Instructions to use armand0e/Qwen3.5-9B-Pi-Agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use armand0e/Qwen3.5-9B-Pi-Agent with Transformers:

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

pipe = pipeline("image-text-to-text", model="armand0e/Qwen3.5-9B-Pi-Agent")
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("armand0e/Qwen3.5-9B-Pi-Agent")
model = AutoModelForImageTextToText.from_pretrained("armand0e/Qwen3.5-9B-Pi-Agent")
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

vLLM

How to use armand0e/Qwen3.5-9B-Pi-Agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "armand0e/Qwen3.5-9B-Pi-Agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "armand0e/Qwen3.5-9B-Pi-Agent",
		"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/armand0e/Qwen3.5-9B-Pi-Agent

SGLang

How to use armand0e/Qwen3.5-9B-Pi-Agent 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 "armand0e/Qwen3.5-9B-Pi-Agent" \
    --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": "armand0e/Qwen3.5-9B-Pi-Agent",
		"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 "armand0e/Qwen3.5-9B-Pi-Agent" \
        --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": "armand0e/Qwen3.5-9B-Pi-Agent",
		"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 armand0e/Qwen3.5-9B-Pi-Agent 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 armand0e/Qwen3.5-9B-Pi-Agent 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 armand0e/Qwen3.5-9B-Pi-Agent to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for armand0e/Qwen3.5-9B-Pi-Agent to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="armand0e/Qwen3.5-9B-Pi-Agent",
    max_seq_length=2048,
)

Docker Model Runner
How to use armand0e/Qwen3.5-9B-Pi-Agent with Docker Model Runner:
```
docker model run hf.co/armand0e/Qwen3.5-9B-Pi-Agent
```

Qwen3.5 9B - Pi mono tune

Training Data

This model was trained on badlogicgames/pi-mono agent traces at 24k context (80% of the training examples), TeichAI/Claude-Opus-4.6-Reasoning-887x was downsampled and mixed in for stability.

Furthermore the qwen3.6 chat template was used to tune this model. preserve_thinking and enable_thinking are both supported by the model now. (preserve_thinking is still experimental)

Goal

The goal was to make a model small enough to run on consumer hardware, capable of working in long context agent scenarios

How it turned out

The model seems like it picked up some stylistic qualities from both the various models, will have to try again later with some other data from the same model source.

Other than that though, overall it's ability to work in agent harnesses have improved. Code quality is potentially degraded (unsure as i haven't used the base model as a coding agent for reference).

I recommend using the model with the pi agent harness, connected via the pi lm_studio extension.

LoRA adapter can be found here

Developed by: armand0e
License: apache-2.0
Finetuned from model : unsloth/Qwen3.5-9B

This qwen3_5 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 26

Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for armand0e/Qwen3.5-9B-Pi-Agent

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

unsloth/Qwen3.5-9B

Finetuned

(81)

this model

armand0e
/

Qwen3.5-9B-Pi-Agent

Qwen3.5 9B - Pi mono tune

Training Data

Goal

How it turned out

Model tree for armand0e/Qwen3.5-9B-Pi-Agent

Datasets used to train armand0e/Qwen3.5-9B-Pi-Agent