--- language: - en - fr - es - pt tags: - falcon3 - llama-cpp - gguf-my-repo base_model: tiiuae/Falcon3-1B-Instruct license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html library_name: transformers --- # Triangle104/Falcon3-1B-Instruct-Q5_K_S-GGUF This model was converted to GGUF format from [`tiiuae/Falcon3-1B-Instruct`](https://huggingface.co/tiiuae/Falcon3-1B-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/Falcon3-1B-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 parameters. This repository contains the Falcon3-1B-Instruct. It achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-1B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 8K. Model Details Architecture Transformer-based causal decoder-only architecture 18 decoder blocks Grouped Query Attention (GQA) for faster inference: 8 query heads and 4 key-value heads Wider head dimension: 256 High RoPE value to support long context understanding: 1000042 Uses SwiGLU and RMSNorm 8K context length 131K vocab size Pruned and healed using larger Falcon models (3B and 7B respectively) on only 80 Gigatokens of datasets comprising of web, code, STEM, high quality and multilingual data using 256 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 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/Falcon3-1B-Instruct-Q5_K_S-GGUF --hf-file falcon3-1b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Falcon3-1B-Instruct-Q5_K_S-GGUF --hf-file falcon3-1b-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/Falcon3-1B-Instruct-Q5_K_S-GGUF --hf-file falcon3-1b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Falcon3-1B-Instruct-Q5_K_S-GGUF --hf-file falcon3-1b-instruct-q5_k_s.gguf -c 2048 ```