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config.json ADDED
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+ {
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+ "_name_or_path": "decapoda-research/llama-30b-hf",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "bos_token_id": 0,
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+ "custom_pipeline": {
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+ "text-generation": {
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+ "impl": "h2oai_pipeline.H2OTextGenerationPipeline",
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+ "pt": "AutoModelForCausalLM"
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+ }
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+ },
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+ "eos_token_id": 1,
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+ "hidden_act": "silu",
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+ "hidden_size": 6656,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 17920,
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+ "max_position_embeddings": 2048,
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+ "max_sequence_length": 2048,
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+ "model_type": "llama",
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+ "num_attention_heads": 52,
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+ "num_hidden_layers": 60,
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+ "pad_token_id": -1,
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+ "rms_norm_eps": 1e-06,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.28.1",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 0,
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+ "eos_token_id": 1,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.28.1"
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+ }
h2oai_pipeline.py ADDED
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+ from transformers import TextGenerationPipeline
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+ from transformers.pipelines.text_generation import ReturnType
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+
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+ human = "<human>:"
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+ bot = "<bot>:"
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+
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+ # human-bot interaction like OIG dataset
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+ prompt = """{human} {instruction}
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+ {bot}""".format(
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+ human=human,
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+ instruction="{instruction}",
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+ bot=bot,
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+ )
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+
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+
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+ class H2OTextGenerationPipeline(TextGenerationPipeline):
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+ def __init__(self, *args, **kwargs):
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+ super().__init__(*args, **kwargs)
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+
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+ def preprocess(self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs):
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+ prompt_text = prompt.format(instruction=prompt_text)
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+ return super().preprocess(prompt_text, prefix=prefix, handle_long_generation=handle_long_generation,
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+ **generate_kwargs)
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+
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+ def postprocess(self, model_outputs, return_type=ReturnType.FULL_TEXT, clean_up_tokenization_spaces=True):
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+ records = super().postprocess(model_outputs, return_type=return_type,
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+ clean_up_tokenization_spaces=clean_up_tokenization_spaces)
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+ for rec in records:
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+ rec['generated_text'] = rec['generated_text'].split(bot)[1].strip().split(human)[0].strip()
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+ return records
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