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---
license: mit
base_model:
- mistralai/Mistral-Small-24B-Instruct-2501
tags:
- symbolic-ai
- reasoning
- deductive-logic
- glyph-code-logic-flow
- mistral
- mlx
- gguf
- fine-tuned
- experimental
---
# Glyphstral-24B-v1 (Preview)
## Model Description
This is a **preview release (Version 1)** of a fine-tuned language model, **Glyphstral-24B-v1**, designed to understand and utilize the **Glyph Code Logic Flow (GCLF)** framework for structured, deductive symbolic reasoning.
This model is based on **Mistral-Small-24b** and has been fine-tuned using **MLX** with **DoRA (Decomposed Relative Attention)** at 4-bit quantization on Apple Silicon.
**Glyph Code Logic Flow (GCLF)** is a novel approach to symbolic AI aimed at enhancing reasoning and multi-dimensional thinking. It provides a structured method for deductive reasoning using a symbolic language. You can explore the conceptual framework in detail here:
[Computational-Model-for-Symbolic-Representations GitHub Repository](https://github.com/severian42/Computational-Model-for-Symbolic-Representations/tree/main)
**Key Features (Version 1 - Preview):**
* **Specialized for Glyph Code Logic Flow:** Fine-tuned to interpret and process instructions based on the GCLF framework.
* **Deductive Reasoning Focus:** Encourages structured, step-by-step deductive reasoning over probabilistic inference.
* **Symbolic Manipulation:** Trained to understand and manipulate symbolic representations within the GCLF framework.
* **MLX Format:** Currently provided in MLX format for efficient inference on Apple Silicon.
* **Quantization:** Fine-tuned and quantized to 4-bit for reduced memory footprint and faster inference (using MLX DoRA).
* **Experimental V1 Release:** This is an initial release to showcase the potential of GCLF training. Expect ongoing development and improvements.
## Intended Use
This model is intended for **experimental use and research** in the following areas:
* **Exploring Symbolic AI:** Investigating the capabilities of language models for structured symbolic reasoning.
* **Deductive Logic Applications:** Building systems that require step-by-step, logically sound reasoning processes.
* **Glyph Code Logic Flow Development:** Experimenting with and refining the GCLF framework.
* **Educational Purposes:** Learning about symbolic AI, deductive reasoning, and structured knowledge representation.
**Limitations:**
* **Version 1 - Preview:** This is an early version and may have limitations in robustness and generalization.
* **Specialized Domain:** Performance is optimized for tasks related to Glyph Code Logic Flow. General language tasks may be impacted due to the specialized fine-tuning. (Further evaluation is ongoing)
* **Experimental Nature:** The GCLF framework itself is under development and this model reflects an early attempt to train an LLM for it.
* **MLX Format (Initial):** Currently primarily available in MLX format, which may limit accessibility for users outside the Apple Silicon/MLX ecosystem (GGUF quantization is in progress).
## Training Data and Process
* **Base Model:** Mistral-Small-24b
* **Fine-tuning Method:** MLX-DoRA (Decomposed Relative Attention) at 4-bit quantization.
* **Training Hardware:** Apple M2 (128GB RAM)
* **Training Dataset:** Custom dataset of approximately 4500 examples specifically designed for Glyph Code Logic Flow. Each example was around 30,000 tokens in length, focused on detailed system instructions and GCLF tasks.
* **Training Tokens:** Approximately 27 million tokens from the custom GCLF dataset.
* **Training Duration:** 7 days (continuous 24/7 training).
* **Initial Experiments:** Initial training attempts were made with Deepeek R1-Qwen-14 and QWQ-32, but Mistral-Small-24b was found to be more receptive to the GCLF framework due to potentially less conflicting pre-trained reasoning biases.
## How to Use
**System Instructions**
## !! GGUF Quantization (Coming Soon) !!
# Version 2 and Future Development
Version 2 (In Development):
GRPO (Gradient Ratio Policy Optimization): Utilizing GRPO for potentially more stable and effective fine-tuning.
Newer Dataset: Training on an expanded and refined dataset for Glyph Code Logic Flow.
GGUF Release: Aiming for a GGUF release for wider accessibility and compatibility.
Improved Documentation: Comprehensive documentation and examples for using the model and understanding GCLF.
Ongoing Efforts:
Refining GCLF Framework: Continuously developing and improving the Glyph Code Logic Flow framework itself.
Performance Evaluation: Conducting thorough evaluations of the model's performance on GCLF tasks and general language understanding.
Community Feedback: Seeking feedback from the community to guide further development and improvements.
---
# Known Issues
The custom dataset and heavy use of symbols and operators seems to have potentially altered the models tool use. I've found that it often want to use it's `[TOOL_CALLS]` function at the end of it's response (sometimes also calling out `<SPECIAL_#>` tokens at the end). I think I know where this is stemming from, so hopefully v2 can avoid this potential issue altogether.
If you are seeing the `[TOOL_CALLS]` and `<SPECIAL_>` outputs, you can set them as the EOS and it will align the model back into a more fluid conversation.
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