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Fix CI: DeepSeek-V3 test config and serve without uvicorn #2

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Merged
akanyaani merged 1 commit into from
Oct 23, 2026
Merged

Fix CI: DeepSeek-V3 test config and serve without uvicorn #2

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2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -34,7 +34,7 @@ serve = ["fastapi>=0.110", "uvicorn>=0.29"]
vllm = ["fastapi>=0.110", "uvicorn>=0.29", "vllm>=0.12"] # decision-model support: vLLM nightly from 2026-10-15
fla = ["flash-linear-attention>=0.5"] # fast Gated DeltaNet kernels for Qwen3.5/3.8 backbones
data = ["datasets>=3"]
dev = ["pytest>=8", "ruff>=0.6", "mypy>=1.11", "fastapi>=0.110", "httpx>=0.27"]
dev = ["pytest>=8", "ruff>=0.6", "mypy>=1.11", "fastapi>=0.110", "uvicorn>=0.29", "httpx>=0.27"]

[project.scripts]
decisionformer = "decisionformer.cli:main"
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2 changes: 1 addition & 1 deletion src/decisionformer/serve.py
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Expand Up @@ -90,7 +90,7 @@ def load_decider(model: str, *, backend: str = "transformers", device: str | Non

def serve(model: str, *, host: str = "127.0.0.1", port: int = 8000, device: str | None = None,
backend: str = "transformers", api_key_env: str = "DECISIONFORMER_API_KEY", **load_options: Any) -> None:
decider = load_decider(model, backend=backend, device=device, **load_options) # model errors first
import uvicorn

decider = load_decider(model, backend=backend, device=device, **load_options)
uvicorn.run(create_app(decider, api_key=os.environ.get(api_key_env)), host=host, port=port)
8 changes: 5 additions & 3 deletions tests/conftest.py
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Expand Up @@ -212,7 +212,8 @@ def data_dir(tmp_path_factory) -> Path:
"qwen3_moe": {"num_experts": 4, "num_experts_per_tok": 2, "moe_intermediate_size": 32, "decoder_sparse_step": 1},
"olmoe": {"num_experts": 4, "num_experts_per_tok": 2},
"gpt_oss": {**_MOE, "layer_types": ["sliding_attention", "full_attention"], "sliding_window": 64},
"deepseek_v3": {"n_routed_experts": 4, "num_experts_per_tok": 2, "moe_intermediate_size": 32,
# multi-head latent attention has as many key/value heads as query heads
"deepseek_v3": {"n_routed_experts": 4, "num_experts_per_tok": 2, "moe_intermediate_size": 32, "num_key_value_heads": 4,
"first_k_dense_replace": 1, "kv_lora_rank": 16, "q_lora_rank": 16, "qk_rope_head_dim": 8,
"qk_nope_head_dim": 8, "v_head_dim": 8, "n_group": 1, "topk_group": 1},
# hybrid linear attention (Gated DeltaNet) + full attention
Expand All @@ -236,8 +237,9 @@ def build_zoo_model(root: Path, model_type: str) -> Path:
tokenizer = make_tokenizer("encoder" if encoder else "decoder")
kwargs = dict(_TINY, vocab_size=len(tokenizer), **(ZOO_ENCODERS if encoder else ZOO_DECODERS)[model_type])
if not encoder:
kwargs.update(num_key_value_heads=2, eos_token_id=SPECIALS.index("<|end|>"), bos_token_id=None,
tie_word_embeddings=True, **({} if model_type == "falcon" else {"head_dim": 8}))
kwargs = {"num_key_value_heads": 2, **kwargs}
kwargs.update(eos_token_id=SPECIALS.index("<|end|>"), bos_token_id=None, tie_word_embeddings=True,
**({} if model_type == "falcon" else {"head_dim": 8}))
torch.manual_seed(0)
config = AutoConfig.for_model(model_type, **kwargs)
if getattr(config, "is_encoder_decoder", False):
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