初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Magellanic-Qwen-14B-R1
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- math
+- code
+- reasoning
+- R1
+---
+
+![vvvvvvvvvvv.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/cFNcCXQNciTqiIqEdtggJ.png)
+
+# **Magellanic-Qwen-14B-R1**
+
+> **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.
+
+## **Key Improvements**
+1. **Mathematical Reasoning Enhancements**  
+   Optimized with datasets targeting arithmetic, algebra, calculus, and formal logic, improving step-by-step solution generation and explanation accuracy.  
+
+2. **Coding Reasoning Enhancements**  
+   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.  
+
+3. **Enhanced General Knowledge**  
+   Broad knowledge base across various domains enables accurate and coherent responses for diverse topics.  
+
+4. **Improved Instruction Following**  
+   Better handling of complex, multi-step instructions with structured and logically coherent outputs.  
+
+5. **Versatile Adaptability**  
+   Resilient across open-ended and structured prompts, adapting well to different interaction styles and subject areas.  
+
+6. **Long-Context Support**  
+   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.  
+
+## **Quickstart with transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Magellanic-Qwen-14B-R1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain how quicksort works with an example in Python."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant skilled in coding and reasoning tasks."},
+    {"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):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## **Intended Use**
+
+1. **Mathematics and Logic Tasks**  
+   Solve and explain math problems, logical puzzles, and formula-based reasoning tasks step-by-step.  
+
+2. **Programming and Development**  
+   Assist in generating code, debugging, documenting functions, and solving algorithmic problems across multiple languages.  
+
+3. **General-Purpose Reasoning**  
+   Handle a wide variety of questions with accurate, contextual responses based on general knowledge and logic.  
+
+4. **Educational Assistance**  
+   Help students and educators with clear, structured explanations in STEM and non-STEM subjects.  
+
+5. **Conversational AI & Chatbots**  
+   Power intelligent assistants that require contextual awareness and technically sound responses.  
+
+6. **Multilingual Applications**  
+   Translate, summarize, and generate multilingual content for global users.  
+
+7. **Long-Form Content Generation**  
+   Generate coherent long articles, research summaries, and reports, especially with structured technical content.
+
+## **Limitations**
+
+1. **High Resource Usage**  
+   Requires high-memory GPUs/TPUs for efficient inference, especially when utilizing 128K context.  
+
+2. **Bias and Hallucination Risk**  
+   May reflect biases from pretraining data and occasionally hallucinate plausible-sounding but incorrect facts.  
+
+3. **Variability in Creative Tasks**  
+   Less consistent in producing high-quality creative writing or highly subjective content.  
+
+4. **Training Cutoff Constraints**  
+   No access to real-world events beyond the last training snapshot.  
+
+5. **Error Propagation in Long Outputs**  
+   Minor early mistakes can compound in very long outputs.  
+
+6. **Prompt Sensitivity**  
+   Performance may vary depending on prompt clarity and structure.