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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- math |
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- code |
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- reasoning |
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- R1 |
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--- |
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# **Magellanic-Qwen-14B-R1** |
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> **Magellanic-Qwen-14B-R1** is based on the **DeepSeek-R1-Distill-Qwen-14B** modality architecture, enhanced specifically for **mathematical reasoning** and **coding reasoning**. This model advances the capabilities of 14B-parameter architectures, excelling in logic-based problem solving, programming tasks, and context-rich dialogue generation. It is fine-tuned with extended chain-of-thought reasoning and domain-specific datasets for improved comprehension, structured generation, and precision in technical tasks. |
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## **Key Improvements** |
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1. **Mathematical Reasoning Enhancements** |
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Optimized with datasets targeting arithmetic, algebra, calculus, and formal logic, improving step-by-step solution generation and explanation accuracy. |
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2. **Coding Reasoning Enhancements** |
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Fine-tuned on diverse programming languages and reasoning-based coding problems (e.g., LeetCode, Codeforces, and real-world engineering tasks), significantly improving code generation, debugging, and documentation. |
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3. **Enhanced General Knowledge** |
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Broad knowledge base across various domains enables accurate and coherent responses for diverse topics. |
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4. **Improved Instruction Following** |
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Better handling of complex, multi-step instructions with structured and logically coherent outputs. |
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5. **Versatile Adaptability** |
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Resilient across open-ended and structured prompts, adapting well to different interaction styles and subject areas. |
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6. **Long-Context Support** |
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Supports up to **128K tokens** of input context and can generate up to **8K tokens** of output—ideal for in-depth technical and academic outputs. |
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## **Quickstart with transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Magellanic-Qwen-14B-R1" |
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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 = "Explain how quicksort works with an example in Python." |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant skilled in coding and reasoning tasks."}, |
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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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1. **Mathematics and Logic Tasks** |
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Solve and explain math problems, logical puzzles, and formula-based reasoning tasks step-by-step. |
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2. **Programming and Development** |
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Assist in generating code, debugging, documenting functions, and solving algorithmic problems across multiple languages. |
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3. **General-Purpose Reasoning** |
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Handle a wide variety of questions with accurate, contextual responses based on general knowledge and logic. |
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4. **Educational Assistance** |
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Help students and educators with clear, structured explanations in STEM and non-STEM subjects. |
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5. **Conversational AI & Chatbots** |
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Power intelligent assistants that require contextual awareness and technically sound responses. |
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6. **Multilingual Applications** |
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Translate, summarize, and generate multilingual content for global users. |
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7. **Long-Form Content Generation** |
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Generate coherent long articles, research summaries, and reports, especially with structured technical content. |
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## **Limitations** |
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1. **High Resource Usage** |
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Requires high-memory GPUs/TPUs for efficient inference, especially when utilizing 128K context. |
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2. **Bias and Hallucination Risk** |
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May reflect biases from pretraining data and occasionally hallucinate plausible-sounding but incorrect facts. |
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3. **Variability in Creative Tasks** |
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Less consistent in producing high-quality creative writing or highly subjective content. |
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4. **Training Cutoff Constraints** |
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No access to real-world events beyond the last training snapshot. |
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5. **Error Propagation in Long Outputs** |
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Minor early mistakes can compound in very long outputs. |
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6. **Prompt Sensitivity** |
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Performance may vary depending on prompt clarity and structure. |
