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Llama-3.2-SUN-2.5B-chat

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Sleeping

Remove the GGUF Naming in the README.md or Else Switch the Whole thing to it.

-> https://huggingface.co/spaces/mkurman/Llama-3.2-SUN-2.5B-chat/blob/main/README.md

I tried it, and it's working great.

Code Snippet
import spaces
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

llm = None
llm_model = None

hf_hub_download(
repo_id="meditsolutions/Llama-3.2-SUN-2.5B-chat-gguf",
filename="llama-3.2-medit-sun-2.5B-chat.gguf",
local_dir="./models"
)

def get_messages_formatter_type(model_name):
return MessagesFormatterType.LLAMA_3

@spaces.GPU(duration=23)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
top_k,
repeat_penalty,
):
global llm
global llm_model
model = "llama-3.2-medit-sun-2.5B-chat.gguf"

chat_template = get_messages_formatter_type(model)

if llm is None or llm_model != model:
    llm = Llama(
        model_path=f"models/{model}",
        flash_attn=True,
        n_gpu_layers=81,
        n_batch=1024,
        n_ctx=8192,
    )
    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

demo = gr.ChatInterface(
fn=respond,
theme="bethecloud/storj_theme",
additional_inputs=[
gr.Textbox(value="You are a helpful assistant.", label="System message"),
gr.Slider(minimum=1, maximum=8192, value=2048, 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",
chatbot=gr.Chatbot(
scale=1,
show_copy_button=True
)
)

if name == "main":
demo.launch()

requirements.txt

huggingface_hub
llama-cpp-agent
scikit-build-core
https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.90-cu124/llama_cpp_python-0.2.90-cp310-cp310-linux_x86_64.whl