35cacf020c
Signed-off-by: Mengjie Zhao <zmj0129@gmail.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>
41 lines
1.6 KiB
YAML
41 lines
1.6 KiB
YAML
# Gepard-1.0: single-stage native-AR FSQ/NanoCodec TTS (~0.5B).
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# Target 1x L4 24GB. Zero-shot only; enforce_eager until the perf PR.
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#
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# The pipeline pin is required, not cosmetic: the checkpoint's config.json
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# self-identifies as qwen3_5_text, so model_type inference cannot reach the
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# gepard registry entry, and the architectures fallback would match
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# Qwen3_5ForCausalLM against the diffusion pipeline.
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pipeline: gepard
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async_chunk: false
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dtype: bfloat16
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trust_remote_code: true
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stages:
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- stage_id: 0
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max_num_seqs: 4
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gpu_memory_utilization: 0.3 # AR LM + NanoCodec fit in ~2 GiB
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enforce_eager: true
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enable_prefix_caching: false
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async_scheduling: true
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max_num_batched_tokens: 4096
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max_model_len: 4096
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devices: "0"
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# Override architectures so vLLM resolves our talker rather than the native
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# Qwen3.5 text model. Do NOT override model_type: the backbone's own
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# model_type check needs it to stay qwen3_5_text.
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hf_overrides:
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architectures: ["GepardTalkerForConditionalGeneration"]
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default_sampling_params:
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# All 32 heads are sampled in-model, so vLLM's sampler is pinned
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# deterministic and just argmaxes what forward chose.
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temperature: 0.0
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top_p: 1.0
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top_k: -1
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# One output token is one frame, so this is the frame budget: 1000 frames
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# is ~46 s at 21.5 fps. A backstop, not a target -- normal utterances are
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# 40-420 frames -- but a load-bearing one, because this checkpoint's stop
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# head does not fire on every batched trajectory.
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max_tokens: 1000
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seed: 42
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detokenize: false
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