Update README.md

README.md

@@ -1,15 +1,99 @@
 ---
 license: mit
-metrics:
-- accuracy
-- character
-pipeline_tag: text-generation
-library_name: transformers
+model_name: Avern Prism 1.0X
+version: 1.0X
 tags:
-- code
-- Qwen
-- unsloth
+- text-generation
 - LLM
 - PyTorch
-- text-generation-inference
----
+- 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))