89 lines
3.9 KiB
Markdown
89 lines
3.9 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- AI-MO/NuminaMath-CoT
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- prithivMLmods/Math-Solve
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- amphora/QwQ-LongCoT-130K
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- prithivMLmods/Deepthink-Reasoning
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- NovaSky-AI/Sky-T1_data_17k
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language:
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- en
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- QwQ
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- Distill
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- R1
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- Deepseek
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- Qwen2.5
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- text-generation-inference
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---
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# **QWQ R1 [Reasoning] Distill 1.5B CoT**
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QWQ R1 [Reasoning] Distill 1.5B CoT is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5 R1 Distill from the DeepSeek base model and has been fine-tuned on chain-of-thought (CoT) reasoning datasets, focusing on 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.
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# **Quickstart with Transformers**
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/QwQ-R1-Distill-1.5B-CoT"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "How many r in strawberry."
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messages = [
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{"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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# **Intended Use**
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**QWQ R1 [Reasoning] Distill 1.5B CoT** is specifically designed for tasks requiring advanced reasoning, structured thinking, and detailed explanations. Its intended applications include:
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1. **Instruction-Following Tasks**: Performing step-by-step tasks based on user instructions.
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2. **Logical Reasoning**: Solving problems that demand multi-step logical processing and inference.
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3. **Text Generation**: Crafting coherent and contextually appropriate text for various domains.
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4. **Educational Tools**: Assisting in learning environments, providing explanations for complex topics, or guiding through reasoning exercises.
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5. **Problem-Solving**: Addressing computational or real-world problems requiring chain-of-thought reasoning.
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6. **AI-Assisted Decision-Making**: Supporting users in making informed decisions with logical analysis.
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# **Limitations**
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While the model excels in reasoning and explanation tasks, it has certain constraints:
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1. **Context Length**: Limited ability to process or generate outputs for inputs exceeding its maximum token limit.
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2. **Domain Knowledge**: It may lack detailed expertise in niche domains not covered during training.
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3. **Dependence on Training Data**: Performance can be influenced by biases or gaps in the datasets it was fine-tuned on.
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4. **Real-Time Reasoning**: Struggles with tasks requiring dynamic understanding of real-time data or rapidly changing contexts.
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5. **Mathematical Precision**: May produce errors in calculations or fail to interpret ambiguous mathematical problems.
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6. **Factual Accuracy**: Occasionally generates incorrect or outdated information when dealing with facts.
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7. **Language Nuances**: Subtle linguistic or cultural nuances might be misunderstood or misrepresented.
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8. **Complex CoT Chains**: For extremely lengthy or convoluted reasoning chains, the model may lose track of earlier context or steps.
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