81 lines
3.9 KiB
Markdown
81 lines
3.9 KiB
Markdown
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---
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license: apache-2.0
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base_model:
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- prithivMLmods/QwQ-LCoT2-7B-Instruct
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datasets:
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- open-r1/OpenR1-Math-220k
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- open
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- r1
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- math
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- QwQ
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---
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# **Open-R1-Math-7B-Instruct**
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The *Open-R1-Math-7B-Instruct* is a fine-tuned language model designed for advanced reasoning and instruction‐following tasks. It leverages the Qwen2.5-7B base model and has been fine-tuned on a chain of thought reasoning dataset derived from [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k). 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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Below is a code snippet using `apply_chat_template` to show how to load the tokenizer and model and how to generate content:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Open-R1-Math-7B-Instruct"
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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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The Open-R1-Math-7B-Instruct model is designed for advanced reasoning and instruction-following tasks, with specific applications including:
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1. **Instruction Following**: Providing detailed and step-by-step guidance for a wide range of user queries.
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2. **Logical Reasoning**: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios.
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3. **Text Generation**: Crafting coherent, contextually relevant, and well-structured text in response to prompts.
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4. **Problem-Solving**: Analyzing and addressing tasks that require chain-of-thought (CoT) reasoning, making it ideal for education, tutoring, and technical support.
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5. **Knowledge Enhancement**: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.
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# **Limitations**
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1. **Data Bias**: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data.
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2. **Context Limitation**: Performance may degrade for tasks requiring knowledge or reasoning that significantly exceeds the model's pretraining or fine-tuning context.
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3. **Complexity Ceiling**: While optimized for multi-step reasoning, exceedingly complex or abstract problems may result in incomplete or incorrect outputs.
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4. **Dependency on Prompt Quality**: The quality and specificity of the user prompt heavily influence the model's responses.
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5. **Non-Factual Outputs**: Despite being fine-tuned for reasoning, the model can still generate hallucinated or factually inaccurate content, particularly for niche or unverified topics.
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6. **Computational Requirements**: Running the model effectively requires significant computational resources, particularly when generating long sequences or handling high-concurrency workloads.
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---
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This version reflects the new name *Open-R1-Math-7B-Instruct* and specifies that its fine-tuning data comes from the [OpenR1-Math-220k dataset](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k).
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