---
language:
- en
- fr
- es
- pt
tags:
- falcon3
- llama-cpp
- gguf-my-repo
base_model: tiiuae/Falcon3-7B-Instruct
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
library_name: transformers
---

# Triangle104/Falcon3-7B-Instruct-Q5_K_S-GGUF
This model was converted to GGUF format from [`tiiuae/Falcon3-7B-Instruct`](https://huggingface.co/tiiuae/Falcon3-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/tiiuae/Falcon3-7B-Instruct) for more details on the model.

---
Model details:
-
Falcon3 family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.

This repository contains the Falcon3-7B-Instruct. It achieves state of art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-7B-Instruct supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.

Model Details

    Architecture
    
        Transformer based causal decoder only architecture
        28 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
    Pretrained on 14 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
    Postrained on 1.2 million samples of STEM, conversations, code, safety and function call data
    Supports EN, FR, ES, PT
    Developed by Technology Innovation Institute
    License: TII Falcon-LLM License 2.0
    Model Release Date: December 2024

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Falcon3-7B-Instruct-Q5_K_S-GGUF --hf-file falcon3-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Falcon3-7B-Instruct-Q5_K_S-GGUF --hf-file falcon3-7b-instruct-q5_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Falcon3-7B-Instruct-Q5_K_S-GGUF --hf-file falcon3-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Falcon3-7B-Instruct-Q5_K_S-GGUF --hf-file falcon3-7b-instruct-q5_k_s.gguf -c 2048
```