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model-conversion : pass config to from_pretrained (#16963)

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This commit modifies the script `run-org-model.py` to ensure that the
model configuration is explicitly passed to the `from_pretrained` method
when loading the model. It also removes a duplicate configuration
loading which was a mistake.

The motivation for this change is that enables the config object to be
modified and then passed to the model loading function, which can be
useful when testing new models.
This commit is contained in:
Daniel Bevenius
2025-11-03 18:01:59 +01:00
committed by GitHub
parent 48bd26501b
commit ed8aa63320
1 changed files with 4 additions and 5 deletions
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9
examples/model-conversion/scripts/causal/run-org-model.py
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@@ -138,6 +138,9 @@ if model_path is None:
"Model path must be specified either via --model-path argument or MODEL_PATH environment variable" "Model path must be specified either via --model-path argument or MODEL_PATH environment variable"
) )
print("Loading model and tokenizer using AutoTokenizer:", model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
print("Model type: ", config.model_type) print("Model type: ", config.model_type)
@@ -147,10 +150,6 @@ print("Number of layers: ", config.num_hidden_layers)
print("BOS token id: ", config.bos_token_id) print("BOS token id: ", config.bos_token_id)
print("EOS token id: ", config.eos_token_id) print("EOS token id: ", config.eos_token_id)
print("Loading model and tokenizer using AutoTokenizer:", model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
if unreleased_model_name: if unreleased_model_name:
model_name_lower = unreleased_model_name.lower() model_name_lower = unreleased_model_name.lower()
unreleased_module_path = ( unreleased_module_path = (
@@ -171,7 +170,7 @@ if unreleased_model_name:
exit(1) exit(1)
else: else:
model = AutoModelForCausalLM.from_pretrained( model = AutoModelForCausalLM.from_pretrained(
model_path, device_map="auto", offload_folder="offload", trust_remote_code=True model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=config
) )
for name, module in model.named_modules(): for name, module in model.named_modules():