forked from phoenix/litellm-mirror
fix hf conversational task bug
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4 changed files with 11 additions and 0 deletions
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@ -56,6 +56,7 @@ def completion(
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if task == "conversational":
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if task == "conversational":
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inference_params = copy.deepcopy(optional_params)
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inference_params = copy.deepcopy(optional_params)
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inference_params.pop("details")
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inference_params.pop("details")
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inference_params.pop("return_full_text")
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past_user_inputs = []
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past_user_inputs = []
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generated_responses = []
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generated_responses = []
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text = ""
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text = ""
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@ -420,6 +420,16 @@ def test_completion_azure_deployment_id():
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pytest.fail(f"Error occurred: {e}")
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pytest.fail(f"Error occurred: {e}")
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# test_completion_azure_deployment_id()
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# test_completion_azure_deployment_id()
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# def test_hf_conversational_task():
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# try:
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# messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}]
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# # e.g. Call 'facebook/blenderbot-400M-distill' hosted on HF Inference endpoints
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# response = completion(model="huggingface/facebook/blenderbot-400M-distill", messages=messages, task="conversational")
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# print(f"response: {response}")
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# test_hf_conversational_task()
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# Replicate API endpoints are unstable -> throw random CUDA errors -> this means our tests can fail even if our tests weren't incorrect.
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# Replicate API endpoints are unstable -> throw random CUDA errors -> this means our tests can fail even if our tests weren't incorrect.
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# def test_completion_replicate_llama_2():
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# def test_completion_replicate_llama_2():
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