---
license: mit
model_name: Avern Prism 1.0X
version: 1.0X
tags:
- text-generation
- LLM
- PyTorch
- unsloth
- code
- Qwen
- Qwen2.5
- reasoning
- general-intelligence
- programming
- avern
- uk
library_name: transformers
pipeline_tag: text-generation
metrics:
- accuracy
- perplexity
- character

---

# Avern Prism 1.0X

**Avern Prism 1.0X** is a state-of-the-art language model developed by **Avern Technology UKI**, built on the **Qwen2.5 14B** architecture. Optimized using the **Unsloth** framework, Prism 1.0X is designed to perform at the intersection of **reasoning**, **coding**, and **general intelligence**, making it suitable for complex problem-solving, logical tasks, and a wide range of applications from software development to AI-driven research and creative tasks.

## Model Description

- **Base Model**: Qwen2.5 14B
- **Architecture**: Transformer (Decoder-only)
- **Training Framework**: PyTorch + Unsloth
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Context Length**: Up to 4096 tokens
- **Use Cases**: Advanced reasoning, problem-solving, code generation, creative content generation, AI research, knowledge extraction, and more.

## Key Features

- **Reasoning**: Prism 1.0X is optimized for solving complex logical problems, answering deep conceptual questions, and providing step-by-step reasoning for math and algorithmic problems.
- **Code Generation**: It supports multi-language code generation (Python, JavaScript, C++, etc.), making it ideal for helping developers write, debug, and optimize code.
- **General Intelligence**: Prism 1.0X is designed with broad capabilities for general-purpose AI tasks such as understanding abstract concepts, creating creative content, and answering domain-specific queries across multiple fields.
- **Size**: 14B parameters, striking an optimal balance between computational power and versatility.
- **Adaptability**: Capable of being fine-tuned for specific domains, allowing customization for different applications in research, business, education, or entertainment.

## Intended Use

This model is ideal for:
- **Developers**: Assisting with code generation, algorithmic problem solving, and software development tasks.
- **Researchers**: Leveraging its broad general intelligence to assist with exploratory research, hypothesis generation, and complex problem-solving.
- **Educators and Students**: Providing tools for learning programming, mathematics, and critical thinking.
- **Creative Applications**: Writing, brainstorming, and idea generation for creative work.
- **AI Enthusiasts**: Building custom AI-driven applications with advanced reasoning and coding capabilities.

## Training Data

Prism 1.0X was fine-tuned on a combination of datasets:
- **Code**: Datasets featuring a wide variety of programming languages and coding tasks.
- **Reasoning**: Datasets for logical reasoning, problem-solving, mathematics, and algorithm design.
- **General Knowledge**: General-domain knowledge, creative writing, and abstract reasoning datasets, including encyclopedic knowledge and instructional content.
  
**Note**: The training data excludes proprietary or private data.

## Limitations

- **Reasoning and Accuracy**: While Prism 1.0X excels at reasoning, it may not always provide perfect solutions to highly specialized problems or new, unseen domains.
- **Hallucination Risk**: As with most large language models, Prism 1.0X may generate hallucinated or incorrect information, especially in highly abstract or speculative scenarios.
- **Context**: Though highly capable, it can still struggle with maintaining perfect context over long conversations or complex multi-step tasks without fine-tuning.

## How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("avernai/prism-1.0x")
tokenizer = AutoTokenizer.from_pretrained("avernai/prism-1.0x")

# Example: Code generation
prompt = "Write a Python function that calculates the Fibonacci sequence up to n."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Example: Logical reasoning
prompt = "What is the next number in the sequence: 2, 4, 8, 16, ?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Example: General intelligence application
prompt = "Explain the theory of relativity in simple terms."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))