61 lines
1.7 KiB
YAML
61 lines
1.7 KiB
YAML
# Recipe: Qwen3.5-122B-A10B-INT4-Autoround
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# Qwen3.5-122B model in Intel INT4-Autoround quantization
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# Important: set memory utilization in GB, not percentage! Requires --no-ray to fit full context on two sparks.
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# If you experience node shutdown, please limit GPU clocks on the affected node (or both): `sudo nvidia-smi -lgc 200,2150`
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recipe_version: "1"
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name: Qwen3.5-397B-INT4-Autoround
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description: EXPERIMENTAL recipe for Qwen3.5-397B-INT4-Autoround (please refer to README for details! Use with `--no-ray` parameter!)
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# HuggingFace model to download (optional, for --download-model)
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model: Intel/Qwen3.5-397B-A17B-int4-AutoRound
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cluster_only: true
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# Container image to use
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container: vllm-node-tf5
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build_args:
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- --tf5
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# Mod required to fix ROPE syntax error
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mods:
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- mods/fix-qwen3.5-autoround
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- mods/fix-qwen3.5-chat-template
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- mods/gpu-mem-util-gb
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# Default settings (can be overridden via CLI)
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defaults:
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port: 8000
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host: 0.0.0.0
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tensor_parallel: 2
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gpu_memory_utilization: 112
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max_model_len: 262144
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max_num_batched_tokens: 4176
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# Environment variables
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env:
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PYTORCH_CUDA_ALLOC_CONF: "expandable_segments:True"
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VLLM_MARLIN_USE_ATOMIC_ADD: 1
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# The vLLM serve command template
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command: |
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vllm serve Intel/Qwen3.5-397B-A17B-int4-AutoRound \
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--max-model-len {max_model_len} \
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--max-num-seqs 2 \
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--kv-cache-dtype fp8 \
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--gpu-memory-utilization-gb {gpu_memory_utilization} \
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--port {port} \
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--host {host} \
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--enable-prefix-caching \
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--enable-auto-tool-choice \
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--tool-call-parser qwen3_coder \
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--reasoning-parser qwen3 \
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--max-num-batched-tokens {max_num_batched_tokens} \
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--trust-remote-code \
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--chat-template unsloth.jinja \
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-tp {tensor_parallel} \
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--distributed-executor-backend ray
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