mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-28 15:18:26 +01:00
78203641fe
* server : added with_pieces functionality to /tokenize endpoint * server : Add tokenize with pieces tests to server.feature * Handle case if tokenizer splits along utf8 continuation bytes * Add example of token splitting * Remove trailing ws * Fix trailing ws * Maybe fix ci * maybe this fix windows ci? --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
121 lines
5.1 KiB
Gherkin
121 lines
5.1 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 file test-model.gguf
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And a model alias tinyllama-2
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And BOS token is 1
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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 256 KV cache size
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And 32 as batch size
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And 2 slots
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And 64 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 the completion is <truncated> truncated
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And <n_prompt> prompt tokens are processed
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And prometheus metrics are exposed
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And metric llamacpp:tokens_predicted is <n_predicted>
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Examples: Prompts
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| prompt | n_predict | re_content | n_prompt | n_predicted | truncated |
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| I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not |
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| Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 46 | 64 | not |
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Scenario: Completion prompt truncated
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Given a prompt:
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"""
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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
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Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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"""
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And a completion request with no api error
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Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl
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And the completion is truncated
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And 109 prompt tokens are processed
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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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And <n_prompt> prompt tokens are processed
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And the completion is <truncated> truncated
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Examples: Prompts
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| model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated |
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| llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not |
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| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | |
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Scenario Outline: OAI Compatibility w/ response format
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Given a model test
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And a system prompt test
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And a user prompt test
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And a response format <response_format>
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And 10 max tokens to predict
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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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| response_format | n_predicted | re_content |
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| {"type": "json_object", "schema": {"const": "42"}} | 6 | "42" |
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| {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10 | \[ -300 \] |
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| {"type": "json_object"} | 10 | \{ " Jacky. |
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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 detokenized
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And tokens do not begin with BOS
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Scenario: Tokenize w/ BOS
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Given adding special tokens
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When tokenizing:
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"""
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What is the capital of Germany?
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"""
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Then tokens begin with BOS
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Given first token is removed
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Then tokens can be detokenized
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Scenario: Tokenize with pieces
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When tokenizing with pieces:
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"""
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What is the capital of Germany?
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媽
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"""
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Then tokens are given with pieces
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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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