Upload handler.py with huggingface_hub
Browse files- handler.py +8 -13
handler.py
CHANGED
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@@ -52,12 +52,12 @@ class EndpointHandler:
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", {})
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# Default generation parameters
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max_new_tokens = parameters.get("max_new_tokens", 512)
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# Tokenize input
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input_ids = self.tokenizer(
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@@ -67,20 +67,15 @@ class EndpointHandler:
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max_length=self.model.config.max_position_embeddings - max_new_tokens
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).input_ids.to(self.model.device)
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# Generate response with
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with torch.no_grad():
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outputs = self.model.generate(
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input_ids,
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max_new_tokens=max_new_tokens,
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top_k=50, # Add top_k for stability
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do_sample=do_sample,
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repetition_penalty=repetition_penalty,
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pad_token_id=self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id,
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eos_token_id=self.tokenizer.eos_token_id,
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bad_words_ids=None, # Ensure no bad words restriction causing issues
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min_length=1, # Ensure at least 1 token is generated
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)
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# Decode output
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", {})
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# Default generation parameters - use greedy decoding to avoid sampling issues
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max_new_tokens = parameters.get("max_new_tokens", 512)
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# Use greedy decoding (do_sample=False) to avoid probability tensor issues
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# This is more stable for models with potential embedding issues
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do_sample = False # Force greedy decoding
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# Tokenize input
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input_ids = self.tokenizer(
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max_length=self.model.config.max_position_embeddings - max_new_tokens
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).input_ids.to(self.model.device)
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# Generate response with greedy decoding (no sampling)
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with torch.no_grad():
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outputs = self.model.generate(
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input_ids,
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max_new_tokens=max_new_tokens,
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do_sample=False, # Greedy decoding - most stable
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num_beams=1, # No beam search for speed
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pad_token_id=self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id,
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eos_token_id=self.tokenizer.eos_token_id,
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)
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# Decode output
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