Update README.md

README.md

@@ -1,85 +1,24 @@
----
-license: mit
-language:
-- en
-base_model:
-- meta-llama/Llama-3.1-70B-Instruct
----
-
-# Cakrawala-70B
-
-## Model Description
-
-Cakrawala-70B is a fine-tuned variant of the Llama-3.1-70B-Instruct model, specifically optimized for generating rich roleplaying conversations and character interactions. The model uses QLoRA (Quantized Low-Rank Adaptation) fine-tuning techniques to efficiently adapt the large language model for this specialized use case.
-
-## Intended Use
+# 🎭 Cakrawala-70B
 
-### Primary Use Case
-Cakrawala-70B is designed specifically for generating high-quality roleplaying conversations with the following key characteristics:
-- Rich, descriptive character interactions
-- Consistent character voice and emotional development
-- Show-don't-tell emotional states
-- Clear separation between character perspectives
-- Structured turn-taking in conversations
-- Detailed physical descriptions and environmental awareness
+> *"Where Worlds Converge and Adventures Begin!"*
 
-### Target Audience
-- Game developers creating interactive narratives
-- Writers seeking AI assistance in character development
-- RPG platforms and applications
-- Interactive fiction developers
-- Educational platforms teaching creative writing or character development
+## 🌟 What's Special About This Model?
 
-## Training Data
+Cakrawala-70B is a fine-tuned variant of the Llama-3.1-70B-Instruct model, specifically optimised for generating rich roleplaying conversations and character interactions. The model has been trained to excel at producing detailed, contextually appropriate character dialogues with rich descriptions of physical actions, expressions, and emotional states while maintaining consistent character voices and perspectives throughout extended interactions. 
 
-### Dataset Composition
-- Total examples: 5,867 conversation pairs
-- Format: JSON Lines (.jsonl)
-- Structure: Conversations field containing alternating messages between participants
-- Validation split: 5% of total data
+## 🧪 The Secret Sauce
 
-### Data Characteristics
-Each training example consists of:
-1. Character establishment prompts
-2. Multi-turn conversations (12-13 turns minimum)
-3. Rich descriptive elements including:
-   - Physical actions
-   - Facial expressions
-   - Tone indicators
-   - Environmental details
-   - Character reactions
+### Training Diet:
+- Fed with 5,867 conversation pairs
+- Each conversation is a minimum 12-13 turns long
+- Focused heavily details like facial expressions, environmental descriptions, and character reactions that are focused a lot on **keeping the model in character.**
 
-### Data Processing
-- Messages are structured with distinct role and content fields
-- Training focuses exclusively on completion tokens (train_on_inputs: false)
-- Input loss is excluded from calculations
-- Sequence length is set to 2048 tokens
-- Sample packing is enabled for efficient training
+### Tech Wizardry:
+- Trained on the mighty Llama-3.1-70B-Instruct
+- Fine-tuned using QLoRA 
+- Trained over 3 epochs
 
-## Training Details
-
-### Base Model
-- Architecture: meta-llama/Llama-3.1-70B-Instruct
-- Model Type: LlamaForCausalLM
-- Tokenizer: AutoTokenizer
-
-### Fine-tuning Approach
-- Method: QLoRA (Quantized Low-Rank Adaptation)
-- Quantization: 4-bit precision
-- Sequence Length: 2048 tokens
-- Training Duration: 3 epochs
-
-### LoRA Configuration
-- Rank (r): 32
-- Alpha: 64
-- Dropout: 0.1
-- Target Modules:
-  - Query Projection (q_proj)
-  - Key Projection (k_proj)
-  - Value Projection (v_proj)
-  - Output Projection (o_proj)
-
-### Training Parameters
+## Training Parameters
 - Gradient Accumulation Steps: 16
 - Micro Batch Size: 4
 - Learning Rate: 0.0003
@@ -87,19 +26,14 @@ Each training example consists of:
 - Scheduler: Cosine
 - Mixed Precision: BF16 & FP16 with TF32 support
 
-## Performance Characteristics
-
-## Limitations
+## 🔧 Under the Hood
+- Trained on 8 x H100 NVL GPUs
 
-Content Limitations:
-   - Training data size (5,867 examples) may limit variety in some scenarios
-   - Specialized for roleplaying conversations, may not generalize well to other tasks
+## 🎬 License & Credits
 
-## Additional Information
+- Licensed under MIT 
+- Based on meta-llama/Llama-3.1-70B-Instruct 
 
-Special Tokens:
-- Pad Token: <|end_of_text|>
+---
 
-Infrastructure:
-- Supports 8 x H100 NVL configuration
-- Utilizes 128 vCPU and 1509 GB RAM
+*Built with ❤️ for roleplayers, by roleplayers*