Update README.md

README.md

@@ -1,64 +1,34 @@
-
 ---
 language:
 - en
 - fr
 - es
 - pt
-base_model:
-- tiiuae/Falcon3-10B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
 tags:
 - falcon3
-
+base_model: tiiuae/Falcon3-10B-Instruct
 ---
 
-<div align="center">
-    <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
-</div>
-
 # Falcon3-10B-Instruct-GGUF
 
+Tired of needing massive GPUs just to experiment with the latest Large Language Models?  Wish you could run powerful LLMs locally on your laptop or even your phone? This GGUF model makes it possible!
+
+Falcon3-10B-Instruct-GGUF is designed for efficient inference on consumer-grade hardware.  It leverages the GGUF format for optimal performance, allowing you to experience the power of LLMs without the need for expensive hardware.
+
+Whether you're a student, hobbyist, or developer, this model opens up a world of possibilities for exploring natural language processing, text generation, and AI-powered applications right at your fingertips.
+
 
-**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
-
-**Falcon3-10B-Instruct** achieves state-of-the-art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
-Falcon3-10B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
-
-This repository contains the GGUFs instruction-tuned 10B Falcon3 model.
-
-## Model Details
-- Architecture
-  - Transformer-based causal decoder-only architecture
-  - 40 decoder blocks
-  - Grouped Query Attention (GQA) for faster inference: 12 query heads and 4 key-value heads
-  - Wider head dimension: 256
-  - High RoPE value to support long context understanding: 1000042
-  - Uses SwiGLu and RMSNorm
-  - 32K context length
-  - 131K vocab size
-- Depth up-scaled from **Falcon3-7B-Base** with 2 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
-- Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data
-- Supports EN, FR, ES, PT
-- Developed by [Technology Innovation Institute](https://www.tii.ae)
-- License: TII Falcon-LLM License 2.0
-- Model Release Date: December 2024
-- Quantization: q2_K, q3_K_M, q4_0, q4_K_M, q5_0, q5_K_M, q6_K, q8_0
-
-  
 ## Getting started
 
 ### 1. Download GGUF models from hugging face
-
 First, download the model from Hugging Face. You can use the `huggingface_hub` library or download it manually:
 
 ```bash
-pip  install  huggingface_hub
-huggingface-cli  download  {model_name}
+pip install huggingface_hub
+huggingface-cli download {model_name}
 ```
 
-This will download the model to your current directory. Make sure to replace {model_name} with the actual username and model name from your Hugging Face repository.
+This will download the model to your current directory.  Make sure to replace {model_name} with the actual username and model name from your Hugging Face repository.
 
 ## 2. Install llama.cpp
 
@@ -68,19 +38,17 @@ You have several options for installing llama.cpp:
 
 This gives you the most flexibility and control. Follow the instructions in the llama.cpp repository to build from source:
 
-```bash
-
-git  clone  https://github.com/ggerganov/llama.cpp
-cd llama.cpp
-cmake -B build
-cmake --build build --config Release
-```
-
+   ```bash
+   git clone https://github.com/ggerganov/llama.cpp
+   cd llama.cpp
+   cmake -B build
+   cmake --build build --config Release
+   ```
 For more information about how to build llama.cpp from source please refere to llama.cpp documentation on how to build from source: **[llama.cpp build from source](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)**.
 
 **2. Download pre-built binaries:**
 
-If you prefer a quicker setup, you can download pre-built binaries for your operating system. Check the llama.cpp repository for available binaries.
+If you prefer a quicker setup, you can download pre-built binaries for your operating system. Check the llama.cpp repository for available binaries. 
 
 **3. Use Docker:**
 
@@ -88,35 +56,33 @@ For a more contained environment, you can use the official llama.cpp Docker imag
 
 For detailed instructions and more information, please check the llama.cpp documentation on docker: **[llama.cpp docker](https://github.com/ggerganov/llama.cpp/blob/master/docs/docker.mdg)**.
 
+
 ### 3. Start playing with your model
+- <details open>
+    <summary>Run simple text completion</summary>
 
-Run simple text completion
-```bash
-llama-cli -m {path-to-gguf-model}  -p  "I believe the meaning of life is"  -n  128
-```
+    ```bash
+    llama-cli -m {path-to-gguf-model} -p "I believe the meaning of life is" -n 128
+    ```
 
-Run in conversation mode
-```bash
-llama-cli -m {path-to-gguf-model}  -p  "You are a helpful assistant"  -cnv  -co
-```
+    </details>
 
-## Useful links
-- View our [release blogpost](https://huggingface.co/blog/falcon3).
-- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
-  
-## Technical Report
+- <details>
+    <summary>Run in conversation mode</summary>
 
-Coming soon....
+    ```bash
+    llama-cli -m {path-to-gguf-model} -p "You are a helpful assistant" -cnv -co
+    </details>
+    ```
+
+# Citation
+If Falcon3 family were helpful to your work, feel free to give us a cite.
 
-## Citation
-If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
- 
 ```
 @misc{Falcon3,
-    title = {The Falcon 3 Family of Open Models},
-    url = {https://huggingface.co/blog/falcon3},
-    author = {Falcon-LLM Team},
+    title = {The Falcon 3 family of Open Models},
+    author = {TII Team},
     month = {December},
     year = {2024}
 }
-```
+```