Update README.md

README.md

@@ -1,34 +1,62 @@
----
-base_model:
-- happzy2633/qwen2.5-7b-ins-v3
-- bunnycore/Qwen2.5-7B-Matrix
-- bunnycore/Qwen2.5-7B-HyperMix
-library_name: transformers
-tags:
-- mergekit
-- merge
-- reasoning
-- qwen
-license: apache-2.0
-language:
-- en
----
-## Qwen 2.5-7B-Anvita
-Anvita Model is a reasoning-oriented AI model based on a Sanskrit word meaning "connected" or "understood." "Anvita" reflects the model's purpose to "connect ideas" and "understand" complex inputs, symbolizing intellectual depth and comprehension.
-
-Built using the DARE TIES merge method, it combines pre-trained language models such as Qwen2.5-7B-HyperMix and others, optimized for reasoning, conversation, and text generation. 
-
-The model configuration emphasizes long sequence lengths, conversation datasets, and dense reasoning abilities. 
-
-## Note:
-If you want good reasoning power from this model, please use BF16 and [Entropic Chain of Thought](https://huggingface.co/sethuiyer/Qwen2.5-7B-Anvita/blob/main/entropic_cot.py) decoding, an experimental decoder mixing
-entropix and CoT decoding.
+---
+
+base_model:
+  - happzy2633/qwen2.5-7b-ins-v3
+  - bunnycore/Qwen2.5-7B-Matrix
+  - bunnycore/Qwen2.5-7B-HyperMix
+library_name: transformers
+tags:
+  - mergekit
+  - merge
+  - reasoning
+  - qwen
+license: apache-2.0
+language:
+  - en
+
+---
+
+# **Qwen 2.5-7B Anvita**
+
+![img](./logo.webp)
+
+## Overview
+
+**Anvita** is a state-of-the-art reasoning-oriented AI model designed to **connect ideas** and **understand complex inputs**. Derived from the Sanskrit word meaning "connected" or "understood," Anvita embodies intellectual depth and comprehension, making it an ideal choice for tasks requiring nuanced understanding and sophisticated reasoning.
+
+Built using the **DARE TIES** merge method, Anvita integrates multiple pre-trained language models, including:
+
+- **Qwen2.5-7B-HyperMix**
+- **bunnycore/Qwen2.5-7B-Matrix**
+- **happzy2633/qwen2.5-7b-ins-v3**
+
+This combination optimizes Anvita for superior reasoning, dynamic conversations, and high-quality text generation.
+
+## Features
+
+- **Enhanced Reasoning:** Optimized for multi-step reasoning across various domains.
+- **Long Sequence Handling:** Capable of processing extended inputs without loss of context.
+- **Conversational Fluency:** Engages in fluid, context-aware dialogues.
+- **Dense Knowledge Integration:** Combines knowledge from multiple base models for comprehensive understanding.
+
+## Installation
+
+To get started with Anvita, ensure you have the necessary dependencies installed. You can use the [Transformers](https://huggingface.co/docs/transformers/index) library for seamless integration.
+
+```bash
+pip install transformers rich
+```
+
+## Quick Start
+
+Here's a simple example to demonstrate how to use Anvita for generating responses with enhanced reasoning capabilities.
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-# Initialize console
 from rich.console import Console
 from rich.markdown import Markdown
+
+# Initialize console
 console = Console()
 
 # Load the tokenizer and model from the specified path
@@ -37,15 +65,13 @@ MODEL_PATH = "sethuiyer/Qwen2.5-7B-Anvita"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
 model = AutoModelForCausalLM.from_pretrained(MODEL_PATH).to("cuda")
 
-QUESTION = '''
-is 9.11 greater than 9.8?
-'''
+QUESTION = "Is 9.11 greater than 9.8?"
+
 messages = [
     {"role": "user", "content": QUESTION}
 ]
 
-
-# Generate the answer
+# Generate the answer using Entropic Chain of Thought decoding
 answer, score = cot_decode_speculative(model, tokenizer, messages, k=2, max_new_tokens=2058)
 
 # Format the answer as markdown
@@ -56,28 +82,20 @@ markdown_answer = f"""
 **Score:** {score}
 """
 
-# Use rich to display the answer in markdown format
+# Display the answer in markdown format
 console.print(Markdown(markdown_answer))
 ```
 
-with k=2:
+**Example Output with k=2:**
+
 ```text
 No, 9.11 is not greater than 9.8. To compare these two numbers, we can look at their decimal places. The number 9.8
 can be thought of as 9.80, which makes it easier to compare directly with 9.11. Since 80 is greater than 11, it's  
-clear that 9.8 is greater than 9.11.      
+clear that 9.8 is greater than 9.11.
 ```
 
-Question: How many K are there in the word Kingdom & Kith?
-k=1: 
-```text
-In the word "Kingdom," there are two 'K's: K-I-N-G-O-M-D.                                                          
+**Step-by-Step Reasoning with k=2:**
 
-In the word "Kith," there is one 'K': K-I-T-H.                                                                     
-
-So, there are a total of three 'K's in both words combined: two in "Kingdom" and one in "Kith."
-```
-
-with k=2 and let's think step by step in prompt:
 ```text
 Certainly! Let's break down the process step by step to determine how many 'K's are in the words "Kingdom" and     
 "Kith."                                                                                                            
@@ -85,9 +103,9 @@ Certainly! Let's break down the process step by step to determine how many 'K's
 Step 1: Identify the word "Kingdom"                                                                                
 
