### Model Description

<!-- Provide a longer summary of what this model is. -->

## Uses
<!-- gen_kwargs = {
        "max_new_tokens": 100,
        "top_k": 70,
        "top_p": 0.8,
        "do_sample": True,  
        "no_repeat_ngram_size": 2,
        "bos_token_id": tokenizer.bos_token_id,
        "eos_token_id": tokenizer.eos_token_id,
        "pad_token_id": tokenizer.pad_token_id,
        "temperature": 0.8,
        "use_cache": True,
        "repetition_penalty": 1.2,
        "num_return_sequences": 1
    }
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
ft = 'gpt-j-onlyk_v2'
tokenizer = AutoTokenizer.from_pretrained(ft)
model = AutoModelForCausalLM.from_pretrained(ft, torch_dtype=torch.float16, low_cpu_mem_usage=True)
model.to(device)

prepared = tokenizer.encode(inp, return_tensors='pt').to(model.device)
out = model.generate(input_ids=prepared, **gen_kwargs)
generated = tokenizer.decode(out[0])
-->