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on
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Running
on
T4
ffreemt
commited on
Commit
·
c48ba74
1
Parent(s):
584239a
Update API ready, TODO: fix info
Browse files
app.py
CHANGED
@@ -6,6 +6,30 @@ transformers 4.31.0
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import torch
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torch.cuda.empty_cache()
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"""
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# pylint: disable=line-too-long, invalid-name, no-member, redefined-outer-name, missing-function-docstring, missing-class-docstring, broad-except,
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import gc
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@@ -14,7 +38,9 @@ import sys
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import time
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from collections import deque
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from dataclasses import asdict, dataclass
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from types import SimpleNamespace
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import gradio as gr
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import torch
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@@ -100,6 +126,10 @@ model = None
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gc.collect()
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torch.cuda.empty_cache()
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model = gen_model(model_name)
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@@ -136,6 +166,7 @@ def bot(chat_history, **kwargs):
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chat_history[:-1].append(["message", str(exc)])
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return chat_history
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def bot_stream(chat_history, **kwargs):
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logger.trace(f"{chat_history=}")
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logger.trace(f"{kwargs=}")
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@@ -149,14 +180,17 @@ def bot_stream(chat_history, **kwargs):
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# for elm in model.chat_stream(tokenizer, message, chat_history):
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model.generation_config.update(**kwargs)
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for elm in model.chat_stream(tokenizer, message, chat_history):
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chat_history[-1] = [message, elm]
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yield chat_history
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-
logger.debug(f"
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SYSTEM_PROMPT = "You are a helpful assistant."
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-
MAX_MAX_NEW_TOKENS =
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MAX_NEW_TOKENS = 256
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@@ -172,6 +206,72 @@ class Config:
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# stats_default = SimpleNamespace(llm=model, system_prompt=SYSTEM_PROMPT, config=Config())
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stats_default = SimpleNamespace(llm=None, system_prompt=SYSTEM_PROMPT, config=Config())
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theme = gr.themes.Soft(text_size="sm")
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with gr.Blocks(
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theme=theme,
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@@ -179,24 +279,69 @@ with gr.Blocks(
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css=css,
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) as block:
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stats = gr.State(stats_default)
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if not torch.cuda.is_available():
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raise gr.Error("GPU not available, cant run. Turn on GPU and restart")
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config = asdict(stats.value.config)
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def bot_stream_state(chat_history):
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logger.trace(f"{chat_history=}")
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yield from bot_stream(chat_history, **config)
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with gr.Accordion("🎈 Info", open=False):
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gr.Markdown(
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-
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elem_classes="xsmall",
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)
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@@ -367,5 +512,31 @@ with gr.Blocks(
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elem_classes=["disclaimer"],
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)
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if __name__ == "__main__":
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block.queue(max_size=8).launch(debug=True)
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import torch
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torch.cuda.empty_cache()
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model.chat(
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tokenizer: transformers.tokenization_utils.PreTrainedTokenizer,
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query: str,
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history: Optional[List[Tuple[str, str]]],
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system: str = 'You are a helpful assistant.',
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append_history: bool = True,
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stream: Optional[bool] = <object object at 0x7f905797ec20>,
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stop_words_ids: Optional[List[List[int]]] = None,
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**kwargs) -> Tuple[str, List[Tuple[str, str]]]
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)
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model.generation_config
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GenerationConfig {
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"chat_format": "chatml",
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"do_sample": true,
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"eos_token_id": 151643,
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"max_new_tokens": 512,
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"max_window_size": 6144,
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"pad_token_id": 151643,
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"top_k": 0,
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"top_p": 0.5,
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"transformers_version": "4.31.0",
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"trust_remote_code": true
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}
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"""
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# pylint: disable=line-too-long, invalid-name, no-member, redefined-outer-name, missing-function-docstring, missing-class-docstring, broad-except,
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import gc
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import time
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from collections import deque
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from dataclasses import asdict, dataclass
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from textwrap import dedent
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from types import SimpleNamespace
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from typing import List, Optional
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import gradio as gr
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import torch
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gc.collect()
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torch.cuda.empty_cache()
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if not torch.cuda.is_available():
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# raise gr.Error("GPU not available, cant run. Turn on GPU and retry")
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raise SystemExit("GPU not available, cant run. Turn on GPU and retry")
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model = gen_model(model_name)
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chat_history[:-1].append(["message", str(exc)])
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return chat_history
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def bot_stream(chat_history, **kwargs):
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logger.trace(f"{chat_history=}")
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logger.trace(f"{kwargs=}")
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# for elm in model.chat_stream(tokenizer, message, chat_history):
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model.generation_config.update(**kwargs)
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response = ""
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for elm in model.chat_stream(tokenizer, message, chat_history):
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chat_history[-1] = [message, elm]
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response = elm
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yield chat_history
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logger.debug(f"{model.generation_config=}")
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logger.debug(f"{response=}")
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SYSTEM_PROMPT = "You are a helpful assistant."
