THaLLE-0.1-7B-fa-GGUF/README.md

base_model, language, license, pipeline_tag, tags

base_model	language	license	pipeline_tag	tags
KBTG-Labs/THaLLE-0.1-7B-fa	en	apache-2.0	text-generation	finance llama-cpp

KBTG-Labs/THaLLE-0.1-7B-fa-GGUF

This model was converted to GGUF format from KBTG-Labs/THaLLE-0.1-7B-fa using llama.cpp. Refer to the original model card for more details on the model.

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 with your perfered quantization level ("q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0", "f16"). Smaller quantization is faster and use less memory, but will be less accurate.

CLI:

llama-cli --hf-repo KBTG-Labs/THaLLE-0.1-7B-fa-GGUF --hf-file thalle-0.1-7b-fa-<QUANTIZATION_LEVEL>.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo KBTG-Labs/THaLLE-0.1-7B-fa-GGUF --hf-file thalle-0.1-7b-fa-<QUANTIZATION_LEVEL>.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 KBTG-Labs/THaLLE-0.1-7B-fa-GGUF --hf-file thalle-0.1-7b-fa-<QUANTIZATION_LEVEL>.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo KBTG-Labs/THaLLE-0.1-7B-fa-GGUF --hf-file thalle-0.1-7b-fa-<QUANTIZATION_LEVEL>.gguf -c 2048