XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF_Legacy

ZhafranR/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF

This model was converted to GGUF format from XeAI/LLaMa_3.2_3B_Instruct_Text2SQL using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama-cpp-python

from llama_cpp import Llama

# Load the model
model = Llama(
    model_path="path_to_your_model.gguf",
    n_ctx=2048,
    n_batch=512,
    n_threads=6
)

# Generate text
output = model.create_completion(
    "Your prompt here",
    max_tokens=512,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repeat_penalty=1.1
)
print(output['choices'][0]['text'])

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 XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF --hf-file llama_3.2_3b_instruct_text2sql-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF --hf-file llama_3.2_3b_instruct_text2sql-q4_k_m.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 XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF --hf-file llama_3.2_3b_instruct_text2sql-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF --hf-file llama_3.2_3b_instruct_text2sql-q4_k_m.gguf -c 2048