mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-04 09:32:00 +00:00
930b178026
* server: logs - always use JSON logger, add add thread_id in message, log task_id and slot_id * server : skip GH copilot requests from logging * server : change message format of server_log() * server : no need to repeat log in comment * server : log style consistency * server : fix compile warning * server : fix tests regex patterns on M2 Ultra * server: logs: PR feedback on log level * server: logs: allow to choose log format in json or plain text * server: tests: output server logs in text * server: logs switch init logs to server logs macro * server: logs ensure value json value does not raised error * server: logs reduce level VERBOSE to VERB to max 4 chars * server: logs lower case as other log messages * server: logs avoid static in general Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * server: logs PR feedback: change text log format to: LEVEL [function_name] message | additional=data --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
85 lines
3.0 KiB
Gherkin
85 lines
3.0 KiB
Gherkin
@llama.cpp
|
|
Feature: llama.cpp server
|
|
|
|
Background: Server startup
|
|
Given a server listening on localhost:8080
|
|
And a model file stories260K.gguf
|
|
And a model alias tinyllama-2
|
|
And 42 as server seed
|
|
# KV Cache corresponds to the total amount of tokens
|
|
# that can be stored across all independent sequences: #4130
|
|
# see --ctx-size and #5568
|
|
And 32 KV cache size
|
|
And 1 slots
|
|
And embeddings extraction
|
|
And 32 server max tokens to predict
|
|
And prometheus compatible metrics exposed
|
|
Then the server is starting
|
|
Then the server is healthy
|
|
|
|
Scenario: Health
|
|
Then the server is ready
|
|
And all slots are idle
|
|
|
|
Scenario Outline: Completion
|
|
Given a prompt <prompt>
|
|
And <n_predict> max tokens to predict
|
|
And a completion request with no api error
|
|
Then <n_predicted> tokens are predicted matching <re_content>
|
|
And prometheus metrics are exposed
|
|
|
|
Examples: Prompts
|
|
| prompt | n_predict | re_content | n_predicted |
|
|
| I believe the meaning of life is | 8 | (read<or>going)+ | 8 |
|
|
| Write a joke about AI | 64 | (park<or>friends<or>scared<or>always)+ | 32 |
|
|
|
|
Scenario Outline: OAI Compatibility
|
|
Given a model <model>
|
|
And a system prompt <system_prompt>
|
|
And a user prompt <user_prompt>
|
|
And <max_tokens> max tokens to predict
|
|
And streaming is <enable_streaming>
|
|
Given an OAI compatible chat completions request with no api error
|
|
Then <n_predicted> tokens are predicted matching <re_content>
|
|
|
|
Examples: Prompts
|
|
| model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming |
|
|
| llama-2 | Book | What is the best book | 8 | (Mom<or>what)+ | 8 | disabled |
|
|
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks<or>happy<or>bird)+ | 32 | enabled |
|
|
|
|
Scenario: Embedding
|
|
When embeddings are computed for:
|
|
"""
|
|
What is the capital of Bulgaria ?
|
|
"""
|
|
Then embeddings are generated
|
|
|
|
Scenario: OAI Embeddings compatibility
|
|
Given a model tinyllama-2
|
|
When an OAI compatible embeddings computation request for:
|
|
"""
|
|
What is the capital of Spain ?
|
|
"""
|
|
Then embeddings are generated
|
|
|
|
Scenario: OAI Embeddings compatibility with multiple inputs
|
|
Given a model tinyllama-2
|
|
Given a prompt:
|
|
"""
|
|
In which country Paris is located ?
|
|
"""
|
|
And a prompt:
|
|
"""
|
|
Is Madrid the capital of Spain ?
|
|
"""
|
|
When an OAI compatible embeddings computation request for multiple inputs
|
|
Then embeddings are generated
|
|
|
|
|
|
Scenario: Tokenize / Detokenize
|
|
When tokenizing:
|
|
"""
|
|
What is the capital of France ?
|
|
"""
|
|
Then tokens can be detokenize
|