mirror of
https://github.com/ggerganov/llama.cpp.git
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2002bc96bf
* server : refactoring (wip) * server : remove llava/clip objects from build * server : fix empty prompt handling + all slots idle logic * server : normalize id vars * server : code style * server : simplify model chat template validation * server : code style * server : minor * llama : llama_chat_apply_template support null buf * server : do not process embedding requests when disabled * server : reorganize structs and enums + naming fixes * server : merge oai.hpp in utils.hpp * server : refactor system prompt update at start * server : disable cached prompts with self-extend * server : do not process more than n_batch tokens per iter * server: tests: embeddings use a real embeddings model (#5908) * server, tests : bump batch to fit 1 embedding prompt * server: tests: embeddings fix build type Debug is randomly failing (#5911) * server: tests: embeddings, use different KV Cache size * server: tests: embeddings, fixed prompt do not exceed n_batch, increase embedding timeout, reduce number of concurrent embeddings * server: tests: embeddings, no need to wait for server idle as it can timout * server: refactor: clean up http code (#5912) * server : avoid n_available var ggml-ci * server: refactor: better http codes * server : simplify json parsing + add comment about t_last * server : rename server structs * server : allow to override FQDN in tests ggml-ci * server : add comments --------- Co-authored-by: Pierrick Hymbert <pierrick.hymbert@gmail.com>
64 lines
2.5 KiB
Gherkin
64 lines
2.5 KiB
Gherkin
@llama.cpp
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@server
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Feature: llama.cpp server
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Background: Server startup
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Given a server listening on localhost:8080
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And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
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And a model alias tinyllama-2
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And 42 as server seed
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# KV Cache corresponds to the total amount of tokens
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# that can be stored across all independent sequences: #4130
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# see --ctx-size and #5568
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And 32 KV cache size
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And 512 as batch size
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And 1 slots
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And embeddings extraction
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And 32 server max tokens to predict
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And prometheus compatible metrics exposed
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Then the server is starting
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Then the server is healthy
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Scenario: Health
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Then the server is ready
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And all slots are idle
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Scenario Outline: Completion
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Given a prompt <prompt>
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And <n_predict> max tokens to predict
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And a completion request with no api error
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Then <n_predicted> tokens are predicted matching <re_content>
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And prometheus metrics are exposed
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Examples: Prompts
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| prompt | n_predict | re_content | n_predicted |
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| I believe the meaning of life is | 8 | (read\|going)+ | 8 |
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| Write a joke about AI | 64 | (park\|friends\|scared\|always)+ | 32 |
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Scenario Outline: OAI Compatibility
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Given a model <model>
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And a system prompt <system_prompt>
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And a user prompt <user_prompt>
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And <max_tokens> max tokens to predict
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And streaming is <enable_streaming>
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Given an OAI compatible chat completions request with no api error
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Then <n_predicted> tokens are predicted matching <re_content>
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Examples: Prompts
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| model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming |
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| llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled |
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| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled |
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Scenario: Tokenize / Detokenize
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When tokenizing:
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"""
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What is the capital of France ?
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"""
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Then tokens can be detokenize
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Scenario: Models available
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Given available models
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Then 1 models are supported
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Then model 0 is identified by tinyllama-2
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Then model 0 is trained on 128 tokens context
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