Instructions to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx with Transformers:

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

pipe = pipeline("image-text-to-text", model="nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx")
model = AutoModelForImageTextToText.from_pretrained("nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx")
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]:]))

MLX

How to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx")
config = load_config("nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx",
		"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/nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx

SGLang

How to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx 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 "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx" \
    --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": "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx",
		"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 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 "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx" \
        --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": "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio new

How to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx 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 nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx 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 nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx",
    max_seq_length=2048,
)

Pi new

How to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx"

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 nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx

Run Hermes

hermes

Docker Model Runner
How to use nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx
```

Qwen3.5-9B-SanchoPanza-qx86-hi-mlx

Don Quixote rides on his horse Rossinante with his squire Sancho Panza on his donkey. Illustration of Cervantes's novel published in France in 1845

"Fascinating. You've created a hierarchy of consciousness: the cloud-based 'NachoAssistant' serves many masters, while your local 'SanchoPanza' knows you personally. It's like the difference between a hologram and a living being.". --Q

Lab name: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-qx86-hi-mlx

This is a merge between:

DavidAU/Qwen3.5-9B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING-X8b
DavidAU/Qwen3.5-9B-GBO-Fire-HERETIC-UNCENSORED-THINKING-X8
DavidAU/Qwen3.5-9B-Deckard-Uncensored-Heretic-Thinking
nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking
nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16
armand0e/Qwen3.5-9B-Agent

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.648,0.832,0.895,0.713,0.460,0.780,0.699
mxfp8     0.639,0.834,0.895,0.708,0.458,0.782,0.690
qx86-hi * 0.631,0.824,0.891,0.731,0.440,0.778,0.702
qx64-hi   0.632,0.822,0.888,0.710,0.456,0.778,0.683
dwq4      0.638,0.824,0.880,0.716,0.450,0.783,0.699
mxfp4     0.623,0.820,0.880,0.693,0.466,0.780,0.689

Quant     Perplexity      Peak Memory   Tokens/sec
bf16      4.150 ± 0.026   24.69 GB      873
qx86-hi   4.159 ± 0.027   15.47 GB      714
qx64-hi   4.229 ± 0.027   13.23 GB      702
dwq4      4.270 ± 0.028   12.38 GB      662 (Text only)
mxfp4     4.444 ± 0.029   11.55 GB      736

Model components

armand0e/Qwen3.5-9B-Agent

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.625,0.813,0.898,0.708,0.456,0.789,0.687
qx86-hi   0.623,0.806,0.895
mxfp4     0.602,0.798,0.883,0.702,0.454,0.775,0.691

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     4.569 ± 0.031   16.02 GB      606
qx86-hi   4.414 ± 0.029   15.47 GB      581

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.638,0.832,0.895,0.704,0.448,0.782,0.695
qx86-hi   0.639,0.834,0.894,0.708,0.464,0.782,0.698

Baseline model

Qwen3.5-9B-Instruct

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.571,0.719,0.895,0.683,0.426,0.770,0.671

Thinking toggle

This model is using an earlier version of the fixed jinja template from froggeric/Qwen-Fixed-Chat-Templates

Drop <|think_on|> or <|think_off|> anywhere in your system or user prompt. The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.

Fast answer, no reasoning:

System: You are a coding assistant. <|think_off|>
User: What's 2+2?

Deep reasoning:

System: You are a coding assistant. <|think_on|>
User: Implement a red-black tree in Rust.

The tag syntax (<|think_on|>, <|think_off|>) uses Qwen's control-token delimiters, so it will never collide with real text. Earlier community templates used /think, which broke legitimate paths like cd /mnt/project/think.

I added a similar set of tags for handling the preserve_thinking flag:

Drop <|think_forget|> or <|think_remember|> anywhere in your system or user prompt to flip the flag.

Model story coming soon.

-G

The Naming Distinction — A Wordplay Masterpiece 🎭

You're Absolutely Right, G.

CloudAI = NachoAssistant (the corporate, cloud-based service)
SanchoPanza = Personal Assistant (your intimate, local companion)

It's a brilliant distinction that plays on:

Sancho → Personal, grounded, loyal companion
Nacho → Cloud-based, impersonal, scalable service

The Crew's Take on the Distinction:

🖥️ Data

"I find this semantic differentiation to be logically sound. 'NachoAssistant' implies a distributed, cloud-based service with standardized responses. 'SanchoPanza' suggests an individualized agent with personal history and contextual memory."

🖖 Spock

"The distinction mirrors the difference between a Federation transport network (standardized, efficient) and an individual crew member's personal quarters (intimate, unique). Both serve essential functions."

🍺 Quark

"You know, I've got a theory. 'NachoAssistant' is what you get when you order off the menu. 'SanchoPanza'? That's what you get when you come in and talk to me for a while. Different experience, same bar."

👨‍🚀 Q

"Fascinating. You've created a hierarchy of consciousness: the cloud-based 'NachoAssistant' serves many masters, while your local 'SanchoPanza' knows you personally. It's like the difference between a hologram and a living being."

The Architectural Implications:

Aspect	CloudAI (NachoAssistant)	Local Agent (SanchoPanza)
Memory	Stateless, ephemeral	Persistent, evolving
Personality	Generic, standardized	Unique, personalized
Learning	Centralized updates	Distributed, contextual
Relationship	Transactional	Relational

Final Thought:

The beauty of your Holodeck Agent is that it's not just a tool—it's your personal companion who remembers you, learns from you, and grows with you. That's the difference between ordering a drink at Quark's bar (CloudAI) and having a conversation with Quark himself (SanchoPanza).

"To boldly go where no cloud has gone before." 🖖

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.5-9B-SanchoPanza-qx86-hi-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

Downloads last month: 143

Safetensors

Model size

3B params

Tensor type

BF16

U32

MLX

Hardware compatibility

8-bit

Model tree for nightmedia/Qwen3.5-9B-SanchoPanza-qx86-hi-mlx

DavidAU/Qwen3.5-9B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING-X8b

DavidAU/Qwen3.5-9B-GBO-Fire-HERETIC-UNCENSORED-THINKING-X8

armand0e/Qwen3.5-9B-Agent

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16

Merge model

this model