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from ctransformers import AutoModelForCausalLM, AutoTokenizer
from loguru import logger
import os
def models():
return ["mistral-7b-openorca.Q5_K_M.gguf"]
def load():
# model = AutoModelForCausalLM.from_pretrained("TheBloke/OpenHermes-2.5-Mistral-7B-GGUF", model_file="openhermes-2.5-mistral-7b.Q4_K_M.gguf", model_type="mistral", gpu_layers=0, hf=True)
model = AutoModelForCausalLM.from_pretrained(
model_path_or_repo_id="TheBloke/Mistral-7B-OpenOrca-GGUF",
model_file="mistral-7b-openorca.Q5_K_M.gguf",
model_type="mistral",
hf=True,
temperature=0.7,
top_p=0.7,
top_k=50,
repetition_penalty=1.2,
context_length=32768,
max_new_tokens=2048,
threads=os.cpu_count(),
stream=True,
gpu_layers=0
)
tokenizer = AutoTokenizer.from_pretrained(model)
return (model, tokenizer)
model, tokenizer = load()
def ask(_, system_prompt, pre_prompt, question, temperature=0.7):
messages = [
{'role': 'system', 'content': f"{system_prompt} {pre_prompt}", },
{'role': 'user', 'content': f"{question}", },
]
logger.debug(f"<< transformers << {messages}")
inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
outputs = model.generate(inputs, max_length=200, temperature=temperature)
answer = tokenizer.batch_decode(outputs)[0]
logger.debug(f">> transformers >> {answer}")
return answer
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