README.md

metadata

library_name: peft
license: apache-2.0

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: nf4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_dtype: bfloat16

Framework versions

• PEFT 0.5.0

---

library_name: peft tags: - code - instruct - gpt2 datasets: - HuggingFaceH4/no_robots base_model: gpt2 license: apache-2.0

Finetuning Overview:

Model Used: gpt2

Dataset: HuggingFaceH4/no_robots

Dataset Insights:

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.

Finetuning Details:

With the utilization of MonsterAPI's LLM finetuner, this finetuning:

• Was achieved with great cost-effectiveness.
• Completed in a total duration of 3mins 40s for 1 epoch using an A6000 48GB GPU.
• Costed $0.101 for the entire epoch.

Hyperparameters & Additional Details:

• Epochs: 1
• Cost Per Epoch: $0.101
• Total Finetuning Cost: $0.101
• Model Path: gpt2
• Learning Rate: 0.0002
• Data Split: 100% train
• Gradient Accumulation Steps: 4
• lora r: 32
• lora alpha: 64

Prompt Structure

<|system|> <|endoftext|> <|user|> [USER PROMPT]<|endoftext|> <|assistant|> [ASSISTANT ANSWER] <|endoftext|>

Training loss :

license: apache-2.0