From c11db6e8f92fc818a87d18998bec6e478a33a824 Mon Sep 17 00:00:00 2001 From: Paul Buetow Date: Sun, 24 May 2026 12:24:01 +0300 Subject: cleanup --- hyperstack-vm.toml | 197 ----------------------------------------------------- 1 file changed, 197 deletions(-) delete mode 100644 hyperstack-vm.toml (limited to 'hyperstack-vm.toml') diff --git a/hyperstack-vm.toml b/hyperstack-vm.toml deleted file mode 100644 index 88f2a19..0000000 --- a/hyperstack-vm.toml +++ /dev/null @@ -1,197 +0,0 @@ -[auth] -api_key_file = "~/.hyperstack" - -[hyperstack] -base_url = "https://infrahub-api.nexgencloud.com/v1" - -[state] -file = ".hyperstack-vm-state.json" - -[vm] -name_prefix = "hyperstack" -hostname = "hyperstack" -environment_name = "snonux-ollama" - -# A100-80GB is the cost-first default for gpt-oss-120b inference. -# Switch this to n3-H100x1 if you want safer throughput and compatibility headroom. -flavor_name = "n3-A100x1" -image_name = "Ubuntu Server 24.04 LTS R570 CUDA 12.8 with Docker" -assign_floating_ip = true -create_bootable_volume = false -enable_port_randomization = false -labels = ["gpt-oss-120b", "wireguard"] - -[ssh] -username = "ubuntu" -private_key_path = "~/.ssh/id_rsa" -hyperstack_key_name = "earth" -port = 22 -connect_timeout_sec = 10 - -[network] -wireguard_udp_port = 56710 -wireguard_subnet = "192.168.3.0/24" -# Secure default: "auto" resolves your current public egress IP to /32 at runtime. -# Override with explicit CIDRs if you deploy from multiple networks or want broader access. -allowed_ssh_cidrs = ["auto"] -allowed_wireguard_cidrs = ["auto"] -# Port 11434 is shared by both Ollama and vLLM for firewall compatibility. -ollama_port = 11434 - -[bootstrap] -enable_guest_bootstrap = true -install_wireguard = true -configure_ufw = true -configure_ollama_host = false - -[ollama] -# Disabled in favour of vLLM; set install = true to switch back to Ollama. -install = false -models_dir = "/ephemeral/ollama/models" -listen_host = "0.0.0.0:11434" -gpu_overhead_mb = 2000 -num_parallel = 1 -context_length = 32768 -pull_models = ["qwen3-coder-next", "qwen3-coder:30b", "gpt-oss:20b", "gpt-oss:120b", "nemotron-3-super"] - -# vLLM serves one model via Docker on the OpenAI-compatible API. -# Use --vllm / --no-vllm CLI flags to override install at runtime. -[vllm] -install = true -model = "openai/gpt-oss-120b" -# HuggingFace model cache on ephemeral NVMe (fast; survives reboots on most providers). -hug_cache_dir = "/ephemeral/hug" -container_name = "vllm_gpt_oss_120b" -# Hard architecture limit: max_position_embeddings=131072 in model config.json. -max_model_len = 131072 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -# tool_call_parser="" disables --enable-auto-tool-choice; the llama3_json parser crashes -# on gpt-oss responses (vLLM 0.17.1 adds token_ids to responses, breaking the parser API). -tool_call_parser = "" - -# Named model presets for 'ruby hyperstack.rb model switch '. -# Each preset overrides the matching [vllm] field; unset fields fall back to [vllm] defaults. -# Switch examples: -# ruby hyperstack.rb model switch qwen3-coder-next # fast coding, 256k context -# ruby hyperstack.rb model switch nemotron-super # extended analysis, 131k context - -[vllm.presets.qwen3-coder-next] -model = "bullpoint/Qwen3-Coder-Next-AWQ-4bit" -container_name = "vllm_qwen3" -max_model_len = 262144 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "qwen3_coder" - -# NVIDIA Nemotron-3-Super-120B-A12B AWQ 4-bit — hybrid Mamba+MoE (12B active / 120B total). -# ~60 GB weights on A100 80GB. Uses NoPE (no positional embeddings) so context can be set to -# 1M by just raising max_model_len; no YaRN needed. May OOM above 256K on A100 80GB. -# Requires trust_remote_code=true for the nemotron_h architecture. -# Note: cyankiwi AWQ has model_type="nemotron_nas" (underscore); vLLM keys on "nemotron-nas" -# (hyphen), so vLLM may not recognise it without trust_remote_code and latest vLLM. -# NVIDIA Nemotron-3-Super uses the same XML tool call format as Qwen3 XML: -# value -# qwen3_xml handles this format and is compatible with Nemotron's chat template. -[vllm.presets.nemotron-super] -model = "cyankiwi/NVIDIA-Nemotron-3-Super-120B-A12B-AWQ-4bit" -container_name = "vllm_nemotron_super" -max_model_len = 262144 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "qwen3_xml" -trust_remote_code = true -# nemotron_v3 reasoning parser exposes tokens as reasoning_content in the API. -extra_vllm_args = ["--reasoning-parser", "nemotron_v3"] - -# OpenAI GPT-OSS 20B — ultra-fast MoE (3.6B active / 20B total, MXFP4), ~14 GB on A100. -# Native MXFP4 quantization; vLLM auto-detects it (no --quantization flag needed). -# With only 14 GB weights, most of the 80 GB is available for KV cache (64K+ context). -# tool_call_parser = "" disables --enable-auto-tool-choice: the llama3_json parser crashes -# on gpt-oss responses (vLLM 0.17.1 adds token_ids to responses, breaking the parser API). -[vllm.presets.gpt-oss-20b] -model = "openai/gpt-oss-20b" -container_name = "vllm_gpt_oss_20b" -max_model_len = 65536 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "" - -# OpenAI GPT-OSS 120B — powerful MoE (5.1B active / 117B total, MXFP4), ~65 GB on A100. -# Hard architecture limit: max_position_embeddings=131072 in model config.json. -# 131072 is the absolute ceiling — exceeding it causes NaN or CUDA OOB errors. -# For sessions approaching this limit, start a fresh Pi conversation. -# tool_call_parser = "" disables --enable-auto-tool-choice (same reason as gpt-oss-20b). -[vllm.presets.gpt-oss-120b] -model = "openai/gpt-oss-120b" -container_name = "vllm_gpt_oss_120b" -max_model_len = 131072 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "" - -# Qwen2.5-Coder-32B-Instruct AWQ — best-in-class open coding model at 32B, ~18 GB on A100. -# Official Qwen AWQ release; max_position_embeddings=32768 per model config.json. -[vllm.presets.qwen25-coder-32b] -model = "Qwen/Qwen2.5-Coder-32B-Instruct-AWQ" -container_name = "vllm_qwen25_coder32b" -max_model_len = 32768 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "hermes" - -# Qwen3-Coder-30B-A3B AWQ — Qwen3 generation coding MoE (3B active / 30B total), ~18 GB. -# Note: model card warns of significant quality loss at 4-bit for this MoE architecture. -[vllm.presets.qwen3-coder-30b] -model = "QuantTrio/Qwen3-Coder-30B-A3B-Instruct-AWQ" -container_name = "vllm_qwen3_coder30b" -max_model_len = 65536 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "qwen3_coder" - -# DeepSeek-R1-Distill-Qwen-32B AWQ — R1 reasoning distillation of Qwen 32B, ~18 GB on A100. -# Generates reasoning tokens; --reasoning-parser deepseek_r1 exposes them in the API. -# tool_call_parser="" disables tool calling (reasoning models don't support it reliably). -[vllm.presets.deepseek-r1-32b] -model = "casperhansen/deepseek-r1-distill-qwen-32b-awq" -container_name = "vllm_deepseek_r1_32b" -max_model_len = 32768 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "" -extra_vllm_args = ["--reasoning-parser", "deepseek_r1"] - -# Qwen3-32B AWQ — dense 32B reasoning model with extended context, ~18 GB on A100. -# Native thinking mode; --reasoning-parser deepseek_r1 is compatible with Qwen3 thinking format. -# tool_call_parser="" disables tool calling (reasoning models don't support it reliably). -[vllm.presets.qwen3-32b] -model = "Qwen/Qwen3-32B-AWQ" -container_name = "vllm_qwen3_32b" -max_model_len = 32768 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "" -extra_vllm_args = ["--reasoning-parser", "deepseek_r1"] - -# Devstral-Small-2507 AWQ — Mistral's coding agent model (~15 GB on A100). -# Uses HF safetensors weights but Mistral tokenizer (tekken.json) and config (params.json). -# --load_format mistral is NOT used: AWQ weights are in standard HF safetensors format. -# --tokenizer_mode mistral and --config_format mistral handle the Mistral-native files. -[vllm.presets.devstral] -model = "cyankiwi/Devstral-Small-2507-AWQ-4bit" -container_name = "vllm_devstral" -max_model_len = 32768 -gpu_memory_utilization = 0.92 -tensor_parallel_size = 1 -tool_call_parser = "mistral" -extra_vllm_args = ["--tokenizer_mode", "mistral", "--config_format", "mistral"] - -[wireguard] -auto_setup = true -setup_script = "./wg1-setup.sh" - -[local_client] -check_wg1_service = true -interface_name = "wg1" -config_path = "/etc/wireguard/wg1.conf" -- cgit v1.2.3