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MAX_MAX_NEW_TOKENS = 2048 # sequence length 2048
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MAX_NEW_TOKENS = 256
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# stats_default = SimpleNamespace(llm=model, system_prompt=SYSTEM_PROMPT, config=Config())
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stats_default = SimpleNamespace(llm=None, system_prompt=SYSTEM_PROMPT, config=Config())
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# input max_new_tokens temperature repetition_penalty top_k top_p system_prompt history
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def api_fn( # pylint: disable=too-many-arguments
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input_text: Optional[str],
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# max_length: int = 256,
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max_new_tokens: int = stats_default.config.max_new_tokens,
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temperature: float = stats_default.config.temperature,
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repetition_penalty: float = stats_default.config.repetition_penalty,
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top_k: int = stats_default.config.top_k,
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top_p: int = stats_default.config.top_p,
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system_prompt: Optional[str] = None,
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history: Optional[List[str]] = None,
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):
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if input_text is None:
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input_text = ""
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try:
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input_text = str(input_text).strip()
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except Exception as exc:
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logger.error(exc)
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input_text = ""
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if not input_text:
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return ""
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if history is None:
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history = []
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try:
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temperature = float(temperature)
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except Exception:
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temperature = stats_default.config.temperature
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if system_prompt is None:
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system_prompt = stats_default.system_prompt
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# if max_length < 10: max_length = 4096
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if max_new_tokens < 10:
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max_new_tokens = stats_default.config.max_new_tokens
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if top_p < 0.1 or top_p > 1:
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top_p = stats_default.config.top_p
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if temperature <= 0.5:
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temperature = stats_default.config.temperature
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_ = {
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"max_new_tokens": max_new_tokens,
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"temperature": temperature,
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"repetition_penalty": repetition_penalty,
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"top_k": top_k,
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"top_p": top_p,
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}
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model.generation_config.update(**_)
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try:
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res, _ = model.chat(
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tokenizer,
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input_text,
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history=history,
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# max_length=max_length,
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append_history=False,
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)
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# logger.debug(f"{res=} \n{_=}")
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except Exception as exc:
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logger.error(f"{exc=}")
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res = str(exc)
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logger.debug(f"api {model.generation_config=}")
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logger.debug(f"api {res=}")
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return res
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theme = gr.themes.Soft(text_size="sm")
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with gr.Blocks(
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theme=theme,
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css=css,
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) as block:
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stats = gr.State(stats_default)
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# would this reset model?
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model.generation_config = GenerationConfig.from_pretrained(
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model_name,
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trust_remote_code=True,
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)
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config = asdict(stats.value.config)
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def bot_stream_state(chat_history):
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logger.trace(f"{chat_history=}")
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yield from bot_stream(chat_history, **config)
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with gr.Accordion("🎈 Info", open=False):
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gr.Markdown(
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dedent(
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f"""
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## {model_name.lower()}
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* temperature range: .51 and up; higher temperature implies more random outputs. Suggested temperature for chatting and creative writing is around 1.1 while it should be set to 0.51-1.0 for summerizing and translation for example.
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* Set `repetition_penalty` to 2.1 or higher for a chatty conversation (more unpredictable and undesirable output). Lower it to 1.1 or smaller if more focused anwsers are desired (for example for translations or fact-oriented queries).
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* Smaller `top_k` probably will result in smoothier sentences.
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(`top_k=0` is equivalent to `top_k` equal to very very big though.) Consult `transformers` documentation for more details.
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* If you inadvertanyl messed up the parameters or the model, reset it in Advanced Options or reload the browser.
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<p></p>
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An api is available at, well, https://mikeee-qwen-7b-chat.hf.space/, e.g. in python
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```python
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from gradio_client import Client
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client = Client("https://7cff5e13976c7ba889.gradio.live/")
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result = client.predict(
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"你好!", # user prompt
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256, # max_new_tokens
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0.951, # temperature
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1.1, # repetition_penalty
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0, # top_k
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0.9, # top_p
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"You are a help assistant", # system_prompt
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None, # history
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api_name="/api"
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)
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print(result)
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```
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or in javascript
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```js
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import {{ client }} from "@gradio/client";
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const app = await client("https://mikeee-qwen-7b-chat.hf.space/");
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const result = await app.predict("api", [...]);
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console.log(result.data);
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```
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Check documentation and examples by clicking `Use via API` at the very bottom of this page.
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<p></p>
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Most examples are meant for another model.
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You probably should try to test
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some related prompts."""
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),
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elem_classes="xsmall",
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)
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elem_classes=["disclaimer"],
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)
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with gr.Accordion("For Chat/Translation API", open=False, visible=False):
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input_text = gr.Text()
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api_history = gr.Chatbot(value=[])
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api_btn = gr.Button("Go", variant="primary")
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out_text = gr.Text()
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# api_fn args order
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# input_text max_new_tokens temperature repetition_penalty top_k top_p system_prompt history
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api_btn.click(
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api_fn,
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[
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input_text,
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max_new_tokens,
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temperature,
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repetition_penalty,
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top_k,
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top_p,
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system_prompt,
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api_history, # dont know how to pass this in gradio_client.Client calls
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],
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out_text,
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api_name="api",
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)
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if __name__ == "__main__":
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logger.info("Just record start time")
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block.queue(max_size=8).launch(debug=True)
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