---
title: "Choosing the Right Open-Source Small LLMs and Tools for Effective Learning and Practical Fine-Tuning"
created: 2026-04-15
updated: 2026-04-15
type: summary
tags: [LLM, finetuning, models, notes, telegram]
external: http://go/ui/posts/2409/
---

**user**: What are the popular open source small LLMs can be used for me to learn llm and fine tuning?  one of the examples is https://github.com/arman-bd/guppylm?tab=readme-ov-file

**chatgpt**: Great question—and you’re already on the right track with something like GuppyLM (tiny, educational). Let’s structure this properly so you can **learn LLM internals + actually fine-tune models on your own machine**.

---

# 🧠 1. First: How to think about “small LLMs”
For learning + fine-tuning, you want models in roughly:

- **<1B params** → understand architecture / train from scratch
- **1B–8B params** → practical fine-tuning (LoRA / QLoRA)
- **>8B** → more “production”, less beginner-friendly

Key insight:
> Fine-tuning small models can match much larger models on specific tasks if done well academia12

---

# 🧪 2. Best “toy / educational” small LLMs (like GuppyLM)

These are **great for learning internals**, not performance:

### 🟢 Minimal / from-scratch style
- **GuppyLM (your example)**  
- **nanoGPT (Karpathy)**
- **minGPT**
- **tinygrad + LLM demos**

👉 Why use them:
- Fully readable code (hundreds of lines)
- Train on laptop / CPU
- Understand attention, tokenization, training loop

👉 Downside:
- Not useful for real-world tasks

---

# ⚙️ 3. Best small models for *real fine-tuning* (IMPORTANT)

This is where you should spend most of your time.

## 🔥 Tier A (best balance today)

### 1. LLaMA family (Meta)
- Sizes: **1B, 3B, 8B**
- Most tutorials + ecosystem
- Strong general-purpose

👉 Why:
- “Default starting point” for open-source LLMs   
- Huge ecosystem (LoRA, datasets, tools)

---

### 2. Qwen (Alibaba)
- Sizes: **0.5B → 7B+**
- Very strong performance per size

👉 Why:
- Excellent multilingual + coding ability
- Small versions (0.5B–4B) are perfect for laptops

👉 Real-world insight:
> Fine-tuned Qwen 4B can match much larger models in tasks reddit23

---

### 3. Gemma (Google)
- Sizes: **2B, 7B**
- Designed for efficiency

👉 Why:
- Clean architecture
- Runs well on consumer GPUs
- Good for structured tasks

---

### 4. Mistral (7B)
- Very strong performance vs size

👉 Why:
- Efficient transformer design
- Great baseline for production-style learning

---

## 🧠 Tier B (research / interesting)

### 5. Phi (Microsoft)
- Sizes: **1.3B / 2.7B**
- Trained on synthetic high-quality data

👉 Why:
- Extremely data-efficient
- Good for experimentation

---

### 6. RWKV
- RNN-style alternative to transformers

👉 Why:
- Different architecture (important for learning)
- Very memory efficient

---

### 7. Pythia (EleutherAI)
- Sizes: 70M → 12B

👉 Why:
- Designed for interpretability research
- Great for studying scaling laws

---

# 🧰 4. Best tools for fine-tuning (don’t skip this)

Models alone are not enough—tooling matters more.

### 🔥 Must-know tools:

- **Unsloth**
  - 2–5× faster training, less VRAM   
  - Can fine-tune 8B on single GPU

- **Axolotl**
  - Supports LLaMA, Mistral, Qwen, etc.   
  - YAML configs → reproducible pipelines

- **HuggingFace Transformers + PEFT**
  - Industry standard

---

# 🧭 5. Recommended learning path (optimized)

If your goal = **deep understanding + practical skill**

## Step 1 — Understand internals
- nanoGPT / GuppyLM
- Train on tiny dataset

## Step 2 — First real fine-tune
- Model: **LLaMA 3 1B or 3B**
- Method: **LoRA / QLoRA**
- Tool: Unsloth

## Step 3 — Compare models
- Try:
  - Qwen 4B
  - Gemma 2B
- Same dataset → compare results

## Step 4 — Advanced
- Instruction tuning
- Dataset curation
- RLHF / DPO

---

# ⚖️ 6. Quick comparison (what to pick)

| Goal | Best model |
|------|-----------|
| Learn from scratch | nanoGPT / GuppyLM |
| First real fine-tune | LLaMA 3 (1B–3B) |
| Best small performance | Qwen 4B |
| Lightweight experiments | Gemma 2B |
| Research / interpretability | Pythia |

---

# 🚀 My strong recommendation (based on your background)

Given you're a **DevOps + ML engineer**, don’t stay too long in toy models.

👉 Best stack for you:

- Model: **LLaMA 3 3B or Qwen 4B**
- Tool: **Unsloth**
- Method: **QLoRA**
- Infra: single GPU (even 12–24GB VRAM works)

This gives you:
- Real-world relevance
- Transferable skills
- Production-level understanding

---

If you want, I can:
- Suggest a **hands-on project (like fine-tuning your own coding assistant)**
- Or give you a **step-by-step repo setup (Mac / local GPU / cloud)**