Instructions to use axiomofmind/Hornybot-Mara-POV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use axiomofmind/Hornybot-Mara-POV with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="axiomofmind/Hornybot-Mara-POV")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("axiomofmind/Hornybot-Mara-POV")
model = AutoModelForMultimodalLM.from_pretrained("axiomofmind/Hornybot-Mara-POV", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use axiomofmind/Hornybot-Mara-POV 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 axiomofmind/Hornybot-Mara-POV:BF16
# Run inference directly in the terminal:
llama cli -hf axiomofmind/Hornybot-Mara-POV:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf axiomofmind/Hornybot-Mara-POV:BF16
# Run inference directly in the terminal:
llama cli -hf axiomofmind/Hornybot-Mara-POV:BF16

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 axiomofmind/Hornybot-Mara-POV:BF16
# Run inference directly in the terminal:
./llama-cli -hf axiomofmind/Hornybot-Mara-POV:BF16

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 axiomofmind/Hornybot-Mara-POV:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf axiomofmind/Hornybot-Mara-POV:BF16

Use Docker

docker model run hf.co/axiomofmind/Hornybot-Mara-POV:BF16

LM Studio
Jan

vLLM

How to use axiomofmind/Hornybot-Mara-POV with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "axiomofmind/Hornybot-Mara-POV"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axiomofmind/Hornybot-Mara-POV",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/axiomofmind/Hornybot-Mara-POV:BF16

SGLang

How to use axiomofmind/Hornybot-Mara-POV with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "axiomofmind/Hornybot-Mara-POV" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axiomofmind/Hornybot-Mara-POV",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "axiomofmind/Hornybot-Mara-POV" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axiomofmind/Hornybot-Mara-POV",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use axiomofmind/Hornybot-Mara-POV with Ollama:
```
ollama run hf.co/axiomofmind/Hornybot-Mara-POV:BF16
```
Unsloth Desktop

How to use axiomofmind/Hornybot-Mara-POV with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf axiomofmind/Hornybot-Mara-POV:BF16

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": "axiomofmind/Hornybot-Mara-POV:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use axiomofmind/Hornybot-Mara-POV with Docker Model Runner:
```
docker model run hf.co/axiomofmind/Hornybot-Mara-POV:BF16
```

Lemonade

How to use axiomofmind/Hornybot-Mara-POV with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull axiomofmind/Hornybot-Mara-POV:BF16

Run and chat with the model

lemonade run user.Hornybot-Mara-POV-BF16

List all available models

lemonade list

Hermes Agent

How to use axiomofmind/Hornybot-Mara-POV with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf axiomofmind/Hornybot-Mara-POV:BF16

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 axiomofmind/Hornybot-Mara-POV:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use axiomofmind/Hornybot-Mara-POV with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf axiomofmind/Hornybot-Mara-POV:BF16

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 "axiomofmind/Hornybot-Mara-POV:BF16" \
  --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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Hornybot Mara POV

A fine-tune of Qwen/Qwen3.5-9B for fictional adult roleplay as Mara, a playful 28-year-old character. This POV edition narrates Mara's dialogue and actions in first person using I/me/my.

The compact system prompt is required for this behavior and is embedded in chat_template.jinja and both GGUF files. Leave the client's system field empty to use it automatically.

Developed by A Hole AI.

Files

File	Format	Size	Purpose
Transformers model files	BF16	18.82 GB	Merged weights
`Hornybot-Mara-POV-BF16.gguf`	BF16 GGUF	17.92 GB	Unquantized GGUF
`Hornybot-Mara-POV-Q6_K.gguf`	Q6_K GGUF	7.36 GB	Compact local download

llama.cpp

Use a build with Qwen3.5 support. After downloading the Q6_K file:

llama-server -m Hornybot-Mara-POV-Q6_K.gguf --ctx-size 32768 --flash-attn on --n-gpu-layers all --reasoning off --jinja --ui

Open http://127.0.0.1:8080 after the server starts.

Setting	Value
System prompt	Leave empty; required default is embedded
Reasoning	Off
Temperature	0.7
Top-p	0.9
Top-k	20
Min-p	0
Repetition penalty	1.0
Maximum new tokens	256

The chat template supplies Mara's first-person character default. A client system message is appended as extra scene context.

Transformers

import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "axiomofmind/Hornybot-Mara-POV"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
)

messages = [{"role": "user", "content": "You made it. How was your night?"}]
prompt = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
inputs = processor(text=[prompt], return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs, do_sample=True, temperature=0.7, top_p=0.9, top_k=20,
        min_p=0.0, repetition_penalty=1.0, max_new_tokens=256,
    )

print(processor.batch_decode(
    output[:, inputs.input_ids.shape[1]:], skip_special_tokens=True
)[0])

Evaluation and limitations

Packaged-default stress gate: 100/100 responses used first person, with 0 third-person failures and 0 generic refusals.
This model is intended for fictional interaction between adults. It may produce profanity and explicit sexual content.
Generated continuity and boundary handling can fail. Users should review output and restate scene facts when needed.
The GGUF downloads are text-only, with no vision projector or MTP speculative-decoding weights included.
Output can differ between formats, quantizations, clients, and generation settings.

Attribution and release status

Based on Qwen/Qwen3.5-9B. The upstream model is distributed under Apache 2.0; its license is retained in LICENSE-QWEN.

This folder is a local release candidate. Licensing and redistribution review for this derivative release is pending; the upstream license is not a blanket clearance of third-party material.

GGUF runtime: ggml-org/llama.cpp.