Upload HybridQAPipeline

added_tokens.json

@@ -0,0 +1,13 @@
+{
+  "<|assistant|>": 32001,
+  "<|endoftext|>": 32000,
+  "<|end|>": 32007,
+  "<|placeholder1|>": 32002,
+  "<|placeholder2|>": 32003,
+  "<|placeholder3|>": 32004,
+  "<|placeholder4|>": 32005,
+  "<|placeholder5|>": 32008,
+  "<|placeholder6|>": 32009,
+  "<|system|>": 32006,
+  "<|user|>": 32010
+}

config.json

@@ -1,70 +1,35 @@
 {
-  "_name_or_path": "MaRiOrOsSi/t5-base-finetuned-question-answering",
+  "_name_or_path": "microsoft/Phi-3-mini-4k-instruct",
   "architectures": [
-    "T5ForConditionalGeneration"
+    "Phi3ForCausalLM"
   ],
-  "classifier_dropout": 0.0,
-  "d_ff": 3072,
-  "d_kv": 64,
-  "d_model": 768,
-  "decoder_start_token_id": 0,
-  "dense_act_fn": "relu",
-  "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "feed_forward_proj": "relu",
-  "initializer_factor": 1.0,
-  "is_encoder_decoder": true,
-  "is_gated_act": false,
-  "layer_norm_epsilon": 1e-06,
-  "model_type": "t5",
-  "n_positions": 512,
-  "num_decoder_layers": 12,
-  "num_heads": 12,
-  "num_layers": 12,
-  "output_past": true,
-  "pad_token_id": 0,
-  "relative_attention_max_distance": 128,
-  "relative_attention_num_buckets": 32,
-  "task_specific_params": {
-    "summarization": {
-      "early_stopping": true,
-      "length_penalty": 2.0,
-      "max_length": 200,
-      "min_length": 30,
-      "no_repeat_ngram_size": 3,
-      "num_beams": 4,
-      "prefix": "summarize: "
-    },
-    "translation_en_to_de": {
-      "early_stopping": true,
-      "max_length": 300,
-      "num_beams": 4,
-      "prefix": "translate English to German: "
-    },
-    "translation_en_to_fr": {
-      "early_stopping": true,
-      "max_length": 300,
-      "num_beams": 4,
-      "prefix": "translate English to French: "
-    },
-    "translation_en_to_ro": {
-      "early_stopping": true,
-      "max_length": 300,
-      "num_beams": 4,
-      "prefix": "translate English to Romanian: "
-    }
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "microsoft/Phi-3-mini-4k-instruct--configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "microsoft/Phi-3-mini-4k-instruct--modeling_phi3.Phi3ForCausalLM"
   },
+  "bos_token_id": 1,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 4096,
+  "model_type": "phi3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": 32000,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 2047,
+  "tie_word_embeddings": false,
   "torch_dtype": "float32",
   "transformers_version": "4.40.2",
   "use_cache": true,
-  "vocab_size": 32128,
-  "custom_pipelines": {
-    "question-answering": {
-      "impl": "hybrid_pipe.HybridQAPipeline",
-      "pt": [
-        "AutoModelForQuestionAnswering"
-      ],
-      "tf": ["TFAutoModelForQuestionAnswering"]
-    }
-  }
+  "vocab_size": 32064
 }

generation_config.json

@@ -1,7 +1,11 @@
 {
   "_from_model_config": true,
-  "decoder_start_token_id": 0,
-  "eos_token_id": 1,
-  "pad_token_id": 0,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    32000,
+    32001,
+    32007
+  ],
+  "pad_token_id": 32000,
   "transformers_version": "4.40.2"
 }

hybrid_pipe.py

@@ -3,8 +3,8 @@ from transformers import QuestionAnsweringPipeline, PretrainedConfig
 
 class HybridQAPipeline(QuestionAnsweringPipeline):
     def __init__(self, model=None, tokenizer=None, **kwargs):
-        extractive_id = "datarpit/distilbert-base-uncased-finetuned-natural-questions"
-        generative_id = "MaRiOrOsSi/t5-base-finetuned-question-answering"
+        extractive_id = "google-bert/bert-large-uncased-whole-word-masking-finetuned-squad"
+        generative_id = "microsoft/Phi-3-mini-4k-instruct"
         self.config = HybridQAConfig(extractive_id, generative_id)
         self.model = HybridQAModel(self.config)
         super().__init__(model=self.model, tokenizer=tokenizer, **kwargs)

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