Instructions to use end000/Qwen3.5-0.8B-WSPForge-Math-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use end000/Qwen3.5-0.8B-WSPForge-Math-v1 with Transformers:

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

pipe = pipeline("text-generation", model="end000/Qwen3.5-0.8B-WSPForge-Math-v1")
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("end000/Qwen3.5-0.8B-WSPForge-Math-v1")
model = AutoModelForMultimodalLM.from_pretrained("end000/Qwen3.5-0.8B-WSPForge-Math-v1", 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 end000/Qwen3.5-0.8B-WSPForge-Math-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "end000/Qwen3.5-0.8B-WSPForge-Math-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "end000/Qwen3.5-0.8B-WSPForge-Math-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/end000/Qwen3.5-0.8B-WSPForge-Math-v1

SGLang

How to use end000/Qwen3.5-0.8B-WSPForge-Math-v1 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 "end000/Qwen3.5-0.8B-WSPForge-Math-v1" \
    --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": "end000/Qwen3.5-0.8B-WSPForge-Math-v1",
		"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 "end000/Qwen3.5-0.8B-WSPForge-Math-v1" \
        --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": "end000/Qwen3.5-0.8B-WSPForge-Math-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use end000/Qwen3.5-0.8B-WSPForge-Math-v1 with Docker Model Runner:
```
docker model run hf.co/end000/Qwen3.5-0.8B-WSPForge-Math-v1
```

Qwen3.5-0.8B-WSPForge-Math-v1 / wspforge /research_code /src /common.py

end000

Release Sol014 mathematical specialization with paired evaluations, regressions and exact replay artifacts

fc32096 verified 1 day ago

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import hashlib
import json
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]


def inside(path):
    path = Path(path).resolve()
    if not path.is_relative_to(ROOT):
        raise ValueError('Путь должен находиться внутри лаборатории')
    return path


def permit_compute():
    if not (ROOT / 'checkpoints/COMPUTE_AUTHORIZED').exists():
        raise SystemExit('Вычисления заблокированы: отсутствует локальный разрешающий файл.')


def save_json(path, value):
    path = inside(path)
    path.parent.mkdir(parents=True, exist_ok=True)
    temp = path.with_suffix(path.suffix + '.tmp')
    temp.write_text(json.dumps(value, ensure_ascii=False, indent=2))
    temp.replace(path)


def rows(path):
    with inside(path).open() as f:
        return [json.loads(line) for line in f if line.strip()]


def stable_seed(item_id, sample, seed=0):
    key = f'{seed}:{item_id}:{sample}'.encode()
    return int.from_bytes(hashlib.sha256(key).digest()[:4], 'little') & 0x7fffffff


def append(path, row):
    path = inside(path)
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open('a') as f:
        f.write(json.dumps(row, ensure_ascii=False) + '\n')
        f.flush()

	import hashlib
	import json
	from pathlib import Path

	ROOT = Path(__file__).resolve().parents[1]


	def inside(path):
	path = Path(path).resolve()
	if not path.is_relative_to(ROOT):
	raise ValueError('Путь должен находиться внутри лаборатории')
	return path


	def permit_compute():
	if not (ROOT / 'checkpoints/COMPUTE_AUTHORIZED').exists():
	raise SystemExit('Вычисления заблокированы: отсутствует локальный разрешающий файл.')


	def save_json(path, value):
	path = inside(path)
	path.parent.mkdir(parents=True, exist_ok=True)
	temp = path.with_suffix(path.suffix + '.tmp')
	temp.write_text(json.dumps(value, ensure_ascii=False, indent=2))
	temp.replace(path)


	def rows(path):
	with inside(path).open() as f:
	return [json.loads(line) for line in f if line.strip()]


	def stable_seed(item_id, sample, seed=0):
	key = f'{seed}:{item_id}:{sample}'.encode()
	return int.from_bytes(hashlib.sha256(key).digest()[:4], 'little') & 0x7fffffff


	def append(path, row):
	path = inside(path)
	path.parent.mkdir(parents=True, exist_ok=True)
	with path.open('a') as f:
	f.write(json.dumps(row, ensure_ascii=False) + '\n')
	f.flush()