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

Libraries

How to use armand0e/Qwen3.5-9B-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-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-Agent")
model = AutoModelForImageTextToText.from_pretrained("armand0e/Qwen3.5-9B-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-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-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-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-Agent

SGLang

How to use armand0e/Qwen3.5-9B-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-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-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-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-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-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-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-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-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-Agent",
    max_seq_length=2048,
)

Docker Model Runner
How to use armand0e/Qwen3.5-9B-Agent with Docker Model Runner:
```
docker model run hf.co/armand0e/Qwen3.5-9B-Agent
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model was trained on the following datasets using the qwen3.6 chat template (training was done with enable_thinking and preserve_thinking set to True):

armand0e/badlogicgames-pi-mono-opus-filtered - Pi traces from Claude Opus (mainly 4.5)
armand0e/kimi-k2.6-claude-code-traces - Claude Code traces from kimi k2.6
armand0e/kimi-k2.6-agent - Codex traces from kimi k2.6
armand0e/minimax-m2.7-agent - Pi traces from minimax m2.7
TeichAI/Claude-Opus-4.6-Reasoning-887x (Downsampled to 200 examples, only present to stabilize chat behavior)

I recommend using the following sampling parameters:

temp: 1.0
top_k: 20 (though higher values like 40 still seem to work and be stable with tool calling and agentic tasks)
top_p: 0.95
min_p: 0.00
repeat_penalty: 1.0
presence_penalty: 1.5

Training code:

MAX_SEQ_LEN = 49152

from unsloth import FastModel
import torch

model = FastModel.get_peft_model(
    model,
    finetune_vision_layers     = False, # Turn off for just text!
    finetune_language_layers   = True,  # Should leave on!
    finetune_attention_modules = True,  # Attention good for GRPO
    finetune_mlp_modules       = True,  # Should leave on always!

    r = 64,           # Larger = higher accuracy, but might overfit
    lora_alpha = 64,  # Recommended alpha == r at least
    lora_dropout = 0,
    bias = "none",
    random_state = 3407,
)

from teich import prepare_data

train_dataset = prepare_data(
    {
        "opus-agent": {
            "source": "armand0e/badlogicgames-pi-mono-opus-filtered",
        },
        "kimi-claude": {
            "source": "armand0e/kimi-k2.6-claude-code-traces",
        },
        "kimi-codex": {
            "source": "armand0e/kimi-k2.6-agent",
        },
        "minimax-m2.7": {
            "source": "armand0e/minimax-m2.7-agent",
        },
        "chat": {
            "source": "TeichAI/Claude-Opus-4.6-Reasoning-887x",
            "max_examples": 200,
        }
    },
    tokenizer,
    split="train",
    hf_token=HF_TOKEN,
    chat_template_kwargs={"enable_thinking": True, "preserve_thinking": True},
    max_length=MAX_SEQ_LEN,
    drop_oversized_examples=True,
    trim_oversized_followups=True,
    tokenize=True,
    strict=True,
)

from trl import SFTConfig, SFTTrainer

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=None,
    args=SFTConfig(
        dataset_text_field="text",
        dataset_num_proc=1,
        max_length=MAX_SEQ_LEN,
        packing=False,
        per_device_train_batch_size=1,
        gradient_accumulation_steps=8,
        warmup_steps= 5,
        num_train_epochs=2,
        learning_rate=2e-4,
        logging_steps=1,
        save_steps=100,
        save_total_limit=3,
        optim="adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="linear",
        output_dir=OUTPUT_DIR,
        seed=3407,
        report_to="none",
    ),
)

from teich import mask_data

trainer = mask_data(
    trainer,
    tokenizer=tokenizer,
    train_on_reasoning=True,
    train_on_final_answers=True,
    train_on_tools=True,
)

This tune was very data limited, but still impresses me. I encourage everyone to generate their own high quality data for their own use cases, they can all be aggregated together.