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--- |
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library_name: peft |
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license: apache-2.0 |
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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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- quant_method: bitsandbytes |
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- load_in_8bit: False |
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- load_in_4bit: True |
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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: nf4 |
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- bnb_4bit_use_double_quant: True |
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- bnb_4bit_compute_dtype: bfloat16 |
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### Framework versions |
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- PEFT 0.5.0 |
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- --- |
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library_name: peft |
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tags: |
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- code |
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- instruct |
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- gpt2 |
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datasets: |
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- HuggingFaceH4/no_robots |
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base_model: gpt2 |
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license: apache-2.0 |
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--- |
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### Finetuning Overview: |
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**Model Used:** gpt2 |
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**Dataset:** HuggingFaceH4/no_robots |
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#### Dataset Insights: |
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[No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. |
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#### Finetuning Details: |
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With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning: |
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- Was achieved with great cost-effectiveness. |
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- Completed in a total duration of 3mins 40s for 1 epoch using an A6000 48GB GPU. |
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- Costed `$0.101` for the entire epoch. |
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#### Hyperparameters & Additional Details: |
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- **Epochs:** 1 |
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- **Cost Per Epoch:** $0.101 |
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- **Total Finetuning Cost:** $0.101 |
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- **Model Path:** gpt2 |
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- **Learning Rate:** 0.0002 |
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- **Data Split:** 100% train |
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- **Gradient Accumulation Steps:** 4 |
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- **lora r:** 32 |
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- **lora alpha:** 64 |
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#### Prompt Structure |
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``` |
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<|system|> <|endoftext|> <|user|> [USER PROMPT]<|endoftext|> <|assistant|> [ASSISTANT ANSWER] <|endoftext|> |
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``` |
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#### Training loss : |
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license: apache-2.0 |
