Instructions to use K1zE/BPM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use K1zE/BPM with Transformers:

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

pipe = pipeline("text-generation", model="K1zE/BPM")
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("K1zE/BPM")
model = AutoModelForMultimodalLM.from_pretrained("K1zE/BPM", 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 K1zE/BPM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "K1zE/BPM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "K1zE/BPM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/K1zE/BPM

SGLang

How to use K1zE/BPM 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 "K1zE/BPM" \
    --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": "K1zE/BPM",
		"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 "K1zE/BPM" \
        --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": "K1zE/BPM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use K1zE/BPM with Docker Model Runner:
```
docker model run hf.co/K1zE/BPM
```

BPM / README.md

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model: Qwen/Qwen3.5-2B
	language:
	- en
	tags:
	- on-policy-distillation
	- cross-tokenizer
	- knowledge-distillation
	- mathematics
	- code
	---

	# BPM · GLM-Z1-9B → Qwen3.5-2B

	[arXiv:2607.22334](https://arxiv.org/abs/2607.22334) · [Project page](https://bpm-opd.github.io/)

	Qwen3.5-2B distilled from GLM-Z1-9B-0414 with BPM (Byte-Prefix Marginalization), a
	cross-tokenizer on-policy distillation method. Forward-KL arm, step 149.

	## Results

	Every cell is `avg@8 / pass@8`, matching Table 1 of the paper. Sampling: temperature 0.6,
	top-p 0.95, top-k 20, 8 samples per prompt, thinking enabled.

	\| Model \| AIME 2026 \| HMMT 2026 \| MATH-500 \| HumanEval+ \| LiveCodeBench \| TACO \| Avg \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| GLM-Z1-9B-0414 (teacher) \| 63.3 / 90.0 \| 33.3 / 48.5 \| 92.3 / 97.4 \| 89.8 / 96.9 \| 45.6 / 63.2 \| 53.5 / 64.0 \| 63.0 / 76.7 \|
	\| Qwen3.5-2B (base) \| 7.5 / 26.7 \| 10.6 / 18.2 \| 60.5 / 84.6 \| 52.5 / 84.0 \| 11.6 / 17.6 \| 5.3 / 11.7 \| 24.7 / 40.5 \|
	\| SimCT \| 15.8 / 30.0 \| 12.5 / 27.3 \| 58.6 / 89.2 \| 50.2 / 78.5 \| 12.6 / 23.6 \| 8.4 / 21.2 \| 26.3 / 45.0 \|
	\| ULD \| 17.5 / 40.0 \| 12.5 / 24.2 \| 81.2 / 94.4 \| 66.0 / 88.3 \| 15.8 / 25.8 \| 12.4 / 27.2 \| 34.2 / 50.0 \|
	\| GOLD \| 22.1 / 53.3 \| 17.4 / 33.3 \| 81.8 / 96.4 \| 63.0 / 89.0 \| 9.0 / 23.6 \| 4.8 / 15.5 \| 33.0 / 51.9 \|
	\| SeqKD \| 23.3 / 50.0 \| 14.8 / 27.3 \| 74.4 / 93.4 \| 60.1 / 87.1 \| 20.1 / 31.3 \| 16.8 / 34.3 \| 34.9 / 53.9 \|
	\| BPM \| 35.4 / 63.3 \| 21.2 / 36.4 \| 84.8 / 95.4 \| 68.8 / 91.4 \| 22.3 / 33.5 \| 16.5 / 36.4 \| 41.5 / 59.4 \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	tok = AutoTokenizer.from_pretrained("K1zE/BPM")
	model = AutoModelForCausalLM.from_pretrained("K1zE/BPM", dtype="auto", device_map="auto")

	msgs = [{"role": "user", "content": "What is the remainder of 7^100 modulo 13?"}]
	ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
	print(tok.decode(model.generate(ids, max_new_tokens=2048)[0][ids.shape[-1]:], skip_special_tokens=True))
	```

	Prompts: [K1zE/BPM](https://huggingface.co/datasets/K1zE/BPM). Research checkpoint, not
	instruction-tuned for general use.

	## Citation

	```bibtex
	@misc{wang2026crosstokenizeronpolicydistillationbyteprefix,
	title={Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization},
	author={Hao Wang and Kun Yuan and Wenlin Zhong and Minglei Zhang and Han Xiao and Ming Sun and Honggang Qi},
	year={2026},
	eprint={2607.22334},
	archivePrefix={arXiv},
	primaryClass={cs.LG},
	url={https://arxiv.org/abs/2607.22334},
	}
	```