An LLM (Large Language Model) is a model that generates text by predicting the next token based on the current context.

In real products, you typically combine:

• LLM: generates the next token (text output)
• RAG: retrieves external documents and injects them into context
• Agent: software that uses LLM + tools + feedback loop to complete tasks

A practical mental model:

• 1) Learned experience = Parameters / Weights
  • This is the “knowledge compressed into numbers” learned during training (e.g., 7B/8B/70B parameters).

• 2) What it can produce = Vocabulary
  • The fixed set of tokens the model can output.

• 3) Temporary memory = Context window
  • The maximum number of tokens the model can see at once (prompt + history + retrieved docs + output).

Note: The Tokenizer is a separate component that converts text into token IDs (it is not the model’s learned experience).

• parameter /pəˈræmɪtər/: tham số (trọng số học được)
• weight /weɪt/: trọng số (một kiểu parameter)
• tokenizer /ˈtoʊkəˌnaɪzər/: bộ tách & mã hóa text thành token
• vocabulary /vəˈkæbjəˌleri/: từ điển token
• context window /ˈkɑːntekst ˈwɪndoʊ/: cửa sổ ngữ cảnh (trí nhớ ngắn hạn)

Technically, an LLM is a probabilistic sequence model: it estimates a distribution over the next token given previous tokens.

Key implications:

• It does not have consciousness or human-like understanding.
• It can *appear* to understand because it learned patterns from massive data.
• It does not inherently know whether its output is true or false.
• Bad / biased / outdated training data can lead to bad answers.
• Ambiguous prompts can trigger “plausible but wrong” outputs (hallucination).

Tokens can be:

• word pieces, characters, punctuation, whitespace
• e.g., “Ha” + “ Noi” can be separate tokens

Generated tokens come from the model’s vocabulary (token dictionary):

• Vocabulary is fixed at model creation time.
• At each step, the model outputs probabilities across the entire vocabulary and selects a token (greedy or sampling).

So:

• The model is not “fetching text from the internet” while generating.
• If you want fresh facts, you must use tools (RAG / browsing / APIs).

Number of distinct tokens the model can output.

• Fixed (e.g., ~100K tokens depending on tokenizer/model).

Number of tokens in the dataset used for training.

• Often measured in trillions (T).
• Represents “experience volume,” not a stored Q&A database.

Maximum number of tokens the model can “see” at once.

• Includes prompt + chat history + retrieved docs + the output being generated.
• Exceed it and the model loses earlier parts.

These refer to parameter count:

• B = Billion parameters (weights).
• 7B ≈ 7 billion parameters, 70B ≈ 70 billion parameters.

Parameters are the model’s learned numeric weights—its compressed “experience.”

A typical LLM package includes:

• Tokenizer: rules to split text into tokens
• Vocabulary: mapping token ↔ ID
• Embedding table: maps token IDs to vectors
• Transformer layers: attention + feed-forward blocks
• Parameters (weights): billions of learned numbers
• Output head: converts final representation to next-token probabilities (softmax)

What is NOT inside:

• No database of memorized articles or Q&A pairs.
• No built-in truth checker.

• Image → encoder → vector
• Store vectors in a DB (FAISS / Milvus / pgvector)
• Query vector → nearest neighbors via cosine similarity
• Returns an existing item from the DB

• Text → tokens → embeddings
• Attention compares vectors internally to build a context representation
• Output is a probability distribution over the vocabulary
• Select next token, repeat

Core difference:

• Image search returns a stored item.
• LLM produces new text token-by-token.

RAG adds:

• Text → embedding → vector DB retrieval → inject documents → LLM answers based on retrieved sources.

So:

• Vector DB = searching
• LLM = composing/explaining

An Agent is a software system that uses an LLM to decide actions, execute tools, observe results, and iterate.

