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  - chat
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  ---
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  # Qwen2-Math
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  ## Introduction
@@ -60,7 +61,7 @@ model = AutoModelForCausalLM.from_pretrained(
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  )
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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- prompt = "Carlos is planting a lemon tree. The tree will cost $90 to plant. Each year it will grow 7 lemons, which he can sell for $1.5 each. It costs $3 a year to water and feed the tree. How many years will it take before he starts earning money on the lemon tree?"
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  messages = [
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  {"role": "system", "content": "You are a helpful assistant."},
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  {"role": "user", "content": prompt}
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  ## Citation
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- If you find our work helpful, feel free to give us a cite.
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  ```
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- WIP
 
 
 
 
 
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  ```
 
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  - chat
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  ---
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+
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  # Qwen2-Math
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  ## Introduction
 
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  )
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
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  messages = [
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  {"role": "system", "content": "You are a helpful assistant."},
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  {"role": "user", "content": prompt}
 
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  ## Citation
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+ If you find our work helpful, feel free to give us a citation.
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  ```
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+ @article{yang2024qwen2,
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+ title={Qwen2 technical report},
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+ author={Yang, An and Yang, Baosong and Hui, Binyuan and Zheng, Bo and Yu, Bowen and Zhou, Chang and Li, Chengpeng and Li, Chengyuan and Liu, Dayiheng and Huang, Fei and others},
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+ journal={arXiv preprint arXiv:2407.10671},
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+ year={2024}
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+ }
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  ```