README.md

---
language:
- en
tags:
- falcon3
- falcon3_mamba
- falcon_mamba
- llama-cpp
- gguf-my-repo
base_model: tiiuae/Falcon3-Mamba-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-Mamba-7B-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`tiiuae/Falcon3-Mamba-7B-Instruct`](https://huggingface.co/tiiuae/Falcon3-Mamba-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-Mamba-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-Mamba-7B-Instruct. It achieves, compared to similar SSM-based models of the same size, state of art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-Mamba-7B-Instruct supports a context length up to 32K and was mainly trained on english corpus.

Model Details

    Architecture (same as Falcon-Mamba-7b)
    
        Mamba1 based causal decoder only architecture trained on a causal language modeling task (i.e., predict the next token).
        64 decoder blocks
        width: 4096
        state_size: 16
        32k context length
        65k vocab size
    Continue Pretrained from Falcon-Mamba-7b, with another 1500 Gigatokens of data consisting of web, code, STEM and high quality data.
    Postrained on 1.2 million samples of STEM, conversations, code, and safety.
    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-Mamba-7B-Instruct-Q8_0-GGUF --hf-file falcon3-mamba-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Falcon3-Mamba-7B-Instruct-Q8_0-GGUF --hf-file falcon3-mamba-7b-instruct-q8_0.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-Mamba-7B-Instruct-Q8_0-GGUF --hf-file falcon3-mamba-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Falcon3-Mamba-7B-Instruct-Q8_0-GGUF --hf-file falcon3-mamba-7b-instruct-q8_0.gguf -c 2048
```