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From a model to your own agent

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Course: from a model to your own agent — index

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  1. 00Course: from a model to your own agent — index
  2. 01Local models from the ground up — a hands-on course
  3. 02Part 2 — Model internals: feel what happens underneath
  4. 03Part 3 — Control: how to force a local model to obey
  5. 04Part 4 — Memory: RAG and context engineering from scratch
  6. 05Part 5 — Multimodality: an agent that sees, hears and speaks
  7. 06Part 6 — The Agent Loop from Scratch
  8. 07Part 7 — Sandbox, Security and Monitoring
  9. 08Part 8 — Offline optimization: make it fast on your own hardware
  10. 09Part 9 — The open-source tool landscape
  11. 10Part 10 — Hybrid: local model + Claude API

Created: 22 September 2026. For Michał.

Purpose of the whole thing. After working through this course you can build from scratch your own agent in the OpenClaw / Claude Code class — a loop that reads files, writes code, runs commands, sees images, listens and speaks, remembers context and works offline on your own hardware — and you understand every layer underneath it.

Not "you can use a framework". You understand why a framework does what it does, and you can write it yourself. Only then do you consciously choose off-the-shelf tools.

The path

  PART 1 ──► you talk to the model, you understand the stack
     │        tokens/weights/runtime/server, llama-cli, llama-server, curl
     ▼
  PART 2 ──► you FEEL the model
     │        tokenizer, logits, sampling, KV-cache, embeddings
     ▼
  PART 3 ──► you FORCE the model to obey
     │        chat template, GBNF grammars, determinism, evaluation
     ▼
  PART 4 ──► you give the model MEMORY
     │        embeddings, vector database, chunking, citations, compaction
     ▼
  PART 5 ──► you give it SENSES
     │        vision, Whisper (hearing), Piper (speech) — plus a hardware list
     ▼
  PART 6 ──► you build the AGENT LOOP          ◄── this is where "Your OpenClaw" is born
     │        tools, file editing, shell, errors, subagents, journal
     ▼
  PART 7 ──► you lock it in a CAGE
     │        bwrap, limits, network, prompt injection, audit, monitoring
     ▼
  PART 8 ──► you make it FAST
     │        prompt cache, speculative decoding, quantization, LoRA, 4 cards
     ▼
  PART 9 ──► you get to know the ECOSYSTEM
     │        alternative engines, OCR, databases, frameworks — rated for RDNA4
     ▼
  PART 10 ─► optional: a HYBRID with the Claude API
              routing, privacy boundary, offline-first

⚠️ The order is not decoration. Part 6 cannot be understood without part 3 (without grammars a local model will not call a tool reliably). Part 8 cannot be understood without part 2 (without the KV-cache you do not know what you are optimizing). Do not skip ahead — except for part 9, which you can read at any time.

Files

# File Level What you will learn Time
1 LOCAL-MODELS-FUNDAMENTALS.md ⭐ ✅ you have it — the stack, downloading, llama-cli, llama-server, llama-swap, first tools —
2 02-MODEL-INTERNALS.md ⭐⭐ tokenizer, logits, sampling, KV-cache, embeddings, perplexity ~4 h
3 03-CONTROL-AND-RELIABILITY.md ⭐⭐⭐ raw chat template, ⭐ GBNF grammars, JSON Schema, determinism, evaluation harness ~5 h
4 04-MEMORY-RAG-AND-CONTEXT.md ⭐⭐⭐⭐ embeddings, vector database from scratch, chunking, BM25, hybrid, citations, compaction ~6 h
5 05-MULTIMODALITY.md ⭐⭐⭐⭐ vision (llama-mtmd), Whisper, Piper, voice loop + ⭐ hardware shopping list ~6 h
6 06-AGENT-LOOP-FROM-SCRATCH.md ⭐⭐⭐⭐ ⭐⭐ agent loop, tool protocol, read/edit/bash, guard, subagents, journal ~8 h
7 07-SANDBOX-SECURITY-MONITORING.md ⭐⭐⭐⭐ bwrap, systemd-run, network control, ⭐ prompt injection, audit, monitoring ~5 h
8 08-OPTIMIZATION-OFFLINE.md ⭐⭐⭐⭐⭐ prompt cache, ⭐ speculative decoding, routing, custom quantization, LoRA, 4 cards ~7 h
9 09-TOOL-LANDSCAPE.md ⭐⭐⭐ ⭐ tool catalogue rated for RDNA4: engines, OCR, databases, frameworks, evaluation ~3 h
10 10-HYBRID-LOCAL-PLUS-CLAUDE.md ⭐⭐⭐ privacy boundary, hybrid router, costs, offline-first ~3 h

Total: ~47 hours of hands-on-keyboard work. This is not a course to be read.

Conventions in all files

The llama.cpp binaries are not in PATH. Run this once per terminal session:

cd ~/Desktop/github_repos/llm-education   # the repository directory (yours may differ)
source rig-env.sh

Create this file once:

cat > $LLMEDU/rig-env.sh <<'EOF'
# Binaries
export LB=~/llama.cpp/build/bin
export WB=$NARZEDZIA/whisper.cpp/build/bin          # after part 5

# Models
export M=~/models
export BIELIK=$M/bielik-11b/Bielik-11B-v3.0-Instruct.Q8_0.gguf
export OSS20=$M/gpt-oss-20b/gpt-oss-20b-MXFP4.gguf
export QWEN32=$M/qwen3-32b/Qwen_Qwen3-32B-Q4_K_M.gguf
export QWEN80=$M/qwen3-next-80b/Q4_1/Qwen3-Next-80B-A3B-Instruct-Q4_1-00001-of-00002.gguf
export DRAFT=$M/gpt-oss-120b/eagle3-gpt-oss-120b-Q8_0.gguf
export EMB=$M/embed/bge-m3-f16.gguf        # after part 4
export VL=$M/vl/Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf          # after part 5
export VLMM=$M/vl/mmproj-Qwen2.5-VL-7B-Instruct-f16.gguf    # after part 5
export WMODEL=$NARZEDZIA/whisper.cpp/models/ggml-large-v3-turbo.bin  # after part 5

