---
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`

```bash
# 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`

```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

```bash
# 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:

```bibtex
@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](https://huggingface.co/Qwen) by Alibaba Cloud. Quantized using [llama.cpp](https://github.com/ggerganov/llama.cpp).