Spaces:
Sleeping
Sleeping
File size: 3,905 Bytes
01f3c1d 44e1f43 01f3c1d 44e1f43 01f3c1d 44e1f43 01f3c1d efcaeaf 01f3c1d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
import json
import subprocess
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppPythonProvider
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
import gradio as gr
from huggingface_hub import hf_hub_download
# Download models
hf_hub_download(
repo_id="OEvortex/HelpingAI-3B-hindi",
filename="HelpingAI-3B-hindi.Q4_K_M.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="OEvortex/HelpingAI-3B-chat",
filename="helpingai-3b-chat-q4_k_m.gguf",
local_dir="./models"
)
llm = None
llm_model = None
def respond(
message,
history: list[tuple[str, str]],
model,
system_message,
max_tokens,
temperature,
top_p,
top_k,
repeat_penalty,
):
chat_template = MessagesFormatterType.CHATML
global llm
global llm_model
if llm is None or llm_model != model:
llm = Llama(
model_path=f"models/{model}",
n_ctx=2048, # Reduced context size for CPU
n_threads=4, # Adjust this based on your CPU cores
n_gpu_layers=50
)
llm_model = model
provider = LlamaCppPythonProvider(llm)
agent = LlamaCppAgent(
provider,
system_prompt=f"{system_message}",
predefined_messages_formatter_type=chat_template,
debug_output=True
)
settings = provider.get_provider_default_settings()
settings.temperature = temperature
settings.top_k = top_k
settings.top_p = top_p
settings.max_tokens = max_tokens
settings.repeat_penalty = repeat_penalty
settings.stream = True
messages = BasicChatHistory()
for msn in history:
user = {
'role': Roles.user,
'content': msn[0]
}
assistant = {
'role': Roles.assistant,
'content': msn[1]
}
messages.add_message(user)
messages.add_message(assistant)
stream = agent.get_chat_response(
message,
llm_sampling_settings=settings,
chat_history=messages,
returns_streaming_generator=True,
print_output=False
)
outputs = ""
for output in stream:
outputs += output
yield outputs
description = "HelpingAI-3B-chat: The Compact Yet Powerful Small Language Model (SLM) for Emotionally Intelligent Conversations 🌟"
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Dropdown([
'helpingai-3b-chat-q4_k_m.gguf',
'HelpingAI-3B-hindi.Q4_K_M.gguf'
],
value="HelpingAI-3B-hindi.Q4_K_M.gguf",
label="Model"
),
gr.Textbox(value="You are HelpingAI, an emotionally intelligent AI designed to provide empathetic and supportive responses in HelpingAI style.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p",
),
gr.Slider(
minimum=0,
maximum=100,
value=40,
step=1,
label="Top-k",
),
gr.Slider(
minimum=0.0,
maximum=2.0,
value=1.1,
step=0.1,
label="Repetition penalty",
),
],
retry_btn="Retry",
undo_btn="Undo",
clear_btn="Clear",
submit_btn="Send",
title="Chat with HelpingAI-3B using llama.cpp",
description=description,
chatbot=gr.Chatbot(
scale=1,
likeable=False,
show_copy_button=True
)
)
if __name__ == "__main__":
demo.launch() |