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# Inference
import gradio as gr
from huggingface_hub import InferenceClient
model = "meta-llama/Llama-3.2-3B-Instruct"
client = InferenceClient(model)
def fn(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
#messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "bot", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens = max_tokens,
temperature = temperature,
top_p = top_p,
stream = True,
):
token = message.choices[0].delta.content
response += token
yield response
app = gr.ChatInterface(
fn = fn,
additional_inputs = [
gr.Textbox(value="You are a friendly Chatbot.", label="System Message"),
gr.Slider(minimum=1, maximum=2048, value=512, 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"),
],
title = "Meta Llama",
description = model,
examples = [
["Hello, World."]
]
)
if __name__ == "__main__":
app.launch()
"""
app = gr.load(
"meta-llama/Llama-3.2-3B-Instruct",
src = "models",
inputs = [gr.Textbox(label = "Input")],
outputs = [gr.Textbox(label = "Output")],
title = "Meta Llama",
description = "Inference",
examples = [
["Hello, World."]
]
).launch()
"""
# Pipeline
import gradio as gr
from transformers import pipeline
pipe = pipeline(model = "meta-llama/Llama-3.2-3B-Instruct")
def fn(input):
output = pipe(
input,
max_new_tokens = 2048
)
return output[0]["generated_text"]#[len(input):]
app = gr.Interface(
fn = fn,
inputs = [gr.Textbox(label = "Input")],
outputs = [gr.Textbox(label = "Output")],
title = "Meta Llama",
description = "Pipeline",
examples = [
["Hello, World."]
]
).launch() |