Triangle104/Falcon3-Mamba-7B-Instruct-Q6_K-GGUF

This model was converted to GGUF format from tiiuae/Falcon3-Mamba-7B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Falcon3-Mamba-7B-Instruct-Q6_K-GGUF --hf-file falcon3-mamba-7b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Falcon3-Mamba-7B-Instruct-Q6_K-GGUF --hf-file falcon3-mamba-7b-instruct-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps 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-Q6_K-GGUF --hf-file falcon3-mamba-7b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Falcon3-Mamba-7B-Instruct-Q6_K-GGUF --hf-file falcon3-mamba-7b-instruct-q6_k.gguf -c 2048