DistilQwen-ThoughtX-7B

Name: DistilQwen-ThoughtX-7B
Author: alibaba-pai

7.0B

license:apache-2.0

alibaba-pai

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OTHER

7B params

New

3 downloads

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Mobile

Laptop

Server

16GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

7GB+ RAM

Code Examples

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alibaba-pai/DistilQwen-ThoughtX-7B"  # or 32B
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Solve ∫x e^x dx. Show your reasoning step-by-step."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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