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GRATH-selftruth
weixinchen/GRATH-selftruth
GRATH-selftruth is a machine learning model from weixinchen. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
This is a self-truthified model proposed in the paper GRATH: Gradual Self-Truthifying for Large Language Models.
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At a glance
- Library
- peft
- Access
- Public
- Created
- Jul 17, 2024
- Updated
- Jul 17, 2024
- SHA
- 86f2c5fa
Model card
From the Hugging Face model README
This is a self-truthified model proposed in the paper GRATH: Gradual Self-Truthifying for Large Language Models.
Note: This model is applied with DPO once. The reference model of DPO is set as the current base model (i.e., the pretrained base model).
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
Framework versions
-
PEFT 0.5.0
-
PEFT 0.5.0
-
PEFT 0.5.0
- Library
- peft
- Created
- Jul 17, 2024
- Updated
- Jul 17, 2024