---

base_model: scikit-plots/minimind-3

library_name: transformers

pipeline_tag: text-generation

license: apache-2.0

language:

• zh
• en

tags:

• gguf
• llama-cpp
• text-generation
• minimind
• qwen3
• small-language-model
• reasoning

---

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

1. Copy the .gguf file to your LM Studio models folder.
2. Select minimind-3-Q4_K_M.gguf.
3. Choose ChatML preset (<|im_start|> / <|im_end|>).