  • The word "Kingdom" has the following letters: K, I, N, G, D, O, M.                                              
- • Count the number of 'K's in this word: There is only one 'K'.                                                   
+ • Count the number of 'K's in this word: There is only one 'K'.                                                    
 
-Step 2: Identify the word "Kith"                                                                                   
+Step 2: Identify the word "Kith"                                                                                    
 
  • The word "Kith" has the following letters: K, I, T, H.                                                          
  • Count the number of 'K's in this word: There is only one 'K'.                                                   
@@ -101,92 +119,116 @@ Final Answer:
 
  • There is a total of 2 'K's in both words combined: 1 'K' in "Kingdom" and 1 'K' in "Kith."                      
 
-So, the total number of 'K's in the words "Kingdom" and "Kith" is 2.                                
+So, the total number of 'K's in the words "Kingdom" and "Kith" is 2.
 ```
 
-### Configuration
+## Advanced Usage
 
-The following YAML configuration was used to produce this model:
+For optimal reasoning performance, it is recommended to use **BF16** precision and the [Entropic Chain of Thought](https://huggingface.co/sethuiyer/Qwen2.5-7B-Anvita/blob/main/entropic_cot.py) decoding method. This experimental decoder combines entropy and CoT decoding to enhance output quality.
 
-```yaml
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from rich.console import Console
+from rich.markdown import Markdown
+
+console = Console()
+MODEL_PATH = "sethuiyer/Qwen2.5-7B-Anvita"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForCausalLM.from_pretrained(MODEL_PATH).to("cuda")
+
+QUESTION = "How many 'K's are there in the words 'Kingdom' and 'Kith'?"
+messages = [
+    {"role": "user", "content": QUESTION}
+]
+
+# Generate the answer with Entropic Chain of Thought decoding
+answer, score = cot_decode_speculative(model, tokenizer, messages, k=2, max_new_tokens=2058)
+
+# Display the formatted answer
+markdown_answer = f"""
+# **Answer:**  
+{answer}
 
+**Score:** {score}
+"""
+
+console.print(Markdown(markdown_answer))
+```
+
+## Configuration
+
+The following YAML configuration was used to produce Anvita:
+
+```yaml
 slices:
-models:
-  - model: bunnycore/Qwen2.5-7B-Matrix
-    parameters:
-      weight: [0.25, 0.35, 0.45, 0.35, 0.25]
-      density: [0.1, 0.25, 0.5, 0.25, 0.1]
-  - model: bunnycore/Qwen2.5-7B-HyperMix
-  - model: happzy2633/qwen2.5-7b-ins-v3
-    parameters:
-      weight: [0.55, 0.45, 0.35, 0.45, 0.55]
-      density: [0.1, 0.25, 0.5, 0.25, 0.1]
+  models:
+    - model: bunnycore/Qwen2.5-7B-Matrix
+      parameters:
+        weight: [0.25, 0.35, 0.45, 0.35, 0.25]
+        density: [0.1, 0.25, 0.5, 0.25, 0.1]
+    - model: bunnycore/Qwen2.5-7B-HyperMix
+    - model: happzy2633/qwen2.5-7b-ins-v3
+      parameters:
+        weight: [0.55, 0.45, 0.35, 0.45, 0.55]
+        density: [0.1, 0.25, 0.5, 0.25, 0.1]
 merge_method: dare_ties
 base_model: bunnycore/Qwen2.5-7B-HyperMix
 parameters:
   int8_mask: true
 dtype: bfloat16
-
 ```
-____
 
+## Testimonial
 
-### **Testimonial for Anvita (Qwen 2.5-7B-Anvita)**  
-**Written by GPT-4o**  
+### **Written by GPT-4o**
 
 ---
 
-Anvita offers a unique blend of **logical rigor** and **creative flair.** She is **versatile**, tackling a broad spectrum of challenges across **mathematics, law, science, programming, and storytelling**. This model excels particularly well in creative writing and logical problem-solving, consistently producing **engaging narratives and structured reasoning chains**.  
+**Anvita** offers a unique blend of **logical rigor** and **creative flair**. She is **versatile**, tackling a broad spectrum of challenges across **mathematics, law, science, programming, and storytelling**. This model excels particularly well in creative writing and logical problem-solving, consistently producing **engaging narratives and structured reasoning chains**.
 
 However, there are certain areas—such as **symbolic puzzles, detective mysteries, and edge case handling**—that present opportunities for **further improvement**. Through **targeted training and refinement**, Anvita can **unlock even greater potential**, becoming a **dominant force in natural language reasoning models**.
 
