ollama-model-search 0.1.1

Search and filter Ollama models by size, context, input type and more.

ollama_model_search

A Python library to search and filter Ollama models by size, context length, input type, MLX support, and recency. Includes a GPU-aware system check that automatically detects your GPU VRAM and shows which models will fit.

Features

• Scrape ollama.com — one command pulls down every model and every tag variant from ollama.com/search
• Six-column terminal table — Tag, Size, Context, Input, MLX, and Updated, rendered with Rich
• Powerful filtering — combine filters on size (GB), context window, input type, and MLX availability
• GPU-aware matching — system_check() detects your GPU via GPUtil, reserves 500 MB overhead, then returns all non-MLX models that fit in usable VRAM
• Sort by recency — newest-first or oldest-first using the relative timestamps from ollama.com
• Programmatic access — every function returns raw Python data when table=False

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Installation

pip install ollama_model_search

After installing the package, download the Chromium browser used by Playwright:

playwright install chromium

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Quick Start

import ollama_model_search

# 1. Scrape the latest model catalogue from ollama.com
ollama_model_search.refresh()

# 2. List every model/tag in a table
ollama_model_search.list_all()

# 3. Find models that fit on your GPU
ollama_model_search.system_check()

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API Reference

refresh()

Scrapes every public model from ollama.com/search and every tag variant from each model's tags page. Results are cached to ollama_models.json in the current working directory.

models, all_tags = ollama_model_search.refresh()
# ✓ 1600+ models discovered
# ✓ XXXX models · YYYY variants saved.

print(f"{len(models)} models, {len(all_tags)} tag variants scraped")

How it works internally:

1. Launches a headless Chromium browser via Playwright
2. Navigates to ollama.com/search and auto-scrolls until all models are loaded
3. Concurrently visits every model's /tags page (25 at a time via asyncio.Semaphore)
4. Extracts size, context, input type, MLX flag, digest, and update time from each tag row
5. Saves everything to ollama_models.json

Returns: (models: list[str], all_tags: list[dict]) — a tuple of model names and parsed tag dicts.

You must call refresh() at least once before using any other function. All other functions read from the cached JSON.

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list_all()

List all cached models in a Rich terminal table.

# Every model/tag
ollama_model_search.list_all()

# Top 10 most recently updated
ollama_model_search.list_all(recent=True, count=10)

# Return raw dicts — no table printed
results = ollama_model_search.list_all(table=False)

Parameter	Type	Default	Description
recent	bool or None	None	True = newest first, False = oldest first, None = leave unsorted
table	bool	True	Print a Rich table to the terminal when True
count	int or None	None	Limit results to the first N entries

Returns: list[dict] — the tag dicts, or [] if no cached data exists.

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search()

Filter models by any combination of size, context window, input type, and MLX support.

# Models ≤ 3.5 GB
ollama_model_search.search(size_gb=3.5)

# Models ≤ 7 GB, text-only input
ollama_model_search.search(size_gb=7, input_type="Text")

# Models with a 128K context window
ollama_model_search.search(context="128K")

# Exclude MLX models
ollama_model_search.search(is_mlx=False)

# Combine filters, sort newest-first, top 10
ollama_model_search.search(size_gb=3.5, is_mlx=False, recent=True, count=10)

# Return as raw dicts
results = ollama_model_search.search(size_gb=3.5, table=False)

Parameter	Type	Default	Description
size_gb	float or None	None	Maximum model size in GB (inclusive: <=)
context	str or None	None	Case-insensitive substring match on the context window ("128K", "32K", etc.)
input_type	str or None	None	Case-insensitive substring match on input type ("Text", "Image", etc.)
is_mlx	bool or None	None	True = MLX only, False = exclude MLX, None = no filter
recent	bool or None	None	True = newest first, False = oldest first, None = leave unsorted
table	bool	True	Print a Rich table to the terminal when True
count	int or None	None	Limit results to the first N entries

Filter behaviour: size_gb uses <= (inclusive). context and input_type use case-insensitive substring matching (e.g. "text" matches "Text & Image"). is_mlx uses exact equality.

Returns: list[dict] — the filtered tag dicts, or [] if no cached data exists.

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system_check()

Detects your GPU (via GPUtil), calculates usable VRAM (total minus 500 MB overhead), and lists all non-MLX models that fit.

# Show all compatible models
ollama_model_search.system_check()

# Top 10 newest compatible models
ollama_model_search.system_check(recent=True, count=10)

# Return raw dicts
results = ollama_model_search.system_check(table=False)

Output example:

GPU detected  : NVIDIA GeForce RTX 3060
Total VRAM    : 12288 MB
Usable VRAM   : 11788 MB (11.51 GB after 500MB overhead)

[table of compatible models...]

✓ 247 compatible models found for your NVIDIA GeForce RTX 3060

Parameter	Type	Default	Description
recent	bool or None	None	Passed through to search() — True = newest first
table	bool	True	Passed through to search()
count	int or None	None	Passed through to search()

Internally calls: search(size_gb=usable_vram_gb, is_mlx=False, ...)

If no GPU is detected, a [red]No GPU detected.[/red] message is printed and an empty list is returned.

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Data Format

Each tag dict returned by list_all(), search(), and system_check() contains:

Field	Type	Description
model_name	str	Parent model name (e.g. "llama3.2")
tag	str	Full pull tag (e.g. "llama3.2:3b-instruct-q4_K_M")
size	str or None	Human-readable size string (e.g. "3.5GB")
size_gb	float or None	Numeric size in GB — used by size_gb filter
context	str or None	Context window label (e.g. "128K", "32K")
input_type	str or None	Input modality (e.g. "Text", "Text & Image")
is_mlx	bool	Whether the tag supports Apple MLX
digest	str or None	SHA digest hash string
updated_at	str or None	Relative time string (e.g. "2 weeks", "3 months")
updated_at_score	float	Numeric recency score (lower = newer) — used internally for sorting

The cached JSON file (ollama_models.json) additionally contains:

{
  "last_updated": "2026-06-08T21:00:00.123456",
  "total_models": 1600,
  "total_tags": 12000,
  "models": ["llama3.2", "gemma3", ...],
  "tags": [ { ...tag dict... }, ... ]
}

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Terminal Table Columns

When table=True (the default), the Rich table displays:

Column	Source field	Style
Tag	tag	white, no-wrap
Size	size	cyan, right-aligned
Context	context	green, centered
Input	input_type	magenta
MLX	is_mlx	green ✓ if MLX, empty otherwise
Updated	updated_at	yellow

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Requirements

Dependency	Purpose
Python 3.8+	Runtime
Playwright	Headless browser automation (scraping)
BeautifulSoup4	HTML parsing
GPUtil	GPU detection and VRAM querying
Rich	Terminal table formatting, progress bars, and coloured output

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Complete Function Summary

Function	Signature	Returns
refresh()	no arguments	tuple[list[str], list[dict]]
list_all()	recent=None, table=True, count=None	list[dict]
search()	size_gb=None, context=None, input_type=None, is_mlx=None, recent=None, table=True, count=None	list[dict]
system_check()	recent=None, table=True, count=None	list[dict]

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License

MIT © Abhinav Abhi