---
license: apache-2.0
base_model:
- prithivMLmods/Sombrero-Opus-14B-Elite6
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- code
- math
- R1
language:
- en
---

![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/IUa2oAhsMWrD9qwTrEEAD.png)

# **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**

```python
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.