Agent formula:

• Agent = program logic + LLM + tools + memory + feedback loop

Compared to a normal chatbot:

• Chatbot: one-shot answer
• Agent: plan → act → check → fix → repeat

Required components:

• Goal: clear objective and success criteria
• Tools: shell/API/DB/filesystem/etc.
• Memory: logs / state (short-term and optionally long-term)
• Loop: repeat until success or stop condition
• Guardrails: tool allowlist, sandboxing, max steps, timeouts, logging

Because free-form text is hard to validate.

Common patterns:

• JSON-only output
• JSON + schema validation
• Function calling / tool calling

This allows:

• parse → validate → execute → verify → retry if needed

For each model below, capture:

• 1) Learned experience = Parameters / Weights (e.g., 7B/8B/70B)
• 2) What it can produce = Vocabulary (tokenizer + vocab size; fixed per model)
• 3) Temporary memory = Context window (max tokens visible at once)

• Parameters/Weights: Not publicly disclosed (varies by product/version).
• Vocabulary: Proprietary tokenizer; vocab size not consistently published.
• Context window: Varies by model tier/version (check product docs).
• Notes: Strong general reasoning + tool ecosystem.

• Parameters/Weights: Not publicly disclosed.
• Vocabulary: Proprietary tokenizer.
• Context window: Varies by model tier/version (check product docs).
• Notes: Strong long-form writing and code assistance.

• Parameters/Weights: Not publicly disclosed.
• Vocabulary: Proprietary tokenizer.
• Context window: Varies by model tier/version (check product docs).
• Notes: Strong multimodal and large-context options (depending on version).

• Parameters/Weights: Common sizes include 7B/8B/13B/70B (depends on generation).
• Vocabulary: Fixed per LLaMA generation (tokenizer + vocab size depends on version).
• Context window: Varies by generation (older versions smaller; newer may be larger).
• Local use: Best with quantized GGUF via llama.cpp / Ollama / LM Studio.

• Parameters/Weights: Mistral commonly 7B-class; Mixtral uses MoE (Mixture-of-Experts) variants.
• Vocabulary: Fixed per model release (tokenizer-specific).
• Context window: Varies by release/version.
• Local use: Mistral 7B-class is popular for fast local inference.

• Parameters/Weights: Multiple sizes (small → large; common local picks: ~7B-class).
• Vocabulary: Fixed per Qwen generation (tokenizer-specific).
• Context window: Varies by release/version (some versions support larger contexts).
• Local use: Often strong multilingual performance.

• Parameters/Weights: Multiple sizes; common local coder models are ~6–7B-class.
• Vocabulary: Fixed per model/tokenizer version.
• Context window: Varies by release/version.
• Local use: Code-focused variants are widely used for dev tasks.

• Parameters/Weights: Small models (often ~2–4B-class depending on version).
• Vocabulary: Fixed per Phi release/tokenizer.
• Context window: Varies by version.
• Local use: Great for low-resource devices; fast inference.

• Purpose: Simplest local runner (download + run models easily).
• Works best with: Quantized GGUF models.

• Purpose: GUI app to download, run, and chat with local models.
• Works best with: Quantized GGUF models, easy model management.

• Purpose: High-performance local inference engine for GGUF models.
• Works best with: Fine-grained control and optimization on CPU/Metal.

• parameter /pəˈræmɪtər/: tham số (trọng số học được)
• weight /weɪt/: trọng số
• vocabulary /vəˈkæbjəˌleri/: từ điển token (tập token có thể sinh)
• token /ˈtoʊkən/: đơn vị nhỏ (mảnh chữ) của văn bản
• context window /ˈkɑːntekst ˈwɪndoʊ/: cửa sổ ngữ cảnh (trí nhớ tạm thời)
• proprietary /prəˈpraɪəˌteri/: độc quyền, không công khai
• open-weight /ˌoʊpən ˈweɪt/: mở trọng số (công bố weights)
• quantized /ˈkwɑːntaɪzd/: đã lượng tử hóa (nén độ chính xác số)
• runtime /ˈrʌnˌtaɪm/: môi trường/chương trình chạy
• variant /ˈveriənt/: biến thể/phiên bản
• Mixture-of-Experts (MoE) /ˈmɪkstʃər əv ˈekspɜːrts/: kiến trúc “nhiều chuyên gia” (chỉ kích hoạt một phần model mỗi bước)

Disk/RAM depends heavily on quantization.

