tessl install tessl/pypi-vllm@0.10.0A high-throughput and memory-efficient inference and serving engine for LLMs
Agent Success
Agent success rate when using this tile
69%
Improvement
Agent success rate improvement when using this tile compared to baseline
1.33x
Baseline
Agent success rate without this tile
52%
{
"context": "This evaluation assesses how effectively the engineer uses vLLM's model loading and initialization capabilities to implement a model configuration service with different deployment modes. The focus is on proper use of the LLM class constructor and its configuration parameters.",
"type": "weighted_checklist",
"checklist": [
{
"name": "LLM Class Usage",
"description": "Uses the vLLM LLM class to initialize models. The solution should import and instantiate the LLM class with appropriate model parameter.",
"max_score": 20
},
{
"name": "GPU Memory Configuration",
"description": "Correctly uses the gpu_memory_utilization parameter in the LLM constructor. Local mode should set it to 0.5, production mode should set it to 0.9.",
"max_score": 25
},
{
"name": "Load Format Specification",
"description": "Properly uses the load_format parameter to specify safetensors format for testing mode. Should pass load_format='safetensors' or load_format='auto' to the LLM constructor.",
"max_score": 20
},
{
"name": "Model Path Handling",
"description": "Correctly passes the model_path parameter to the LLM constructor's model parameter for all modes, supporting both local paths and HuggingFace identifiers.",
"max_score": 20
},
{
"name": "Error Handling",
"description": "Implements proper validation to raise ValueError for invalid mode values before attempting model initialization.",
"max_score": 15
}
]
}