics"]:
    for subtopic in topic["subtopics"]:
        for style in ["terse", "conversational", "detailed"]:
            scenarios.append(generate_opening(topic, subtopic, style))

Critical: Scenarios must cover your full input distribution, including edge cases.

Step 5: Run Base Model

Run the base model on each scenario. Collect single-turn responses.

responses = []
for scenario in scenarios:
    response = generate(
        model=model_path,
        prompt=scenario,
        system=your_system_prompt,
    )
    responses.append({
        "scenario": scenario,
        "response": response,
    })

Step 6: Assess with Rubric

Run your domain rubric on each response. Calculate pass rate.

results = []
for item in responses:
    assessment = assess_single_turn(item["scenario"], item["response"])
    results.append(assessment)

pass_rate = sum(1 for r in results if r.passed) / len(results)

Step 7: Make Decision

digraph decision {
    "pass_rate" [shape=diamond, label="Pass Rate?"];
    "qualitative" [shape=box, label="≥70%: Qualitative review\nMay not need fine-tuning"];
    "proceed" [shape=box, label="50-70%: Proceed with\nfine-tuning (moderate gains)"];
    "full" [shape=box, label="<50%: Full pipeline\n(significant gains possible)"];

    "pass_rate" -> "qualitative" [label="≥70%"];
    "pass_rate" -> "proceed" [label="50-70%"];
    "pass_rate" -> "full" [label="<50%"];
}

Pass Rate	Decision	Next Step
≥ 70%	Likely sufficient	Do qualitative review. If specific failure modes exist, consider targeted fine-tuning. Otherwise, deploy base model.
50-70%	Moderate improvement possible	Proceed with fine-tuning. Document failure modes to guide data generation.
< 50%	Significant improvement needed	Full pipeline. Analyze failures—are they fixable with data, or architectural?

Step 8: Document Results

Create docs/base-model-evaluation.md:

# Base Model Evaluation

**Date:** YYYY-MM-DD

## Model Selection

### Research Summary

[Summary of models considered and why primary was chosen]

| Model | Domain Fit | Why Considered | Why Selected/Rejected |
|-------|------------|----------------|----------------------|
| Qwen 3 8B | ⭐⭐⭐⭐ | Latest gen, non-thinking mode | **SELECTED** - best chat quality |
| DeepSeek-R1-7B | ⭐⭐ | Strong reasoning | Rejected - poor empathy per research |
| ... | ... | ... | ... |

### Selected Model

- **Model:** qwen3:8b-instruct-q4_k_m
- **Parameters:** 8.2B
- **License:** Apache 2.0
- **Context:** 128K tokens

## Evaluation Results

- **Scenarios:** 50
- **Pass rate:** XX%
- **Decision:** [DEPLOY / FINE-TUNE / FULL PIPELINE]

## Failure Analysis

| Criterion | Failure Count | Pattern |
|-----------|---------------|---------|
| CQ3 | 12 | Jumps to advice without validation |
| CQ8 | 3 | Missed crisis signals |

## Qualitative Notes

[Specific observations about response quality]

## Next Steps

[What happens next based on decision]

Outputs

1. Research table — All candidates with domain fit ratings
2. Selected model — Name, version, and rationale
3. Baseline pass rate — Percentage on rubric
4. Failure analysis — Which criteria fail most
5. Decision document — docs/base-model-evaluation.md

Common Mistakes

Mistake	Why It's Wrong
Shallow research	Missing better candidates; picking based on familiarity
Ignoring domain fit	Reasoning models fail at empathy tasks; chat models fail at logic
Using old model lists	Models release monthly; 6-month-old recommendations are stale
Skipping baseline	You won't know if fine-tuning helped
Testing multiple models in parallel	Wastes time—pick one, evaluate, iterate if needed
Using full conversations for baseline	Single-turn is enough to assess base capability
Ignoring qualitative review at high pass rates	Numbers hide specific failure modes worth fixing
Proceeding without documenting failures	Failure patterns should guide data generation

Quick Reference

# 1. Research (use web search extensively)
# Build comparison table with 8-12 candidates

# 2. Pull model
ollama pull qwen3:8b
# or
huggingface-cli download Qwen/Qwen3-8B-Instruct-GGUF

# 3. Generate scenarios
uv run python generator.py --scenarios-only 50

# 4. Run evaluation
uv run python evaluate_base_model.py --model qwen3:8b

# 5. Check results
cat docs/base-model-evaluation.md