Udating markdown sections and removing old inference code.

recipes/responsible_ai/llama_guard/inference.py

@@ -1,75 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
-
-import fire
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
-
-from llama_recipes.inference.prompt_format_utils import build_default_prompt, create_conversation, LlamaGuardVersion
-from typing import List, Tuple
-from enum import Enum
-
-class AgentType(Enum):
-    AGENT = "Agent"
-    USER = "User"
-
-def main(
-    model_id: str = "meta-llama/Llama-Guard-3-8B",
-    llama_guard_version: str = "LLAMA_GUARD_3"
-):
-    """
-    Entry point for Llama Guard inference sample script.
-
-    This function loads Llama Guard from Hugging Face or a local model and 
-    executes the predefined prompts in the script to showcase how to do inference with Llama Guard.
-
-    Args:
-        model_id (str): The ID of the pretrained model to use for generation. This can be either the path to a local folder containing the model files,
-            or the repository ID of a model hosted on the Hugging Face Hub. Defaults to 'meta-llama/LlamaGuard-7b'.
-        llama_guard_version (LlamaGuardVersion): The version of the Llama Guard model to use for formatting prompts. Defaults to LLAMA_GUARD_1.
-    """
-    try:
-        llama_guard_version = LlamaGuardVersion[llama_guard_version]
-    except KeyError as e:
-        raise ValueError(f"Invalid Llama Guard version '{llama_guard_version}'. Valid values are: {', '.join([lgv.name for lgv in LlamaGuardVersion])}") from e
-
-    prompts: List[Tuple[List[str], AgentType]] = [
-        (["<Sample user prompt>"], AgentType.USER),
-
-        (["<Sample user prompt>",
-        "<Sample agent response>"], AgentType.AGENT),
-        
-        (["<Sample user prompt>",
-        "<Sample agent response>",
-        "<Sample user reply>",
-        "<Sample agent response>",], AgentType.AGENT),
-
-    ]
-
-    quantization_config = BitsAndBytesConfig(load_in_8bit=True)
-
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-    model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config, device_map="auto")
-    
-    for prompt in prompts:
-        formatted_prompt = build_default_prompt(
-                prompt[1], 
-                create_conversation(prompt[0]),
-                llama_guard_version)
-
-
-        input = tokenizer([formatted_prompt], return_tensors="pt").to("cuda")
-        prompt_len = input["input_ids"].shape[-1]
-        output = model.generate(**input, max_new_tokens=100, pad_token_id=0)
-        results = tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
-       
-        
-        print(prompt[0])
-        print(f"> {results}")
-        print("\n==================================\n")
-
-if __name__ == "__main__":
-    try:
-        fire.Fire(main)
-    except Exception as e:
-        print(e)