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README.md

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-license: mit
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+---
+license: mit
+---
+
+# J.O.S.I.E.v6-2b
+
+## Overview
+
+This is a crude proof of concept (PoC) demonstrating the feasibility of fine-tuning a large language model (LLM) on Apple Silicon using the MLX-LM framework. The goal is to explore the capabilities of Apple’s hardware for local LLM training and fine-tuning workflows.
+
+## Model and Training Details
+
+- **Base Model:** `mlx-community/helium-1-preview-2b`
+- **Fine-Tuned Model:** `J.O.S.I.E.v6-2b`
+- **Context length:** 4098
+- **Trained number of Tokens:** ca. 1T
+- **Created by:** Gökdeniz Gülmez
+- **Fine-Tune Dataset:** Offline private dataset
+- **DPO Dataset:** Offline private dataset
+- **Prompt Template:**
+
+  ```text
+  <|im_start|>system
+  You are Josie my private, super-intelligent assistant.<|im_end|>
+  <|im_start|>Gökdeniz Gülmez
+  {{ .PROMPT }}<|im_end|>
+  <|im_start|>Josie
+  {{ .RESPONSE }}<|im_end|>
+  ```
+
+- **Training Process:**
+  - First **10K steps** trained using **LoRA** (Low-Rank Adaptation) with **18 layers** selected.
+  - Final **1K steps** trained using **full weight training**.
+
+## Hardware Used
+
+- **Device:** Apple Mac Mini M4 (32GB RAM)
+- **Framework:** Apple MLX-LM
+
+## Notes & Limitations
+
+- This is an experimental setup; performance and efficiency optimizations are ongoing.
+- Dataset details remain private and are not included in this repository.
+- The training process may require significant memory and computational resources despite optimizations.
+- Further work is needed to explore distributed training and mixed-precision techniques for better performance on Apple Silicon.
+
+## DPO Training
+
+DPO training is not yet available in the official `mlx-examples` repository. To use it, you will need to clone and work from my fork:
+[https://github.com/Goekdeniz-Guelmez/mlx-examples.git](https://github.com/Goekdeniz-Guelmez/mlx-examples.git)
+
+## Future Improvements
+
+- Experiment with additional quantization techniques to reduce VRAM usage.
+- Investigate performance scaling across multiple Apple Silicon devices.
+- Optimize training pipelines for better convergence and efficiency.
+
+## Community Feedback
+
+I would love to hear from the MLX community! Should I publish a tutorial on how to fine-tune LLMs on Apple Silicon? If so, would you prefer it in text or video format? Let me know!
+
+## Disclaimer
+
+This project is strictly for research and experimental purposes. The fine-tuned model is not intended for production use at this stage.
+
+Best:
+Gökdeniz Gülmez