File size: 11,431 Bytes
692f4a4 f0b399b 692f4a4 b760773 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 ec9e02c f1ba107 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 3208b6e 692f4a4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 |
import gradio as gr
import random
models=[
"facebook/MobileLLM-125M",
"facebook/MobileLLM-350M",
"facebook/MobileLLM-600M",
"facebook/MobileLLM-1B",
]
client_z=[]
def load_models(inp,new_models):
if not new_models:
new_models=models
out_box=[gr.Chatbot(),gr.Chatbot(),gr.Chatbot(),gr.Chatbot()]
print(type(inp))
print(inp)
#print(new_models[inp[0]])
client_z.clear()
for z,ea in enumerate(inp):
client_z.append(gr.Interface("models/" + new_models[inp[z]]))
out_box[z]=(gr.update(label=new_models[inp[z]]))
return out_box[0],out_box[1],out_box[2],out_box[3]
def format_prompt_default(message, history):
prompt = ""
if history:
#<start_of_turn>userHow does the brain work?<end_of_turn><start_of_turn>model
for user_prompt, bot_response in history:
prompt += f"{user_prompt}\n"
print(prompt)
prompt += f"{bot_response}\n"
print(prompt)
prompt += f"{message}\n"
return prompt
def format_prompt_gemma(message, history):
prompt = ""
if history:
#<start_of_turn>userHow does the brain work?<end_of_turn><start_of_turn>model
for user_prompt, bot_response in history:
prompt += f"{user_prompt}\n"
print(prompt)
prompt += f"{bot_response}\n"
print(prompt)
prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model"
return prompt
def format_prompt_mixtral(message, history):
prompt = "<s>"
if history:
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def format_prompt_choose(message, history, model_name, new_models=None):
if not new_models:
new_models=models
if "gemma" in new_models[model_name].lower() and "it" in new_models[model_name].lower():
return format_prompt_gemma(message,history)
if "mixtral" in new_models[model_name].lower():
return format_prompt_mixtral(message,history)
else:
return format_prompt_mixtral(message,history)
mega_hist=[[],[],[],[]]
def chat_inf_tree(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
client=client_z[int(hid_val)-1]
#client = gr.load()
if history:
mega_hist[hid_val-1]=history
#history = []
hist_len=0
generate_kwargs = dict(
temperature=temp,
max_new_tokens=tokens,
top_p=top_p,
repetition_penalty=rep_p,
do_sample=True,
seed=seed,
)
#formatted_prompt=prompt
formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", mega_hist[hid_val-1])
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield [(prompt,output)]
mega_hist[hid_val-1].append((prompt,output))
yield mega_hist[hid_val-1]
else:
yield None
def chat_inf_a(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
if system_prompt:
system_prompt=f'{system_prompt}, '
#client1=client_z[int(hid_val)-1]
client1=gr.load("models/" + models[0])
if not history:
history = []
hist_len=0
generate_kwargs = dict(
temperature=temp,
max_new_tokens=tokens,
top_p=top_p,
repetition_penalty=rep_p,
do_sample=True,
seed=seed,
)
#formatted_prompt=prompt
formatted_prompt = format_prompt_choose(f"{system_prompt}{prompt}", history, client_choice[0])
stream1 = client1.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream1:
output += response.token.text
yield [(prompt,output)]
history.append((prompt,output))
yield history
else:
yield None
def chat_inf_b(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
if system_prompt:
system_prompt=f'{system_prompt}, '
client2=client_z[int(hid_val)-1]
#client2=gr.load("models/" + models[1])
if not history:
history = []
hist_len=0
generate_kwargs = dict(
temperature=temp,
max_new_tokens=tokens,
top_p=top_p,
repetition_penalty=rep_p,
do_sample=True,
seed=seed,
)
#formatted_prompt=prompt
formatted_prompt = format_prompt_choose(f"{system_prompt}{prompt}", history, client_choice[1])
stream2 = client2.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream2:
output += response.token.text
yield [(prompt,output)]
history.append((prompt,output))
yield history
else:
yield None
def chat_inf_c(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
if system_prompt:
system_prompt=f'{system_prompt}, '
client3=client_z[int(hid_val)-1]
#client3=gr.load("models/" + models[2])
if not history:
history = []
hist_len=0
generate_kwargs = dict(
