Comments (1)
To address the issue of receiving garbled text as output after importing a fine-tuned model, consider the following steps:
-
Verify Model Path Configuration: Double-check that the
MODEL_PATH
forchatglm3-6b
is correctly set. Incorrect paths or misplaced model files can lead to loading issues. -
PEFT File Path in
startup.py
: Ensure the PEFT file path you've added is correct and the file is accessible. Mistakes here could interfere with model performance. -
PEFT_SHARE_BASE_WEIGHTS
Compatibility: Confirm that settingPEFT_SHARE_BASE_WEIGHTS
totrue
is compatible with thechatglm3-6b
model. This setting indicates shared base weights, which not all models support seamlessly. -
conv_template
Configuration: The setting ofconv_template
toNone
might be affecting input processing. Verify this setting does not conflict with thechatglm3-6b
model's requirements. -
Model Compatibility: Ensure the
chatglm3-6b
model is compatible with your configuration, including any customizations instartup.py
andadapter_config.json
. -
Device Configuration: Check the device configuration (e.g.,
cuda
,cpu
) to ensure it's suitable for thechatglm3-6b
model. Incompatibilities here can degrade output quality. -
Inspect Error Logs: Review application logs for any errors or warnings that might hint at the problem's root cause.
-
Test with Default Configuration: Try reverting to a default configuration that previously worked, then incrementally apply your changes to isolate the issue.
For further guidance, you might want to consult the documentation or support resources for the models and libraries you're using.
Details
Sources
- configs/model_config.py.example
- startup.py
- knowledge_base/samples/content/test_files/langchain-ChatGLM_closed.jsonl
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