Awesome let’s make a catalog file for the 200+ models.
I’ll prepare a Markdown table (easy to read, can also be converted into JSON or CSV) with the following columns:
- Model ID (Hugging Face repo)
- Family (LLaMA, Mistral, Falcon, etc.)
- Parameters (e.g., 7B, 13B, 70B)
- Type (Base / Chat / Instruct / Quantized)
📑 AI Model Catalog (200+ Models)
Here’s the starter catalog (excerpt — the full file will be large, but I’ll generate all entries if you want the full dump):
⚡ This excerpt covers just ~50 entries.
Here’s a cleaned, compressed, blog-friendly version of your large model table. I’ve grouped by Family to make it easy to read, and removed repetition while keeping all the key details (Model, Params, Type).
Popular Open-Source LLMs (Sorted by Family)
| Family | Models |
|---|---|
| LLaMA 2 | - Llama-2-7B (Base / Chat / Quantized) - Llama-2-13B (Base / Chat / Quantized) - Llama-2-70B (Base / Chat / Quantized) |
| LLaMA 3 | - Meta-Llama-3-8B (Base / Instruct / Quantized) - Meta-Llama-3-70B (Base / Instruct / Quantized) |
| Mistral / Mixtral | - Mistral-7B (Base / Instruct / Quantized) - Mixtral-8×7B (Base / Instruct / Quantized) - Mixtral-8×22B (Instruct) |
| Gemma (Google) | - Gemma-2B (Base / Instruct / Quantized) - Gemma-7B (Base / Instruct / Quantized) |
| Vicuna | - Vicuna-7B (Chat / Quantized) - Vicuna-13B (Chat / Quantized) - Vicuna-33B (Chat / Quantized) |
| Falcon | - Falcon-7B (Base / Instruct / Quantized) - Falcon-40B (Base / Instruct / Quantized) |
✅ This condensed layout avoids repetition, is scannable for blog readers, and still communicates:
- Model Family
- Parameter sizes
- Types (Base / Chat / Instruct / Quantized)
The full table (200+ rows) will be long, but if readers want then they can generate it by themselves as:
- A Markdown file (
models_catalog.md) → easy for human reading. - A JSON file (
models_catalog.json) → easy for programmatic use. - A CSV file (
models_catalog.csv) → easy for Excel/Google Sheets.
Perfect ✅
Here’s the final ready-to-use blog table with parameters shown separately for quick comparison:
Choosing the right Large Language Model (LLM) can be tricky with so many options available. To make it easier, here’s a quick comparison of the most popular open-source LLM families. The table highlights their parameter sizes and available variants (Base, Chat, Instruct, Quantized), so you can quickly see which models fit your project’s needs.
# Popular Open-Source LLMs (Sorted by Family)
| **Family** | **Parameters** | **Models / Types** |
|------------|----------------|---------------------|
| **LLaMA 2** | 7B, 13B, 70B | Base / Chat / Quantized |
| **LLaMA 3** | 8B, 70B | Base / Instruct / Quantized |
| **Mistral / Mixtral** | 7B, 8×7B, 8×22B | Base / Instruct / Quantized |
| **Gemma (Google)** | 2B, 7B | Base / Instruct / Quantized |
| **Vicuna** | 7B, 13B, 33B | Chat / Quantized |
| **Falcon** | 7B, 40B | Base / Instruct / Quantized |
This way, your blog readers can instantly compare model families, parameter sizes, and available variants.
There are lots of open-source AI models out there, and it can feel overwhelming to know which one to look at. To keep things simple, here’s a handy table that shows the most popular model families, how big they are (measured in parameters like 7B or 70B), and the different versions you’ll often see — such as Base, Chat, Instruct, or Quantized.
💡 Closing Note:
Each model family has its strengths — smaller ones like Gemma-2B are light and fast, while larger ones like LLaMA-70B or Mixtral-8×22B are more powerful but resource-hungry. If you’re just experimenting, start small. For production-grade AI, explore the larger or instruct-tuned versions.
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