Instructions to use abenzerps/Apodex-1.1-mini-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 abenzerps/Apodex-1.1-mini-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 abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

Use Docker

docker model run hf.co/abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use abenzerps/Apodex-1.1-mini-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abenzerps/Apodex-1.1-mini-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": "abenzerps/Apodex-1.1-mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

Ollama
How to use abenzerps/Apodex-1.1-mini-GGUF with Ollama:
```
ollama run hf.co/abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M
```

Unsloth Studio

How to use abenzerps/Apodex-1.1-mini-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for abenzerps/Apodex-1.1-mini-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for abenzerps/Apodex-1.1-mini-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for abenzerps/Apodex-1.1-mini-GGUF to start chatting

How to use abenzerps/Apodex-1.1-mini-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

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": "abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use abenzerps/Apodex-1.1-mini-GGUF with Docker Model Runner:
```
docker model run hf.co/abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M
```

Lemonade

How to use abenzerps/Apodex-1.1-mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Apodex-1.1-mini-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use abenzerps/Apodex-1.1-mini-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 abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

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 abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/Apodex-1.1-mini-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M

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 "abenzerps/Apodex-1.1-mini-GGUF:Q4_K_M" \
  --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"

Apodex-1.1-mini GGUF

GGUF quantizations of apodex/Apodex-1.1-mini, a 35.95B-parameter Qwen3.5 MoE model designed for research, data, files, code, and tool-driven work.

Every model file includes the checkpoint's native MTP/NextN head. Image input requires the F16 vision projector included in this repository.

Q4 Selective

Q4 Selective is a compact custom Q4 build. It uses importance-matrix-guided expert quantization and preserves selected tensors at higher precision. It is an alternative to the standard Q4_K_M; Q4_K_M remains the recommended default.

Upstream benchmarks

Both figures are reproduced from the upstream model card. They report evaluations performed with FrontierAgent, Apodex's open-source agent runtime and evaluation suite, using its ReAct and Agent Team workflows. They do not measure the GGUF files in this repository.

Upstream Apodex-1.1 evaluation results using FrontierAgent's ReAct and Agent Team workflows.

Upstream Apodex-1.1-mini evaluation results using FrontierAgent. With the Agent Team workflow, the model scored 27.7 on APEX-Agent, 50.2 on FrontierFinance, and 51.7 on FrontierScience-Research.

Files

File	Size	Notes
`Apodex-1.1-mini-IQ1_M.gguf`	8.82 GB	Ultra-low-memory, importance-aware build; substantial quality trade-off
`Apodex-1.1-mini-IQ2_M.gguf`	12.2 GB	Importance-aware; smallest recommended build
`Apodex-1.1-mini-Q2_K.gguf`	13.2 GB	Maximum-compression legacy K-quant
`Apodex-1.1-mini-IQ3_M.gguf`	16.0 GB	Importance-aware balance for constrained systems
`Apodex-1.1-mini-Q3_K_M.gguf`	17.2 GB	Standard Q3 K-quant
`Apodex-1.1-mini-Q4_K_S.gguf`	20.4 GB	Smaller Q4 option
`Apodex-1.1-mini-Q4_Selective-Q4_K_M.gguf`	19.8 GB	Compact custom Q4 alternative
`Apodex-1.1-mini-Q4_K_M.gguf`	21.7 GB	Recommended default
`Apodex-1.1-mini-Q5_K_S.gguf`	24.6 GB	Smaller Q5 option
`Apodex-1.1-mini-Q5_K_M.gguf`	25.3 GB	Higher-quality Q5 option
`Apodex-1.1-mini-Q6_K.gguf`	29.2 GB	High quality
`Apodex-1.1-mini-Q8_0.gguf`	37.8 GB	Near-lossless reference quantization
`mmproj-Apodex-1.1-mini-F16.gguf`	899 MB	F16 vision projector
`imatrix/Apodex-1.1-mini-imatrix-Q6_K-wiki-100x512.gguf`	192 MB	Importance matrix used by the IQ builds

IQ1_M, IQ2_M, and IQ3_M use an importance matrix. Their embedded MTP layer is retained at Q5_K because that auxiliary layer is not exercised while collecting the matrix. IQ1_M is a 1.75-bit-per-weight format intended for severe memory constraints; use IQ2_M or higher when possible.

