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
File size: 2,355 Bytes
fc32096 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import json
import time
def completed_outputs(engine, prompts, samplings, stream=False):
if not stream:
for index, output in enumerate(engine.generate(prompts,samplings,use_tqdm=False)):
yield index, output
return
identifiers = engine.enqueue(prompts,samplings,use_tqdm=False)
processor = getattr(engine.llm_engine,'output_processor',None)
if processor is not None:
indices = {processor.request_states[request_id].external_req_id:index for index,request_id in enumerate(identifiers)}
else:
indices = {request_id:index for index,request_id in enumerate(identifiers)}
finished = set()
finished_tokens = 0
start = last = time.monotonic()
while engine.llm_engine.has_unfinished_requests():
for output in engine.llm_engine.step():
if not output.finished:
continue
if output.request_id not in indices or output.request_id in finished:
raise ValueError('Unknown or duplicate completed request')
finished.add(output.request_id)
finished_tokens += sum(len(completion.token_ids) for completion in getattr(output,'outputs',[]))
yield indices[output.request_id],output
now = time.monotonic()
if now-last >= 30:
progress = {'type':'queue_progress','completed':len(finished),'queued':len(indices),
'unfinished':engine.llm_engine.get_num_unfinished_requests(),'seconds':round(now-start,1),
'completed_response_tokens':finished_tokens}
if processor is not None:
states = [processor.request_states[request_id] for request_id in identifiers
if request_id in processor.request_states]
detokenizers = [getattr(state,'detokenizer',None) for state in states]
if all(detokenizer is not None for detokenizer in detokenizers):
progress['unfinished_response_tokens'] = sum(d.num_output_tokens() for d in detokenizers)
progress['generated_response_tokens'] = finished_tokens + progress['unfinished_response_tokens']
print(json.dumps(progress),flush=True)
last = now
if len(finished) != len(indices):
raise RuntimeError('Engine stopped with incomplete requests')
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