Text Generation
Transformers
Safetensors
English
Russian
qwen3_5
image-text-to-text
weight-space-programming
wspforge
math
experimental
conversational
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
- Transformers
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
Download wspforge/research_code/src/compare.py from end000/Qwen3.5-0.8B-WSPForge-Math-v1: direct link, hf CLI and curl.
- Browser
- Download file 2.57 kB
-
https://huggingface.co/end000/Qwen3.5-0.8B-WSPForge-Math-v1/resolve/main/wspforge/research_code/src/compare.py
- Command line
-
hf download hf://end000/Qwen3.5-0.8B-WSPForge-Math-v1/wspforge/research_code/src/compare.py
-
curl -L -o compare.py https://huggingface.co/end000/Qwen3.5-0.8B-WSPForge-Math-v1/resolve/main/wspforge/research_code/src/compare.py
2.57 kB
| import argparse | |
| import json | |
| import math | |
| import numpy as np | |
| from scipy.stats import binomtest | |
| from common import rows, save_json, inside | |
| def compare(original, edited): | |
| def index(records): | |
| result = {(r['id'], r['sample']): r for r in records} | |
| if len(result) != len(records): | |
| raise ValueError('Повторные результаты') | |
| return result | |
| a, b = index(original), index(edited) | |
| if a.keys() != b.keys() or not a: | |
| raise ValueError('Нужны одинаковые непустые наборы задач и образцов') | |
| groups = {} | |
| rescues = losses = 0 | |
| for key in sorted(a): | |
| if a[key]['seed'] != b[key]['seed']: | |
| raise ValueError('Seed не совпадает') | |
| delta = int(b[key]['correct']) - int(a[key]['correct']) | |
| groups.setdefault(key[0], []).append(delta) | |
| rescues += delta == 1 | |
| losses += delta == -1 | |
| per_problem = np.array([np.mean(x) for x in groups.values()]) | |
| gain = float(per_problem.mean()) | |
| se = float(per_problem.std(ddof=1) / math.sqrt(len(per_problem))) if len(per_problem) > 1 else None | |
| rng = np.random.default_rng(1701) | |
| boot = np.array([rng.choice(per_problem, len(per_problem), replace=True).mean() for _ in range(10000)]) | |
| return {'problems': len(groups), 'samples': len(a), 'gain': gain, 'cluster_paired_se': se, | |
| 'problem_bootstrap_ci95': np.quantile(boot, [.025, .975]).tolist(), | |
| 'rescues': rescues, 'regressions': losses, | |
| 'mcnemar_exact_p': binomtest(rescues, rescues + losses).pvalue if rescues + losses and all(len(v) == 1 for v in groups.values()) else None, | |
| 'warning': 'Повторные образцы одной задачи объединены перед оценкой неопределённости. Подтверждение допустимо только на отложенной выборке.'} | |
| if __name__ == '__main__': | |
| p = argparse.ArgumentParser() | |
| p.add_argument('original') | |
| p.add_argument('edited') | |
| p.add_argument('--out', required=True) | |
| a = p.parse_args() | |
| ja = json.loads((inside(a.original).parent / 'job.json').read_text()) | |
| jb = json.loads((inside(a.edited).parent / 'job.json').read_text()) | |
| for key in ('data_sha256', 'protocol', 'samples', 'max_tokens', 'runtime_versions', 'limit', 'set', 'seed_scheme'): | |
| if ja[key] != jb[key]: | |
| raise ValueError(f'Несопоставимые запуски: {key}') | |
| save_json(a.out, compare(rows(a.original), rows(a.edited))) | |