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- Drop single-VM default hyperstack-vm.toml and @config_path/@config_explicit machinery
- Add global --vm flag (default: 1) mapping to hyperstack-vm1.toml and/or hyperstack-vm2.toml
- Fold create-both and delete-both into create/delete --vm both
- Teach status, watch, test, model to accept --vm (default: 1)
- Update help text and README/AGENTS/fish abbreviations accordingly
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Move the former hyperstack-vm1-coder.toml to hyperstack-vm1.toml as the
standard VM1 profile (Qwen3-Coder-Next on single GPU). Preserve the
dual-H100 Nemotron-3-Super stack as hyperstack-vm1-nemotron.toml. Point
create-both at hyperstack-vm1.toml and refresh README for current defaults.
Made-with: Cursor
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Adds docker_image and pre_start_cmd config fields to config.rb and
provisioning.rb so the Gemma 4 31B workarounds are baked in:
- docker_image = "vllm/vllm-openai:nightly" (stable lacks Gemma 4 support)
- pre_start_cmd = "pip install -q transformers==5.5.0" (stable pins <5)
- extra_docker_env = ["CUDA_VISIBLE_DEVICES=0"] (required with --entrypoint bash)
When pre_start_cmd is set, the provisioner switches to --entrypoint bash and
chains the patch command before launching vLLM, so create-both works end-to-end
without manual container replacement.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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VM1 (hyperstack-vm1-coder.toml, renamed from hyperstack-vm1-gptoss.toml):
- Default model switched from gpt-oss-120b to qwen3-coder-next
- Config file renamed to reflect actual default model
VM2 (hyperstack-vm2.toml):
- Default model switched from qwen3-coder-next to Gemma 4 31B AWQ
- Uses vLLM nightly image + transformers==5.5.0 workaround: Gemma 4
architecture is registered in transformers 5.x but vLLM stable pins <5
- max_model_len=131072 (128K context); KV cache fills ~95% of H100-80GB VRAM
- Added gemma4-31b preset
watcher.rb:
- Add loading_status field to VmSnapshot to show live model-load progress
(last relevant log line during startup instead of generic "loading" message)
- fetch_vm_stats now captures both Engine 0 stats and loading-phase log lines
in a single SSH call using a shell variable to avoid two docker log invocations
- clean_log_line() strips vLLM PID/timestamp prefix for readable display
cli.rb: update all hardcoded hyperstack-vm1-gptoss.toml references to
hyperstack-vm1-coder.toml
hypr.fish: replace pi-hyperstack-nemotron with pi-hyperstack-coder (VM1),
add pi-hyperstack-gemma4 (VM2)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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- Add --reasoning-parser openai_gptoss to gpt-oss-120b vLLM config in
all three toml files; extracts <|channel|>analysis thinking blocks
into reasoning_content in API responses
- Mark gpt-oss-120b as reasoning: true in pi/agent/models.json for all
three providers (hyperstack, hyperstack1, hyperstack2)
- Update vm1 state file
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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- hyperstack-vm1/vm2.toml: reduce nemotron-super max_model_len 262144→131072
and add --enforce-eager to disable CUDA graph capture (~3-4 GB overhead).
Nemotron 120B weights (~60 GB) leave too little VRAM headroom for KV cache
allocation and CUDA graph buffers at 262K context on a single A100 80GB.
131K context with eager mode is stable. README VRAM table updated to match.
- hyperstack.fish: add hyperstack-create/delete/test and hyperstack-create/delete-both
abbreviations for VM lifecycle management alongside the existing pi-* aliases.
- README.md: add "Automated setup reference" section with single-VM and two-VM
command flows before the manual vLLM Docker setup section.
End-to-end tested: single VM (GPT-OSS 120B), dual VM (Nemotron + Qwen3-Coder),
pi queries on all three models — all passed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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