Magellanic-Qwen-25B-R999

Magellanic-Qwen-25B-R999 is a general-purpose large language model (LLM) based on the Qwen 2.5 architecture. It is designed for coding, mathematical problem-solving, and general conversational tasks. This model excels in logical reasoning, structured problem-solving, and multi-step calculations, making it suitable for both technical and everyday use. Fine-tuned with diverse datasets, it delivers high-accuracy outputs with a focus on clarity, coherence, and precision.

Key Features

1. Advanced Coding Assistance: Supports multiple programming languages, offering debugging, optimization, and algorithmic guidance.
2. Mathematical Reasoning: Excels in algebra, calculus, number theory, and logical deduction, providing structured and step-by-step solutions.
3. General Chat and Knowledge Retrieval: Handles open-ended questions, discussions, and knowledge-based queries across various domains.
4. Long-Context Processing: Supports up to 128K tokens in input context and generates up to 8K tokens in output, ideal for in-depth problem-solving.
5. Multilingual Support: Works with over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Arabic, and more.

Quickstart with Transformers

Below is a code snippet demonstrating how to load the tokenizer and model using apply_chat_template:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Magellanic-Qwen-25B-R999"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python function to check if a number is prime."
messages = [
    {"role": "system", "content": "You are a coding assistant specialized in algorithm development and problem-solving."},
    {"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. Programming and Development:

   • Writing, debugging, and optimizing code.
   • Generating explanations for algorithms and data structures.
   • Assisting with code documentation and best practices.

2. Mathematical and Logical Reasoning:

   • Solving mathematical equations and complex computations.
   • Providing theorem proofs and step-by-step derivations.
   • Supporting scientific and engineering applications.

3. General-Purpose Chat:

   • Engaging in meaningful discussions across a wide range of topics.
   • Providing accurate and informative responses in multilingual settings.
   • Assisting in creative writing, content generation, and structured discussions.

4. Research and Knowledge Discovery:

   • Summarizing papers, research articles, and technical documents.
   • Assisting with STEM education and research applications.
   • Providing logical explanations for scientific concepts.

Limitations

1. Hardware Requirements:
   Requires high-memory GPUs or TPUs for efficient inference due to its 25B parameters and long-context capabilities.

2. Possible Biases:
   While trained on diverse datasets, some responses may inherit biases from the underlying training data.

3. Abstract Problem-Solving Limitations:
   May struggle with highly abstract problems that require intuition beyond formal computational reasoning.

4. Error Accumulation in Long Outputs:
   In multi-step reasoning or long-form content, minor errors in earlier steps can propagate through the response.

5. Prompt Sensitivity:
   Response quality depends on how well the input is structured and framed.