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
```

armand0e commited on 8 days ago

Commit

8ab4058

verified ·

1 Parent(s): 666a56c

Update README.md

Browse files

Files changed (1) hide show

README.md +12 -3

README.md CHANGED Viewed

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/Qwen3.5-9B
 tags:
 - text-generation-inference
 - transformers
@@ -25,7 +25,16 @@ This model was trained on the following datasets using the qwen3.6 chat template
   - `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)
-Training specs:
 ```py
 MAX_SEQ_LEN = 49152
@@ -61,7 +70,7 @@ train_dataset = prepare_data(
             "source": "armand0e/kimi-k2.6-agent",
         },
         "minimax-m2.7": {
-            "source": "armand0e/ag-datagen-v2-test",
         },
         "chat": {
             "source": "TeichAI/Claude-Opus-4.6-Reasoning-887x",

 ---
+base_model: armand0e/Qwen3.5-9B-Agent
 tags:
 - text-generation-inference
 - transformers
   - `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:
 ```py
 MAX_SEQ_LEN = 49152
             "source": "armand0e/kimi-k2.6-agent",
         },
         "minimax-m2.7": {
+            "source": "armand0e/minimax-m2.7-agent",
         },
         "chat": {
             "source": "TeichAI/Claude-Opus-4.6-Reasoning-887x",