Triangle104/Falcon3-3B-Instruct-Q6_K-GGUF

This model was converted to GGUF format from tiiuae/Falcon3-3B-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.

---

Model details:

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

Falcon3-3B-Instruct achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-3B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K. Model Details

Architecture
    Transformer-based causal decoder-only architecture
    22 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
Pruned and healed from Falcon3-7B-Base on only 100 Gigatokens 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
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)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Falcon3-3B-Instruct-Q6_K-GGUF --hf-file falcon3-3b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Falcon3-3B-Instruct-Q6_K-GGUF --hf-file falcon3-3b-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/Falcon3-3B-Instruct-Q6_K-GGUF --hf-file falcon3-3b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Falcon3-3B-Instruct-Q6_K-GGUF --hf-file falcon3-3b-instruct-q6_k.gguf -c 2048