Typical sizes (rough guidance):

• FP16 (no quantization): ~14–16 GB for 7B/8B (not recommended on thin laptops)
• Q8: ~8–9 GB
• Q4/Q5 (most common for local use):
  • 7B Q4: ~4–5 GB
  • 8B Q4: ~4.5–5.5 GB
  • Q5 adds ~0.5–1.5 GB

Mac uses unified memory, so CPU+GPU share RAM.

Practical reality:

• Training a foundation model from scratch: not realistic on a laptop.
• Running (inference) 7B/8B quantized locally: realistic.
• Fine-tuning with LoRA: usually done on cloud GPU; laptop can prep data and run inference tests.

There are three main approaches:

• Changes only the context (runtime behavior).
• Does not change weights.

• Adds domain documents (guidelines, protocols, FAQ) into context.
• No weight changes.
• Easy to update and safer for fresh knowledge.

• Changes parameters via a small adapter.
• Best for behavior: formatting, refusal policy, questioning flow, tone.
• Not ideal for injecting large factual knowledge (use RAG for that).

• Freeze base model weights.
• Train a small LoRA adapter (delta weights).
• During inference: base + delta.
• Can enable/disable adapters or swap adapters per domain.

1. Step 1: Choose a base model (7B/8B common).
2. Step 2: Prepare instruction→response dataset (high-quality, domain-shaped).
3. Step 3: Train LoRA/QLoRA using a framework (e.g., HuggingFace PEFT / Axolotl), typically on cloud GPUs.
4. Step 4: Export adapter files (e.g., safetensors).
5. Step 5: Load base model + adapter locally for inference.

Prefer training:

• structured response format
• asking clarifying questions
• refusal behavior and escalation cues
• citing sources (especially combined with RAG)

Avoid training:

• private patient records
• uncontrolled “medical knowledge dumps”

Common directions:

• Multi-agent setups: planner, executor, critic, safety checker
• Self-critique / verification loops
• World models / simulation before action
• Stronger human-in-the-loop controls

• token /ˈtoʊkən/: unit of text used by the model (often a word piece)
• vocabulary /vəˈkæbjəˌleri/: the model’s token dictionary
• context /ˈkɑːntekst/: the visible token window during inference
• parameter /pəˈræmɪtər/: a learned weight value in the network
• quantization /ˌkwɑːntəˈzeɪʃən/: reducing numeric precision to shrink models
• embedding /ɪmˈbedɪŋ/: vector representation of tokens/items
• attention /əˈtenʃən/: mechanism that mixes information across tokens
• softmax /ˈsɔːftˌmæks/: converts scores into probabilities
• hallucination /həˌluːsɪˈneɪʃən/: plausible-sounding but incorrect output
• agent /ˈeɪdʒənt/: autonomous software using LLM + tools
• tool calling /tuːl ˈkɔːlɪŋ/: structured calls to tools/functions
• RAG /ræɡ/: Retrieval-Augmented Generation (retrieve docs then generate)
• fine-tune /ˌfaɪnˈtuːn/: adapting a model with additional training
• LoRA /ˈloʊrə/: Low-Rank Adaptation (lightweight fine-tune adapter)
• QLoRA /kjuːˈloʊrə/: LoRA + quantization for memory-efficient training

1. Need accurate, updatable domain knowledge: do RAG first.
2. Need consistent behavior/formatting: add LoRA.
3. Need task execution: build an Agent with:
   • strict JSON/tool outputs
   • schema validation
   • allowlisted tools + sandbox
   • logs + max steps + stop rules