Instructions to use Nubinu/Qwen3.5-4B-MiniFantasy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nubinu/Qwen3.5-4B-MiniFantasy with Transformers:

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

pipe = pipeline("text-generation", model="Nubinu/Qwen3.5-4B-MiniFantasy")
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("Nubinu/Qwen3.5-4B-MiniFantasy")
model = AutoModelForImageTextToText.from_pretrained("Nubinu/Qwen3.5-4B-MiniFantasy")
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 Nubinu/Qwen3.5-4B-MiniFantasy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nubinu/Qwen3.5-4B-MiniFantasy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nubinu/Qwen3.5-4B-MiniFantasy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nubinu/Qwen3.5-4B-MiniFantasy

SGLang

How to use Nubinu/Qwen3.5-4B-MiniFantasy 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 "Nubinu/Qwen3.5-4B-MiniFantasy" \
    --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": "Nubinu/Qwen3.5-4B-MiniFantasy",
		"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 "Nubinu/Qwen3.5-4B-MiniFantasy" \
        --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": "Nubinu/Qwen3.5-4B-MiniFantasy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Nubinu/Qwen3.5-4B-MiniFantasy 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 Nubinu/Qwen3.5-4B-MiniFantasy 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 Nubinu/Qwen3.5-4B-MiniFantasy to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nubinu/Qwen3.5-4B-MiniFantasy to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Nubinu/Qwen3.5-4B-MiniFantasy",
    max_seq_length=2048,
)

Docker Model Runner
How to use Nubinu/Qwen3.5-4B-MiniFantasy with Docker Model Runner:
```
docker model run hf.co/Nubinu/Qwen3.5-4B-MiniFantasy
```

Nubinu commited on Mar 25

Commit

0c0102b

verified ·

1 Parent(s): 5af0498

Upload base_test_sample_geechan.json

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Files changed (1) hide show

base_test_sample_geechan.json +123 -0

base_test_sample_geechan.json ADDED Viewed

	@@ -0,0 +1,123 @@

+{
+    "temp": 1,
+    "temperature_last": true,
+    "top_p": 1,
+    "top_k": 200,
+    "top_a": 0,
+    "tfs": 1,
+    "epsilon_cutoff": 0,
+    "eta_cutoff": 0,
+    "typical_p": 1,
+    "min_p": 0.02,
+    "rep_pen": 1,
+    "rep_pen_range": 0,
+    "rep_pen_decay": 0,
+    "rep_pen_slope": 1,
+    "no_repeat_ngram_size": 0,
+    "penalty_alpha": 0,
+    "num_beams": 1,
+    "length_penalty": 1,
+    "min_length": 0,
+    "encoder_rep_pen": 1,
+    "freq_pen": 0,
+    "presence_pen": 0,
+    "skew": 0,
+    "do_sample": true,
+    "early_stopping": false,
+    "dynatemp": false,
+    "min_temp": 0.6,
+    "max_temp": 2,
+    "dynatemp_exponent": 1,
+    "smoothing_factor": 0,
+    "smoothing_curve": 1,
+    "dry_allowed_length": 4,
+    "dry_multiplier": 0.8,
+    "dry_base": 1.75,
+    "dry_sequence_breakers": "[\"\\n\", \":\", \"\\\"\", \"*\", \"\"]",
+    "dry_penalty_last_n": 0,
+    "add_bos_token": true,
+    "ban_eos_token": false,
+    "skip_special_tokens": false,
+    "mirostat_mode": 0,
+    "mirostat_tau": 5,
+    "mirostat_eta": 0.1,
+    "guidance_scale": 1,
+    "negative_prompt": "",
+    "grammar_string": "",
+    "json_schema": {},
+    "json_schema_allow_empty": false,
+    "banned_tokens": "",
+    "sampler_priority": [
+        "repetition_penalty",
+        "presence_penalty",
+        "frequency_penalty",
+        "dry",
+        "top_k",
+        "min_p",
+        "top_p",
+        "top_n_sigma",
+        "tfs",
+        "typical_p",
+        "epsilon_cutoff",
+        "eta_cutoff",
+        "top_a",
+        "mirostat",
+        "quadratic_sampling",
+        "dynamic_temperature",
+        "temperature",
+        "xtc",
+        "encoder_repetition_penalty",
+        "no_repeat_ngram"
+    ],
+    "samplers": [
+        "top_k",
+        "penalties",
+        "dry",
+        "top_n_sigma",
+        "typ_p",
+        "top_p",
+        "min_p",
+        "xtc",
+        "temperature",
+        "adaptive_p"
+    ],
+    "samplers_priorities": [
+        "dry",
+        "penalties",
+        "no_repeat_ngram",
+        "temperature",
+        "top_nsigma",
+        "top_p_top_k",
+        "top_a",
+        "min_p",
+        "tfs",
+        "eta_cutoff",
+        "epsilon_cutoff",
+        "typical_p",
+        "quadratic",
+        "xtc"
+    ],
+    "ignore_eos_token": false,
+    "spaces_between_special_tokens": true,
+    "speculative_ngram": false,
+    "sampler_order": [
+        6,
+        2,
+        0,
+        1,
+        3,
+        4,
+        5
+    ],
+    "logit_bias": [],
+    "xtc_threshold": 0,
+    "xtc_probability": 0,
+    "nsigma": 0,
+    "min_keep": 0,
+    "extensions": {},
+    "adaptive_target": -0.01,
+    "adaptive_decay": 0.9,
+    "rep_pen_size": 0,
+    "genamt": 4096,
+    "max_length": 32768
+}