Best Practices for Using Needle: A Complete Guide to Tool Calling, Extraction, and Fine-Tuning

Needle is a 45M-parameter self-contained model that provides a unified Python interface for tool calling, structured data extraction, and LoRA fine-tuning on edge devices.

If you're building AI-powered applications that need to run offline or on resource-constrained hardware, Needle offers a compelling alternative to cloud-dependent APIs. This guide covers the essential best practices for using Needle effectively, drawn directly from the cactus-compute/needle source code.

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Understand Needle's Architecture

Before diving into code, you should understand how Needle achieves its tiny footprint. The model is built around a Simple Attention Network implemented in [needle/model/architecture.py](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py). Key architectural decisions include:

• Hadamard-based MLP for efficient feedforward computations
• GQA (Grouped Query Attention) for memory-efficient attention
• Byte-level grammar compilation from Pydantic schemas, ensuring syntactically valid JSON output

The entire model ships as a single 14MB binary (.cact file) managed by the engine in [needle/model/run.py](https://github.com/cactus-compute/needle/blob/main/needle/model/run.py). This engine is downloaded once and cached locally for all subsequent inferences.

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Follow the Tool Contract

The most important best practice for using Needle is precise tool definitions. Needle uses Python type hints and docstrings to construct tool schemas automatically.

Write Clear Tool Functions

import needle

@needle.tool
def get_weather(city: str):
    """Get the current weather for a city."""
    # Real implementation would call an API; here we mock it.

    return {"city": city, "temp_c": 27, "sky": "clear"}

agent = needle.Needle(tools=[get_weather])
response = agent.run("What's the weather like in Lagos right now?")
print(response["results"])

# [{'city': 'Lagos', 'temp_c': 27, 'sky': 'clear'}]

Key requirements:

• Use explicit type hints for all arguments—these become the JSON schema
• Write concise, descriptive docstrings—the model uses these to decide when to invoke the tool
• Keep the tool catalog focused—while Needle has a retrieval head for large catalogs, it performs best with ≤20 tools

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Leverage Grammar-Based Structured Extraction

One of Needle's standout features is guaranteed-valid JSON output. When you pass a Pydantic model to needle.extract, the decoder in [needle/model/decode.py](https://github.com/cactus-compute/needle/blob/main/needle/model/decode.py) compiles a byte-level grammar that restricts token generation token-by-token.

from pydantic import BaseModel
import needle

class Invoice(BaseModel):
    vendor: str
    total: float
    due_date: str

text = "Invoice from Acme Corp, $1,200.00, due 2026-09-01"
invoice = needle.extract(text, Invoice)
print(invoice.vendor, invoice.total)   # → Acme Corp 1200.0

This approach eliminates post-processing and validation errors common with generic LLM outputs.

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Use Confidence Gating for Production Reliability

Every response from Needle includes a calibrated confidence score. The inference loop in [needle/model/run.py](https://github.com/cactus-compute/needle/blob/main/needle/model/run.py) exposes this for threshold-based decision making:

response = agent.run("Dim the kitchen lights to 10%")

if response["confidence"] > 0.85:
    execute_action(response["results"])
else:
    escalate_to_human_review(response)

Set thresholds based on your application's risk tolerance. Lower thresholds for exploratory tasks, higher thresholds for consequential actions.

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Fine-Tune Only When Necessary

The base 45M model handles most tool-calling scenarios. However, if you need higher accuracy on a specialized tool set, Needle supports LoRA fine-tuning with a reproducible CLI workflow:

1. Generate Synthetic Training Data (Optional)

export OPENROUTER_API_KEY=sk-...
needle generate-data --tools my_tools.json --num-samples 500 --output data.jsonl

2. Fine-Tune with LoRA

needle finetune data.jsonl --epochs 10 --lora-rank 16 --lora-alpha 32

3. Export a Merged Model

needle build checkpoints/needle2.pkl --lora checkpoints/needle_lora.pkl --out my_needle.cact

4. Load Your Tuned Model

import needle

agent = needle.Needle(weights="my_needle.cact", tools=[get_weather])
print(agent.run("Dim the kitchen lights to 10%"))

Best practice: Start with the base model, measure performance on your target tasks, and fine-tune only if accuracy gaps exist.

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Cache the Engine for Offline Deployment

The Needle engine downloads from Hugging Face on first use. For production deployments:

• Include the .cact file in your deployment artifacts
• Set appropriate cache directories if running in containerized environments
• Verify the 28MB RAM footprint stays constant regardless of conversation length, thanks to the 256-token sliding window with KV-sinks

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Prefer the CLI for Reproducibility

Needle's command-line interface in [needle/cli.py](https://github.com/cactus-compute/needle/blob/main/needle/cli.py) encapsulates the full pipeline:

Command	Purpose
needle build	Merge base weights with LoRA adapters
needle finetune	Train LoRA adapters on your data
needle generate-data	Create synthetic training examples
needle playground	Interactive testing environment

Using these commands ensures your workflows are version-controllable and shareable.

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Summary

• Describe tools precisely with type hints and concise docstrings
• Use Pydantic schemas for guaranteed-valid structured extraction
• Start with the base model and fine-tune only when accuracy requires it
• Cache .cact files locally for offline, self-contained deployments
• Apply confidence thresholds to filter low-certainty responses
• Keep tool catalogs focused for optimal retrieval performance
• Use CLI commands to ensure reproducible workflows

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Frequently Asked Questions

How large is the Needle model and what are its system requirements?

Needle is a 45M-parameter model that ships as a 14MB binary. At runtime, it uses approximately 28MB of RAM regardless of conversation length, thanks to bounded memory techniques including a 256-token sliding window with KV-sinks. This makes it suitable for edge devices and offline deployments.

Does Needle support function calling with multiple tools?

Yes. Needle can handle multiple tools through the @needle.tool decorator and the Needle(tools=[...]) interface. For large tool catalogs, the retrieval head in [needle/model/run.py](https://github.com/cactus-compute/needle/blob/main/needle/model/run.py) automatically selects the top 5 candidate tools per turn to maintain fast inference, though best practice is to keep catalogs at 20 or fewer tools.

How does Needle ensure JSON output is always valid?

Needle compiles a byte-level grammar from your Pydantic schema in [needle/model/decode.py](https://github.com/cactus-compute/needle/blob/main/needle/model/decode.py). This grammar constrains the decoder to generate only syntactically valid JSON tokens, eliminating the need for post-hoc parsing or retry logic common with unconstrained language models.

Can I use Needle without an internet connection?

Yes, after the initial engine download. Best practice is to cache the .cact file locally and include it in deployment artifacts. Once cached, Needle operates entirely offline with no API calls or network dependencies.

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