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@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
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>
Examples: Prompts
| prompt | n_predict | re_content | n_predicted |
| I believe the meaning of life is | 8 | read | 8 |
| Write a joke about AI | 64 | (park<or>friends<or>scared)+ | 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
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