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Add files using upload-large-folder tool

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  1. README.md +12 -12
README.md CHANGED
@@ -42,15 +42,13 @@ TRIPLES:
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  Output as markdown table with Type and Triple columns.
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  ```
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- ## ms-swift PtEngine
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  ```python
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  import os
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  os.environ.setdefault("USE_HF", "1")
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  import torch
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- from huggingface_hub import snapshot_download
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- from swift.llm import InferRequest, PtEngine, RequestConfig, get_model_tokenizer, get_template
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- from swift.tuners import Swift
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  BASE_MODEL = "Qwen/Qwen3-4B"
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  ADAPTER_ID = "Loria-MosAIk/xqdt-e2e-qwen3-4b"
@@ -61,18 +59,20 @@ TRIPLES:
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  1. [S] Blue Spice [P] area [O] city centre
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  2. [S] Blue Spice [P] eat type [O] coffee shop
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  Output as markdown table with Type and Triple columns."""
 
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- adapter_path = snapshot_download(ADAPTER_ID)
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- model, tokenizer = get_model_tokenizer(
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  BASE_MODEL,
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- model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloat16},
 
 
 
 
 
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  )
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- model = Swift.from_pretrained(model, model_id=adapter_path, adapter_name="default")
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- template = get_template("qwen3", tokenizer, default_system=SYSTEM_PROMPT)
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- engine = PtEngine.from_model_template(model, template, max_batch_size=1)
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  response = engine.infer(
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- [InferRequest(messages=[{"role": "user", "content": QUERY}])],
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- RequestConfig(max_tokens=1024, temperature=0.3),
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  use_tqdm=False,
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  )[0]
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  print(response.choices[0].message.content)
 
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  Output as markdown table with Type and Triple columns.
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  ```
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+ ## ms-swift
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  ```python
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  import os
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  os.environ.setdefault("USE_HF", "1")
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  import torch
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+ from swift.infer_engine import InferRequest, RequestConfig, TransformersEngine
 
 
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  BASE_MODEL = "Qwen/Qwen3-4B"
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  ADAPTER_ID = "Loria-MosAIk/xqdt-e2e-qwen3-4b"
 
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  1. [S] Blue Spice [P] area [O] city centre
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  2. [S] Blue Spice [P] eat type [O] coffee shop
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  Output as markdown table with Type and Triple columns."""
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+ MESSAGES = [{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": QUERY}]
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+ engine = TransformersEngine(
 
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  BASE_MODEL,
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+ adapters=[ADAPTER_ID],
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+ max_batch_size=1,
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+ torch_dtype=torch.bfloat16,
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+ device_map="auto",
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+ template_type="qwen3",
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+ use_hf=True,
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  )
 
 
 
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  response = engine.infer(
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+ [InferRequest(messages=MESSAGES)],
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+ RequestConfig(max_tokens=1024, temperature=0.3, seed=2023),
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  use_tqdm=False,
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  )[0]
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  print(response.choices[0].message.content)