Qwen3-0.6B-f16/Qwen3-0.6B-f16-Q2_K/README.md

---
license: apache-2.0
tags:
  - gguf
  - qwen
  - qwen3-0.6b
  - qwen3-0.6b-q2
  - qwen3-0.6b-q2_k
  - qwen3-0.6b-q2_k-gguf
  - llama.cpp
  - quantized
  - text-generation
  - chat
  - edge-ai
  - tiny-model
base_model: Qwen/Qwen3-0.6B
author: geoffmunn
---

# Qwen3-0.6B-f16:Q2_K

Quantized version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) at **Q2_K** level, derived from **f16** base weights.

## Model Info

- **Format**: GGUF (for llama.cpp and compatible runtimes)
- **Size**: 347 MB
- **Precision**: Q2_K
- **Base Model**: [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
- **Conversion Tool**: [llama.cpp](https://github.com/ggerganov/llama.cpp)

## Quality & Performance

| Metric             | Value                                                           |
|--------------------|-----------------------------------------------------------------|
| **Speed**          | ⚡ Fast                                                          |
| **RAM Required**   | ~0.6 GB                                                         |
| **Recommendation** | 🚨 **DO NOT USE.** Could not provide an answer to any question. |

## Prompt Template (ChatML)

This model uses the **ChatML** format used by Qwen:

```text
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

Set this in your app (LM Studio, OpenWebUI, etc.) for best results.

## Generation Parameters

Recommended defaults:

| Parameter      | Value |
|----------------|-------|
| Temperature    | 0.6   |
| Top-P          | 0.95  |
| Top-K          | 20    |
| Min-P          | 0.0   |
| Repeat Penalty | 1.1   |

Stop sequences: `<|im_end|>`, `<|im_start|>`

> ⚠️ Due to model size, avoid temperatures above 0.9 — outputs become highly unpredictable.

## 💡 Usage Tips

> This model is best suited for lightweight tasks:
>
> ### ✅ Ideal Uses
> - Quick replies and canned responses
> - Intent classification (e.g., “Is this user asking for help?”)
> - UI prototyping and local AI testing
> - Embedded/NPU deployment
>
> ### ❌ Limitations
> - No complex reasoning or multi-step logic
> - Poor math and code generation
> - Limited world knowledge
> - May repeat or hallucinate frequently at higher temps
>
> ---
>
> 🔄 **Fast Iteration Friendly**  
> Perfect for developers building prompt templates or testing UI integrations.
>
> 🔋 **Runs on Almost Anything**  
> Even Raspberry Pi Zero W can run Q2_K with swap enabled.
>
> 📦 **Tiny Footprint**  
> Fits easily on USB drives, microSD cards, or IoT devices.

## Customisation & Troubleshooting

Importing directly into Ollama should work, but you might encounter this error: `Error: invalid character '<' looking for beginning of value`.
In this case try these steps:

1. `wget https://huggingface.co/geoffmunn/Qwen3-0.6B-f16/resolve/main/Qwen3-0.6B-f16%3AQ2_K.gguf`
2. `nano Modelfile` and enter these details:
```text
FROM ./Qwen3-0.6B-f16:Q2_K.gguf
 
# Chat template using ChatML (used by Qwen)
SYSTEM You are a helpful assistant

TEMPLATE "{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>

# Default sampling
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER min_p 0.0
PARAMETER repeat_penalty 1.1
PARAMETER num_ctx 4096
```

The `num_ctx` value has been dropped to increase speed significantly.

3. Then run this command: `ollama create Qwen3-0.6B-f16:Q2_K -f Modelfile`

You will now see "Qwen3-0.6B-f16:Q2_K" in your Ollama model list.

These import steps are also useful if you want to customise the default parameters or system prompt.

## 🖥️ CLI Example Using Ollama or TGI Server

Here’s how you can query this model via API using `curl` and `jq`. Replace the endpoint with your local server (e.g., Ollama, Text Generation Inference).

```bash
curl http://localhost:11434/api/generate -s -N -d '{
  "model": "hf.co/geoffmunn/Qwen3-0.6B-f16:Q2_K",
  "prompt": "Respond exactly as follows: Repeat the word 'hello' five times separated by commas.",
  "temperature": 0.1,
  "top_p": 0.95,
  "top_k": 20,
  "min_p": 0.0,
  "repeat_penalty": 1.1,
  "stream": false
}' | jq -r '.response'
```

🎯 **Why this works well**:
- The prompt is meaningful yet achievable for a tiny model.
- Temperature tuned appropriately: lower for deterministic output (`0.1`), higher for jokes (`0.8`).
- Uses `jq` to extract clean response.

> 💬 Tip: For ultra-low-latency use, try `Q3_K_M` or `Q4_K_S` on older laptops.

## Verification

Check integrity:

```bash
sha256sum -c ../SHA256SUMS.txt
```

## Usage

Compatible with:
- [LM Studio](https://lmstudio.ai) – local AI model runner
- [OpenWebUI](https://openwebui.com) – self-hosted AI interface
- [GPT4All](https://gpt4all.io) – private, offline AI chatbot
- Directly via `llama.cpp`

## License

Apache 2.0 – see base model for full terms.