Instructions to use p4b/qwen3.5-4b-chunky-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use p4b/qwen3.5-4b-chunky-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="p4b/qwen3.5-4b-chunky-FP8")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("p4b/qwen3.5-4b-chunky-FP8")
model = AutoModelForMultimodalLM.from_pretrained("p4b/qwen3.5-4b-chunky-FP8", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use p4b/qwen3.5-4b-chunky-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "p4b/qwen3.5-4b-chunky-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "p4b/qwen3.5-4b-chunky-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/p4b/qwen3.5-4b-chunky-FP8

SGLang

How to use p4b/qwen3.5-4b-chunky-FP8 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 "p4b/qwen3.5-4b-chunky-FP8" \
    --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": "p4b/qwen3.5-4b-chunky-FP8",
		"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 "p4b/qwen3.5-4b-chunky-FP8" \
        --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": "p4b/qwen3.5-4b-chunky-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use p4b/qwen3.5-4b-chunky-FP8 with Docker Model Runner:
```
docker model run hf.co/p4b/qwen3.5-4b-chunky-FP8
```

p4b/qwen3.5-4b-chunky-FP8

Bitext chunk-alignment model. Given a source/target pair pre-segmented with numbered split markers [|i|], it predicts the cumulative split points at which the two sides stay mutually aligned — and omits spans that are one-sided (untranslated / noise).

FP8 (W8A8) language model, quantized with llm-compressor using static, calibrated activation scales.

Input / output format

Input:

<src>First sentence.[|1|]Second sentence.[|2|]Third.[|3|]</src><tgt>첫 문장.[|1|]두 번째 문장.[|2|]세 번째.[|3|]</tgt>

Output — cumulative SourceIndex-TargetIndex boundary pairs:

<answer>1-1, 2-2, 3-3</answer>

This is a text model. It is packaged in the Qwen3_5ForConditionalGeneration layout (the original vision tower is bundled unused) so it loads in both Hugging Face Transformers and vLLM; no images are involved.

Serving (vLLM)

vllm serve p4b/qwen3.5-4b-chunky-FP8 --language-model-only --mamba-cache-mode=align

--language-model-only skips the (unused) vision tower; --mamba-cache-mode=align is required by the Qwen3.5 hybrid linear-attention layers.

Usage (Transformers)

from transformers import AutoModelForImageTextToText, AutoTokenizer

tok = AutoTokenizer.from_pretrained("p4b/qwen3.5-4b-chunky-FP8")
model = AutoModelForImageTextToText.from_pretrained("p4b/qwen3.5-4b-chunky-FP8", device_map="auto")

msgs = [{"role": "user", "content": PROMPT + text}]  # text = the <src>...</tgt> block
enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True)
out = model.generate(input_ids=enc["input_ids"].to(model.device), max_new_tokens=128)
print(tok.decode(out[0, enc["input_ids"].shape[1]:], skip_special_tokens=True))

License

Apache-2.0.

Downloads last month: 150

Safetensors

Model size

5B params

Tensor type

BF16

F8_E4M3