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 /prepare_coding_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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2.86 kB

import ast
import gzip
import hashlib
import json
import subprocess
from pathlib import Path
from download_model import ROOT, fetch

provenance = json.loads((ROOT/'data/provenance.json').read_text())
revision = next(r['revision'] for r in provenance if r.get('repo')=='google-research/google-research')
mbpp_path = ROOT/'sources/mbpp/sanitized-mbpp.json'
fetch(f'https://raw.githubusercontent.com/google-research/google-research/{revision}/mbpp/sanitized-mbpp.json',mbpp_path)
records = []
with gzip.open(ROOT/'sources/human-eval/data/HumanEval.jsonl.gz','rt') as f:
    for row in map(json.loads,f):
        records.append({'id':'humaneval:'+row['task_id'],'source':'humaneval','problem':row['prompt'],
                        'entry_point':row['entry_point'],'test_code':row['test'],
                        'reference_code':row['prompt']+row['canonical_solution'],'prelude':''})
skipped = []
for row in json.loads(mbpp_path.read_text()):
    tree = ast.parse(row['code'])
    functions = {node.name:node for node in tree.body if isinstance(node,ast.FunctionDef)}
    entry = None
    for test in row['test_list']:
        entry = next((node.func.id for node in ast.walk(ast.parse(test)) if isinstance(node,ast.Call) and isinstance(node.func,ast.Name) and node.func.id in functions),None)
        if entry:
            break
    if entry is None:
        skipped.append(row['task_id'])
        continue
    node = functions[entry]
    signature = 'def '+entry+'('+ast.unparse(node.args)+')'+(' -> '+ast.unparse(node.returns) if node.returns else '')+':'
    imports = '\n'.join(row.get('test_imports',[]))
    tests = '\n'.join('    '+line for test in row['test_list'] for line in test.split('\n'))
    records.append({'id':'mbpp:'+str(row['task_id']),'source':'mbpp','problem':row['prompt']+'\nRequired signature:\n'+signature,
                    'entry_point':entry,'test_code':imports+'\ndef check(candidate):\n    '+entry+' = candidate\n'+tests,
                    'reference_code':imports+'\n'+row['code'],'prelude':imports})
out = ROOT/'data/coding'
out.mkdir(parents=True,exist_ok=True)
(out/'all_tasks.jsonl').write_text(''.join(json.dumps(r,ensure_ascii=False)+'\n' for r in records))
human_commit = subprocess.check_output(['git','rev-parse','HEAD'],cwd=ROOT/'sources/human-eval',text=True).strip()
(out/'provenance.json').write_text(json.dumps({'humaneval':{'repo':'openai/human-eval','revision':human_commit},
                                           'mbpp':{'repo':'google-research/google-research','revision':revision,
                                                   'file_sha256':hashlib.sha256(mbpp_path.read_bytes()).hexdigest()},
                                           'rows':len(records),'unresolved_mbpp_entries':skipped},indent=2))
print('Prepared',len(records),'coding tasks; splits follow canonical sandbox validation.')

	import ast
	import gzip
	import hashlib
	import json
	import subprocess
	from pathlib import Path
	from download_model import ROOT, fetch

	provenance = json.loads((ROOT/'data/provenance.json').read_text())
	revision = next(r['revision'] for r in provenance if r.get('repo')=='google-research/google-research')
	mbpp_path = ROOT/'sources/mbpp/sanitized-mbpp.json'
	fetch(f'https://raw.githubusercontent.com/google-research/google-research/{revision}/mbpp/sanitized-mbpp.json',mbpp_path)
	records = []
	with gzip.open(ROOT/'sources/human-eval/data/HumanEval.jsonl.gz','rt') as f:
	for row in map(json.loads,f):
	records.append({'id':'humaneval:'+row['task_id'],'source':'humaneval','problem':row['prompt'],
	'entry_point':row['entry_point'],'test_code':row['test'],
	'reference_code':row['prompt']+row['canonical_solution'],'prelude':''})
	skipped = []
	for row in json.loads(mbpp_path.read_text()):
	tree = ast.parse(row['code'])
	functions = {node.name:node for node in tree.body if isinstance(node,ast.FunctionDef)}
	entry = None
	for test in row['test_list']:
	entry = next((node.func.id for node in ast.walk(ast.parse(test)) if isinstance(node,ast.Call) and isinstance(node.func,ast.Name) and node.func.id in functions),None)
	if entry:
	break
	if entry is None:
	skipped.append(row['task_id'])
	continue
	node = functions[entry]
	signature = 'def '+entry+'('+ast.unparse(node.args)+')'+(' -> '+ast.unparse(node.returns) if node.returns else '')+':'
	imports = '\n'.join(row.get('test_imports',[]))
	tests = '\n'.join(' '+line for test in row['test_list'] for line in test.split('\n'))
	records.append({'id':'mbpp:'+str(row['task_id']),'source':'mbpp','problem':row['prompt']+'\nRequired signature:\n'+signature,
	'entry_point':entry,'test_code':imports+'\ndef check(candidate):\n '+entry+' = candidate\n'+tests,
	'reference_code':imports+'\n'+row['code'],'prelude':imports})
	out = ROOT/'data/coding'
	out.mkdir(parents=True,exist_ok=True)
	(out/'all_tasks.jsonl').write_text(''.join(json.dumps(r,ensure_ascii=False)+'\n' for r in records))
	human_commit = subprocess.check_output(['git','rev-parse','HEAD'],cwd=ROOT/'sources/human-eval',text=True).strip()
	(out/'provenance.json').write_text(json.dumps({'humaneval':{'repo':'openai/human-eval','revision':human_commit},
	'mbpp':{'repo':'google-research/google-research','revision':revision,
	'file_sha256':hashlib.sha256(mbpp_path.read_bytes()).hexdigest()},
	'rows':len(records),'unresolved_mbpp_entries':skipped},indent=2))
	print('Prepared',len(records),'coding tasks; splits follow canonical sandbox validation.')