Add README

README.md

@@ -1,59 +1,21 @@
 ---
-base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
-library_name: transformers
-model_name: test-model
 tags:
-- generated_from_trainer
+- gguf
+- llama.cpp
 - unsloth
-- sft
-- trl
-licence: license
----
-
-# Model Card for test-model
-
-This model is a fine-tuned version of [unsloth/llama-3.2-3b-instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-3b-instruct-bnb-4bit).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-
-## Quick start
-
-```python
-from transformers import pipeline
-
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="hellstone1918/test-model", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
 
-## Training procedure
-
- 
-
-
-This model was trained with SFT.
-
-### Framework versions
+---
 
-- TRL: 0.24.0
-- Transformers: 4.57.2
-- Pytorch: 2.9.0
-- Datasets: 4.3.0
-- Tokenizers: 0.22.1
+# test-model - GGUF
 
-## Citations
+This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
 
+**Example usage**:
+- For text only LLMs:    **llama-cli** **--hf** repo_id/model_name **-p** "why is the sky blue?"
+- For multimodal models: **llama-mtmd-cli** **-m** model_name.gguf **--mmproj** mmproj_file.gguf
 
+## Available Model files:
+- `Llama-3.2-3B-Instruct.Q4_K_M.gguf`
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+## Ollama
+An Ollama Modelfile is included for easy deployment.