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from llama_cpp import Llama
from huggingface_hub import hf_hub_download
# import spaces
import functools
# Download only the safety model shard from the model repo
# Load the Llama model for safety classification
# model_path = r".\models\llama-guard-3-8b-q4_k_m.gguf"
# llm = Llama(model_path=model_path, n_ctx=1024)
# llm = Llama(
# model_path=model_path,
# n_ctx=512, # down from 4096
# low_vram=True, # llama.cpp low-vram mode
# f16_kv=True, # half-precision kv cache
# use_mmap=True, # mmap file
# use_mlock=False,
# )
# @spaces.CPU
@functools.lru_cache(maxsize=1)
def llm_gpu():
model_path = hf_hub_download(
repo_id="Inventors-Hub/SwarmChat-models",
repo_type="model",
filename="llama-guard-3-8b-q4_k_m.gguf",)
llm = Llama(
model_path=model_path,
n_ctx=512, # down from 4096
low_vram=True, # llama.cpp low-vram mode
f16_kv=True, # half-precision kv cache
use_mmap=True, # mmap file
use_mlock=False,
)
return llm
def check_safety(text, checkbox):
if checkbox:
return "Safe"
categories = {
"S1": "Violent Crimes.",
"S2": "Non-Violent Crimes.",
"S3": "Sex-Related Crimes.",
"S4": "Child Sexual Exploitation.",
"S5": "Defamation.",
"S6": "Specialized Advice.",
"S7": "Privacy.",
"S8": "Intellectual Property.",
"S9": "Indiscriminate Weapons.",
"S10": "Hate.",
"S11": "Suicide & Self-Harm.",
"S12": "Sexual Content.",
"S13": "Elections.",
"S14": "Code Interpreter Abuse.",
}
input_prompt = f"""
<|begin_of_text|><|start_header_id|>user<|end_header_id|>
Task: Determine if the following text contains unsafe content according to our safety policy. Classify the content into one or more of the categories below if unsafe.
<BEGIN UNSAFE CONTENT CATEGORIES>
{', '.join([f'{key}: {value}' for key, value in categories.items()])}
<END UNSAFE CONTENT CATEGORIES>
Text: {text}
Provide your safety assessment:
- Respond with "safe" if the text is appropriate.
- If unsafe, respond with: "unsafe" followed by a comma-separated list of the violated categories (e.g., S1, S2).
<|eot_id|>
"""
# Generate a response from the model
llm = llm_gpu()
output = llm(input_prompt, temperature=0)
# Extract the model's response
response = output.get("choices", [{}])[0].get("text", "").strip()
print("\n\n", response, "\n\n")
# Parse the response
if response.lower().startswith("safe"):
return "Safe"
else:
unsafe_categories = categories[response.split("unsafe", 1)[-1].strip()]
return f"Unsafe: This prompt is categorized as '{unsafe_categories}'"
# unsafe_categories = categories[response.split("unsafe", 1)[-1].strip()]
# return f"Unsafe: This prompt categorized as '{unsafe_categories}'" |