Hardware guidance

Available VRAM	Suggested starting point	Expected placement
8 GB	IQ1_M	CPU/GPU hybrid offload
12 GB	IQ1_M or IQ2_M	IQ1_M full offload; IQ2_M may require hybrid offload
16 GB	IQ2_M or IQ3_M	IQ2_M full offload; IQ3_M may require hybrid offload

These are starting points, not guarantees. Context length, KV-cache type, backend overhead, and other GPU workloads also consume memory. Prefer Q4_K_M or higher when system memory permits.

Chat template

The included chat_template.jinja is synchronized with upstream revision 62583b4. Use it with a current llama.cpp build to enable the upstream identity, thinking-history, and tool-argument behavior.

llama-server -m Apodex-1.1-mini-Q4_K_M.gguf --jinja --chat-template-file chat_template.jinja

Usage

Use llama.cpp commit f280b26983ad0fdb705a0d9ebf0503e76f2899b0 or a newer compatible revision.

Text generation:

llama-cli \
  -m Apodex-1.1-mini-Q4_K_M.gguf \
  -c 4096 -n 512 --temp 1.0 --top-p 0.95 \
  --jinja --chat-template-file chat_template.jinja \
  -p "Explain why reproducible builds matter."

MTP speculative decoding requires no separate draft model:

llama-server \
  -m Apodex-1.1-mini-Q4_K_M.gguf \
  -c 32768 --host 0.0.0.0 --port 8080 \
  --spec-type draft-mtp --spec-draft-n-max 3 \
  --jinja --chat-template-file chat_template.jinja

Image input:

llama-mtmd-cli \
  -m Apodex-1.1-mini-Q4_K_M.gguf \
  -mm mmproj-Apodex-1.1-mini-F16.gguf \
  --image image.jpg -p "Describe this image." \
  -c 4096 -n 256

Checksums

File	SHA-256
`Apodex-1.1-mini-IQ1_M.gguf`	`1e84d8adf7837e96fb18712882a8a114becc7e53554372e2f612c5e0c6276cd4`
`Apodex-1.1-mini-IQ2_M.gguf`	`450afff9cd60e19ad485b4be4c22adf561df35072e4b4879bcc981e6fad88a8f`
`Apodex-1.1-mini-Q2_K.gguf`	`cbe8ea39285cf03d24d119ac69f6a2d9f126777d31c7486138cbaea7b6897619`
`Apodex-1.1-mini-IQ3_M.gguf`	`8620c43276492c59be49269b0cce52ca4f6698c73154751274fa73eb831fb38a`
`Apodex-1.1-mini-Q3_K_M.gguf`	`c8c51f058f2fb4be7f064dedd0dcd8c07eb690dbc985335bb7f15f3b5754381b`
`Apodex-1.1-mini-Q4_K_S.gguf`	`9d0e92ea8b9de608de713b960bb2005277636a2d0ba3c815d5f2e58cb4b97c6a`
`Apodex-1.1-mini-Q4_Selective-Q4_K_M.gguf`	`1226a6dad081cddc4406aa6376152364b1d991cfecc51df2ba485770c336e0f2`
`Apodex-1.1-mini-Q4_K_M.gguf`	`13a580b1ab9350b90085c221cf66e8715a594b2428e5a80a7a956d8e0420be16`
`Apodex-1.1-mini-Q5_K_S.gguf`	`69df27cae68b3c012c9e1d508835d62d51044c55b735a14eca397c8375619e8c`
`Apodex-1.1-mini-Q5_K_M.gguf`	`b55bea4022cd4963af12f4446887966ebcdbf0ea1f326c79fcf8abba48f38cb7`
`Apodex-1.1-mini-Q6_K.gguf`	`a29a4071b7a034e7f15a8379dd2d70407dbeb20ab4d1340bf4954fc747a4ed73`
`Apodex-1.1-mini-Q8_0.gguf`	`75340c1561b8e6cb8b6314299a6c4a865ba49f0109e2193dcef48d3478564068`
`mmproj-Apodex-1.1-mini-F16.gguf`	`d0ccf814138020651bcab603d9ce5c080a0ada46f482c116f0aca0fbb84e091e`
`imatrix/Apodex-1.1-mini-imatrix-Q6_K-wiki-100x512.gguf`	`ecc7798b0c08125e4cfc3f25ba060f5bc46ea09cccca7f558aaacebf6dd5d244`

sha256sum -c SHA256SUMS

License

The source model was released by Apodex AI under the Apache License 2.0. The same license and attribution requirements apply to these conversions.

Downloads last month: 45

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for abenzerps/Apodex-1.1-mini-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

apodex/Apodex-1.1-mini

Quantized

(12)

this model