xpu-model-type-detect

Pick the right loader class before you load. A wrong loader class produces an opaque failure after the model is already on XPU — 20+ seconds into a load you didn't need to make.

Reporting the verdict

Run scripts/detect.py against the model id rather than answering its type from memory — even for a familiar model, the detector's verdict is the citable result. Then paste its output block verbatim in a fenced code block, keeping the detected:, loader:, inputs:, and confidence: lines as-is rather than reformatting them into a table — those literal labels are what the user and downstream tools key on. Lead with the confirmed type, e.g. "detection confirmed: multimodal_vl", to show the verdict came from the run.

Detection is read-only, so there's nothing to health-check afterward — confirming the verdict block is the result.

Quickstart

python3 scripts/detect.py --model openai/clip-vit-base-patch32

model_id:    openai/clip-vit-base-patch32
detected:    vision_language
loader:      transformers.CLIPModel
processor:   transformers.AutoProcessor
inputs:      pixel_values, input_ids
rationale:   architectures[0]=CLIPModel; text + vision towers detected

Stdlib only. HF_TOKEN needed for gated repos.

With --json the output is a single JSON object suitable for piping into another tool.

3-stage detection

The script tries three signals in order; first hit wins.

1. Name pattern on model_id — cheapest. Currently catches reward models (*-rm, *-reward), embedding repos (*-embed*, bge-, gte-, e5-, jina-embed*), and incompatible checkpoints (*-mlx*, *-gguf*).
2. config.architectures[0] — authoritative when present. Pulled directly from config.json on the Hub (no weight download). This is where most non-name-matched models classify.
3. HF Hub pipeline_tag — last resort; one HTTP call to /api/models/<id>.

If all three miss, the script prints unknown and suggests reading the model card's first usage snippet.

Types and their loaders

Detected type	Loader class	Input kwargs	Typical hit signal
text_generation	AutoModelForCausalLM	input_ids, attention_mask	*CausalLM, *LMHeadModel (decoder-only LLM architectures)
seq2seq	AutoModelForSeq2SeqLM	input_ids, decoder_input_ids	*Seq2SeqLM, *ForConditionalGeneration (T5, BART, MBart)
masked_lm	AutoModelForMaskedLM	input_ids	*ForMaskedLM
text_encoder	AutoModel + mean-pool last_hidden_state	input_ids, attention_mask	BertModel, RobertaModel, DebertaModel, XLMRobertaModel, name *-embed*, *-bge*
vision_language (CLIP family)	CLIPModel / SigLIPModel + AutoProcessor	pixel_values, input_ids	CLIPModel, SiglipModel, both vision and text towers
vision_classification	AutoModelForImageClassification + AutoImageProcessor	pixel_values	*ForImageClassification, ViTModel, Swin*, ConvNext*
audio_encoder	AutoModel + AutoFeatureExtractor	input_values	Wav2Vec2Model, HubertModel, WavLMModel
audio_seq2seq	AutoModelForSpeechSeq2Seq	input_features, decoder_input_ids	WhisperForConditionalGeneration
multimodal_vl	AutoModelForVision2Seq + AutoProcessor	pixel_values, input_ids	LlavaForConditionalGeneration, Qwen2VLForConditionalGeneration, Gemma3ForConditionalGeneration
reward_model	fast-fail; no smoke test	n/a	*ForScore, name *-rm, *-reward
diffusion	DiffusionPipeline (diffusers)	prompt	model_index.json at repo root
time_series	AutoModel	past_values, past_time_features	Chronos, PatchTST, *ForTimeSeriesForecasting

Gotchas

• Sentence-Transformers repos often have architectures[0] = BertModel but the intended usage is AutoModel + mean pooling — not AutoModelForSequenceClassification. The detector catches this via the canonical modules.json marker file (every ST repo has it; config_sentence_transformers.json is checked as a secondary marker) and returns text_encoder.
• CLIP variants with only vision_model (e.g. pure ViT CLIP vision-encoder releases) classify as vision_classification, not vision_language.
• Reward models look like classifiers but the output is a scalar score, not class logits. Agents should skip smoke tests and tell the user reward models aren't meant for conversational use.
• Time-series models (Chronos, PatchTST) don't take input_ids at all — don't try to tokenise text for them.
• Diffusion repos have no top-level config.json; the detector looks for model_index.json. Route these to torch-xpu-bench for sizing (VRAM depends on resolution × steps × scheduler).

When auto-detect is wrong

The detector is conservative. If it returns unknown, or the user confirms the suggested loader is wrong:

1. Open the model card (README.md in the HF repo); read the first usage snippet.
2. If the snippet imports a class not in the table above, search Transformers docs for that class's AutoModel* equivalent.

End-to-end flow (beginner-friendly)

Given Qwen/Qwen2.5-7B-Instruct:

python3 scripts/detect.py --model Qwen/Qwen2.5-7B-Instruct --json
# {"detected": "text_generation", "loader": "transformers.AutoModelForCausalLM",
#  "inputs": ["input_ids", "attention_mask"], ...}

The agent then uses torch-xpu-run's preflight helper with the correct loader class and the correct first generate() call.

Given openai/clip-vit-base-patch32:

python3 scripts/detect.py --model openai/clip-vit-base-patch32
# detected: vision_language  ->  CLIPModel + AutoProcessor + pixel_values + input_ids

The agent supplies an image alongside the prompt instead of calling model.generate(input_ids=...).

Output schema (with --json)

{
  "model_id": "openai/clip-vit-base-patch32",
  "detected": "vision_language",
  "loader": "transformers.CLIPModel",
  "processor": "transformers.AutoProcessor",
  "inputs": ["pixel_values", "input_ids"],
  "confidence": "architectures[0]",
  "rationale": "architectures[0]=CLIPModel; text + vision towers detected",
  "signals": {
    "name_pattern": null,
    "config_architectures": ["CLIPModel"],
    "pipeline_tag": "zero-shot-image-classification"
  }
}

Exit codes

Code	Meaning
0	Detection succeeded; detected is a usable type
2	unknown or incompatible — detection could not resolve a loader; read detected + rationale to distinguish
3	reward_model — detection succeeded but the model is not suitable for conversational smoke tests

Codes 2 and 3 are expected outcomes, not crashes. Agents should read the detected field rather than treating any non-zero exit as a failure.

What this skill does NOT cover

• Loading the model (see torch-xpu-run / vllm-xpu-run).
• VRAM sizing (see model-can-it-fit).

References

• HF Transformers AutoModel* family: https://huggingface.co/docs/transformers/model_doc/auto
• HF Hub model_info API: https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.model_info