model.py

import os
from threading import Thread
from typing import Iterator

import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
from huggingface_hub import login

model_id = 'jaymojnidar/Llama-2-7b-chat-hf-sharded-bf16-5GBMAX'

if not torch.cuda.is_available():
    tok = os.environ['HF_TOKEN']
    
    device_map = {
        "transformer.word_embeddings": "cpu",
        "transformer.word_embeddings_layernorm": "cpu",
        "lm_head": "cpu",
        "transformer.h": "cpu",
        "transformer.ln_f": "cpu",
        "model.layers": "cpu",
        "model.norm": "cpu",
        }
  
    login(new_session=True,
          write_permission=False,
          token=tok
          
          #, token="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"
          )
    quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True, load_in_8bit=True,llm_int8_threshold=200.0)

    config = AutoConfig.from_pretrained(model_id,
        use_auth_token=True)
    config.pretraining_tp = 1
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        config=config,
        quantization_config=quantization_config,
        torch_dtype=torch.float16,
        device_map=device_map,
        use_auth_token=True
    )
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(torch.device) #.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)