# P12/P32: the iGPU is GPU[2] — exclude it
export HIP_VISIBLE_DEVICES=0,1

# P4: P2P hangs the host with 4 cards
export NCCL_P2P_DISABLE=1
export NCCL_PROTO=Simple
EOF

source $LLMEDU/rig-env.sh
$LB/llama-cli --version    # → 0.4.1-dev (build 1, commit 987498f)

Models I use in the examples

Shorthand Size Role in the course
$BIELIK 11.9 GB Polish, Q8 — language exercises
$OSS20 12.1 GB ⭐ main agent model — fast
$QWEN32 19.8 GB smarter, dense — the large one in the router
$QWEN80 49.7 GB MoE — experiments with -cmoe
$DRAFT 849 MB ⭐ draft model — speculative decoding (part 8)

Rules I follow throughout the course

  1. No frameworks until you have written it yourself. You will meet frameworks in part 9 and you will know what they hide.
  2. Pure Python, no numpy and no torch. A dot product written as a loop teaches more than np.dot(). Where a library is needed, I say so openly.
  3. Every command has an explanation of what it does and what output to expect.
  4. Every chapter ends with an exercise that actually produces something.
  5. Do not optimize without measuring. Every lever in part 8 has a number before and after.
  6. Pitfalls (P…) are numbered jointly with TECHNIKA.md. Hardware: P1–P18. Software, from this course: P20–P52.

Register of software pitfalls

ID Pitfall Part
P20 Counting tokens with another model's tokenizer 2
P21 repeat_penalty > 1.0 breaks JSON generation 2
P22 Context is VRAM: weights + KV-cache together 2
P23 Context overflow can be silent 2
P24 A generative model used as an embedding model — weak 2
P25 Not every model has a system role 3
P26 A grammar with no exit loops the generation 3
P27 A grammar costs 10–30% tok/s 3
P28 One sampling profile for the whole agent 3
P29 A chunk without metadata — poorly retrieved 4
P30 Vectors are useless for identifiers 4
P31 Compaction loses exact numbers 4
P32 The iGPU as GPU[2] — models land on 2 GB 5
P33 mmproj incompatible with the model 5
P34 OuteTTS speaks Polish poorly 5
P35 TTS without number normalization 5
P36 Whisper hallucinates on silence and noise 5
P37 The model forgets to call odpowiedz 6
P38 No freshness check — the agent eats the human's changes 6
P39 A command allowlist is not a sandbox 6
P40 A single observation fills the whole context window 6
P41 Delegation has a fixed cost 6
P42 --bind instead of --ro-bind on a system directory 7
P43 Mounting the home directory ends the sandbox 7
P44 No --new-session — injection into the terminal 7
P45 HTTP_PROXY is a convention, not enforcement 7
P46 A changing system prompt kills the prompt cache 8
P47 The draft model eats VRAM and may slow things down 8
P48 Routing between servers invalidates the cache 8
P49 PPL on calibration text is artificially good 8
P50 -np splits the context, it does not multiply throughput 8
P51 A regex filter lets through what you did not anticipate 10
P52 Silent dependence on the cloud 10

Where things live

File Role
HANDOFF.md project state — what is done, what is next
TECHNIKA.md procedures on this machine — inventory, hardware pitfalls P1–P18
KARTY-3-I-4-PLAN-PODLACZENIA.md expansion to 4 cards
course 00–10 how it works and how to build an agent on it
BIZNES.md thesis, market, product

Directories that will appear during the course:

$KURS/       measurement tools, RAG, evaluation sets
$AGENT/      ⭐ your agent loop, sandbox, audit, journals
$NARZEDZIA/whisper.cpp/  STT (part 5)
$NARZEDZIA/piper/      TTS (part 5)
~/models/     models: embed/, vl/, tts/, wlasne/

Before you start — check the workstation

# 1. Cards visible? (expect 2x R9700 + the iGPU as GPU[2])
rocm-smi --showid

# 2. Binaries working?
source $LLMEDU/rig-env.sh && $LB/llama-cli --version

# 3. Models in place? (expect 7 files)
find ~/models -name '*.gguf' | wc -l

# 4. Disk space? (expect ~1.5 T)
df -h /home | tail -1

⚠️ Pitfall P12/P32 — the iGPU is also a ROCm device. rocm-smi shows three devices: GPU[0], GPU[1] are the R9700 cards (34.2 GB each), GPU[2] is the integrated graphics with 2 GB. Small models can land there and run 50× slower. HIP_VISIBLE_DEVICES=0,1 is in rig-env.sh precisely for this reason.

⚠️ After adding cards 3 and 4 the numbering will change again (P13). Remap it with amd-smi list and update rig-env.sh.

Start: 02-MODEL-INTERNALS.md.

After this course: course 2

course_agentic_apps/00-INDEX.md — ⭐ agentic applications on local models. Nineteen parts, ~102 h. Course 1 teaches how a model works. Course 2 teaches how to build a system around it that can be trusted:

a harness across many context windows · bi-temporal memory · code-mode
production sandbox · proactivity · a fleet of three agents
evaluation and traces · OWASP ASI01–ASI10 · releases and runbook

It builds one growing system $OSA/ with three personal agents. Every part has an experiment with an empty table for your measurement. Pitfalls P60–P139.

⚠️ Do not start course 2 before part 6 of course 1 — the whole of course 2 stands on the agent loop you build there.

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