Where to Find Pre‑Trained Weights for Needle 2: Hugging Face and CLI Download Guide

Pre‑trained weights for Needle 2 are hosted on Hugging Face at Cactus-Compute/needle2 and can be downloaded manually or fetched automatically via the needle download CLI command.

If you're building with the Needle 2 inference engine, you'll need the base weights to power your agent. The Cactus Compute team distributes these as a compact binary package through Hugging Face, with multiple retrieval options to fit your workflow.

Hugging Face Hub: Direct Download

The official source for Needle 2 pre‑trained weights is the Hugging Face repository Cactus‑Compute/needle2:

# Direct URL: https://huggingface.co/Cactus-Compute/needle2

According to the project README, this hub page stores the baked‑in 14 MB engine file named needle‑2.cact — the complete weight bundle required for inference.

To use a manually downloaded file, pass its path to the Needle constructor:

import needle

agent = needle.Needle(
    weights="~/needle-2.cact",  # path to your downloaded .cact file

    tools=[...]
)
result = agent.run("What is the weather in London?")
print(result["results"])

CLI Download: Automated Fetch and Cache

The needle package provides a purpose‑built command for retrieving the engine. As documented in doc/apis.md (lines 58–63), running:

needle download Cactus-Compute/needle2

This command:

1. Detects your current platform
2. Pulls the appropriate engine binary from Hugging Face
3. Caches it at ~/.cache/cactus-needle/<engine‑version>/

Once cached, the engine (with embedded weights) is available for completely offline inference.

You can verify the download location:

needle download Cactus-Compute/needle2 --print-path

Automatic Lazy Loading: Zero‑Configuration Option

If you skip the weights argument entirely, Needle 2 handles retrieval automatically. The constructor in needle/__init__.py triggers a lazy download on first use:

import needle

# No explicit weights — library fetches engine when first needed

agent = needle.Needle(tools=[...])
response = agent.run("Turn on the kitchen lights")
print(response["results"])

This approach suits rapid prototyping but requires network access on the initial run.

Key Implementation Details

Component	Location	Purpose
README.md (line 15)	Repository root	Announces Hugging Face weight location
doc/apis.md (section "Offline devices")	Documentation	Documents needle download command and cache behavior
needle/__init__.py	Package core	Implements weights parameter handling
needle/cli.py	CLI module	Implements download command logic

Summary

• Primary source: Hugging Face repository Cactus-Compute/needle2 hosts the official needle‑2.cact weights file
• Manual workflow: Download from Hugging Face, then pass file path to needle.Needle(weights=...)
• CLI workflow: Run needle download Cactus-Compute/needle2 to cache platform‑specific engine
• Automatic workflow: Omit weights argument for lazy first‑time download
• Cache location: ~/.cache/cactus-needle/<engine‑version>/ enables offline operation

Frequently Asked Questions

How large are the Needle 2 pre‑trained weights?

The complete engine file is 14 MB, as stated in the repository README. This compact size enables fast distribution and embedding in edge deployments.

Can I run Needle 2 without internet access?

Yes. After running needle download Cactus-Compute/needle2 once, the cached engine in ~/.cache/cactus-needle/ supports fully offline inference with no subsequent network requirements.

What file format contains the Needle 2 weights?

The weights are packaged as a .cact file — a Cactus‑specific binary format that bundles the neural network weights with the optimized inference engine.

Is the needle download command required, or can I use standard Hugging Face tools?

You can download needle‑2.cact directly through the Hugging Face web interface or huggingface-cli, but the needle download command automatically selects the correct platform binary and manages cache placement for you.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}