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import spaces |
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import subprocess |
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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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from llama_cpp import Llama |
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from llama_cpp_agent import LlamaCppAgent |
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from llama_cpp_agent import MessagesFormatterType |
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from llama_cpp_agent.providers import LlamaCppPythonProvider |
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subprocess.run('pip install llama-cpp-python==0.2.75 --no-build-isolation --no-cache-dir --upgrade --only-binary=:all: --extra-index-url=https://abetlen.github.io/llama-cpp-python/whl/cu124', env={'CMAKE_ARGS': "-DLLAMA_CUDA=on"}, shell=True) |
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hf_hub_download(repo_id="TheBloke/Mistral-7B-Instruct-v0.2-GGUF", filename="mistral-7b-instruct-v0.2.Q6_K.gguf", local_dir = "./models") |
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@spaces.GPU(duration=120) |
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def respond( |
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message, |
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history: list[tuple[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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): |
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llama_model = Llama(r"models/mistral-7b-instruct-v0.2.Q6_K.gguf", n_batch=1024, n_threads=0, n_gpu_layers=33, n_ctx=8192, verbose=False) |
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provider = LlamaCppPythonProvider(llama_model) |
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agent = LlamaCppAgent( |
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provider, |
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system_prompt=f"{system_message}", |
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predefined_messages_formatter_type=MessagesFormatterType.MISTRAL, |
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debug_output=True |
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) |
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settings = provider.get_provider_default_settings() |
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settings.stream = True |
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settings.max_tokens = max_tokens |
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settings.temperature = temperature |
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settings.top_p = top_p |
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yield agent.get_chat_response(message, llm_sampling_settings=settings) |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="You are a helpful assistant.", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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) |
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if __name__ == "__main__": |
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demo.launch() |
