kon172verma/intent-classifier

Intent Classifier — Release Models

Small, fine-tuned language models for routing a user request to one tool. Each model returns a compact routing label, not a natural-language response.

The current recommended release is v2.1 Qwen2.5-0.5B (v2.1-qwen2.5-0.5b): it achieved 99.9% test accuracy in the v2.1 evaluation.

Choose a release

Use v2.1 or v2.0 when your application can map a positional ID back to its tool. Use v1.0 only when your integration requires the model to emit a tool name directly.

Output contract

These are generative routing models. Build the prompt as plain text exactly in the relevant format below, tokenize that text directly, and read only the generated continuation. Do not wrap the prompt in the base model's chat template: the examples below are the complete prompt content before any model tokenizer adds its own special or chat-template tokens.

Example: v1.0 (tool-name output)

For Call my wife., the expected raw model output is call_handler.

You are a tool router.

Rules:
- Return only the tool name.
- Return "none" if no tool matches.
- Do not explain.

Available Tools:
Name: health_monitor
Description: Monitors driver biometrics such as heart rate, blood oxygen, and cumulative fatigue score from wearables.

Name: insurance_claims
Description: Initiates, documents, and tracks vehicle insurance claims including photo upload and damage assessment.

Name: nav_route_planner
Description: Plans optimal routes, provides turn-by-turn navigation, and recalculates routes around obstacles.

Name: call_handler
Description: Initiates, receives, holds, and terminates hands-free phone calls.

Name: remote_vehicle_lock
Description: Remotely locks or unlocks vehicle doors, boot, and windows and reports current lock state.

User Request:
Call my wife.

Selected Tool:

Example: v2.x (positional-ID output)

For the same request, call_handler is the fourth displayed tool, so the expected raw model output is d. Convert d back to call_handler using the tool list before calculating accuracy or calling the tool.

You are a tool router.

Available tools are listed with id, name, and description.

Rules:
- Return only the tool id.
- Use the id from the available tools list.
- Return "-" if no tool matches.
- Do not explain.

Available Tools:
ID | Name | Description
a | health_monitor | Monitors driver biometrics such as heart rate, blood oxygen, and cumulative fatigue score from wearables.
b | insurance_claims | Initiates, documents, and tracks vehicle insurance claims including photo upload and damage assessment.
c | nav_route_planner | Plans optimal routes, provides turn-by-turn navigation, and recalculates routes around obstacles.
d | call_handler | Initiates, receives, holds, and terminates hands-free phone calls.
e | remote_vehicle_lock | Remotely locks or unlocks vehicle doors, boot, and windows and reports current lock state.

User Request:
Call my wife.

Selected Tool:

Artifacts

Each release/model folder contains the same inference formats.

• —transformers/: merged full-weight Transformers checkpoint in safetensors format, plus tokenizer and generation configuration.
• —gguf/<quant>/model.gguf: llama.cpp-compatible exports (Q4_K_M, Q6_K, and Q8_0).
• —onnx/<variant>/: ONNX Runtime exports. Keep every file in a variant folder together; model.onnx can reference an adjacent external-data file.

The individual release README and benchmark_provenance.json record the exact base-model revision, adapter configuration, prompt format, and artifact provenance.

Licensing and notices

This is a mixed-license repository, so its Hugging Face metadata is marked other rather than apache-2.0. Fine-tuning, merging, quantizing, and exporting a model do not replace the license of its base-model weights. The Llama release artifacts are Built with Llama.

For a release containing Llama artifacts, include the Llama 3.2 Community License Agreement and a Notice file with this attribution:

Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

Before publishing, also confirm that the training/evaluation data and any third-party conversion code permit the intended distribution. This summary is operational guidance, not legal advice.

Related repositories

• —Training code: <https://github.com/kon172verma/intent-classifier>
• —Inference and benchmarking: <https://github.com/kon172verma/intent-classifier-inference>
• —Experiment adapters: <https://huggingface.co/kon172verma/intent-classifier-experiments>