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Model: geoffmunn/Qwen3-0.6B-f16 Source: Original Platform
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Qwen3-0.6B-f16-Q4_K_M/README.md
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Qwen3-0.6B-f16-Q4_K_M/README.md
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---
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license: apache-2.0
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tags:
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- gguf
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- qwen
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- qwen3-0.6b
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- qwen3-0.6b-q4
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- qwen3-0.6b-q4_k_m
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- qwen3-0.6b-q4_k_m-gguf
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- llama.cpp
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- quantized
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- text-generation
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- chat
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- edge-ai
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- tiny-model
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base_model: Qwen/Qwen3-0.6B
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author: geoffmunn
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---
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# Qwen3-0.6B-f16:Q4_K_M
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Quantized version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) at **Q4_K_M** level, derived from **f16** base weights.
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## Model Info
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- **Format**: GGUF (for llama.cpp and compatible runtimes)
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- **Size**: 484 MB
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- **Precision**: Q4_K_M
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- **Base Model**: [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
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- **Conversion Tool**: [llama.cpp](https://github.com/ggerganov/llama.cpp)
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## Quality & Performance
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| Metric | Value |
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|--------------------|--------------------------------------------------|
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| **Speed** | 🚀 Fast |
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| **RAM Required** | ~1.0 GB |
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| **Recommendation** | Showed up in a few results, but not recommended. |
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## Prompt Template (ChatML)
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This model uses the **ChatML** format used by Qwen:
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```text
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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Set this in your app (LM Studio, OpenWebUI, etc.) for best results.
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## Generation Parameters
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Recommended defaults:
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| Parameter | Value |
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|----------------|-------|
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| Temperature | 0.6 |
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| Top-P | 0.95 |
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| Top-K | 20 |
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| Min-P | 0.0 |
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| Repeat Penalty | 1.1 |
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Stop sequences: `<|im_end|>`, `<|im_start|>`
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> ⚠️ Due to model size, avoid temperatures above 0.9 — outputs become highly unpredictable.
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## 💡 Usage Tips
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> This model is best suited for lightweight tasks:
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>
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> ### ✅ Ideal Uses
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> - Quick replies and canned responses
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> - Intent classification (e.g., “Is this user asking for help?”)
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> - UI prototyping and local AI testing
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> - Embedded/NPU deployment
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>
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> ### ❌ Limitations
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> - No complex reasoning or multi-step logic
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> - Poor math and code generation
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> - Limited world knowledge
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> - May repeat or hallucinate frequently at higher temps
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>
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> ---
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>
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> 🔄 **Fast Iteration Friendly**
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> Perfect for developers building prompt templates or testing UI integrations.
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>
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> 🔋 **Runs on Almost Anything**
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> Even Raspberry Pi Zero W can run Q2_K with swap enabled.
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>
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> 📦 **Tiny Footprint**
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> Fits easily on USB drives, microSD cards, or IoT devices.
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## Customisation & Troubleshooting
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Importing directly into Ollama should work, but you might encounter this error: `Error: invalid character '<' looking for beginning of value`.
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In this case try these steps:
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1. `wget https://huggingface.co/geoffmunn/Qwen3-0.6B-f16/resolve/main/Qwen3-0.6B-f16%3AQ4_K_M.gguf`
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2. `nano Modelfile` and enter these details:
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```text
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FROM ./Qwen3-0.6B-f16:Q4_K_M.gguf
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# Chat template using ChatML (used by Qwen)
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SYSTEM You are a helpful assistant
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TEMPLATE "{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>{{ end }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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<|im_start|>assistant
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"
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PARAMETER stop <|im_start|>
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PARAMETER stop <|im_end|>
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# Default sampling
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PARAMETER temperature 0.6
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PARAMETER top_p 0.95
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PARAMETER top_k 20
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PARAMETER min_p 0.0
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PARAMETER repeat_penalty 1.1
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PARAMETER num_ctx 4096
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```
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The `num_ctx` value has been dropped to increase speed significantly.
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3. Then run this command: `ollama create Qwen3-0.6B-f16:Q4_K_M -f Modelfile`
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You will now see "Qwen3-0.6B-f16:Q4_K_M" in your Ollama model list.
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These import steps are also useful if you want to customise the default parameters or system prompt.
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## 🖥️ CLI Example Using Ollama or TGI Server
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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).
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```bash
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curl http://localhost:11434/api/generate -s -N -d '{
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"model": "hf.co/geoffmunn/Qwen3-0.6B-f16:Q4_K_M",
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"prompt": "Respond exactly as follows: Write a short joke about cats.",
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"temperature": 0.8,
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"top_p": 0.95,
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"top_k": 20,
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"min_p": 0.0,
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"repeat_penalty": 1.1,
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"stream": false
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}' | jq -r '.response'
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```
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🎯 **Why this works well**:
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- The prompt is meaningful yet achievable for a tiny model.
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- Temperature tuned appropriately: lower for deterministic output (`0.1`), higher for jokes (`0.8`).
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- Uses `jq` to extract clean response.
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> 💬 Tip: For ultra-low-latency use, try `Q3_K_M` or `Q4_K_S` on older laptops.
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## Verification
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Check integrity:
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```bash
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sha256sum -c ../SHA256SUMS.txt
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```
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## Usage
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Compatible with:
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- [LM Studio](https://lmstudio.ai) – local AI model runner
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- [OpenWebUI](https://openwebui.com) – self-hosted AI interface
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- [GPT4All](https://gpt4all.io) – private, offline AI chatbot
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- Directly via `llama.cpp`
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## License
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Apache 2.0 – see base model for full terms.
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