Instructions to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF
# Run inference directly in the terminal:
llama cli -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF
# Run inference directly in the terminal:
llama cli -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF
# Run inference directly in the terminal:
./build/bin/llama cli -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Use Docker

docker model run hf.co/IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Llama new
LM Studio
Jan

vLLM

How to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Ollama
How to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with Ollama:
```
ollama run hf.co/IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF
```
Unsloth Desktop

How to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF
```

Lemonade

How to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Run and chat with the model

lemonade run user.ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "IsValorum/ThinkingCap-Gemma-4-26B-A4B-it-APEX-I-MiniPlus-V2.1-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

ThinkingCap Gemma-4 26B-A4B — MiniPlus

APEX Quantization Series by IsValorum

Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.

If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies.

Family cross-links

Tier	Footprint	Base	Abliterated
MiniPlus	12.03 GiB	Current	—
NanoPlus	9.92 GiB	Base	—

Architecture and precision

Base model: bottlecapai/ThinkingCap-gemma-4-26B-A4B-it
Architecture: gemma4
iMatrix: model:bottlecapai/ThinkingCap-gemma-4-26B-A4B-it-GGUF/ThinkingCap-gemma-4-26B-A4B-it-imatrix.gguf@bb52b322579d218a9931261d6c9cd7403d80eb96 (used file; external provenance not inferred)
Recurrent protections (if present): F32 (271 tensors observed)

Surgical Tensor Quantization Map (Audited from GGUF)

The table below is generated from the audited physical GGUF.

Complete per-tensor map.

Layer Group	Sub-Component / Tensor	Qty	Precision	Engineering Rationale
Attention and gates	`blk.[N].attn_k.weight`	30	`Q4_K`	Audited attention projection
Attention and gates	`blk.[N].attn_output.weight`	25	`Q4_K`	Audited attention projection
Attention and gates	`blk.[N].attn_output.weight`	5	`Q6_K`	Audited attention projection
Attention and gates	`blk.[N].attn_q.weight`	30	`Q4_K`	Audited attention projection
Attention and gates	`blk.[N].attn_v.weight`	25	`Q4_K`	Audited attention projection
Dense FFN / MLP	`blk.[N].ffn_down.weight`	30	`Q5_1`	Audited dense MLP projection
Dense FFN / MLP	`blk.[N].ffn_gate.weight`	30	`Q5_K`	Audited dense MLP projection
Dense FFN / MLP	`blk.[N].ffn_up.weight`	30	`Q5_K`	Audited dense MLP projection
Global tensors	`rope_freqs.weight`	1	`F32`	Audited global tensor
Global tensors	`token_embd.weight`	1	`Q6_K`	Audited global tensor
Normalizations	`blk.[N].attn_k_norm.weight`	30	`F32`	Verified physical normalization type
Normalizations	`blk.[N].attn_norm.weight`	30	`F32`	Verified physical normalization type
Normalizations	`blk.[N].attn_q_norm.weight`	30	`F32`	Verified physical normalization type
Normalizations	`blk.[N].ffn_norm.weight`	30	`F32`	Verified physical normalization type
Normalizations	`blk.[N].post_attention_norm.weight`	30	`F32`	Verified physical normalization type
Normalizations	`blk.[N].post_ffw_norm.weight`	30	`F32`	Verified physical normalization type
Normalizations	`blk.[N].post_ffw_norm_1.weight`	30	`F32`	Verified physical normalization type
Normalizations	`blk.[N].post_ffw_norm_2.weight`	30	`F32`	Verified physical normalization type
Normalizations	`blk.[N].pre_ffw_norm_2.weight`	30	`F32`	Verified physical normalization type
Normalizations	`output_norm.weight`	1	`F32`	Verified physical normalization type
Other block components	`blk.[N].layer_output_scale.weight`	30	`F32`	Audited structural parameter
Routed experts	`blk.[N].ffn_down_exps.scale`	30	`F32`	Audited routed-expert projection
Routed experts	`blk.[N].ffn_down_exps.weight`	10	`IQ4_NL`	Audited routed-expert projection
Routed experts	`blk.[N].ffn_down_exps.weight`	20	`Q5_0`	Audited routed-expert projection
Routed experts	`blk.[N].ffn_gate_up_exps.weight`	10	`IQ3_XXS`	Audited routed-expert projection
Routed experts	`blk.[N].ffn_gate_up_exps.weight`	20	`Q3_K`	Audited routed-expert projection
Routing and indexer	`blk.[N].ffn_gate_inp.scale`	30	`F32`	Audited routing/indexer parameter
Routing and indexer	`blk.[N].ffn_gate_inp.weight`	30	`F32`	Audited routing/indexer parameter