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@@ -31,7 +31,7 @@ def main(
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temperature: float=1.0, # [optional] The value used to modulate the next token probabilities.
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top_k: int=50, # [optional] The number of highest probability vocabulary tokens to keep for top-k-filtering.
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repetition_penalty: float=1.0, #The parameter for repetition penalty. 1.0 means no penalty.
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- length_penalty: int=1, #[optional] Exponential penalty to the length that is used with beam-based generation.
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+ length_penalty: int=1, #[optional] Exponential penalty to the length that is used with beam-based generation.
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enable_azure_content_safety: bool=False, # Enable safety check with Azure content safety api
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enable_sensitive_topics: bool=False, # Enable check for sensitive topics using AuditNLG APIs
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enable_salesforce_content_safety: bool=True, # Enable safety check with Salesforce safety flan t5
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@@ -98,12 +98,12 @@ def main(
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top_k=top_k,
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repetition_penalty=repetition_penalty,
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length_penalty=length_penalty,
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- **kwargs
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+ **kwargs
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)
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e2e_inference_time = (time.perf_counter()-start)*1000
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print(f"the inference time is {e2e_inference_time} ms")
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output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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-
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+
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# Safety check of the model output
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safety_results = [check(output_text, agent_type=AgentType.AGENT, user_prompt=user_prompt) for check in safety_checker]
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are_safe = all([r[1] for r in safety_results])
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@@ -156,7 +156,7 @@ def main(
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label="Output",
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)
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],
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- title="Llama2 Playground",
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+ title="Meta Llama3 Playground",
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description="https://github.com/facebookresearch/llama-recipes",
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).queue().launch(server_name="0.0.0.0", share=True)
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HamidShojanazeri