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 Studio

How to use kon172verma/intent-classifier with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kon172verma/intent-classifier to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kon172verma/intent-classifier to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kon172verma/intent-classifier to start chatting

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 @mariozechner/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

Hermes Agent new

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 new

OpenClaw new

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"

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

Intent Classifier (Release)

This Hugging Face repo contains only the final released models for the intent-classifier project.

It is intentionally limited to release artifacts:

merged full-weight model checkpoints
GGUF exports for llama.cpp
ONNX exports for runtime backends.

Current release

Current stable release: v1.0 To use this exact release, select v1.0 in the Files and versions tab or load the repo with revision="v1.0".

Models

qwen3-0.6b
llama3.2-1b

Both models are fine-tuned for intent classification and exported in multiple inference formats.

Transformers / Safetensors: Full-weight Hugging Face checkpoints for standard Transformers inference and downstream conversion.
GGUF: GGUF files are provided for llama.cpp inference.
ONNX: ONNX exports are provided for runtime backends.

Transformers / Safetensors

The Transformers folders contain merged full-weight checkpoints in safetensors format.

These are the canonical Hugging Face model artifacts for each selected release model and are the best starting point if you want to:

run inference with Transformers,
inspect tokenizer and config files,
convert to another serving format,
fine-tune further from the released checkpoint.

GGUF

The GGUF files are intended for inference with llama.cpp.

Available quantization formats include:

Q4_K_M
Q6_K
Q8_0
F16

ONNX

The ONNX folders contain exported model variants for ONNX Runtime backends.

These artifacts are intended for deployment and benchmarking across runtimes such as CPU, CoreML, CUDA, or TensorRT pipelines, depending on the exported variant.

When available, the ONNX exports may include multiple precision or quantization variants such as fp16 or int8.

Versioning

Stable releases are published as git tags such as v1.0. The README describes the latest intended stable release, while the Files and versions tab lets you browse or load a specific tagged revision.

Repository structure

intent-classifier/
├── qwen3-0.6b/
│   ├── Transformers / Safetensors
│   ├── GGUF
│   └── ONNX
│
├── llama3.2-1b/
│   ├── Transformers / Safetensors
│   ├── GGUF
│   └── ONNX
│
└── README.md

Related repositories

Training code and experiment artifacts are maintained separately.

Training code: https://github.com/kon172verma/intent-classifier
Inference/benchmarking: https://github.com/kon172verma/intent-classifier-inference
Experiments (all adapters): https://huggingface.co/kon172verma/intent-classifier-experiments

Downloads last month: 91

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

Model tree for kon172verma/intent-classifier

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Quantized

(378)

this model