Files

Gahow Wang 445e491123 Add vLLM v0.18.1 source tree with KV transfer abort fix

third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:

  vllm/v1/core/sched/scheduler.py:
    Replace fatal assert with graceful skip when KV transfer callback
    arrives for an already-aborted request during PD disaggregated serving.

Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-05-22 00:30:38 +08:00

configs

Add vLLM v0.18.1 source tree with KV transfer abort fix

2026-05-22 00:30:38 +08:00

__init__.py

Add vLLM v0.18.1 source tree with KV transfer abort fix

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conftest.py

Add vLLM v0.18.1 source tree with KV transfer abort fix

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README.md

Add vLLM v0.18.1 source tree with KV transfer abort fix

2026-05-22 00:30:38 +08:00

test_gpqa_correctness.py

Add vLLM v0.18.1 source tree with KV transfer abort fix

2026-05-22 00:30:38 +08:00

README.md

GPQA Evaluation using GPT-OSS

This directory contains GPQA evaluation tests using the GPT-OSS evaluation package and vLLM server.

Usage

Run tests with pytest (like buildkite)

# H200
pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py \
    --config-list-file=configs/models-h200.txt

# B200
pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py \
    --config-list-file=configs/models-b200.txt

Configuration Format

Model configs in configs/ directory use this YAML format:

model_name: "openai/gpt-oss-20b"
metric_threshold: 0.568          # Minimum expected accuracy
reasoning_effort: "low"          # Reasoning effort level (default: "low")
server_args: "--tensor-parallel-size 2"  # Server arguments
startup_max_wait_seconds: 1800   # Max wait for server startup (default: 1800)
env:                             # Environment variables (optional)
  SOME_VAR: "value"

The server_args field accepts any arguments that can be passed to vllm serve.

The env field accepts a dictionary of environment variables to set for the server process.

Adding New Models

Create a new YAML config file in the configs/ directory
Add the filename to the appropriate models-*.txt file

Tiktoken Encoding Files

The tiktoken encoding files required by the vLLM server are automatically downloaded from OpenAI's public blob storage on first run:

cl100k_base.tiktoken
o200k_base.tiktoken

Files are cached in the data/ directory. The TIKTOKEN_ENCODINGS_BASE environment variable is automatically set to point to this directory when running evaluations.