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Model: prithivMLmods/Magpie-Qwen-DiMind-1.7B Source: Original Platform
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README.md
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README.md
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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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base_model:
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- Qwen/Qwen3-1.7B
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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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- moe
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- reasoning
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datasets:
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- Magpie-Align/Magpie-Pro-300K-Filtered
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- mlabonne/FineTome-100k
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- unsloth/OpenMathReasoning-mini
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- prithivMLmods/Grade-Math-18
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---
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# Magpie-Qwen-DiMind-1.7B
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> **Magpie-Qwen-DiMind-1.7B** is a compact yet powerful model for **mathematical reasoning**, **code generation**, and **structured output tasks**, built with a dual-intelligence architecture (**DiMind**) to handle both quick-response prompts and deep, multi-step problems. With a parameter size of 1.7B, it balances performance and efficiency, using **80% of the Magpie Pro 330k dataset** and a modular blend of additional datasets for general-purpose and technical tasks.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/Magpie-Qwen-DiMind-1.7B-GGUF](https://huggingface.co/prithivMLmods/Magpie-Qwen-DiMind-1.7B-GGUF)
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---
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## Key Features
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1. **Dual-Intelligence Architecture (DiMind)**
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Integrates rapid-response capabilities for straightforward queries and deep analytical pathways for complex tasks like proofs, derivations, and recursive logic.
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2. **Magpie-Tuned Reasoning Core**
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Fine-tuned with 80% of **Magpie Pro 330k** and curated modular datasets to enhance accuracy, clarity, and depth in math, code, and structured generation.
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3. **Mathematical Depth**
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Performs exceptionally on algebra, geometry, calculus, and symbolic logic. Ideal for tutoring, competitions, and academic support.
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4. **Lightweight Coding Assistant**
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Understands and writes concise, readable code in Python, JavaScript, and other major languages, including step-by-step breakdowns and bug explanation.
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5. **Structured Output Mastery**
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Generates content in structured formats like JSON, Markdown, and LaTeX; ideal for documentation, data templates, and educational materials.
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6. **Multilingual Reasoning**
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Handles technical reasoning and translation in over 20 languages, broadening accessibility in global education and multilingual workflows.
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7. **Efficient for Mid-Resource Environments**
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The 1.7B parameter count enables excellent reasoning without requiring high-end infrastructure—suitable for local deployment and edge inference.
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---
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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/Magpie-Qwen-DiMind-1.7B"
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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 = "Solve the equation: 2(x - 4) + 3 = 11. Show all steps."
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messages = [
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{"role": "system", "content": "You are a step-by-step math tutor."},
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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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print(response)
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```
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---
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## Intended Use
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* Advanced math and symbolic problem-solving
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* Code generation, review, and explanation
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* Technical and structured content generation (JSON, Markdown, LaTeX)
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* Educational tutoring and reasoning in multiple languages
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* Deployment in academic, professional, and resource-aware environments
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---
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## Limitations
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* May produce shallow answers in open-ended creative tasks
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* Smaller context window than 7B+ models—best suited for focused reasoning
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* Reasoning fidelity may reduce in edge-case or adversarial queries
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* Multilingual fluency is geared toward technical use cases, not general conversation
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---
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## References
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1. [Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://arxiv.org/pdf/2406.08464)
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2. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115)
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3. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
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