smashingLearning

smashingLearning: local LLM pipeline — dataset → fine-tune → serve → minimal agents.

Setup (fast)

git clone https://github.com/ConardLi/easy-dataset.git
git clone https://github.com/hiyouga/LLaMA-Factory.git

# easy-dataset (dev)
cd easy-dataset && npm install && npm run start   # http://localhost:1717

# llma-factory (fine-tune)
cd ../LLaMA-Factory && pip install -r requirements.txt

Quick operations

• Prepare training data with easy-dataset and export JSONL.
• Fine-tune via LLaMA-Factory (see repo for params).
• Serve model locally (Ollama or any http-compatible server).

Notes

• See STATE_OF_ART_RECAP.md and STATE_OF_ART_RECAP_IT.md for current recommendations (RAG, agent-loop, swarm).
• Replace model names with your served model identifiers.

Ollama & Testing

See ollamaDocs/ for official Ollama documentation (core API, tool calling, streaming, web search).

Test local models with ollama_test_llms.py:

cd ollamaExperiments
python ollama_test_llms.py                        # print to stdout
python ollama_test_llms.py > ollama_test_llms.log 2>&1  # log to file

What it tests

Each test is independent. All except tools work on any model, including models that don't support function calling.

Flag	What it tests	Passes if
reasoning	Distributed caching system-design question	Response mentions eviction/coherence/consistency keywords
code	Generate a factorial(n) function	Code is extracted and executed — factorial(5)==120
finance	BUY/SELL/HOLD analysis of AAPL	Response starts with one of the three words
stream	Streaming token generation	>5 chunks received, >30 words total
security	Identify reentrancy bug in a Solidity contract	Response mentions "reentrancy" or "check-effects"
tools	4 scenarios (see below)	Model invokes the expected tools

Tools test scenarios

Requires a model that supports Ollama function-calling (e.g. llama3.1, mistral, qwen2.5). Each scenario runs independently.

Scenario	Prompt	Expected
tools/calc	Complex arithmetic expression	Calls calculate_expression
tools/weather	"What's the weather in Paris?"	Calls get_weather
tools/multi-tool	"Weather and time in Tokyo?"	Calls both get_weather + get_current_time (≥2 calls)
tools/search	"Search for LLM prompt engineering tips"	Calls search_web, summarizes results

The tools runner uses a full agentic loop: model calls tool → receives result → can call more tools → produces final answer.

Available mock tools (deterministic, no external calls):

• calculate_expression(expression) — safe AST-based math (no eval)
• get_weather(city) — mock weather data
• get_current_time(timezone) — mock UTC time
• search_web(query) — mock search results

Config — edit CONFIG at the top of the file:

CONFIG = {
    "models": ["phi3:3.8b", "llama3.1:8b"],  # empty list → all available models
    "reasoning": True,
    "code":      True,
    "tools":     True,   # skip if model doesn't support function calling
    "finance":   True,
    "stream":    True,
    "security":  True,
}

Output format

MODEL: llama3.1:8b
  [PASS] reasoning     1243ms  Response mentions LRU, TTL, consistency...
  [PASS] code          876ms   function executed correctly: factorial(5)=120
  [PASS] finance       654ms   HOLD — growth is moderate but FCF is strong...
  [PASS] stream        3201ms  chunks=187 words=312 ~97 words/s
  [PASS] security      891ms   Reentrancy vulnerability — attacker calls...
  [PASS] tools/calc    1102ms  tool_calls=1 result=16226741...
  [FAIL] tools/multi-tool 2341ms  tool_calls=1 (expected ≥2)...

Summary table at the end shows PASS/FAIL/SKIP per test per model.

Resources

See resources_sorted.md and resources_extendex.md for 2026 tech stack: AI frameworks, data tools, trading bots, e-commerce platforms, automation, and more.

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