mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 10:54:36 +00:00
9731134296
* server: tests: add models endpoint scenario * server: /v1/models add some metadata * server: tests: add debug field in context before scenario * server: tests: download model from HF, add batch size * server: tests: add passkey test * server: tests: add group attention params * server: do not truncate prompt tokens if self-extend through group attention is enabled * server: logs: do not truncate log values * server: tests - passkey - first good working value of nga * server: tests: fix server timeout * server: tests: fix passkey, add doc, fix regex content matching, fix timeout * server: tests: fix regex content matching * server: tests: schedule slow tests on master * server: metrics: fix when no prompt processed * server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1 * server: tests: increase timeout for completion * server: tests: keep only the PHI-2 test * server: tests: passkey add a negative test
92 lines
3.2 KiB
Gherkin
92 lines
3.2 KiB
Gherkin
@llama.cpp
|
|
@server
|
|
Feature: llama.cpp server
|
|
|
|
Background: Server startup
|
|
Given a server listening on localhost:8080
|
|
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
|
|
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 512 as batch 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\|going)+ | 8 |
|
|
| Write a joke about AI | 64 | (park\|friends\|scared\|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\|what)+ | 8 | disabled |
|
|
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|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
|
|
|
|
Scenario: Models available
|
|
Given available models
|
|
Then 1 models are supported
|
|
Then model 0 is identified by tinyllama-2
|
|
Then model 0 is trained on 128 tokens context
|