La-Superba-14B-Y.2

La-Superba-14B-Y.2 is a next-generation language model built on the Qwen 2.5 14B architecture. It is meticulously optimized for mathematical reasoning, programming, and general-purpose logic-based tasks. With its advanced comprehension, structured problem-solving capabilities, and long-context handling, it serves as a powerful assistant for technical, educational, and reasoning-intensive workflows.

Key Improvements

1. Exceptional Mathematical Reasoning
   Specially trained for handling symbolic math, arithmetic, algebra, calculus, and applied mathematics with step-by-step clarity and logical precision.

2. Advanced Coding & Debugging Intelligence
   Proficient in code generation, multi-language programming support (Python, JavaScript, C++, etc.), and automatic debugging. It can explain, optimize, and refactor code with minimal prompting.

3. Superior General-Purpose Reasoning
   Fine-tuned to manage logical deduction, multi-step reasoning, and contextual understanding across a wide array of domains.

4. Instruction-Following Accuracy
   Capable of precisely interpreting nested, multi-part instructions and returning structured, coherent responses that follow the prompt intent faithfully.

5. Extended Context Support
   Handles up to 128K tokens of input with 8K token output capacity, making it suitable for long documents, codebases, and detailed walkthroughs.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/La-Superba-14B-Y.2"

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 whether a number is prime and explain each step."
messages = [
    {"role": "system", "content": "You are a highly capable assistant in math, programming, and logical reasoning."},
    {"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. Mathematical Problem Solving
   Solves math questions with detailed steps, symbolic manipulation, and numerical precision—ideal for students, educators, and professionals.

2. Programming & Automation
   Assists in writing clean, correct code and helps debug and explain errors in software development tasks.

3. Technical Support and Tutoring
   Can be deployed as a tutor or assistant in educational platforms focused on logic, STEM, and engineering disciplines.

4. General-Purpose Reasoning Agent
   Useful in applications requiring thoughtful multi-turn reasoning, structured outputs, and logical consistency.

5. Multilingual Knowledge Assistant
   Enables intelligent communication and content generation across various languages and technical contexts.

6. Structured and Long-Form Output
   Can produce well-formatted JSON, tables, documents, and full-length guides and reports while maintaining coherence.

Limitations

1. High Hardware Demand
   Best performance requires high-RAM GPUs or TPUs due to its parameter size and context window.

2. Bias and Factual Limits
   Some inherited training data biases and occasional factual inaccuracies may still appear.

3. Not Real-Time Aware
   It does not have access to current events or real-time information post-training.

4. Creative Limitations
   Less consistent with storytelling, poetry, or heavily subjective tasks.

5. Prompt Sensitivity
   Output quality and structure can vary based on prompt clarity and format.