Falcon-7b-instruct ALWAYS returns SHORT ANSWERS on inference endpoint
Hey,I put the falcon-7b-instruct on huggingface inference endpoint.The issue I have is that the output it gives me is very short. Even when prompted: ‘Write a 300 word poem’ or given a question plus document prompt, it returns answers (60 to 100 tokens long ~ 10-15 words).Is there something fundamentally wrong?Many thanks,A.P.S.I changed nothing in the tiiuae/falcon-7b-instruct repo apart from the below:handler.pyimport torchfrom typing import Any, Dictfrom transformers import AutoModelForCausalLM, AutoTokenizerclass EndpointHandler:def __init__(self, path=""):
    self.tokenizer = AutoTokenizer.from_pretrained(path)
    self.model = AutoModelForCausalLM.from_pretrained(
        path, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True
    )
    self.device = "cuda" if torch.cuda.is_available() else "cpu"
    self.max_length = 4096
    self.max_new_tokens = 4096
    self.top_k = 100
    self.top_p = 0.95
    self.temperature = 0.9

def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
    inputs = data.pop("inputs", data)
    parameters = data.pop("parameters", {})

    inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)

    parameters["max_length"] = self.max_length
    parameters["max_new_tokens"] = self.max_new_tokens
    parameters["top_k"] = self.top_k
    parameters["top_p"] = self.top_p
    parameters["temperature"] = self.temperature
    outputs = self.model.generate(**inputs, **parameters)

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

    return [{"generated_text": prediction}]
2023-09-05T08:17:07Z
facing same issue with falconsometimes it returns one liners and when it does not know the answer, it generates some random question-answer pairs as response