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This is a Finetuning of GPT-J-6B using LoRa - https://huggingface.co/EleutherAI/gpt-j-6B |
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The dataset is the cleaned version of the Alpaca dataset - https://github.com/gururise/AlpacaDataCleaned |
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A model similar to this has been talked about |
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The performance is good but not as good as the orginal Alpaca trained from a base model of LLaMa |
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This is mostly due to the LLaMa 7B model being pretrained on 1T tokens and GPT-J-6B being trained on 300-400M tokens |
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You will need a 3090 or A100 to run it, unfortunately this current version won't work on a T4. |
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--- |
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library_name: peft |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- Text Generation |
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--- |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- load_in_8bit: True |
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- load_in_4bit: False |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: fp4 |
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- bnb_4bit_use_double_quant: False |
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- bnb_4bit_compute_dtype: float32 |
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The following `bitsandbytes` quantization config was used during training: |
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- load_in_8bit: True |
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- load_in_4bit: False |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: fp4 |
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- bnb_4bit_use_double_quant: False |
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- bnb_4bit_compute_dtype: float32 |
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|
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The following `bitsandbytes` quantization config was used during training: |
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- load_in_8bit: True |
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- load_in_4bit: False |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: fp4 |
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- bnb_4bit_use_double_quant: False |
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- bnb_4bit_compute_dtype: float32 |
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### Framework versions |
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- PEFT 0.4.0.dev0 |
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- PEFT 0.4.0.dev0 |
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- PEFT 0.4.0.dev0 |