A high-throughput and memory-efficient inference and serving engine for LLMs
Overall
score
69%
Evaluation — 69%
↑ 1.33xAgent success when using this tile
Synchronous and Asynchronous Engines
LLMEngine Initialization
0%
0%
add_request() Usage
0%
0%
step() Execution
0%
0%
has_unfinished_requests() Check
0%
0%
abort_request() Implementation
0%
0%
AsyncLLMEngine Initialization
0%
0%
Async Generator Pattern
100%
100%
AsyncLLMEngine.generate() Streaming
0%
0%
Text Generation and Completion
LLM initialization
0%
100%
generate() method usage
0%
100%
SamplingParams configuration
0%
100%
Output extraction
0%
93%
Error handling
100%
100%
Basic Memory Management
LLM class import
100%
100%
LLM initialization
100%
100%
gpu_memory_utilization parameter
100%
60%
swap_space parameter
100%
60%
Combined configuration
100%
80%
Default configuration
100%
100%
Model Loading and Initialization
LLM Class Usage
100%
90%
GPU Memory Configuration
100%
100%
Load Format Specification
100%
100%
Model Path Handling
100%
100%
Error Handling
100%
100%
Beam Search and Advanced Sampling
LLM Initialization
0%
0%
Beam Search Method
0%
0%
Beam Width Configuration
0%
0%
Length Penalty
0%
0%
Max Tokens Control
0%
0%
Temperature Parameter
0%
0%
Vocabulary Restriction
0%
0%
Output Processing
0%
0%
Sampling Parameters
LLM Initialization
0%
100%
SamplingParams Import
50%
100%
Default Generation
20%
100%
Temperature Parameter
20%
100%
Top-p Parameter
20%
100%
Multiple Completions
13%
100%
Seed Parameter
30%
100%
Max Tokens
60%
100%
Generation Method
0%
100%
LoRA Adapters and Multi-LoRA Support
LLM Initialization
100%
0%
Max LoRAs Configuration
100%
0%
Max LoRA Rank
100%
0%
LoRA Request Object
100%
0%
Adapter in Generate
100%
0%
Base Model Generation
100%
30%
Chat-based Generation
LLM Initialization
100%
80%
Chat Method Usage
0%
100%
Message Format
0%
100%
SamplingParams Configuration
100%
100%
Response Extraction
100%
100%
Multi-Turn Handling
0%
100%
Multi-Modal Support
LLM initialization
50%
100%
Multi-modal prompt format
0%
100%
Image loading
20%
100%
Single image processing
10%
100%
Multiple image handling
13%
100%
Error handling
100%
100%
Custom Attention Mechanisms
LLM Class Import
100%
100%
SamplingParams Import
100%
100%
LLM Initialization
100%
100%
Attention Backend Configuration
100%
100%
Default Backend Handling
100%
100%
Text Generation
100%
100%
SamplingParams Usage
100%
100%
Output Extraction
100%
100%
Install with Tessl CLI
npx tessl i tessl/pypi-vllmTable of Contents