h2oai

/

h2ogpt-gm-oasst1-multilang-2048-falcon-7b

like 7

File <command-2149863258257856>:12
      2 from transformers import pipeline
      4 generate_text = pipeline(
      5     model="h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b",
      6     torch_dtype=torch.float16,
   (...)
      9     device_map = 'auto',
     10 )
---> 12 res = generate_text(
     13     "What is quantum tunnelling",
     14     min_new_tokens=2,
     15     max_new_tokens=1024,
     16     do_sample=False,
     17     num_beams=1,
     18     temperature=float(0.3),
     19     repetition_penalty=float(1.2),
     20     renormalize_logits=True
     21 )
     22 print(res[0]["generated_text"])

File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/transformers/pipelines/text_generation.py:201, in TextGenerationPipeline.__call__(self, text_inputs, **kwargs)
    160 def __call__(self, text_inputs, **kwargs):
    161     """
    162     Complete the prompt(s) given as inputs.
    163 
   (...)
    199           ids of the generated text.
    200     """
--> 201     return super().__call__(text_inputs, **kwargs)

File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/transformers/pipelines/base.py:1119, in Pipeline.__call__(self, inputs, num_workers, batch_size, *args, **kwargs)
   1111     return next(
   1112         iter(
   1113             self.get_iterator(
   (...)
   1116         )
   1117     )
   1118 else:
-> 1119     return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)

File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/transformers/pipelines/base.py:1125, in Pipeline.run_single(self, inputs, preprocess_params, forward_params, postprocess_params)
   1124 def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
-> 1125     model_inputs = self.preprocess(inputs, **preprocess_params)
   1126     model_outputs = self.forward(model_inputs, **forward_params)
   1127     outputs = self.postprocess(model_outputs, **postprocess_params)

File ~/.cache/huggingface/modules/transformers_modules/h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b/d5781acb7c1b1af6437449a28d8302acbcd08404/h2oai_pipeline.py:16, in H2OTextGenerationPipeline.preprocess(self, prompt_text, prefix, handle_long_generation, **generate_kwargs)
     12 def preprocess(
     13     self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs
     14 ):
     15     prompt_text = self.prompt.format(instruction=prompt_text)
---> 16     return super().preprocess(
     17         prompt_text,
     18         prefix=prefix,
     19         handle_long_generation=handle_long_generation,
     20         **generate_kwargs,
     21     )

File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/transformers/pipelines/text_generation.py:204, in TextGenerationPipeline.preprocess(self, prompt_text, prefix, handle_long_generation, **generate_kwargs)
    203 def preprocess(self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs):
--> 204     inputs = self.tokenizer(
    205         prefix + prompt_text, padding=False, add_special_tokens=False, return_tensors=self.framework
    206     )
    207     inputs["prompt_text"] = prompt_text
    209     if handle_long_generation == "hole":

TypeError: 'NoneType' object is not callable

I thought it was sentencepiece but the error still exists