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import gradio as gr | |
from huggingface_hub import Repository, InferenceClient | |
import os | |
import json | |
import re | |
API_TOKEN = os.environ.get("API_TOKEN") | |
API_ENDPOINTS = { | |
"Falcon": "tiiuae/falcon-180B-chat", | |
"Llama": "meta-llama/Llama-2-70b-chat-hf", | |
"Mistral": "mistralai/Mistral-7B-v0.1", | |
"Mistral*": "mistralai/Mistral-7B-Instruct-v0.1", | |
"Open-3.5": "openchat/openchat_3.5", | |
"Xistral": "mistralai/Mixtral-8x7B-v0.1", | |
"Xistral*": "mistralai/Mixtral-8x7B-Instruct-v0.1", | |
} | |
CHOICES = [] | |
CLIENTS = {} | |
for model_name, model_endpoint in API_ENDPOINTS.items(): | |
CHOICES.append(model_name) | |
CLIENTS[model_name] = InferenceClient(model_endpoint, headers = { "Authorization": f"Bearer {API_TOKEN}" }) | |
def predict(input, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed): | |
stops = json.loads(stop_seqs or '[]') | |
response = CLIENTS[model].text_generation( | |
input, | |
temperature = temperature, | |
max_new_tokens = max_tokens, | |
top_p = top_p, | |
top_k = top_k, | |
repetition_penalty = rep_p, | |
stop_sequences = stops, | |
do_sample = True, | |
seed = seed, | |
stream = False, | |
details = False, | |
return_full_text = False | |
) | |
return response | |
with gr.Blocks() as demo: | |
with gr.Row(variant = "panel"): | |
gr.Markdown("✡️ This is a private LLM Space owned within STC Holdings!") | |
with gr.Row(): | |
with gr.Column(): | |
input = gr.Textbox(label = "Input", value = "", lines = 2) | |
run = gr.Button("▶") | |
with gr.Column(): | |
model = gr.Dropdown(choices = CHOICES, value = next(iter(API_ENDPOINTS)), interactive = True, label = "Model") | |
temperature = gr.Slider( minimum = 0, maximum = 2, value = 1, step = 0.01, interactive = True, label = "Temperature" ) | |
top_p = gr.Slider( minimum = 0.01, maximum = 0.99, value = 0.95, step = 0.01, interactive = True, label = "Top P" ) | |
top_k = gr.Slider( minimum = 1, maximum = 2048, value = 50, step = 1, interactive = True, label = "Top K" ) | |
rep_p = gr.Slider( minimum = 0.01, maximum = 2, value = 1.2, step = 0.01, interactive = True, label = "Repetition Penalty" ) | |
max_tokens = gr.Slider( minimum = 1, maximum = 2048, value = 32, step = 64, interactive = True, label = "Max New Tokens" ) | |
stop_seqs = gr.Textbox( value = "", interactive = True, label = "Stop Sequences ( JSON Array / 4 Max )" ) | |
seed = gr.Slider( minimum = 0, maximum = 9007199254740991, value = 42, step = 1, interactive = True, label = "Seed" ) | |
with gr.Row(): | |
with gr.Column(): | |
output = gr.Textbox(label = "Output", value = "", lines = 50) | |
run.click(predict, inputs = [input, model, temperature, top_p, top_k, rep_p, max_tokens, stop_seqs, seed], outputs = [output], queue = False) | |
demo.launch(show_api = True) |