QwQ-R1-Distill-7B-CoT is based on the Qwen [ KT ] model, which was distilled by DeepSeek-R1-Distill-Qwen-7B. It has been fine-tuned on the long chain-of-thought reasoning model and specialized datasets, focusing on chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
Quickstart with Transformers
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
fromtransformersimportAutoModelForCausalLM,AutoTokenizermodel_name="prithivMLmods/QwQ-R1-Distill-7B-CoT"model=AutoModelForCausalLM.from_pretrained(model_name,torch_dtype="auto",device_map="auto")tokenizer=AutoTokenizer.from_pretrained(model_name)prompt="Give me a short introduction to large language model."messages=[{"role":"system","content":"You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},{"role":"user","content":prompt}]text=tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)model_inputs=tokenizer([text],return_tensors="pt").to(model.device)generated_ids=model.generate(**model_inputs,max_new_tokens=512)generated_ids=[output_ids[len(input_ids):]forinput_ids,output_idsinzip(model_inputs.input_ids,generated_ids)]response=tokenizer.batch_decode(generated_ids,skip_special_tokens=True)[0]
Intended Use:
Instruction-Following: The model excels in understanding and executing detailed instructions, making it ideal for automation systems, virtual assistants, and educational tools.
Text Generation: It can produce coherent, logically structured, and contextually relevant text for use in content creation, summarization, and report writing.
Complex Reasoning Tasks: With its fine-tuning for chain-of-thought reasoning, the model is well-suited for multi-step problem-solving, logical deduction, and question-answering tasks.
Research and Development: It can support researchers and developers in exploring advancements in logical reasoning and fine-tuning methodologies.
Educational Applications: The model can assist in teaching logical reasoning and problem-solving by generating step-by-step solutions.
Limitations:
Domain-Specific Knowledge: While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains.
Hallucination: Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data.
Bias in Training Data: The model's outputs may reflect biases present in the datasets it was fine-tuned on, which could limit its objectivity in certain contexts.
Performance on Non-Reasoning Tasks: The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses.
Resource-Intensive: Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.
Dependence on Input Quality: The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results.
