Instructions to use kon171verma/intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kon171verma/intent-classifier with Transformers:

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

pipe = pipeline("text-classification", model="kon171verma/intent-classifier")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kon171verma/intent-classifier", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kon171verma/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 kon171verma/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kon171verma/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 kon171verma/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kon171verma/intent-classifier:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf kon171verma/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf kon171verma/intent-classifier:Q4_K_M

Use Docker

docker model run hf.co/kon171verma/intent-classifier:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use kon171verma/intent-classifier with Ollama:
```
ollama run hf.co/kon171verma/intent-classifier:Q4_K_M
```
Unsloth Desktop

How to use kon171verma/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 kon171verma/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": "kon171verma/intent-classifier:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kon171verma/intent-classifier with Docker Model Runner:
```
docker model run hf.co/kon171verma/intent-classifier:Q4_K_M
```

Lemonade

How to use kon171verma/intent-classifier with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kon171verma/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 kon171verma/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 kon171verma/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 kon171verma/intent-classifier:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kon171verma/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 kon171verma/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 "kon171verma/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"

Konark Verma commited on Sep 1

Commit

5c72ef3

verified ·

1 Parent(s): 6e9ae35

Document v2.1 SmolLM2-360M release

Browse files

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v2.1-smollm2-360m/README.md +34 -0

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+# Intent Classifier v2.1 — SmolLM2-360M
+This is a merged, fine-tuned intent-classifier release. It is not the base
+model named below.
+## Model identity
+| Field | Value |
+| --- | --- |
+| Release repository | `kon172verma/intent-classifier` |
+| Release subfolder | `v2.1-smollm2-360m` |
+| Release | `v2.1` |
+| Base model | `HuggingFaceTB/SmolLM2-360M-Instruct` |
+| Base revision | `8747f21e7ce7b9209590290fb0937720ba3c0cd5` |
+| Fine-tuning method | `DoRA+`, configuration `D` |
+| Adapter source | `kon172verma/intent-classifier-experiments` at `225a5766f66d752de4090ea228611418c7847a7b` |
+| Adapter subfolder | `v2.1/smollm2-360m_DoRA+_D_10k_20260828-170431` |
+| Dataset size | `10k` |
+| Prompt template | `v2-positional-id` |
+| Native output | `positional_id`; no-tool token `-` |
+## Artifacts
+- **Transformers:** `transformers/`. Load with
+  `subfolder="v2.1-smollm2-360m/transformers"` from `kon172verma/intent-classifier`.
+- **GGUF:** `gguf/<quant>/model.gguf` for llama.cpp.
+- **ONNX:** `onnx/<variant>/` for ONNX Runtime. Keep all files in a variant
+  directory together, including any external-data files referenced by
+  `model.onnx`.
+## Provenance
+`benchmark_provenance.json` records the pinned manifest, source revisions, and
+local artifact metadata used to produce this model folder.