README.md

metadata

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

Qwen3-0.6B-f16-GGUF

This is a GGUF-quantized version of the Qwen/Qwen3-0.6B language model — a compact 600-million-parameter LLM designed for ultra-fast inference on low-resource devices.

Converted for use with llama.cpp, LM Studio, OpenWebUI, and GPT4All, enabling private AI anywhere — even offline.

⚠️ Note: This is a very small model. It will not match larger models (e.g., 4B+) in reasoning, coding, or factual accuracy. However, it shines in speed, portability, and efficiency.

Available Quantizations (from f16)

These variants were built from a f16 base model to ensure consistency across quant levels.

Level	Speed	Size	Recommendation
Q2_K	⚡ Fastest	347 MB	🚨 DO NOT USE. Could not provide an answer to any question.
Q3_K_S	⚡ Fast	390 MB	Not recommended, did not appear in any top 3 results.
Q3_K_M	⚡ Fast	414 MB	First place in the bat & ball question, no other top 3 appearances.
Q4_K_S	🚀 Fast	471 MB	A good option for technical, low-temperature questions.
Q4_K_M	🚀 Fast	484 MB	Showed up in a few results, but not recommended.
🥈 Q5_K_S	🐢 Medium	544 MB	🥈 A very close second place. Good for all query types.
🥇 Q5_K_M	🐢 Medium	551 MB	🥇 Best overall model. Highly recommended for all query types.
Q6_K	🐌 Slow	623 MB	Showed up in a few results, but not recommended.
🥉 Q8_0	🐌 Slow	805 MB	🥉 Very good for non-technical, creative-style questions.

Why Use a 0.6B Model?

While limited in capability compared to larger models, Qwen3-0.6B excels at:

• Running instantly on CPUs without GPU
• Fitting into <2GB RAM, even when quantized
• Enabling offline AI on microcontrollers, phones, or edge devices
• Serving as a fast baseline for lightweight NLP tasks (intent detection, short responses)

It’s ideal for:

• Chatbots with simple flows
• On-device assistants
• Educational demos
• Rapid prototyping

Model anaysis and rankings

I have run each of these models across 6 questions, and ranked them all based on the quality of the anwsers. Qwen3-0.6B-f16:Q5_K_M is the best model across all question types, but if you want to play it safe with a higher precision model, then you could consider using Qwen3-0.6B-f16:Q8_0.

You can read the results here: Qwen3-0.6b-f16-analysis.md

If you find this useful, please give the project a ❤️ like.

Usage

Load this model using:

• OpenWebUI – self-hosted AI interface with RAG & tools
• LM Studio – desktop app with GPU support and chat templates
• GPT4All – private, local AI chatbot (offline-first)
• Or directly via llama.cpp

Each quantized model includes its own README.md and shares a common MODELFILE for optimal configuration.

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%3AQ3_K_M.gguf (replace the quantised version with the one you want)
2. nano Modelfile and enter these details (again, replacing Q3_K_M with the version you want):

FROM ./Qwen3-0.6B-f16:Q3_K_M.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:Q3_K_M -f Modelfile

You will now see "Qwen3-0.6B-f16:Q3_K_M" in your Ollama model list.

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

Author

👤 Geoff Munn (@geoffmunn)
🔗 Hugging Face Profile

Disclaimer

This is a community conversion for local inference. Not affiliated with Alibaba Cloud or the Qwen team.