Instructions to use kon172verma/intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kon172verma/intent-classifier with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="kon172verma/intent-classifier")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kon172verma/intent-classifier", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kon172verma/intent-classifier with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kon172verma/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kon172verma/intent-classifier:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kon172verma/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kon172verma/intent-classifier:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kon172verma/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kon172verma/intent-classifier:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kon172verma/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kon172verma/intent-classifier:Q4_K_M

Use Docker

docker model run hf.co/kon172verma/intent-classifier:Q4_K_M

LM Studio
Jan
Ollama
How to use kon172verma/intent-classifier with Ollama:
```
ollama run hf.co/kon172verma/intent-classifier:Q4_K_M
```
Unsloth Desktop

How to use kon172verma/intent-classifier with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kon172verma/intent-classifier:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kon172verma/intent-classifier:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kon172verma/intent-classifier with Docker Model Runner:
```
docker model run hf.co/kon172verma/intent-classifier:Q4_K_M
```

Lemonade

How to use kon172verma/intent-classifier with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kon172verma/intent-classifier:Q4_K_M

Run and chat with the model

lemonade run user.intent-classifier-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use kon172verma/intent-classifier with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kon172verma/intent-classifier:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kon172verma/intent-classifier:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kon172verma/intent-classifier with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kon172verma/intent-classifier:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "kon172verma/intent-classifier:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

Version	Scope	Recommended completed models
v2.1	Full 10k positional-ID DoRA+ experiments, configuration D	Qwen3-0.6B: 99.8% test; Qwen2.5-0.5B: 99.9% test; SmolLM2-360M: 99.7% test. Recommended: Qwen2.5-0.5B—the highest-scoring and fastest of the three (253 ms P50).
v2.0	1k positional-ID experiment matrix (800/100/100)	DoRA+ Qwen3-0.6B D (99% validation, 100% test) and DoRA+ Llama3.2-1B C (96% validation, 100% test).
v1.0	Initial 1k name-output experiments	LoRA Qwen3-0.6B C (99% test) and LoRA Llama3.2-1B C (100% test).

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.

Release	Native output	No-tool output	Evaluator behavior
v1.0	Tool name, such as `call_handler`	`none`	Used directly as the canonical tool name.
v2.x	Positional tool ID, such as `d`	`-`	Convert the ID to the tool name from the prompt's tool list before scoring or invoking the tool.

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.

Artifacts	Upstream license	What a redistributor must do
Qwen3-0.6B and Qwen2.5-0.5B derivatives	Apache License 2.0	Comply with Apache-2.0, including its license and notice requirements.
SmolLM2-360M derivatives	Apache License 2.0	Comply with Apache-2.0, including its license and notice requirements.
Llama-3.2-1B derivatives	Llama 3.2 Community License	Comply with the agreement and Acceptable Use Policy. Distributed copies must include the agreement and a `Notice` text file containing Meta's required attribution.

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.