Phi-4-mini-instruct-ExecuTorch

Phi-4-mini-instruct — ExecuTorch XNNPACK 8da4w + 8-bit embedding

phi_4_mini_xnnpack_8da4w_e8.pte (2597.9 MB)

• Source: Qwen/Phi-4-mini-instruct
• License: Apache-2.0
• Quantization: 8da4w linear + 8-bit embedding (embedding_quantize: "8,0")
• Export: executorch 1.4.0 export_llm, static shape (seq_len=1), max_seq_length 2048, XNNPACK extended_ops
• Config: llm_params/phi_4_mini_xnnpack_8da4w_e8.yaml

Verification (Mac arm64, 2026-08-21)

llm_params/gen_static.py, token-by-token prefill then greedy decode, a fresh process per prompt so no answer is read through the previous one's cache:

Stopping is the part worth looking at. Chat template is <|user|>...<|end|><|assistant|>, bos 199999, eos [200020, 199999] — the model's own generation_config.json. The upstream example config (examples/models/phi_4_mini/config/phi_4_mini_xnnpack.yaml) carries 151643 for both, which is Qwen's, and a model that never sees its end token does not stop.

Not measured on a phone. Worth noting against the rest of the shelf: this decodes at 49 tok/s where Qwen3.5-4B manages 8.6 at a comparable 2.8 GB. The Qwen3.5 exports run with use_sdpa_with_kv_cache: False and this one with it on.

Converting it

python convert/export_phi_4_mini.py

Not export_llm directly, because its download path cannot reach this model: load_checkpoint_from_pytorch_model reads pytorch_model.bin and this repository ships safetensors only, so the code falls back to torchtune's checkpointer — a dependency with its own torch pin, for a key rename and two splits. The script reads the safetensors, calls upstream's own phi_4_hf_to_meta, and ties the output embedding back on: input and output embeddings are tied here, so there is no lm_head.weight to map to output.weight, and the model refuses a checkpoint without it. The torchtune converter beside it ties them; the safetensors one does not.

Running it

python llm_params/gen_static.py \
  --pte phi_4_mini_xnnpack_8da4w_e8.pte \
  --tokenizer tokenizer.json \
  --prompt '<|user|>What is the capital of France?<|end|><|assistant|>' \
  --eos_ids "[200020,199999]"

The 8-bit embedding needs from executorch.kernels import quantized before the program is loaded. Without it the method will not even load — kernel 'quantized_decomposed::embedding_byte.dtype_out' not found — which reads like a broken export rather than a runtime missing its kernels.

(conversion scripts: executorch-models · iOS sample: executorch-samples)