Update README.md

README.md

@@ -25,9 +25,9 @@ datasets:
 
 # 🤖 LaboAI-0.3.3-3B
 
-This is a mid-sized language model (3B parameters) fine-tuned specifically for generating, understanding, and debugging **Kotlin** code and **Android** development (with a strong emphasis on Jetpack Compose, Coroutines, and modern architectures). 
+This is a lightweight yet capable language model (3B parameters) fine-tuned specifically for generating, understanding, and debugging **Kotlin** code and **Android** development (with a strong emphasis on Jetpack Compose, Coroutines, and modern architectures). 
 
-This version (0.3.3) shares the same training recipe as the 1.5B variant but leverages the increased capacity of the 3B base model for superior reasoning, better handling of complex code structures, and improved generalization across diverse Android development scenarios. It is optimized using **QLoRA (4-bit)** to run efficiently on consumer GPUs with 6-8GB VRAM.
+This version (0.3.3) applies the same proven, multi-dataset training recipe as the 1.5B version, but scaled up to the 3B architecture for improved reasoning, better context understanding, and more reliable code generation. It is optimized using **QLoRA (4-bit)** to run efficiently on consumer hardware (e.g., NVIDIA RTX 3060 12GB, or RTX 4060).
 
 ## 📋 Model Details
 
@@ -41,11 +41,10 @@ This version (0.3.3) shares the same training recipe as the 1.5B variant but lev
 ## 🚀 Uses
 
 ### Direct Use
-- Generating boilerplate for Activities, Fragments, ViewModels, and Repositories in Kotlin.
-- Creating modern UI components with **Jetpack Compose**.
+- Generating robust boilerplate for Activities, Fragments, ViewModels, and Repositories in Kotlin.
+- Creating complex modern UI components with **Jetpack Compose**.
 - Debugging compilation errors or logic flaws in Android code snippets.
 - Translating legacy Java logic into modern, idiomatic Kotlin.
-- Understanding and explaining complex Android architecture patterns (MVVM, MVI, Clean Architecture).
 
 ### Ecosystem Use (Recommended)
 This model shines when used as a local coding assistant via **Ollama** and the **Continue** extension in VS Code. This guarantees complete privacy (your code never leaves your machine) and low latency.
@@ -57,66 +56,14 @@ This model shines when used as a local coding assistant via **Ollama** and the *
 
 ## ⚠️ Limitations and Risks
 - **API Hallucinations:** In rare cases, it might suggest deprecated Android APIs instead of modern alternatives.
-- **Context Window:** Optimized for 2048 tokens. It is not suitable for analyzing massive, multi-thousand-line codebase files all at once.
+- **Context Window:** Optimized for 1024 tokens during training. It is not suitable for analyzing massive, multi-thousand-line codebase files all at once.
 - **Dependencies:** It does not have real-time knowledge of the latest Android library updates.
 
 ## 💻 How to Get Started (Local Setup)
 
-This repository includes both the original format (`safetensors`) and the quantized format (`GGUF` Q4_K_M). To use it on your PC with a 6-8GB VRAM GPU:
+This repository includes both the original format (`safetensors`) and the quantized format (`GGUF` Q4_K_M). 
 
 1. Install [Ollama](https://ollama.com/).
-2. Download the `.gguf` file from this repository (e.g., `LaboAI-0.3.3-3B-Q4_K_M.gguf`).
-3. Create a file named `Modelfile` in the same folder with the following content:
-   ```text
-   FROM ./LaboAI-0.3.3-3B-Q4_K_M.gguf
-   TEMPLATE """{{- if .Messages }}
-   {{- if or .System .Tools }}<|im_start|>system
-   {{- if .System }}
-   {{ .System }}
-   {{- end }}
-   {{- if .Tools }}
-
-   # Tools
-
-   You may call one or more functions to assist with the user query.
-
-   You are provided with function signatures within <tools></tools> XML tags:
-   <tools>
-   {{- range .Tools }}
-   {"type": "function", "function": {{ .Function }}}
-   {{- end }}
-   </tools>
-
-   For each function call, return a json object with function name and arguments within  XML tags:
-   
-   {{- end }}<|im_end|>
-   {{ end }}
-   {{- range $i, $_ := .Messages }}
-   {{- $last := eq (len (slice $.Messages $i)) 1 -}}
-   {{- if eq .Role "user" }}<|im_start|>user
-   {{ .Content }}<|im_end|>
-   {{ else if eq .Role "assistant" }}<|im_start|>assistant
-   {{ if .Content }}{{ .Content }}
-   {{- else if .ToolCalls }}
-   {{- end }}{{ if not $last }}<|im_end|>
-   {{ end }}
-   {{- else if eq .Role "tool" }}<|im_start|>user
-   <tool_response>
-   {{ .Content }}
-   </tool_response><|im_end|>
-   {{ end }}
-   {{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
-   {{ end }}
-   {{- end }}
-   {{- else }}
-   {{- if .System }}<|im_start|>system
-   {{ .System }}<|im_end|>
-   {{ end }}{{ if .Prompt }}<|im_start|>user
-   {{ .Prompt }}<|im_end|>
-   {{ end }}<|im_start|>assistant
-   {{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
-   PARAMETER stop "<|im_end|>"
-   PARAMETER stop "<|endoftext|>"
-   PARAMETER temperature 0.7
-   PARAMETER min_p 0.1
-   SYSTEM """You are LaboAI, a helpful assistant specialized in Kotlin and Android development."""
+2. Run the model directly from Hugging Face:
+   ```bash
+   ollama run hf.co/LaboAI/LaboAI-0.3.3-3B:Q4_K_M