Upload 21 files

config.json

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+{
+  "_name_or_path": "decapoda-research/llama-30b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "custom_pipeline": {
+    "text-generation": {
+      "impl": "h2oai_pipeline.H2OTextGenerationPipeline",
+      "pt": "AutoModelForCausalLM"
+    }
+  },
+  "eos_token_id": 1,
+  "hidden_act": "silu",
+  "hidden_size": 6656,
+  "initializer_range": 0.02,
+  "intermediate_size": 17920,
+  "max_position_embeddings": 2048,
+  "max_sequence_length": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 52,
+  "num_hidden_layers": 60,
+  "pad_token_id": -1,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.28.1"
+}

h2oai_pipeline.py

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+from transformers import TextGenerationPipeline
+from transformers.pipelines.text_generation import ReturnType
+
+human = "<human>:"
+bot = "<bot>:"
+
+# human-bot interaction like OIG dataset
+prompt = """{human} {instruction}
+{bot}""".format(
+    human=human,
+    instruction="{instruction}",
+    bot=bot,
+)
+
+
+class H2OTextGenerationPipeline(TextGenerationPipeline):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def preprocess(self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs):
+        prompt_text = prompt.format(instruction=prompt_text)
+        return super().preprocess(prompt_text, prefix=prefix, handle_long_generation=handle_long_generation,
+                                  **generate_kwargs)
+
+    def postprocess(self, model_outputs, return_type=ReturnType.FULL_TEXT, clean_up_tokenization_spaces=True):
+        records = super().postprocess(model_outputs, return_type=return_type,
+                                      clean_up_tokenization_spaces=clean_up_tokenization_spaces)
+        for rec in records:
+            rec['generated_text'] = rec['generated_text'].split(bot)[1].strip().split(human)[0].strip()
+        return records

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