4.3 KiB
license, language, base_model, pipeline_tag, library_name, tags, datasets
| license | language | base_model | pipeline_tag | library_name | tags | datasets | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
|
Magpie-Qwen-DiMind-1.7B
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.
[!note] GGUF: https://huggingface.co/prithivMLmods/Magpie-Qwen-DiMind-1.7B-GGUF
Key Features
-
Dual-Intelligence Architecture (DiMind) Integrates rapid-response capabilities for straightforward queries and deep analytical pathways for complex tasks like proofs, derivations, and recursive logic.
-
Magpie-Tuned Reasoning Core Fine-tuned with 80% of Magpie Pro 330k and curated modular datasets to enhance accuracy, clarity, and depth in math, code, and structured generation.
-
Mathematical Depth Performs exceptionally on algebra, geometry, calculus, and symbolic logic. Ideal for tutoring, competitions, and academic support.
-
Lightweight Coding Assistant Understands and writes concise, readable code in Python, JavaScript, and other major languages, including step-by-step breakdowns and bug explanation.
-
Structured Output Mastery Generates content in structured formats like JSON, Markdown, and LaTeX; ideal for documentation, data templates, and educational materials.
-
Multilingual Reasoning Handles technical reasoning and translation in over 20 languages, broadening accessibility in global education and multilingual workflows.
-
Efficient for Mid-Resource Environments The 1.7B parameter count enables excellent reasoning without requiring high-end infrastructure—suitable for local deployment and edge inference.
Quickstart with Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Magpie-Qwen-DiMind-1.7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Solve the equation: 2(x - 4) + 3 = 11. Show all steps."
messages = [
{"role": "system", "content": "You are a step-by-step math tutor."},
{"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]
print(response)
Intended Use
- Advanced math and symbolic problem-solving
- Code generation, review, and explanation
- Technical and structured content generation (JSON, Markdown, LaTeX)
- Educational tutoring and reasoning in multiple languages
- Deployment in academic, professional, and resource-aware environments
Limitations
- May produce shallow answers in open-ended creative tasks
- Smaller context window than 7B+ models—best suited for focused reasoning
- Reasoning fidelity may reduce in edge-case or adversarial queries
- Multilingual fluency is geared toward technical use cases, not general conversation