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 /scripts /split_data.py

end000

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

fc32096 verified about 22 hours ago

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1.63 kB

import hashlib
import json
import re
from common import ROOT, rows, save_json


def dump(path, items):
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(''.join(json.dumps(r, ensure_ascii=False) + '\n' for r in items))


def main():
    groups = {}
    for name in ('gsm8k_train', 'math_train', 'aime'):
        for row in rows(ROOT / f'data/practice/{name}.jsonl'):
            canonical = ' '.join(re.findall(r'\w+', row['problem'].lower()))
            digest = hashlib.sha256(('sol-independent-v1:' + canonical).encode()).digest()
            bucket = int.from_bytes(digest[:8], 'big') % 10
            split = 'discovery' if bucket < 7 else 'validation' if bucket < 9 else 'confirmation'
            groups.setdefault(split, []).append(row)
    for split, records in groups.items():
        dump(ROOT / f'data/splits/{split}.jsonl', records)
    diagnostic = []
    for source, count in [('gsm8k', 32), ('math', 64)]:
        subset = [r for r in groups['discovery'] if r['source'] == source]
        diagnostic.extend(sorted(subset, key=lambda r: hashlib.sha256(r['id'].encode()).hexdigest())[:count])
    dump(ROOT / 'data/diagnostic.jsonl', diagnostic)
    manifest = {k: {'count': len(v), 'sha256': hashlib.sha256((ROOT / f'data/splits/{k}.jsonl').read_bytes()).hexdigest()} for k, v in groups.items()}
    manifest['_method'] = 'sha256(sol-independent-v1:normalized_problem), 70/20/10 buckets; identical normalized texts stay together'
    save_json(ROOT / 'data/splits/manifest.json', manifest)
    print(json.dumps({k: len(v) for k, v in groups.items()}))


if __name__ == '__main__':
    main()

	import hashlib
	import json
	import re
	from common import ROOT, rows, save_json


	def dump(path, items):
	path.parent.mkdir(parents=True, exist_ok=True)
	path.write_text(''.join(json.dumps(r, ensure_ascii=False) + '\n' for r in items))


	def main():
	groups = {}
	for name in ('gsm8k_train', 'math_train', 'aime'):
	for row in rows(ROOT / f'data/practice/{name}.jsonl'):
	canonical = ' '.join(re.findall(r'\w+', row['problem'].lower()))
	digest = hashlib.sha256(('sol-independent-v1:' + canonical).encode()).digest()
	bucket = int.from_bytes(digest[:8], 'big') % 10
	split = 'discovery' if bucket < 7 else 'validation' if bucket < 9 else 'confirmation'
	groups.setdefault(split, []).append(row)
	for split, records in groups.items():
	dump(ROOT / f'data/splits/{split}.jsonl', records)
	diagnostic = []
	for source, count in [('gsm8k', 32), ('math', 64)]:
	subset = [r for r in groups['discovery'] if r['source'] == source]
	diagnostic.extend(sorted(subset, key=lambda r: hashlib.sha256(r['id'].encode()).hexdigest())[:count])
	dump(ROOT / 'data/diagnostic.jsonl', diagnostic)
	manifest = {k: {'count': len(v), 'sha256': hashlib.sha256((ROOT / f'data/splits/{k}.jsonl').read_bytes()).hexdigest()} for k, v in groups.items()}
	manifest['_method'] = 'sha256(sol-independent-v1:normalized_problem), 70/20/10 buckets; identical normalized texts stay together'
	save_json(ROOT / 'data/splits/manifest.json', manifest)
	print(json.dumps({k: len(v) for k, v in groups.items()}))


	if __name__ == '__main__':
	main()