Instructions to use Nikhil1581/qwen3.5-0.8b-intent-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Nikhil1581/qwen3.5-0.8b-intent-classification 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 Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nikhil1581/qwen3.5-0.8b-intent-classification: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 Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nikhil1581/qwen3.5-0.8b-intent-classification: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 Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M

Use Docker

docker model run hf.co/Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M

LM Studio
Jan
Ollama
How to use Nikhil1581/qwen3.5-0.8b-intent-classification with Ollama:
```
ollama run hf.co/Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M
```
Unsloth Desktop

How to use Nikhil1581/qwen3.5-0.8b-intent-classification with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nikhil1581/qwen3.5-0.8b-intent-classification: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": "Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Nikhil1581/qwen3.5-0.8b-intent-classification with Docker Model Runner:
```
docker model run hf.co/Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M
```

Lemonade

How to use Nikhil1581/qwen3.5-0.8b-intent-classification with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M

Run and chat with the model

lemonade run user.qwen3.5-0.8b-intent-classification-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Nikhil1581/qwen3.5-0.8b-intent-classification with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nikhil1581/qwen3.5-0.8b-intent-classification: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 Nikhil1581/qwen3.5-0.8b-intent-classification:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Nikhil1581/qwen3.5-0.8b-intent-classification with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nikhil1581/qwen3.5-0.8b-intent-classification: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 "Nikhil1581/qwen3.5-0.8b-intent-classification: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"

qwen3.5-0.8b-intent-classification / README.md

Nikhil1581

Update README.md

ab840c4 verified about 1 month ago

preview code

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metadata

license: mit
tags:
  - conversational
  - intent-classification
  - gguf
  - qwen
  - text-classification
library_name: gguf
language:
  - en
datasets:
  - Nikhil1581/excel_dataset

Qwen3.5-0.8B Intent Classification

A lightweight, quantized GGUF model fine-tuned on top of Qwen3.5-0.8B for conversational intent classification. Designed to run efficiently on consumer hardware with no GPU required.

Model Details

Property	Value
Base Model	Qwen3.5-0.8B
Format	GGUF (Q4_K_M quantization)
Parameters	~0.8 Billion
File Size	529 MB
Architecture	Qwen 3.5
License	MIT
Task	Intent Classification

Intended Use

This model is designed to classify user intents from conversational text. It is suitable for:

Chatbot routing and intent detection
Virtual assistant pipelines
Customer support automation
NLU (Natural Language Understanding) systems

Quickstart

Using `llama.cpp`

# Download the model
huggingface-cli download Nikhil1581/qwen3.5-0.8b-intent-classification Qwen3.5-0.8B.Q4_K_M.gguf --local-dir ./models

# Run inference
./llama-cli -m ./models/Qwen3.5-0.8B.Q4_K_M.gguf \
  -p "Classify the intent of the following message: 'What is the weather like today?'" \
  -n 128

Using `llama-cpp-python`

from llama_cpp import Llama

llm = Llama(
    model_path="./models/Qwen3.5-0.8B.Q4_K_M.gguf",
    n_ctx=2048,
)

prompt = """You are an intent classification assistant. 
Classify the intent of the user message below into a single intent label.

User message: "Book me a flight to New York for next Monday."
Intent:"""

output = llm(prompt, max_tokens=64, stop=["\n"])
print(output["choices"][0]["text"].strip())

Using Ollama

# Create a Modelfile
cat <<EOF > Modelfile
FROM ./models/Qwen3.5-0.8B.Q4_K_M.gguf
SYSTEM "You are an intent classification assistant. Given a user message, respond with the most appropriate intent label."
EOF

ollama create qwen-intent -f Modelfile
ollama run qwen-intent "Cancel my subscription"

Quantization Details

This model uses Q4_K_M quantization, which offers a good balance between size, speed, and accuracy.

Format	Size	Notes
Q4_K_M	529 MB	Recommended — balanced

Hardware Requirements

Setup	Minimum RAM
CPU only	4 GB RAM
GPU offload	2 GB VRAM

Limitations

Output quality depends on prompt formatting — clear, structured prompts yield better results.
As a 0.8B parameter model, performance on complex or ambiguous intents may be limited compared to larger models.
Primarily optimized for English-language inputs.

Citation

If you use this model in your work, please cite:

@misc{nikhil1581-qwen3.5-intent,
  author       = {Nikhil1581},
  title        = {Qwen3.5-0.8B Intent Classification (GGUF)},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/Nikhil1581/qwen3.5-0.8b-intent-classification}
}

Acknowledgements

Built on top of Qwen3.5 by Alibaba Cloud. Quantized using llama.cpp.

Qwen3.5-0.8B Intent Classification

Model Details

Intended Use

Quickstart

Using llama.cpp

Using llama-cpp-python

Using Ollama

Quantization Details

Hardware Requirements

Limitations

Citation

Acknowledgements

Using `llama.cpp`

Using `llama-cpp-python`