Create model.py

model.py

@@ -0,0 +1,74 @@
+from threading import Thread
+from typing import Iterator
+
+import torch
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+model_id = 'meta-llama/Llama-2-13b-chat-hf'
+
+if torch.cuda.is_available():
+    config = AutoConfig.from_pretrained(model_id)
+    config.pretraining_tp = 1
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        config=config,
+        torch_dtype=torch.float16,
+        load_in_4bit=True,
+        device_map='auto'
+    )
+else:
+    model = None
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+
+def get_prompt(message: str, chat_history: list[tuple[str, str]],
+               system_prompt: str) -> str:
+    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
+    # The first user input is _not_ stripped
+    do_strip = False
+    for user_input, response in chat_history:
+        user_input = user_input.strip() if do_strip else user_input
+        do_strip = True
+        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
+    message = message.strip() if do_strip else message
+    texts.append(f'{message} [/INST]')
+    return ''.join(texts)
+
+
+def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
+    prompt = get_prompt(message, chat_history, system_prompt)
+    input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['input_ids']
+    return input_ids.shape[-1]
+
+
+def run(message: str,
+        chat_history: list[tuple[str, str]],
+        system_prompt: str,
+        max_new_tokens: int = 1024,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        top_k: int = 50) -> Iterator[str]:
+    prompt = get_prompt(message, chat_history, system_prompt)
+    inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
+
+    streamer = TextIteratorStreamer(tokenizer,
+                                    timeout=10.,
+                                    skip_prompt=True,
+                                    skip_special_tokens=True)
+    generate_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield ''.join(outputs)