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You can run a compact language model on your own computer and use it to produce structured scores without sending prompts to a hosted model service. That can reduce one route by which sensitive inputs leave your device, but privacy depends on the whole setup—not just the model runtime. And a model’s score is not a validated probability or reliable decision rule unless it has been tested and, where needed, calibrated for your specific task.
What a local scoring setup does—and does not—promise
A local model takes an input, such as a case description or set of evidence fields, and generates an output on the computer running it. If the application processes the prompt locally and does not forward it elsewhere, the prompt and response can remain on that device.
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That is a data-location property, not proof that the score is correct. A syntactically valid value such as 0.82 does not automatically mean an 82% chance of an outcome. Treat the output as a model-generated score until you evaluate it against representative labeled examples and establish what the number means for your use case.
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Choose a local runtime
| Option | Setup and hardware | Structured output | Privacy evidence |
|---|---|---|---|
| Ollama | Emphasizes a convenient local runtime and model workflow. Its policy describes local-device execution. | Check current runtime and model support for the output format you need. | Ollama states that it does not collect, store, transmit, or access content processed locally; the policy distinguishes local use from its cloud-hosted service. |
| llama.cpp | Offers CLI and server workflows, GGUF model files, multiple quantization levels, and CPU/GPU options. Documented backends include CPU, Apple Silicon, NVIDIA CUDA, AMD HIP, Vulkan, and SYCL; hybrid CPU/GPU inference is supported. | Its CLI documentation includes grammar-constrained generation. | Provides a local runtime. The project documentation cited here does not make an equivalent broad privacy-policy claim. |
This is a capability comparison, not a performance or accuracy benchmark. The available documentation does not establish which option is faster, more energy-efficient, or more accurate for scoring.
For more direct control over model files and inference settings, llama.cpp is a reasonable route. If you prefer a simpler local runtime workflow, consider Ollama. In either case, verify the current documentation and configuration for the version you install.
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Set up llama.cpp with a compatible model
- Choose a model and check its details. For the documented llama.cpp workflow, use a compatible GGUF model file. Read the model card for its license, intended use, and limitations, and obtain the file from a source you trust. These details vary by checkpoint; the runtime project does not settle them for you.
- Install and configure the runtime for your machine. llama.cpp supports a range of CPU and GPU backends and can divide inference between CPU and GPU. The practical configuration depends on your hardware and build.
- Test the candidate on the target computer. Model size, quantization, and available memory affect what is practical. Quantization can reduce memory use, but there is no universal RAM or VRAM threshold established by the documentation. Run the model you intend to use on the actual machine and check that it completes representative inputs reliably.
- Keep the model files where you have room. The files require local storage; an external drive is optional if your internal storage is sufficient.
- Run inference locally and verify network exposure. llama.cpp documents both CLI and server workflows. Its server quick-start binds to
127.0.0.1by default; configuring a different address can expose the service beyond the local machine, so check the bind address and network controls before sending sensitive inputs.
Define the score before prompting the model
A score is useful only if its meaning and allowed outputs are clear. Write a score contract before building prompts or downstream automation:
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- Label meaning: define what a low or high score represents. Do not call it a probability unless evaluation supports that interpretation.
- Evidence: identify which input fields the model may use and require it to cite or summarize the relevant fields in its response.
- Abstention: define when the model should return “insufficient evidence” or route a case for human review rather than invent a score.
- Output schema: specify required keys, types, and permitted values so a program can parse the result.
With llama.cpp, grammar-constrained generation can help restrict output to a machine-readable form. That improves the chance of receiving valid syntax; it does not establish that the score is accurate, consistent, or calibrated. Parse and validate every response in downstream code, and handle malformed or out-of-range output explicitly.
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Evaluate scores before using them to make decisions
Build a representative labeled set
Collect examples that reflect the cases, language, and input quality the system will encounter, with trustworthy outcome labels. Keep a portion separate from any data used to adjust prompts or fit calibration so that evaluation measures performance on cases the model has not been tuned against.
Inspect errors and consistency
Review incorrect outputs, not just an aggregate score. Examine whether similar cases receive similar scores, whether small wording changes cause large shifts, and whether performance differs across relevant groups or input conditions. Decide which failure types matter most for the task.
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Check calibration and set thresholds by error cost
If you want to interpret scores as probabilities, compare predicted values with observed outcomes on held-out labeled data and calibrate as needed. Choose any action threshold based on the relative cost of false positives and false negatives, not on a convenient round number. Re-evaluate when the model, prompt, input mix, or decision context changes.
Ollaya says its models ship with per-model calibration and can be refit on labeled data. That is a vendor statement, not independent evidence that a particular model is best or calibrated for your task; verify it on your own representative cases.
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Check the full privacy boundary
Ollama’s policy says: “We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” This is the company’s statement about local use, not an independent audit of every application, plugin, operating system, or network component. The same policy notes limited device and usage metadata collection and treats cloud-hosted models separately. Review the current policy and make sure you are using local inference rather than a cloud-hosted model: Ollama privacy policy.
- Check whether the application or any integration forwards prompts to a cloud service.
- Review logs, crash reports, telemetry, and backups for sensitive inputs or outputs.
- Restrict access to the computer, local model files, and any server interface.
- Confirm which network address the inference server listens on and whether other devices can reach it.
- Review current application settings and policies before processing sensitive material.
For llama.cpp’s supported backends, GGUF workflow, quantization, and grammar features, consult the llama.cpp project documentation.
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