Qwen3-4B-f16-GGUF

This is a GGUF-quantized version of the Qwen/Qwen3-4B language model — a powerful 4-billion-parameter LLM from Alibaba's Qwen series, designed for strong reasoning, agentic workflows, and multilingual fluency on consumer-grade hardware.

Converted for use with llama.cpp, LM Studio, OpenWebUI, GPT4All, and more.

Available Quantizations (from f16)

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

NEW: I have a custom model called Q3_HIFI, which is better than the standard Q3_K_M model. It is higher quality, smaller in size, and nearly the same speed as Q3_K_M.

It is listed under the 'f16' options because it's not an officially recognised type (at the moment).

Q3_HIFI

Pros:

• 🏆 Best quality with lowest perplexity of 16.76 (7.2% better than Q3_K_M, 12.2% better than Q3_K_S)
• 📦 Smaller than Q3_K_M (1.87 vs 1.93 GiB) while being significantly better quality
• 🎯 Uses intelligent layer-sensitive quantization (Q3_HIFI on sensitive layers, mixed q3_K/q4_K elsewhere)
• 📊 Most consistent results (lowest relative standard deviation in perplexity)

Cons:

• 🐢 Slowest inference at 215.1 TPS (5.5% slower than Q3_K_S)
• 🔧 Custom quantization may have less community support

Best for: Production deployments where output quality matters, tasks requiring accuracy (reasoning, coding, complex instructions), or when you want the best quality-to-size ratio.

You can read more about how it compares to Q3_K_M and Q3_K_S here: Q3_Quantization_Comparison.md

You can also view a cross-model comparison of the Q3_HIFI type here.

Level	Speed	Size	Recommendation
Q2_K	⚡ Fastest	1.9 GB	🚨 DO NOT USE. Worst results from all the 4B models.
🥈 Q3_K_S	⚡ Fast	2.2 GB	🥈 Runner up. A very good model for a wide range of queries.
🥇 Q3_K_M	⚡ Fast	2.4 GB	🥇 Best overall model. Highly recommended for all query types.
Q4_K_S	🚀 Fast	2.7 GB	A late showing in low-temperature queries. Probably not recommended.
Q4_K_M	🚀 Fast	2.9 GB	A late showing in high-temperature queries. Probably not recommended.
Q5_K_S	🐢 Medium	3.3 GB	Did not appear in the top 3 for any question. Not recommended.
Q5_K_M	🐢 Medium	3.4 GB	A second place for a high-temperature question, probably not recommended.
Q6_K	🐌 Slow	3.9 GB	Did not appear in the top 3 for any question. Not recommended.
🥉 Q8_0	🐌 Slow	5.1 GB	🥉 If you want to play it safe, this is a good option. Good results across a variety of questions.

Why Use a 4B Model?

The Qwen3-4B model strikes a powerful balance between capability and efficiency, offering:

• Strong reasoning and language understanding—significantly more capable than sub-1B models
• Smooth CPU inference with moderate hardware (no high-end GPU required)
• Memory footprint under ~8GB when quantized (e.g., GGUF Q4_K_M or AWQ)
• Excellent price-to-performance ratio for local or edge deployment

It’s ideal for:

• Local chatbots with contextual memory and richer responses
• On-device AI on laptops or mid-tier edge servers
• Lightweight RAG (Retrieval-Augmented Generation) applications
• Developers needing a capable yet manageable open-weight model

Choose Qwen3-4B when you need more intelligence than a tiny model can provide—but still want to run offline, avoid cloud costs, or maintain full control over your AI stack.

Build notes

All of these models (including Q3_HIFI) where built using these commands:

mkdir build
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DGGML_CUDA=ON -DGGML_VULKAN=OFF -DLLAMA_CURL=OFF
cmake --build build --config Release -j 

NOTE: Vulkan support is specifically turned off here. Vulkan performance was much worse, so if you want Vulkan support you can rebuild these models yourself.

The quantisation for Q3_HIFI also used a 5000 chunk imatrix file for extra precision. You can re-use it here: Qwen3-4B-f16-imatrix-5000.gguf

You can use the Q3_HIFI GitHub repository to build it from source if you're interested (use the Q3_HIFI branch): https://github.com/geoffmunn/llama.cpp.

Model anaysis and rankings

NOTE: This analysis does not include Q3_HIFI.

I have run each of these models across 6 questions, and ranked them all based on the quality of the anwsers. Qwen3-4B-f16:Q3_K_M (or Qwen3-4B-f16:Q3_HIFI) 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 Qwen3-4B-f16:Q8_0.

You can read the results here: Qwen3-4b-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/Qwen3-4B-f16/resolve/main/Qwen3-4B-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 ./Qwen3-4B-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 Qwen3-4B-f16:Q3_K_M -f Modelfile

You will now see "Qwen3-4B-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.