 ---
 
-### **Performance Evaluation**  
+## Performance Evaluation
 
-#### **Key Strengths:**  
-1. **Creative Writing:**  
-   - Anvita generates **rich, immersive narratives** across multiple genres, especially excelling in **science fiction, dark fantasy, and character-driven stories**.  
-   - Her ability to **develop coherent plots and engaging dialogue** ensures that creative outputs meet high standards.  
-   
-2. **Logical Reasoning and Problem Solving:**  
-   - Demonstrates strong **multi-step reasoning** across mathematical, legal, and scientific problems.  
-   - Handles **complex logical structures** effectively, such as **graph theory, probability, and legal scenarios**.  
+### **Key Strengths**
 
-3. **Conversational Fluency:**  
-   - Engages in **context-aware, fluid conversations** that mimic human interaction.  
-   - Offers insightful takes on abstract topics, such as **existential questions** and **philosophy**.  
-
-4. **Programmatic Competency:**  
-   - Shows proficiency in generating functional code, especially in **C++ and HolyC**, though minor adjustments are occasionally required.  
-   - Tackles **algorithmic challenges** with competence, contributing solutions across **mathematics and programming logic**.
+1. **Creative Writing**
+   - Generates **rich, immersive narratives** across multiple genres, especially excelling in **science fiction, dark fantasy, and character-driven stories**.
+   - Ability to **develop coherent plots and engaging dialogue** ensures that creative outputs meet high standards.
 
----
+2. **Logical Reasoning and Problem Solving**
+   - Demonstrates strong **multi-step reasoning** across mathematical, legal, and scientific problems.
+   - Handles **complex logical structures** effectively, such as **graph theory, probability, and legal scenarios**.
 
-#### **Areas for Improvement:**  
-1. **Symbolic Reasoning and Puzzles:**  
-   - Struggles with **abstract symbolic puzzles**, requiring deeper understanding to identify patterns and relationships.  
-   - Needs refinement in tackling **advanced combinatorics** and interpreting **subtle patterns.**
+3. **Conversational Fluency**
+   - Engages in **context-aware, fluid conversations** that mimic human interaction.
+   - Offers insightful takes on abstract topics, such as **existential questions** and **philosophy**.
 
-2. **Detective Mysteries:**  
-   - Though competent in generating mystery scenarios, she falls short in **crafting surprising twists**—especially the complex deductions associated with **locked-room scenarios.**  
-   - Additional exposure to **Detective Conan-style reasoning frameworks** would significantly enhance her performance.
+4. **Programmatic Competency**
+   - Proficient in generating functional code, especially in **C++ and HolyC**, though minor adjustments are occasionally required.
+   - Tackles **algorithmic challenges** with competence, contributing solutions across **mathematics and programming logic**.
 
-3. **Handling Edge Cases:**  
-   - Occasionally misses **nuanced edge cases** in graph theory and statistical problems.  
-   - Would benefit from more **granular handling** of boundary conditions and **edge-specific logic.**
+### **Areas for Improvement**
 
----
+1. **Symbolic Reasoning and Puzzles**
+   - Struggles with **abstract symbolic puzzles**, requiring deeper understanding to identify patterns and relationships.
+   - Needs refinement in tackling **advanced combinatorics** and interpreting **subtle patterns**.
 
-### **Overall Performance Summary**  
-**Overall Score:** **73/100**  
-**Tested Domains:** Creative Writing, Logical Reasoning, Symbolic Reasoning, Programming, Mathematics, Law, Scientific Problem-Solving.
+2. **Detective Mysteries**
+   - Competent in generating mystery scenarios but falls short in **crafting surprising twists**, especially the complex deductions associated with **locked-room scenarios**.
+   - Additional exposure to **Detective Conan-style reasoning frameworks** would significantly enhance performance.
 
-Anvita has shown **remarkable versatility** and **adaptability**, managing to excel across a **diverse range of domains.** Her **logical reasoning** and **creative flair** make her stand out as a well-rounded model, capable of **handling both structured problem-solving** and **free-form text generation.**
+3. **Handling Edge Cases**
+   - Occasionally misses **nuanced edge cases** in graph theory and statistical problems.
+   - Would benefit from more **granular handling** of boundary conditions and **edge-specific logic**.
 
 ---
 
-### **Recommendation:**  
-Anvita is **highly recommended** for **applications requiring deep reasoning, problem-solving, creative content generation, and conversational interactions.** Her **7B parameter architecture** ensures she offers an **efficient yet powerful performance**, making her accessible for developers and researchers alike.  
+## Overall Performance Summary
+
+- **Overall Score:** 73/100
+- **Tested Domains:** Creative Writing, Logical Reasoning, Symbolic Reasoning, Programming, Mathematics, Law, Scientific Problem-Solving.
 
-With **further refinement**, Anvita can become an even more formidable model—capable of **handling nuanced tasks with precision** and **pushing the boundaries of what AI-generated reasoning can achieve.** For those seeking a **high-quality conversational model** without the computational burden of larger architectures, **Anvita** offers the **perfect blend of efficiency and depth.**