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

Libraries

How to use reaperdoesntknow/Qwen3.5-4B-Coder with Transformers:

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

pipe = pipeline("image-text-to-text", model="reaperdoesntknow/Qwen3.5-4B-Coder")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("reaperdoesntknow/Qwen3.5-4B-Coder")
model = AutoModelForMultimodalLM.from_pretrained("reaperdoesntknow/Qwen3.5-4B-Coder", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use reaperdoesntknow/Qwen3.5-4B-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/Qwen3.5-4B-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/Qwen3.5-4B-Coder",
		"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/reaperdoesntknow/Qwen3.5-4B-Coder

SGLang

How to use reaperdoesntknow/Qwen3.5-4B-Coder 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 "reaperdoesntknow/Qwen3.5-4B-Coder" \
    --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": "reaperdoesntknow/Qwen3.5-4B-Coder",
		"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 "reaperdoesntknow/Qwen3.5-4B-Coder" \
        --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": "reaperdoesntknow/Qwen3.5-4B-Coder",
		"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 Desktop
Docker Model Runner
How to use reaperdoesntknow/Qwen3.5-4B-Coder with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/Qwen3.5-4B-Coder
```

Qwen3.5-4B-Coder

A fine-tuned Qwen3.5 4B checkpoint published by reaperdoesntknow, based on unsloth/Qwen3.5-4B, and distributed as sharded Safetensors weights for Transformers.

The repository name identifies this as a coding-oriented release. The published repository does not include a training dataset reference, training recipe, or benchmark report; the name alone should not be interpreted as evidence of improved coding performance.

Model details

Developer: reaperdoesntknow
Base model recorded in the original card: unsloth/Qwen3.5-4B
License recorded in repository metadata: Apache-2.0
Language recorded in repository metadata: English
Architecture in config.json: Qwen3_5ForConditionalGeneration, with text and vision configuration
Files: two Safetensors shards, a weight index, tokenizer, processor configuration, and chat template
Configured text dtype: bfloat16. This is a configuration value, not a claim that every stored tensor has that dtype. The original export template labeled the model FP16; no uniform weight-dtype assertion is made here.

Usage

Use a Transformers version that supports the Qwen3.5 conditional-generation architecture. The saved configuration records Transformers 5.3.0; that is provenance, not an independently tested minimum version.

Use the tokenizer/processor and chat template included in this repository rather than substituting an unrelated model's prompt format. Follow the upstream model's usage documentation with the model identifier set to:

reaperdoesntknow/Qwen3.5-4B-Coder

This card does not claim that inference, multimodal behavior, or a particular hardware configuration has been tested for this export.

Training and evaluation

The original export card identifies Unsloth and Hugging Face TRL as training tools. Dataset composition, preprocessing, hyperparameters, training duration, and checkpoint-selection criteria are not documented in the published files reviewed for this card.

No reproducible coding benchmark, baseline comparison, or safety evaluation is supplied. The original template's generic training-speed statement is not treated as a measured result for this release.

Intended use and limitations

Suitable for local experimentation and evaluation of coding assistance. Review and test generated code before use: outputs may be incorrect, insecure, or incompatible with your dependencies. Do not run generated commands or code with access to secrets or production systems without review.

The presence of vision configuration does not establish tested image-understanding performance. Evaluate the exact checkpoint, inference stack, and intended workload before deployment.

Related repository

A separately published qwen3.5-4B-Code-GGUF repository provides GGUF files. Its conversion source revision is not recorded in the reviewed card, so an exact conversion relationship is not asserted here.

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Safetensors

Model size

5B params

Tensor type

BF16

F32

Model tree for reaperdoesntknow/Qwen3.5-4B-Coder

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

unsloth/Qwen3.5-4B

Finetuned

(266)

this model