PUMA Vault

Ollama — Local LLM Inference Engine

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Ollama — Local LLM Inference Engine

Purpose in PUMA: Ollama is the inference backend that runs Llama 3.2 8B and Mistral 7B locally without GPU or API costs, enabling reproducible, deterministic experiments (seed=42, temperature=0).

Key Properties

PropertyValue
Installcurl -fsSL https://ollama.ai/install.sh | sh
APILocal REST API on localhost:11434
Modelsollama pull llama3.2:8b / ollama pull mistral:7b
Reproducibilityseed=42, temperature=0 via API params
HardwareRuns on CPU with 16GB RAM (quantized 4-bit)
RAM usage~5GB for 8B model (Q4_K_M quantization)

Basic API Call (Python)

import requests
import json
 
def call_ollama(prompt: str, model: str = "llama3.2:8b") -> str:
    """
    Call Ollama local API with deterministic settings.
    Seed and temperature=0 ensure reproducibility.
    """
    response = requests.post(
        "http://localhost:11434/api/generate",
        json={
            "model": model,
            "prompt": prompt,
            "stream": False,
            "options": {
                "seed": 42,
                "temperature": 0,
                "num_predict": 512
            }
        }
   )
    return response.json()["response"]

Model Versions Used in PUMA

ModelTagRAMSpeed (CPU)Use
Llama 3.2llama3.2:8b~5GB~15-40s/queryPrimary
Mistral 7Bmistral:7b~4.5GB~12-35s/queryComparison
Phi-3.5 Miniphi3.5:3.8b~2.5GB~6-15s/queryFallback (latency)

🔗 Related Notes

30 - Permanent/31 Concepts/PN-LLM-Agents | SP-Architecture 60 - Resources/61 Prompts/PT-PUMA-Triage-ZeroShot

id: LN-Tool-ClaudeCode title: “OpenCode — Agentic Coding CLI” type: literature-note subtype: tool tags: [tool, claude-code, coding-agent, cli, anthropic] url: “https://docs.the LLM platform/claude-code” puma_role: “Primary AI coding assistant for implementation” puma_phase: “F1, F2, F3” created: 2026-03-01

OpenCode — Agentic Coding CLI

Purpose in PUMA: OpenCode is used for implementing the PUMA benchmark modules (triage agent, estimation agent, metric calculator), generating test cases from BDD specs, and refactoring code to meet reproducibility standards.

Key Capabilities

  • Reads and edits files directly in the project
  • Runs tests and interprets failures autonomously
  • Understands full codebase context
  • Follows SDD specs when provided as input

Effective Prompt Patterns for PUMA

Pattern 1 — Spec-to-Code Generation

# In terminal with OpenCode active:
claude "Read the spec at docs/specs/SP-Triage-Agent.md and implement 
the TriageAgent class in src/agents/triage_agent.py. 
Requirements:
- Use Ollama API at localhost:11434
- Accept model name and strategy as parameters
- Return structured JSON with prediction and reasoning
- Add type hints and docstrings
- Include unit test skeleton in tests/test_triage_agent.py"

Pattern 2 — Debug with Context

claude "The Wilcoxon test in src/analysis/stats.py is producing p-values 
above 1.0. Here is the error: [paste error]. 
Look at the input data format and fix the issue. 
Explain what was wrong before making changes."

Pattern 3 — Reproducibility Audit

claude "Audit the entire src/ directory for reproducibility issues:
1. Find any random calls without seed=42
2. Find any hardcoded paths that should be configurable
3. Find any missing version pins in requirements.txt
Report findings as a checklist. Do not make changes yet."

🔗 Related Notes

LN-Tool-OpenCode | 60 - Resources/61 Prompts/61.3 Dev-Tools/PT-OpenCode-Agent-Triage

id: LN-Tool-OpenCode title: “OpenCode — Open-Source AI Coding Agent” type: literature-note subtype: tool tags: [tool, opencode, coding-agent, open-source] github: “https://github.com/opencode-ai/opencode” puma_role: “Open-source alternative to OpenCode for code generation” puma_phase: “F2, F3” created: 2026-03-01

OpenCode — Open-Source AI Coding Agent

Purpose in PUMA: OpenCode provides an open-source, locally-runnable alternative to OpenCode — important for the project’s commitment to open-source tooling and reproducibility without API costs.

Configuration for PUMA

# opencode.yaml
model: "ollama/llama3.2:8b"  # or any Ollama model
context:
  include:
    - "src/**/*.py"
    - "docs/specs/*.md"
    - "tests/**/*.py"
tools:
  - read_file
  - write_file
  - run_command
  - search_files

Best Prompt Patterns

# Structured task with context
[CONTEXT]: Working on PUMA benchmark — a reproducible LLM evaluation 
framework for ICT project management.

[TASK]: Implement the function calculate_f1_macro() in 
src/metrics/classification.py

[SPEC]: 
- Input: y_true (list[str]), y_pred (list[str]), classes (list[str])
- Output: dict with keys: f1_macro, f1_per_class, precision, recall
- Use sklearn.metrics internally
- Add comprehensive docstring with example

[CONSTRAINTS]:
- Python 3.11
- No external deps beyond scikit-learn
- Type hints required

🔗 Related Notes

LN-Tool-ClaudeCode | 60 - Resources/61 Prompts/61.3 Dev-Tools/PT-OpenCode-Refactor

id: LN-Tool-BrowserOS title: “Browser OS — AI Web Browsing Agent” type: literature-note subtype: tool tags: [tool, browser-os, web-agent, automation] puma_role: “Automated web research and dataset discovery” puma_phase: “F0, F1” created: 2026-03-01

Browser OS — AI Web Browsing Agent

Purpose in PUMA: Browser OS automates web-based research tasks: navigating academic databases, downloading papers, checking dataset availability, and verifying DOIs — freeing researcher time for higher-value analysis.

Use Cases in PUMA

TaskPrompt Pattern
Check paper availability”Navigate to [DOI URL] and confirm the paper is open-access or provide alternatives”
Dataset verification”Go to zenodo.org/records/5901893 and confirm: file format, size, licence, last update”
Citation count check”Search Semantic Scholar for [paper title] and report citation count and recent citing papers”
Tool documentation”Navigate to ollama.ai/docs and find the API parameter for controlling random seed”

Example PUMA Prompt

ROLE: You are a research assistant with web access.
CONTEXT: I am working on PUMA Project about LLM agents for project management.
OBJECTIVE: Find the 3 most recent papers (2024-2026) that benchmark 
           local LLMs on software engineering classification tasks.
INSTRUCTIONS:
1. Search arXiv.org with query: "local LLM benchmark software engineering 2024"
2. Filter for papers with available code repositories
3. For each paper: extract title, authors, date, arXiv ID, GitHub link
FORMAT: Markdown table with 5 columns: Title | Authors | Date | arXiv | GitHub

🔗 Related Notes

60 - Resources/61 Prompts/61.3 Dev-Tools/PT-BrowserOS-Web-Agent

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