MiniMind 3 GGUF
This repository provides GGUF quantizations for scikit-plots/minimind-3 (from the MiniMind series by Jingyao Gong).
MiniMind-3 is an ultra-compact language model that supports ChatML formatting, reasoning traces (<think>...</think>), and tool use.
All GGUF files were converted from the original safetensors weights using llama.cpp at native F16 precision, followed by standard k-quant quantization.
Available Files and Quantizations
| File | Quant Type | Size | Description / Recommendation |
|---|---|---|---|
minimind-3-F16.gguf |
F16 | 122.16 MB | Full precision base conversion. Highest fidelity reference weights. |
minimind-3-Q8_0.gguf |
Q8_0 | 65.03 MB | Near-lossless 8-bit quantization. Recommended for best quality evaluation. |
minimind-3-Q6_K.gguf |
Q6_K | 53.72 MB | High quality retention with minimal degradation. |
minimind-3-Q5_K_M.gguf |
Q5_K_M | 46.07 MB | Balanced performance and memory footprint. |
minimind-3-Q4_K_M.gguf |
Q4_K_M | 40.51 MB | Lightweight 4-bit quant. Recommended default for microcontroller and edge deployment. |
minimind-3-Q3_K_M.gguf |
Q3_K_M | 32.94 MB | Ultra-compact footprint for tightly memory-constrained devices. |
minimind-3-Q2_K.gguf |
Q2_K | 26.61 MB | Extreme compression (~26 MB). |
Model Architecture Details
- Base Architecture:
Qwen3ForCausalLM(qwen3) - Parameters: ~60M (60,486,912)
- Layers: 8
- Hidden Size: 768
- Attention Heads: 8 query heads / 4 KV heads (GQA), head dimension 96
- Intermediate Size: 2432
- Context Length: 32,768 tokens
- Vocabulary Size: 6,400 (compact BPE tokenizer)
- RoPE Theta: 1,000,000.0
Prompt Format and Chat Template
MiniMind-3 utilizes ChatML format with optional <think> reasoning blocks:
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is 25 * 4?<|im_end|>
<|im_start|>assistant
<think>
Quickstart Guide
1. llama.cpp
Run interactive text generation with llama-cli:
llama-cli -m ./minimind-3-Q4_K_M.gguf \
-p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHello! Who are you?<|im_end|>\n<|im_start|>assistant\n" \
-n 256 \
--temp 0.7 \
--top-p 0.85
Launch a local inference server with llama-server:
llama-server -m ./minimind-3-Q4_K_M.gguf \
--port 8080 \
-c 4096
2. Ollama
Create a Modelfile:
FROM ./minimind-3-Q4_K_M.gguf
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER temperature 0.7
Build and run:
ollama create minimind-3 -f Modelfile
ollama run minimind-3 "Explain what gravity is."
3. LM Studio
- Copy the
.gguffile to your LM Studio models folder. - Select
minimind-3-Q4_K_M.gguf. - Choose ChatML preset (
<|im_start|>/<|im_end|>).