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* server: tests: init scenarios - health and slots endpoints - completion endpoint - OAI compatible chat completion requests w/ and without streaming - completion multi users scenario - multi users scenario on OAI compatible endpoint with streaming - multi users with total number of tokens to predict exceeds the KV Cache size - server wrong usage scenario, like in Infinite loop of "context shift" #3969 - slots shifting - continuous batching - embeddings endpoint - multi users embedding endpoint: Segmentation fault #5655 - OpenAI-compatible embeddings API - tokenize endpoint - CORS and api key scenario * server: CI GitHub workflow --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
22 lines
711 B
Gherkin
22 lines
711 B
Gherkin
# run with ./test.sh --tags wrong_usage
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@wrong_usage
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Feature: Wrong usage of llama.cpp server
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#3969 The user must always set --n-predict option
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# to cap the number of tokens any completion request can generate
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# or pass n_predict/max_tokens in the request.
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Scenario: Infinite loop
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Given a server listening on localhost:8080
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And a model file stories260K.gguf
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# Uncomment below to fix the issue
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#And 64 server max tokens to predict
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Then the server is starting
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Given a prompt:
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"""
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Go to: infinite loop
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"""
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# Uncomment below to fix the issue
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#And 128 max tokens to predict
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Given concurrent completion requests
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Then all prompts are predicted
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