Bepitic/DM-falcon-7b-shared

to use : code:

PEFT_MODEL = "Bepitic/DM-falcon-7b-shared"
config = PeftConfig.from_pretrained(PEFT_MODEL)
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    return_dict=True,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
tokenizer.pad_token = tokenizer.eos_token

model = PeftModel.from_pretrained(model, PEFT_MODEL)

generation_config = model.generation_config
generation_config.max_new_tokens = 200
generation_config.temperature = 0.7
generation_config.top_p = 0.7
generation_config.num_return_sequences = 1
generation_config.pad_token_id = tokenizer.eos_token_id
generation_config.eos_token_id = tokenizer.eos_token_id

# new cell colab
%%time
device = "cuda:0"

prompt = """
<human>: Explain how Aelar Windrider a Human tried to Attack with a melee weapon of Extremely Easy difficulty and got an Catastrophic Failure.
<assistant>:
""".strip()

encoding = tokenizer(prompt, return_tensors="pt").to(device)
with torch.inference_mode():
  outputs = model.generate(
      input_ids = encoding.input_ids,
      attention_mask = encoding.attention_mask,
      generation_config = generation_config
  )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))