Update README.md

README.md

@@ -2,19 +2,45 @@
 tags:
 - gguf
 - llama.cpp
-- unsloth
-
+- agent
+- tool-use
+base_model: WeiboAI/VibeThinker-3B
+pipeline_tag: reinforcement-learning
 ---
 
 # mythos_fast : GGUF
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+## Model Description
+
+mythos_fast is a fine-tuned version of [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B), adapted for custom tool-use and agentic task execution. The base model was trained on a rich dataset of approximately 2 million samples covering multi-step tool calls, function-calling formats, and agent-style reasoning traces, then converted to GGUF format for efficient local inference with llama.cpp.
+
+## Training Details
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf Shadow0482/mythos_fast --jinja`
-- For multimodal models: `llama-mtmd-cli -hf Shadow0482/mythos_fast --jinja`
+- **Base model:** WeiboAI/VibeThinker-3B
+- **Fine-tuning focus:** tool-use / function calling, agentic task completion
+- **Dataset size:** ~2,000,000 samples
+- **Output format:** GGUF (F16)
+
+## Available Model Files
 
-## Available Model files:
 - `VibeThinker-3B.F16.gguf`
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+## Usage
+
+For text-only LLMs:
+```
+llama-cli -hf Shadow0482/mythos_fast --jinja
+```
+
+For multimodal models:
+```
+llama-mtmd-cli -hf Shadow0482/mythos_fast --jinja
+```
+
+## Intended Use
+
+This model is intended for local inference scenarios that require tool-calling and agent-style task execution, such as autonomous agents, function-calling pipelines, and multi-step reasoning workflows.
+
+## Limitations
+
+Performance on tool-use tasks depends on the format and structure of tool definitions provided at inference time. Results may vary outside the training distribution covered by the fine-tuning dataset.