from model import ExLlama, ExLlamaCache, ExLlamaConfig
from tokenizer import ExLlamaTokenizer
from generator import ExLlamaGenerator
import os, glob

# Directory containing model, tokenizer, generator

model_directory =  "/mnt/str/models/llama-13b-4bit-128g/"

# Locate files we need within that directory

tokenizer_path = os.path.join(model_directory, "tokenizer.model")
model_config_path = os.path.join(model_directory, "config.json")
st_pattern = os.path.join(model_directory, "*.safetensors")
model_path = glob.glob(st_pattern)

# Batched prompts

prompts = [
    "Once upon a time,",
    "I don't like to",
    "A turbo encabulator is a",
    "In the words of Mark Twain,"
]

# Create config, model, tokenizer and generator

config = ExLlamaConfig(model_config_path)               # create config from config.json
config.model_path = model_path                          # supply path to model weights file

model = ExLlama(config)                                 # create ExLlama instance and load the weights
tokenizer = ExLlamaTokenizer(tokenizer_path)            # create tokenizer from tokenizer model file

cache = ExLlamaCache(model, batch_size = len(prompts))  # create cache for inference
generator = ExLlamaGenerator(model, tokenizer, cache)   # create generator

# Configure generator

generator.disallow_tokens([tokenizer.eos_token_id])

generator.settings.token_repetition_penalty_max = 1.2
generator.settings.temperature = 0.95
generator.settings.top_p = 0.65
generator.settings.top_k = 100
generator.settings.typical = 0.5

# Generate, batched

for line in prompts:
    print(line)

output = generator.generate_simple(prompts, max_new_tokens = 200)

for line in output:
    print("---")
    print(line)