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import gradio as gr | |
from llama_cpp import Llama | |
# Path to model (downloaded automatically from HF hub) | |
MODEL_PATH = "TheBloke/CodeLlama-7B-Instruct-GGUF" | |
# Load LLM (first time takes time) | |
llm = Llama.from_pretrained( | |
repo_id=MODEL_PATH, | |
filename="codellama-7b-instruct.Q4_K_M.gguf", # 4-bit quantized version | |
n_ctx=2048, | |
verbose=True | |
) | |
# Prompt wrapper | |
def build_prompt(user_prompt): | |
return f"[INST] Write Python code using the Manim library: {user_prompt} [/INST]" | |
def generate_code(prompt): | |
result = llm(build_prompt(prompt), max_tokens=512, temperature=0.7, stop=["</s>"]) | |
return result["choices"][0]["text"] | |
gr.Interface( | |
fn=generate_code, | |
inputs=gr.Textbox(label="Prompt", placeholder="e.g., Animate a bouncing ball in Manim"), | |
outputs=gr.Textbox(label="Generated Python Code"), | |
title="π Manim Code Generator - Code Llama 7B", | |
description="Powered by llama-cpp and Code Llama 7B (Quantized). Runs on CPU!" | |
).launch() | |