temperature=temp,
max_new_tokens=tokens,
top_p=top_p,
repetition_penalty=rep_p,
do_sample=True,
seed=seed,
)
#formatted_prompt=prompt
formatted_prompt = format_prompt_choose(f"{system_prompt}{prompt}", history, client_choice[2])
stream3 = client3.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream3:
output += response.token.text
yield [(prompt,output)]
history.append((prompt,output))
yield history
else:
yield None
def chat_inf_d(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p,hid_val):
if len(client_choice)>=hid_val:
if system_prompt:
system_prompt=f'{system_prompt}, '
client4=client_z[int(hid_val)-1]
#client4=gr.load("models/" + models[3])
if not history:
history = []
hist_len=0
generate_kwargs = dict(
temperature=temp,
max_new_tokens=tokens,
top_p=top_p,
repetition_penalty=rep_p,
do_sample=True,
seed=seed,
)
#formatted_prompt=prompt
formatted_prompt = format_prompt_choose(f"{system_prompt}{prompt}", history, client_choice[3])
stream4 = client4.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream4:
output += response.token.text
yield [(prompt,output)]
history.append((prompt,output))
yield history
else:
yield None
def add_new_model(inp, cur):
cur.append(inp)
return cur,gr.update(choices=[z for z in cur])
def load_new(models=models):
return models
def clear_fn():
return None,None,None,None,None,None
rand_val=random.randint(1,1111111111111111)
def check_rand(inp,val):
if inp==True:
return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1,1111111111111111))
else:
return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
with gr.Blocks() as app:
new_models=gr.State([])
gr.HTML("""<center><h1 style='font-size:xx-large;'>Chatbot Model Compare</h1>""")
with gr.Row():
chat_a = gr.Chatbot(height=500)
chat_b = gr.Chatbot(height=500)
with gr.Row():
chat_c = gr.Chatbot(height=500)
chat_d = gr.Chatbot(height=500)
with gr.Group():
with gr.Row():
with gr.Column(scale=3):
inp = gr.Textbox(label="Prompt")
sys_inp = gr.Textbox(label="System Prompt (optional)")
with gr.Row():
with gr.Column(scale=2):
btn = gr.Button("Chat")
with gr.Column(scale=1):
with gr.Group():
stop_btn=gr.Button("Stop")
clear_btn=gr.Button("Clear")
client_choice=gr.Dropdown(label="Models",type='index', choices=[c for c in models],max_choices=4,multiselect=True,interactive=True)
add_model=gr.Textbox(label="New Model")
add_btn=gr.Button("Add Model")
with gr.Column(scale=1):
with gr.Group():
rand = gr.Checkbox(label="Random Seed", value=True)
seed=gr.Slider(label="Seed", minimum=1, maximum=1111111111111111,step=1, value=rand_val)
tokens = gr.Slider(label="Max new tokens",value=3840,minimum=0,maximum=8000,step=64,interactive=True, visible=True,info="The maximum number of tokens")
temp=gr.Slider(label="Temperature",step=0.01, minimum=0.01, maximum=1.0, value=0.9)
top_p=gr.Slider(label="Top-P",step=0.01, minimum=0.01, maximum=1.0, value=0.9)
rep_p=gr.Slider(label="Repetition Penalty",step=0.1, minimum=0.1, maximum=2.0, value=1.0)
hid1=gr.Number(value=1,visible=False)
hid2=gr.Number(value=2,visible=False)
hid3=gr.Number(value=3,visible=False)
hid4=gr.Number(value=4,visible=False)
app.load(load_new,None,new_models)
add_btn.click(add_new_model,[add_model,new_models],[new_models,client_choice])
client_choice.change(load_models,[client_choice,new_models],[chat_a,chat_b,chat_c,chat_d])
#im_go=im_btn.click(get_screenshot,[chat_b,im_height,im_width,chatblock,theme,wait_time],img)
#chat_sub=inp.submit(check_rand,[rand,seed],seed).then(chat_inf,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p],chat_b)
go1=btn.click(check_rand,[rand,seed],seed).then(chat_inf_a,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p,hid1],chat_a)
go2=btn.click(check_rand,[rand,seed],seed).then(chat_inf_b,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p,hid2],chat_b)
go3=btn.click(check_rand,[rand,seed],seed).then(chat_inf_c,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p,hid3],chat_c)
go4=btn.click(check_rand,[rand,seed],seed).then(chat_inf_d,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p,hid4],chat_d)
stop_btn.click(None,None,None,cancels=[go1,go2,go3,go4])
clear_btn.click(clear_fn,None,[inp,sys_inp,chat_a,chat_b,chat_c,chat_d])
app.queue(default_concurrency_limit=10).launch()
|