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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
---
# **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. |